entry_point
stringlengths 1
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| original_triton_python_code
stringlengths 208
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| optimised_triton_code
stringlengths 1.15k
275k
| repo_name
stringlengths 7
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| module_name
stringlengths 1
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class | uuid
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listlengths 1
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stringlengths 40
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stringlengths 72
180
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|---|---|---|---|---|---|---|---|---|---|---|
Dueling_Critic
|
import torch
import torch.nn.functional as F
import torch.nn as nn
class Dueling_Critic(nn.Module):
def __init__(self, input_size, output_size, hidden_size):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, 1)
self.linear3 = nn.Linear(hidden_size, output_size)
def forward(self, x):
x1 = F.relu(self.linear1(x))
x2 = F.relu(self.linear1(x))
y1 = self.linear2(x1)
y2 = self.linear3(x2)
x3 = y1 + y2 - y2.mean(dim=1, keepdim=True)
return x3
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'output_size': 4, 'hidden_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_mean_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x4 = xindex // 4
x5 = xindex
x3 = xindex // 64
x6 = xindex % 16
tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp4 = tl.load(in_ptr2 + x5, xmask)
tmp6 = tl.load(in_ptr2 + (x6 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp7 = tl.load(in_ptr2 + (16 + x6 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr2 + (32 + x6 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp11 = tl.load(in_ptr2 + (48 + x6 + 64 * x3), xmask, eviction_policy=
'evict_last')
tmp3 = tmp0 + tmp2
tmp5 = tmp3 + tmp4
tmp8 = tmp6 + tmp7
tmp10 = tmp8 + tmp9
tmp12 = tmp10 + tmp11
tmp13 = 4.0
tmp14 = tmp12 / tmp13
tmp15 = tmp5 - tmp14
tl.store(out_ptr0 + x5, tmp15, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (1, 4), (4, 1))
assert_size_stride(primals_5, (1,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
primals_2, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 1), (1, 4), 0), out=buf2)
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf1, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf3)
del primals_7
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_mean_sub_1[grid(256)](buf2, primals_5, buf3,
buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
del buf2
del buf3
del primals_5
return buf4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 4), (4, 1), 0
), primals_6, primals_4, buf5
class Dueling_CriticNew(nn.Module):
def __init__(self, input_size, output_size, hidden_size):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, 1)
self.linear3 = nn.Linear(hidden_size, output_size)
def forward(self, input_0):
primals_1 = self.linear1.weight
primals_2 = self.linear1.bias
primals_4 = self.linear2.weight
primals_5 = self.linear2.bias
primals_6 = self.linear3.weight
primals_7 = self.linear3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
FlickerNiko/ai_lib
|
Dueling_Critic
| false | 13,693 |
[
"MIT"
] | 99 |
7087d4569c9a827d35dd8735b55a080834d31a82
|
https://github.com/FlickerNiko/ai_lib/tree/7087d4569c9a827d35dd8735b55a080834d31a82
|
EncoderImagePrecomp
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_4/inductor_cache/5x/c5xuny2q3w4rfyqbc6h5bf6h7phwdd6eg4xkubmoahyiibq64bkx.py
# Topologically Sorted Source Nodes: [pow_1, sum_1, sqrt, norm, X], Original ATen: [aten.pow, aten.sum, aten.sqrt, aten.add, aten.div]
# Source node to ATen node mapping:
# X => div
# norm => add
# pow_1 => pow_1
# sqrt => sqrt
# sum_1 => sum_1
# Graph fragment:
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_1, 2), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1], True), kwargs = {})
# %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_1,), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-08), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_1, %add), kwargs = {})
triton_poi_fused_add_div_pow_sqrt_sum_0 = async_compile.triton('triton_poi_fused_add_div_pow_sqrt_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_pow_sqrt_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_pow_sqrt_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-08
tmp14 = tmp12 + tmp13
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [features], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [pow_1, sum_1, sqrt, norm, X], Original ATen: [aten.pow, aten.sum, aten.sqrt, aten.add, aten.div]
stream0 = get_raw_stream(0)
triton_poi_fused_add_div_pow_sqrt_sum_0.run(buf0, buf1, 256, grid=grid(256), stream=stream0)
return (buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_div_pow_sqrt_sum_0(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-08
tmp14 = tmp12 + tmp13
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + x2, tmp15, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_div_pow_sqrt_sum_0[grid(256)](buf0, buf1, 256,
XBLOCK=256, num_warps=4, num_stages=1)
return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0
def l2norm(X, dim, eps=1e-08):
"""L2-normalize columns of X
"""
norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
X = torch.div(X, norm)
return X
class EncoderImagePrecompNew(nn.Module):
def __init__(self, img_dim, embed_size, no_imgnorm=False):
super(EncoderImagePrecompNew, self).__init__()
self.embed_size = embed_size
self.no_imgnorm = no_imgnorm
self.fc = nn.Linear(img_dim, embed_size)
self.init_weights()
def init_weights(self):
"""Xavier initialization for the fully connected layer
"""
r = np.sqrt(6.0) / np.sqrt(self.fc.in_features + self.fc.out_features)
self.fc.weight.data.uniform_(-r, r)
self.fc.bias.data.fill_(0)
def load_state_dict(self, state_dict):
"""Copies parameters. overwritting the default one to
accept state_dict from Full model
"""
own_state = self.state_dict()
new_state = OrderedDict()
for name, param in state_dict.items():
if name in own_state:
new_state[name] = param
super(EncoderImagePrecompNew, self).load_state_dict(new_state)
def forward(self, input_0):
primals_1 = self.fc.weight
primals_2 = self.fc.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
ChopinSharp/SCAN
|
EncoderImagePrecomp
| false | 4,994 |
[
"Apache-2.0"
] | 1 |
4a165b2aeb3007685054d0c550540893b2006b17
|
https://github.com/ChopinSharp/SCAN/tree/4a165b2aeb3007685054d0c550540893b2006b17
|
ConcatConv1d
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_7/inductor_cache/nd/cndqmv6opasogda3abybssrolojxtgspiw4l44wajkbuoorfwxgs.py
# Topologically Sorted Source Nodes: [ttx], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# ttx => cat
# Graph fragment:
# %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_2, %primals_1], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 80
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4) % 5
x0 = xindex % 4
x2 = (xindex // 20)
x3 = xindex
tmp0 = x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + (4*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 5, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + (x0 + (4*((-1) + x1)) + (16*x2)), tmp6 & xmask, other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_7/inductor_cache/gi/cgiwilqietbdjis33kn5fz2pnnroyzpbn4tycl3qtynjtmghkqyd.py
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# conv1d => convolution
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_3, %primals_4, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 2) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 1, 4), (4, 4, 1))
assert_size_stride(primals_3, (4, 5, 3), (15, 3, 1))
assert_size_stride(primals_4, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 5, 4), (20, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [ttx], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_2, primals_1, buf0, 80, grid=grid(80), stream=stream0)
del primals_1
del primals_2
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 2), (8, 2, 1))
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf2, primals_4, 32, grid=grid(32), stream=stream0)
del primals_4
return (buf2, primals_3, buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 1, 4), (4, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 5, 3), (15, 3, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 80
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 5
x0 = xindex % 4
x2 = xindex // 20
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 4 * x2), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 5, tl.int64)
tmp9 = tl.load(in_ptr1 + (x0 + 4 * (-1 + x1) + 16 * x2), tmp6 & xmask,
other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x3, tmp10, xmask)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 2 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 1, 4), (4, 4, 1))
assert_size_stride(primals_3, (4, 5, 3), (15, 3, 1))
assert_size_stride(primals_4, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 5, 4), (20, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(80)](primals_2, primals_1, buf0, 80,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
del primals_2
buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 2), (8, 2, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_1[grid(32)](buf2, primals_4, 32,
XBLOCK=32, num_warps=1, num_stages=1)
del primals_4
return buf2, primals_3, buf0
class ConcatConv1dNew(nn.Module):
"""
1d convolution concatenated with time for usage in ODENet.
"""
def __init__(self, dim_in, dim_out, kernel_size=3, stride=1, padding=0,
bias=True, transpose=False):
super(ConcatConv1dNew, self).__init__()
module = nn.ConvTranspose1d if transpose else nn.Conv1d
self._layer = module(dim_in + 1, dim_out, kernel_size=kernel_size,
stride=stride, padding=padding, bias=bias)
def forward(self, input_0, input_1):
primals_3 = self._layer.weight
primals_4 = self._layer.bias
primals_2 = input_0
primals_1 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
puneat/SS-using-NODE
|
ConcatConv1d
| false | 4,147 |
[
"MIT"
] | 0 |
29f053769420a2d1cab1ad45f59a912c2ac737da
|
https://github.com/puneat/SS-using-NODE/tree/29f053769420a2d1cab1ad45f59a912c2ac737da
|
SimpleCumSumModule
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_9/inductor_cache/3c/c3cpu3wz3bo3cso4qi4ii34iyrr3niuqsvkxdjr6hbwpvju6c4oe.py
# Topologically Sorted Source Nodes: [cumsum], Original ATen: [aten.cumsum]
# Source node to ATen node mapping:
# cumsum => cumsum
# Graph fragment:
# %cumsum : [num_users=1] = call_function[target=torch.ops.aten.cumsum.default](args = (%arg0_1, 4), kwargs = {})
triton_per_fused_cumsum_0 = async_compile.triton('triton_per_fused_cumsum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
tmp0 = arg0_0 + arg1_0
return tmp0
@triton_heuristics.persistent_reduction(
size_hints=[256, 4],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_cumsum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_cumsum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 256
rnumel = 4
RBLOCK: tl.constexpr = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + (4*x0)), xmask, other=0.0)
tmp1 = tmp0.to(tl.float32)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp3, = tl.associative_scan((tmp2,), 1, _triton_helper_fn_add0)
tl.store(out_ptr0 + (r1 + (4*x0)), tmp3, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [cumsum], Original ATen: [aten.cumsum]
stream0 = get_raw_stream(0)
triton_per_fused_cumsum_0.run(arg0_1, buf0, 256, 4, grid=grid(256), stream=stream0)
del arg0_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
tmp0 = arg0_0 + arg1_0
return tmp0
@triton.jit
def triton_per_fused_cumsum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl
.constexpr):
xnumel = 256
RBLOCK: tl.constexpr = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 4 * x0), xmask, other=0.0)
tmp1 = tmp0.to(tl.float32)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp3, = tl.associative_scan((tmp2,), 1, _triton_helper_fn_add0)
tl.store(out_ptr0 + (r1 + 4 * x0), tmp3, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
torch.float32)
get_raw_stream(0)
triton_per_fused_cumsum_0[grid(256)](arg0_1, buf0, 256, 4, XBLOCK=
32, num_warps=2, num_stages=1)
del arg0_1
return buf0,
class SimpleCumSumModuleNew(torch.nn.Module):
def __init__(self, dim):
super(SimpleCumSumModuleNew, self).__init__()
self.dim = dim
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
briancoutinho/glow
|
SimpleCumSumModule
| false | 12,570 |
[
"Apache-2.0"
] | 0 |
4c919d60b3c33296c4109aec8020a1733c98f5b5
|
https://github.com/briancoutinho/glow/tree/4c919d60b3c33296c4109aec8020a1733c98f5b5
|
ResNetBlockGroupNorm
|
import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class ResNetBlockGroupNorm(nn.Module):
def __init__(self, inplanes, planes, num_groups, stride=1, activation=
'relu'):
super(ResNetBlockGroupNorm, self).__init__()
assert activation in ['relu', 'elu', 'leaky_relu']
self.conv1 = conv3x3(inplanes, planes, stride)
self.gn1 = nn.GroupNorm(num_groups, planes)
if activation == 'relu':
self.activation = nn.ReLU(inplace=True)
elif activation == 'elu':
self.activation = nn.ELU(inplace=True)
else:
self.activation = nn.LeakyReLU(inplace=True, negative_slope=0.1)
self.conv2 = conv3x3(planes, planes)
self.gn2 = nn.GroupNorm(num_groups, planes)
downsample = None
if stride != 1 or inplanes != planes:
downsample = nn.Sequential(nn.Conv2d(inplanes, planes,
kernel_size=1, stride=stride, bias=False), nn.GroupNorm(
num_groups, planes))
self.downsample = downsample
self.reset_parameters()
def reset_parameters(self):
nn.init.constant_(self.gn1.weight, 1.0)
nn.init.constant_(self.gn1.bias, 0.0)
nn.init.constant_(self.gn2.weight, 1.0)
nn.init.constant_(self.gn2.bias, 0.0)
if self.downsample is not None:
assert isinstance(self.downsample[1], nn.GroupNorm)
nn.init.constant_(self.downsample[1].weight, 1.0)
nn.init.constant_(self.downsample[1].bias, 0.0)
def init(self, x, init_scale=1.0):
with torch.no_grad():
return self(x)
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.gn1(out)
out = self.activation(out)
out = self.conv2(out)
out = self.gn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.activation(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'inplanes': 4, 'planes': 4, 'num_groups': 1}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_native_group_norm_relu_0(in_ptr0, in_ptr1, in_ptr2,
out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 4
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
r3 = rindex // 16
tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
tmp24 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
tmp26 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tl.where(xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 64, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = tmp0 - tmp10
tmp18 = 64.0
tmp19 = tmp16 / tmp18
tmp20 = 1e-05
tmp21 = tmp19 + tmp20
tmp22 = libdevice.rsqrt(tmp21)
tmp23 = tmp17 * tmp22
tmp25 = tmp23 * tmp24
tmp27 = tmp25 + tmp26
tmp28 = tl.full([1, 1], 0, tl.int32)
tmp29 = triton_helpers.maximum(tmp28, tmp27)
tl.store(out_ptr2 + (r1 + 64 * x0), tmp29, xmask)
tl.store(out_ptr3 + x0, tmp22, xmask)
tl.store(out_ptr0 + x0, tmp10, xmask)
@triton.jit
def triton_per_fused_add_native_group_norm_relu_threshold_backward_1(in_ptr0,
in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr2, out_ptr3, out_ptr4,
xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 4
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
r3 = rindex // 16
tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
tmp24 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
tmp26 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
tmp28 = tl.load(in_ptr3 + (r1 + 64 * x0), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tl.where(xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 64, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = tmp0 - tmp10
tmp18 = 64.0
tmp19 = tmp16 / tmp18
tmp20 = 1e-05
tmp21 = tmp19 + tmp20
tmp22 = libdevice.rsqrt(tmp21)
tmp23 = tmp17 * tmp22
tmp25 = tmp23 * tmp24
tmp27 = tmp25 + tmp26
tmp29 = tmp27 + tmp28
tmp30 = tl.full([1, 1], 0, tl.int32)
tmp31 = triton_helpers.maximum(tmp30, tmp29)
tmp32 = 0.0
tmp33 = tmp31 <= tmp32
tl.store(out_ptr2 + (r1 + 64 * x0), tmp31, xmask)
tl.store(out_ptr3 + (r1 + 64 * x0), tmp33, xmask)
tl.store(out_ptr4 + x0, tmp22, xmask)
tl.store(out_ptr0 + x0, tmp10, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf4 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
get_raw_stream(0)
triton_per_fused_native_group_norm_relu_0[grid(4)](buf0, primals_3,
primals_4, buf1, buf5, buf4, 4, 64, XBLOCK=1, num_warps=2,
num_stages=1)
del primals_4
buf6 = extern_kernels.convolution(buf5, primals_5, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
buf7 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf10 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
triton_per_fused_add_native_group_norm_relu_threshold_backward_1[grid
(4)](buf6, primals_6, primals_7, primals_1, buf7, buf11, buf12,
buf10, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
del primals_7
return (buf11, primals_1, primals_2, primals_3, primals_5, primals_6,
buf0, reinterpret_tensor(buf1, (4, 1), (1, 1), 0),
reinterpret_tensor(buf4, (4, 1), (1, 1), 0), buf5, buf6,
reinterpret_tensor(buf7, (4, 1), (1, 1), 0), reinterpret_tensor(
buf10, (4, 1), (1, 1), 0), buf12)
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class ResNetBlockGroupNormNew(nn.Module):
def __init__(self, inplanes, planes, num_groups, stride=1, activation=
'relu'):
super(ResNetBlockGroupNormNew, self).__init__()
assert activation in ['relu', 'elu', 'leaky_relu']
self.conv1 = conv3x3(inplanes, planes, stride)
self.gn1 = nn.GroupNorm(num_groups, planes)
if activation == 'relu':
self.activation = nn.ReLU(inplace=True)
elif activation == 'elu':
self.activation = nn.ELU(inplace=True)
else:
self.activation = nn.LeakyReLU(inplace=True, negative_slope=0.1)
self.conv2 = conv3x3(planes, planes)
self.gn2 = nn.GroupNorm(num_groups, planes)
downsample = None
if stride != 1 or inplanes != planes:
downsample = nn.Sequential(nn.Conv2d(inplanes, planes,
kernel_size=1, stride=stride, bias=False), nn.GroupNorm(
num_groups, planes))
self.downsample = downsample
self.reset_parameters()
def reset_parameters(self):
nn.init.constant_(self.gn1.weight, 1.0)
nn.init.constant_(self.gn1.bias, 0.0)
nn.init.constant_(self.gn2.weight, 1.0)
nn.init.constant_(self.gn2.bias, 0.0)
if self.downsample is not None:
assert isinstance(self.downsample[1], nn.GroupNorm)
nn.init.constant_(self.downsample[1].weight, 1.0)
nn.init.constant_(self.downsample[1].bias, 0.0)
def init(self, x, init_scale=1.0):
with torch.no_grad():
return self(x)
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = self.gn1.weight
primals_4 = self.gn1.bias
primals_5 = self.conv2.weight
primals_6 = self.gn2.weight
primals_7 = self.gn2.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
andrecianflone/wolf
|
ResNetBlockGroupNorm
| false | 14,857 |
[
"Apache-2.0"
] | 75 |
826bbedc58d4d29871110349356868066a3108e6
|
https://github.com/andrecianflone/wolf/tree/826bbedc58d4d29871110349356868066a3108e6
|
InnerProductLayer
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_9/inductor_cache/dx/cdxuxa4usulotytq2uhe2lpzvskzdaihvkxtup7hxtpolwz634sr.py
# Topologically Sorted Source Nodes: [p], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# p => cat
# Graph fragment:
# %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%select, %select_1, %select_2, %select_3, %select_4, %select_5], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4) % 24
x0 = xindex % 4
x2 = (xindex // 96)
x3 = xindex
tmp0 = x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + (4*x1) + (16*x2)), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr0 + (x0 + (4*((-4) + x1)) + (16*x2)), tmp9 & xmask, other=0.0)
tmp11 = tmp0 >= tmp7
tmp12 = tl.full([1], 12, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tmp11 & tmp13
tmp15 = tl.load(in_ptr0 + (x0 + (4*((-8) + x1)) + (16*x2)), tmp14 & xmask, other=0.0)
tmp16 = tmp0 >= tmp12
tmp17 = tl.full([1], 16, tl.int64)
tmp18 = tmp0 < tmp17
tmp19 = tmp16 & tmp18
tmp20 = tl.load(in_ptr0 + (64 + x0 + (4*((-12) + x1)) + (16*x2)), tmp19 & xmask, other=0.0)
tmp21 = tmp0 >= tmp17
tmp22 = tl.full([1], 20, tl.int64)
tmp23 = tmp0 < tmp22
tmp24 = tmp21 & tmp23
tmp25 = tl.load(in_ptr0 + (64 + x0 + (4*((-16) + x1)) + (16*x2)), tmp24 & xmask, other=0.0)
tmp26 = tmp0 >= tmp22
tmp27 = tl.full([1], 24, tl.int64)
tmp28 = tmp0 < tmp27
tmp29 = tl.load(in_ptr0 + (128 + x0 + (4*((-20) + x1)) + (16*x2)), tmp26 & xmask, other=0.0)
tmp30 = tl.where(tmp24, tmp25, tmp29)
tmp31 = tl.where(tmp19, tmp20, tmp30)
tmp32 = tl.where(tmp14, tmp15, tmp31)
tmp33 = tl.where(tmp9, tmp10, tmp32)
tmp34 = tl.where(tmp4, tmp5, tmp33)
tl.store(out_ptr0 + (x3), tmp34, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/3d/c3d5ce25tfocluzh3yh76j6qjeohfmdemkusyhh2wn2xdyzoda7p.py
# Topologically Sorted Source Nodes: [q], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# q => cat_1
# Graph fragment:
# %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%select_6, %select_7, %select_8, %select_9, %select_10, %select_11], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4) % 24
x0 = xindex % 4
x2 = (xindex // 96)
x3 = xindex
tmp0 = x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (64 + x0 + (4*x1) + (16*x2)), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr0 + (128 + x0 + (4*((-4) + x1)) + (16*x2)), tmp9 & xmask, other=0.0)
tmp11 = tmp0 >= tmp7
tmp12 = tl.full([1], 12, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tmp11 & tmp13
tmp15 = tl.load(in_ptr0 + (192 + x0 + (4*((-8) + x1)) + (16*x2)), tmp14 & xmask, other=0.0)
tmp16 = tmp0 >= tmp12
tmp17 = tl.full([1], 16, tl.int64)
tmp18 = tmp0 < tmp17
tmp19 = tmp16 & tmp18
tmp20 = tl.load(in_ptr0 + (128 + x0 + (4*((-12) + x1)) + (16*x2)), tmp19 & xmask, other=0.0)
tmp21 = tmp0 >= tmp17
tmp22 = tl.full([1], 20, tl.int64)
tmp23 = tmp0 < tmp22
tmp24 = tmp21 & tmp23
tmp25 = tl.load(in_ptr0 + (192 + x0 + (4*((-16) + x1)) + (16*x2)), tmp24 & xmask, other=0.0)
tmp26 = tmp0 >= tmp22
tmp27 = tl.full([1], 24, tl.int64)
tmp28 = tmp0 < tmp27
tmp29 = tl.load(in_ptr0 + (192 + x0 + (4*((-20) + x1)) + (16*x2)), tmp26 & xmask, other=0.0)
tmp30 = tl.where(tmp24, tmp25, tmp29)
tmp31 = tl.where(tmp19, tmp20, tmp30)
tmp32 = tl.where(tmp14, tmp15, tmp31)
tmp33 = tl.where(tmp9, tmp10, tmp32)
tmp34 = tl.where(tmp4, tmp5, tmp33)
tl.store(out_ptr0 + (x3), tmp34, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/wm/cwmiv6irrhvdznioij35ss6z35p7cnponuwhiju4tyoert736dwz.py
# Topologically Sorted Source Nodes: [inner_product, inner_product_1], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
# inner_product => mul
# inner_product_1 => sum_1
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%cat, %cat_1), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2], True), kwargs = {})
triton_poi_fused_mul_sum_2 = async_compile.triton('triton_poi_fused_mul_sum_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 96
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp2 = tmp0 * tmp1
tmp5 = tmp3 * tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 * tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 * tmp12
tmp14 = tmp10 + tmp13
tl.store(out_ptr0 + (x0), tmp14, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 24, 4), (96, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [p], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(arg0_1, buf0, 384, grid=grid(384), stream=stream0)
buf1 = empty_strided_cuda((4, 24, 4), (96, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [q], Original ATen: [aten.cat]
triton_poi_fused_cat_1.run(arg0_1, buf1, 384, grid=grid(384), stream=stream0)
del arg0_1
buf2 = empty_strided_cuda((4, 24, 1), (24, 1, 1), torch.float32)
# Topologically Sorted Source Nodes: [inner_product, inner_product_1], Original ATen: [aten.mul, aten.sum]
triton_poi_fused_mul_sum_2.run(buf0, buf1, buf2, 96, grid=grid(96), stream=stream0)
del buf0
del buf1
return (buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from sklearn.metrics import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 24
x0 = xindex % 4
x2 = xindex // 96
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr0 + (x0 + 4 * (-4 + x1) + 16 * x2), tmp9 & xmask,
other=0.0)
tmp11 = tmp0 >= tmp7
tmp12 = tl.full([1], 12, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tmp11 & tmp13
tmp15 = tl.load(in_ptr0 + (x0 + 4 * (-8 + x1) + 16 * x2), tmp14 & xmask,
other=0.0)
tmp16 = tmp0 >= tmp12
tmp17 = tl.full([1], 16, tl.int64)
tmp18 = tmp0 < tmp17
tmp19 = tmp16 & tmp18
tmp20 = tl.load(in_ptr0 + (64 + x0 + 4 * (-12 + x1) + 16 * x2), tmp19 &
xmask, other=0.0)
tmp21 = tmp0 >= tmp17
tmp22 = tl.full([1], 20, tl.int64)
tmp23 = tmp0 < tmp22
tmp24 = tmp21 & tmp23
tmp25 = tl.load(in_ptr0 + (64 + x0 + 4 * (-16 + x1) + 16 * x2), tmp24 &
xmask, other=0.0)
tmp26 = tmp0 >= tmp22
tl.full([1], 24, tl.int64)
tmp29 = tl.load(in_ptr0 + (128 + x0 + 4 * (-20 + x1) + 16 * x2), tmp26 &
xmask, other=0.0)
tmp30 = tl.where(tmp24, tmp25, tmp29)
tmp31 = tl.where(tmp19, tmp20, tmp30)
tmp32 = tl.where(tmp14, tmp15, tmp31)
tmp33 = tl.where(tmp9, tmp10, tmp32)
tmp34 = tl.where(tmp4, tmp5, tmp33)
tl.store(out_ptr0 + x3, tmp34, xmask)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 24
x0 = xindex % 4
x2 = xindex // 96
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (64 + x0 + 4 * x1 + 16 * x2), tmp4 & xmask,
other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr0 + (128 + x0 + 4 * (-4 + x1) + 16 * x2), tmp9 &
xmask, other=0.0)
tmp11 = tmp0 >= tmp7
tmp12 = tl.full([1], 12, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tmp11 & tmp13
tmp15 = tl.load(in_ptr0 + (192 + x0 + 4 * (-8 + x1) + 16 * x2), tmp14 &
xmask, other=0.0)
tmp16 = tmp0 >= tmp12
tmp17 = tl.full([1], 16, tl.int64)
tmp18 = tmp0 < tmp17
tmp19 = tmp16 & tmp18
tmp20 = tl.load(in_ptr0 + (128 + x0 + 4 * (-12 + x1) + 16 * x2), tmp19 &
xmask, other=0.0)
tmp21 = tmp0 >= tmp17
tmp22 = tl.full([1], 20, tl.int64)
tmp23 = tmp0 < tmp22
tmp24 = tmp21 & tmp23
tmp25 = tl.load(in_ptr0 + (192 + x0 + 4 * (-16 + x1) + 16 * x2), tmp24 &
xmask, other=0.0)
tmp26 = tmp0 >= tmp22
tl.full([1], 24, tl.int64)
tmp29 = tl.load(in_ptr0 + (192 + x0 + 4 * (-20 + x1) + 16 * x2), tmp26 &
xmask, other=0.0)
tmp30 = tl.where(tmp24, tmp25, tmp29)
tmp31 = tl.where(tmp19, tmp20, tmp30)
tmp32 = tl.where(tmp14, tmp15, tmp31)
tmp33 = tl.where(tmp9, tmp10, tmp32)
tmp34 = tl.where(tmp4, tmp5, tmp33)
tl.store(out_ptr0 + x3, tmp34, xmask)
@triton.jit
def triton_poi_fused_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 96
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 * tmp1
tmp5 = tmp3 * tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 * tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 * tmp12
tmp14 = tmp10 + tmp13
tl.store(out_ptr0 + x0, tmp14, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 24, 4), (96, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(384)](arg0_1, buf0, 384, XBLOCK=256,
num_warps=4, num_stages=1)
buf1 = empty_strided_cuda((4, 24, 4), (96, 4, 1), torch.float32)
triton_poi_fused_cat_1[grid(384)](arg0_1, buf1, 384, XBLOCK=256,
num_warps=4, num_stages=1)
del arg0_1
buf2 = empty_strided_cuda((4, 24, 1), (24, 1, 1), torch.float32)
triton_poi_fused_mul_sum_2[grid(96)](buf0, buf1, buf2, 96, XBLOCK=
128, num_warps=4, num_stages=1)
del buf0
del buf1
return buf2,
class InnerProductLayerNew(nn.Module):
"""InnerProduct Layer used in PNN that compute the element-wise
product or inner product between feature vectors.
Input shape
- a list of 3D tensor with shape: ``(batch_size,1,embedding_size)``.
Output shape
- 3D tensor with shape: ``(batch_size, N*(N-1)/2 ,1)`` if use reduce_sum. or 3D tensor with shape:
``(batch_size, N*(N-1)/2, embedding_size )`` if not use reduce_sum.
Arguments
- **reduce_sum**: bool. Whether return inner product or element-wise product
References
- [Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//
Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.]
(https://arxiv.org/pdf/1611.00144.pdf)"""
def __init__(self, reduce_sum=True, device='cpu'):
super(InnerProductLayerNew, self).__init__()
self.reduce_sum = reduce_sum
self
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
liyunrui/DeepCTR-Torch
|
InnerProductLayer
| false | 12,951 |
[
"Apache-2.0"
] | 0 |
392fd6d39d9ca0ac854022136cdb4d5c68e3a592
|
https://github.com/liyunrui/DeepCTR-Torch/tree/392fd6d39d9ca0ac854022136cdb4d5c68e3a592
|
LocalNet
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_6/inductor_cache/we/cwerb6bh4wkw66tuuogvvdhshhz6u3cw5s3a4jqb23ftxyfx5pg2.py
# Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
# pad => _unsafe_index, _unsafe_index_1
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 2304
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = (xindex // 6) % 6
x2 = (xindex // 36)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_6/inductor_cache/gd/cgdqnufkmolkhmahgpirghlilfdhf2tzbnkmaswr2jaio34ke2tp.py
# Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
# conv2d => convolution
# x => gt
# Graph fragment:
# %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
triton_poi_fused_convolution_leaky_relu_1 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 16) % 64
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tl.store(out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_6/inductor_cache/4h/c4hp7y3t3avb7y4kdpd3qny63wnyjqlzzqqpgqgesca5dwxpwjbq.py
# Topologically Sorted Source Nodes: [conv2d, x, pad_1], Original ATen: [aten.convolution, aten.leaky_relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# conv2d => convolution
# pad_1 => _unsafe_index_2, _unsafe_index_3
# x => mul, where
# Graph fragment:
# %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.01), kwargs = {})
# %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
# %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_3 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_convolution_leaky_relu_reflection_pad2d_2 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_reflection_pad2d_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16384],
filename=__file__,
triton_meta={'signature': {0: '*i1', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_reflection_pad2d_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_reflection_pad2d_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 9216
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = (xindex // 6) % 6
x4 = (xindex // 36)
x2 = (xindex // 36) % 64
x5 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x4)), xmask, eviction_policy='evict_last').to(tl.int1)
tmp1 = tl.load(in_ptr1 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x4)), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = 0.01
tmp5 = tmp3 * tmp4
tmp6 = tl.where(tmp0, tmp3, tmp5)
tl.store(out_ptr0 + (x5), tmp6, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_6/inductor_cache/tz/ctz2qoenjoyo6tyt4vbajmakep7el2x5bm5bjwz5dxrqesj5uyf2.py
# Topologically Sorted Source Nodes: [conv2d_1, x_1], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
# conv2d_1 => convolution_1
# x_1 => gt_1, mul_1, where_1
# Graph fragment:
# %convolution_1 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_3, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 0.01), kwargs = {})
# %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_1, %mul_1), kwargs = {})
triton_poi_fused_convolution_leaky_relu_3 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 16) % 64
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + (x3), tmp4, None)
tl.store(out_ptr1 + (x3), tmp7, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 16, 4, 4), (256, 16, 4, 1))
assert_size_stride(primals_2, (64, 16, 3, 3), (144, 9, 3, 1))
assert_size_stride(primals_3, (64, ), (1, ))
assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (64, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 16, 6, 6), (576, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 2304, grid=grid(2304), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 64, 4, 4), (1024, 16, 4, 1))
buf2 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.leaky_relu]
triton_poi_fused_convolution_leaky_relu_1.run(buf1, primals_3, buf2, 4096, grid=grid(4096), stream=stream0)
buf3 = empty_strided_cuda((4, 64, 6, 6), (2304, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [conv2d, x, pad_1], Original ATen: [aten.convolution, aten.leaky_relu, aten.reflection_pad2d]
triton_poi_fused_convolution_leaky_relu_reflection_pad2d_2.run(buf2, buf1, primals_3, buf3, 9216, grid=grid(9216), stream=stream0)
del primals_3
# Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1))
buf5 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool)
buf6 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [conv2d_1, x_1], Original ATen: [aten.convolution, aten.leaky_relu]
triton_poi_fused_convolution_leaky_relu_3.run(buf4, primals_5, buf5, buf6, 4096, grid=grid(4096), stream=stream0)
del buf4
del primals_5
return (buf6, primals_2, primals_4, buf0, buf2, buf3, buf5, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 16, 4, 4), (256, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((64, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 2304
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 16 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tl.store(out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_reflection_pad2d_2(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 9216
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x4 = xindex // 36
x2 = xindex // 36 % 64
x5 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x4),
xmask, eviction_policy='evict_last').to(tl.int1)
tmp1 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x4),
xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = 0.01
tmp5 = tmp3 * tmp4
tmp6 = tl.where(tmp0, tmp3, tmp5)
tl.store(out_ptr0 + x5, tmp6, xmask)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 16 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, None)
tl.store(out_ptr1 + x3, tmp7, None)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 16, 4, 4), (256, 16, 4, 1))
assert_size_stride(primals_2, (64, 16, 3, 3), (144, 9, 3, 1))
assert_size_stride(primals_3, (64,), (1,))
assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (64,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 16, 6, 6), (576, 36, 6, 1), torch.float32
)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(2304)](primals_1, buf0,
2304, XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 64, 4, 4), (1024, 16, 4, 1))
buf2 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_leaky_relu_1[grid(4096)](buf1,
primals_3, buf2, 4096, XBLOCK=128, num_warps=4, num_stages=1)
buf3 = empty_strided_cuda((4, 64, 6, 6), (2304, 36, 6, 1), torch.
float32)
triton_poi_fused_convolution_leaky_relu_reflection_pad2d_2[grid(9216)](
buf2, buf1, primals_3, buf3, 9216, XBLOCK=256, num_warps=4,
num_stages=1)
del primals_3
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1))
buf5 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool)
buf6 = buf1
del buf1
triton_poi_fused_convolution_leaky_relu_3[grid(4096)](buf4,
primals_5, buf5, buf6, 4096, XBLOCK=256, num_warps=4, num_stages=1)
del buf4
del primals_5
return buf6, primals_2, primals_4, buf0, buf2, buf3, buf5
class LocalNetNew(nn.Module):
def __init__(self, in_channels=16, out_channels=64):
"""Double convolutional block
:param in_channels: number of input channels
:param out_channels: number of output channels
:returns: N/A
:rtype: N/A
"""
super(LocalNetNew, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, 3, 1, 0, 1)
self.conv2 = nn.Conv2d(out_channels, out_channels, 3, 1, 0, 1)
self.lrelu = nn.LeakyReLU()
self.refpad = nn.ReflectionPad2d(1)
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
ZombaSY/DeepLPF
|
LocalNet
| false | 1,345 |
[
"BSD-3-Clause"
] | 0 |
adce64ae01bc9e32f465a354cb1f6534f0d13597
|
https://github.com/ZombaSY/DeepLPF/tree/adce64ae01bc9e32f465a354cb1f6534f0d13597
|
DilatedResidualLayer
|
import torch
from torch import nn
import torch.nn.functional as F
class DilatedResidualLayer(nn.Module):
def __init__(self, dilation, in_channels, out_channels):
super(DilatedResidualLayer, self).__init__()
self.conv_dilated = nn.Conv1d(in_channels, out_channels, 3, padding
=dilation, dilation=dilation)
self.conv_1x1 = nn.Conv1d(out_channels, out_channels, 1)
self.dropout = nn.Dropout()
def forward(self, x):
out = F.relu(self.conv_dilated(x))
out = self.conv_1x1(out)
out = self.dropout(out)
return x + out
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'dilation': 1, 'in_channels': 4, 'out_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_out_ptr0 + x2, xmask)
tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3), (12, 3, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
4, 4), (16, 4, 1), 0), primals_1, stride=(1,), padding=(1,),
dilation=(1,), transposed=False, output_padding=(0,), groups=1,
bias=None)
assert_size_stride(buf0, (1, 4, 4), (16, 4, 1))
buf1 = reinterpret_tensor(buf0, (4, 4), (4, 1), 0)
del buf0
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(16)](buf1,
primals_2, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_2
buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (1, 4, 4
), (0, 4, 1), 0), primals_4, stride=(1,), padding=(0,),
dilation=(1,), transposed=False, output_padding=(0,), groups=1,
bias=None)
assert_size_stride(buf2, (1, 4, 4), (16, 4, 1))
buf3 = reinterpret_tensor(buf2, (4, 4), (4, 1), 0)
del buf2
triton_poi_fused_add_1[grid(16)](buf3, primals_3, primals_5, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del primals_5
return buf3, primals_1, primals_4, reinterpret_tensor(primals_3, (1, 4,
4), (16, 4, 1), 0), reinterpret_tensor(buf1, (1, 4, 4), (16, 4, 1), 0
), buf4
class DilatedResidualLayerNew(nn.Module):
def __init__(self, dilation, in_channels, out_channels):
super(DilatedResidualLayerNew, self).__init__()
self.conv_dilated = nn.Conv1d(in_channels, out_channels, 3, padding
=dilation, dilation=dilation)
self.conv_1x1 = nn.Conv1d(out_channels, out_channels, 1)
self.dropout = nn.Dropout()
def forward(self, input_0):
primals_1 = self.conv_dilated.weight
primals_2 = self.conv_dilated.bias
primals_4 = self.conv_1x1.weight
primals_5 = self.conv_1x1.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
Jaakik/hydra-ml
|
DilatedResidualLayer
| false | 11,535 |
[
"MIT"
] | 0 |
eae54fc478163130c94450a2a2ddea4f204c1ea9
|
https://github.com/Jaakik/hydra-ml/tree/eae54fc478163130c94450a2a2ddea4f204c1ea9
|
ResidualBlock_noBN
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_2/inductor_cache/nd/cndrb2by2g3ednlxsjtytxjhjdm7bdlrgvzzodhsxvxnpml2di7p.py
# Topologically Sorted Source Nodes: [conv2d, out], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# conv2d => convolution
# out => relu
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1048576],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1048576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 4096) % 64
tmp0 = tl.load(in_out_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_2/inductor_cache/ee/cee5rlyewrt7mgwaib2prtpk3sps6e32amj5myetakd7b423m57w.py
# Topologically Sorted Source Nodes: [out_1, add], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
# add => add
# out_1 => convolution_1
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %convolution_1), kwargs = {})
triton_poi_fused_add_convolution_1 = async_compile.triton('triton_poi_fused_add_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1048576],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 1048576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 4096) % 64
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_out_ptr0 + (x3), None)
tmp2 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 64, 64, 64), (262144, 4096, 64, 1))
assert_size_stride(primals_2, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_3, (64, ), (1, ))
assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (64, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [conv2d, out], Original ATen: [aten.convolution, aten.relu]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_relu_0.run(buf1, primals_3, 1048576, grid=grid(1048576), stream=stream0)
del primals_3
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf3 = buf2; del buf2 # reuse
# Topologically Sorted Source Nodes: [out_1, add], Original ATen: [aten.convolution, aten.add]
triton_poi_fused_add_convolution_1.run(buf3, primals_1, primals_5, 1048576, grid=grid(1048576), stream=stream0)
del primals_5
return (buf3, primals_1, primals_2, primals_4, buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 64, 64, 64), (262144, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
import torch.nn.init as init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 4096 % 64
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_add_convolution_1(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 4096 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_out_ptr0 + x3, None)
tmp2 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + x3, tmp4, None)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 64, 64, 64), (262144, 4096, 64, 1))
assert_size_stride(primals_2, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_3, (64,), (1,))
assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (64,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(1048576)](buf1, primals_3,
1048576, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_3
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf3 = buf2
del buf2
triton_poi_fused_add_convolution_1[grid(1048576)](buf3, primals_1,
primals_5, 1048576, XBLOCK=512, num_warps=8, num_stages=1)
del primals_5
return buf3, primals_1, primals_2, primals_4, buf1
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv3d):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm3d):
init.constant_(m.weight, 1)
init.constant_(m.bias.data, 0.0)
class ResidualBlock_noBNNew(nn.Module):
"""Residual block w/o BN
---Conv-ReLU-Conv-+-
|________________|
"""
def __init__(self, nf=64):
super(ResidualBlock_noBNNew, self).__init__()
self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
initialize_weights([self.conv1, self.conv2], 0.1)
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
LCM1999/VolumeRescaling
|
ResidualBlock_noBN
| false | 17,575 |
[
"Apache-2.0"
] | 4 |
3eeabf057e68804ed945711b440f19e419c10d7a
|
https://github.com/LCM1999/VolumeRescaling/tree/3eeabf057e68804ed945711b440f19e419c10d7a
|
UpsamplingBilinear
|
import torch
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
class UpsamplingBilinear(nn.Module):
def __init__(self):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
def forward(self, x):
x = self.quant(x)
upsample = nn.functional.interpolate(x, scale_factor=2, mode=
'bilinear', align_corners=True)
return self.dequant(upsample)
def fuse_model(self):
pass
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.quantization import QuantStub
from torch.quantization import DeQuantStub
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0(
in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 8 % 8
x0 = xindex % 8
x2 = xindex // 64
x4 = xindex
tmp0 = x1
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.42857142857142855
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tmp7 = tl.full([1], 1, tl.int64)
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 3, tl.int64)
tmp10 = triton_helpers.minimum(tmp8, tmp9)
tmp11 = x0
tmp12 = tmp11.to(tl.float32)
tmp13 = tmp12 * tmp2
tmp14 = triton_helpers.maximum(tmp13, tmp4)
tmp15 = tmp14.to(tl.int32)
tmp16 = tl.load(in_ptr0 + (tmp15 + 4 * tmp10 + 16 * x2), xmask,
eviction_policy='evict_last')
tmp17 = tmp15 + tmp7
tmp18 = triton_helpers.minimum(tmp17, tmp9)
tmp19 = tl.load(in_ptr0 + (tmp18 + 4 * tmp10 + 16 * x2), xmask,
eviction_policy='evict_last')
tmp20 = tmp19 - tmp16
tmp21 = tmp15.to(tl.float32)
tmp22 = tmp14 - tmp21
tmp23 = triton_helpers.maximum(tmp22, tmp4)
tmp24 = 1.0
tmp25 = triton_helpers.minimum(tmp23, tmp24)
tmp26 = tmp20 * tmp25
tmp27 = tmp16 + tmp26
tmp28 = tl.load(in_ptr0 + (tmp15 + 4 * tmp6 + 16 * x2), xmask,
eviction_policy='evict_last')
tmp29 = tl.load(in_ptr0 + (tmp18 + 4 * tmp6 + 16 * x2), xmask,
eviction_policy='evict_last')
tmp30 = tmp29 - tmp28
tmp31 = tmp30 * tmp25
tmp32 = tmp28 + tmp31
tmp33 = tmp27 - tmp32
tmp34 = tmp6.to(tl.float32)
tmp35 = tmp5 - tmp34
tmp36 = triton_helpers.maximum(tmp35, tmp4)
tmp37 = triton_helpers.minimum(tmp36, tmp24)
tmp38 = tmp33 * tmp37
tmp39 = tmp32 + tmp38
tl.store(in_out_ptr0 + x4, tmp39, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0[grid
(1024)](buf1, arg0_1, 1024, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return buf1,
class UpsamplingBilinearNew(nn.Module):
def __init__(self):
super().__init__()
self.quant = QuantStub()
self.dequant = DeQuantStub()
def fuse_model(self):
pass
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Leslie-Fang/incubator-tvm
|
UpsamplingBilinear
| false | 9,303 |
[
"Apache-2.0"
] | 0 |
aa035f4650926f5e714b02cbab6d974f0a17352f
|
https://github.com/Leslie-Fang/incubator-tvm/tree/aa035f4650926f5e714b02cbab6d974f0a17352f
|
AcM
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_2/inductor_cache/pv/cpvq5akoxii6zri2lssiqurrqsl5nuea43ylpa6xzb433ugqbchi.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
# x => tanh
# Graph fragment:
# %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%view_1,), kwargs = {})
triton_poi_fused_tanh_0 = async_compile.triton('triton_poi_fused_tanh_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tl.store(in_out_ptr0 + (x2), tmp3, None)
''', device_str='cuda')
# kernel path: runs/run_shard_2/inductor_cache/vd/cvdwj7vqydgw7z7brh663uauzwfegvbcydkvglc3kwgrl6q4zijb.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
# x_1 => tanh_1
# Graph fragment:
# %tanh_1 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%view_3,), kwargs = {})
triton_poi_fused_tanh_1 = async_compile.triton('triton_poi_fused_tanh_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[2048],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 2048
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 32
tmp0 = tl.load(in_out_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tl.store(in_out_ptr0 + (x2), tmp3, None)
''', device_str='cuda')
# kernel path: runs/run_shard_2/inductor_cache/e4/ce4cl5fwehnchpxxkcnlocfcdupfyowv73biygcxglbtym6a2can.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# x_2 => amax, exp, sub
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_5, [1], True), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_5, %amax), kwargs = {})
# %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = (xindex // 64)
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_2/inductor_cache/4m/c4m3ygzhvntyb2u2ng47arhwfhv5p2yswp6d5b5ltatzm2z6f57y.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# x_2 => div, sum_1
# Graph fragment:
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = (xindex // 64)
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
args.clear()
assert_size_stride(primals_1, (64, 4), (4, 1))
assert_size_stride(primals_2, (64, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (32, 64), (64, 1))
assert_size_stride(primals_5, (32, ), (1, ))
assert_size_stride(primals_6, (4, 32), (32, 1))
assert_size_stride(primals_7, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf0 # reuse
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.tanh]
stream0 = get_raw_stream(0)
triton_poi_fused_tanh_0.run(buf1, primals_2, 4096, grid=grid(4096), stream=stream0)
del primals_2
buf2 = empty_strided_cuda((64, 32), (32, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 32), (1, 64), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 32), (512, 128, 32, 1), 0); del buf2 # reuse
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.tanh]
triton_poi_fused_tanh_1.run(buf3, primals_5, 2048, grid=grid(2048), stream=stream0)
del primals_5
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 32), (32, 1), 0), reinterpret_tensor(primals_6, (32, 4), (1, 32), 0), alpha=1, beta=1, out=buf4)
del primals_7
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._softmax]
triton_poi_fused__softmax_2.run(buf4, buf5, 256, grid=grid(256), stream=stream0)
buf6 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf4 # reuse
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._softmax]
triton_poi_fused__softmax_3.run(buf5, buf6, 256, grid=grid(256), stream=stream0)
del buf5
return (buf6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1, buf3, buf6, primals_6, primals_4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((32, 64), (64, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 32), (32, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, None)
@triton.jit
def triton_poi_fused_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 32
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, None)
@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x3, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x3, tmp8, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (64, 4), (4, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (32, 64), (64, 1))
assert_size_stride(primals_5, (32,), (1,))
assert_size_stride(primals_6, (4, 32), (32, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_tanh_0[grid(4096)](buf1, primals_2, 4096, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 32), (32, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0),
reinterpret_tensor(primals_4, (64, 32), (1, 64), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 32), (512, 128, 32, 1), 0)
del buf2
triton_poi_fused_tanh_1[grid(2048)](buf3, primals_5, 2048, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 32),
(32, 1), 0), reinterpret_tensor(primals_6, (32, 4), (1, 32), 0),
alpha=1, beta=1, out=buf4)
del primals_7
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_2[grid(256)](buf4, buf5, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf6 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf4
triton_poi_fused__softmax_3[grid(256)](buf5, buf6, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del buf5
return buf6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf1, buf3, buf6, primals_6, primals_4
class AcMNew(nn.Module):
def __init__(self, in_dim: 'int', ac_dim: 'int', ac_lim: 'int',
discrete: 'bool'=True):
super().__init__()
self.ac_lim = ac_lim
self.discrete = discrete
self.fc1 = nn.Linear(in_dim, 64)
self.fc2 = nn.Linear(64, 32)
self.fc3 = nn.Linear(32, ac_dim)
def act(self, obs):
action = self.forward(obs)
if self.discrete:
action = torch.argmax(action, dim=1)
return action
def forward(self, input_0):
primals_1 = self.fc1.weight
primals_2 = self.fc1.bias
primals_4 = self.fc2.weight
primals_5 = self.fc2.bias
primals_6 = self.fc3.weight
primals_7 = self.fc3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
MIMUW-RL/spp-rl
|
AcM
| false | 17,644 |
[
"MIT"
] | 7 |
86b96cdd220cc4eae86f7cfd26924c69b498dcc6
|
https://github.com/MIMUW-RL/spp-rl/tree/86b96cdd220cc4eae86f7cfd26924c69b498dcc6
|
net2
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_4/inductor_cache/fp/cfp5jrxxyxrvhcpoq5tio3p5tkhj5ugdrpyur3x4v6meatzih7jn.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 16], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 16
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 4
y1 = (yindex // 4)
tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask & ymask)
tl.store(out_ptr0 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_4/inductor_cache/f4/cf435ljkvsbmsfqmb2jfkuq4aor2tpycnclm7llj5efimpnzxiwz.py
# Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# conv2d => convolution
# x => relu
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_4/inductor_cache/j3/cj3tcccv2au6fwrk7zrqv6aadd7rde25xioifxhrg3rnq277xw2z.py
# Topologically Sorted Source Nodes: [conv2d_1, x_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# conv2d_1 => convolution_1
# x_1 => relu_1
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_2 = async_compile.triton('triton_poi_fused_convolution_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16384],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 256
tmp0 = tl.load(in_out_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_4/inductor_cache/mv/cmvvxenutty4sz3eynxns45xagw56ec2ufreiud5s5acbv345vt5.py
# Topologically Sorted Source Nodes: [conv2d_2, x_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# conv2d_2 => convolution_2
# x_2 => relu_2
# Graph fragment:
# %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
triton_poi_fused_convolution_relu_3 = async_compile.triton('triton_poi_fused_convolution_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8192],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 8192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 128
tmp0 = tl.load(in_out_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_4/inductor_cache/gb/cgbyyfi5lll3xaoryj23lises4aplhm5a4rcgchfqja6bfmq4vbx.py
# Topologically Sorted Source Nodes: [conv2d_3, x_3, x_4], Original ATen: [aten.convolution, aten.relu, aten._log_softmax, aten.threshold_backward]
# Source node to ATen node mapping:
# conv2d_3 => convolution_3
# x_3 => relu_3
# x_4 => amax, sub
# Graph fragment:
# %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_2, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_3 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {})
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%relu_3, [1], True), kwargs = {})
# %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%relu_3, %amax), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_poi_fused__log_softmax_convolution_relu_threshold_backward_4 = async_compile.triton('triton_poi_fused__log_softmax_convolution_relu_threshold_backward_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_convolution_relu_threshold_backward_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_convolution_relu_threshold_backward_4(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 2
x1 = (xindex // 2)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (2*x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr1 + (0))
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp10 = tl.load(in_ptr0 + (1 + (2*x1)), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr1 + (1))
tmp12 = tl.broadcast_to(tmp11, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp8 = tmp5 + tmp7
tmp9 = triton_helpers.maximum(tmp3, tmp8)
tmp13 = tmp10 + tmp12
tmp14 = triton_helpers.maximum(tmp3, tmp13)
tmp15 = triton_helpers.maximum(tmp9, tmp14)
tmp16 = tmp4 - tmp15
tmp17 = 0.0
tmp18 = tmp4 <= tmp17
tl.store(out_ptr0 + (x2), tmp16, xmask)
tl.store(out_ptr1 + (x2), tmp18, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_4/inductor_cache/f6/cf62mfpqwv5pcofy3ro6w6ycq3f2iwdol4c5h7ameyvaum2rwiui.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
# x_4 => exp, log, sub_1, sum_1
# Graph fragment:
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
# %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
triton_poi_fused__log_softmax_5 = async_compile.triton('triton_poi_fused__log_softmax_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8, 16], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 8
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 2
y1 = (yindex // 2)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (2*x2) + (32*y1)), xmask & ymask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + ((2*x2) + (32*y1)), xmask & ymask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (2*x2) + (32*y1)), xmask & ymask, eviction_policy='evict_last')
tmp2 = tl_math.exp(tmp1)
tmp4 = tl_math.exp(tmp3)
tmp5 = tmp2 + tmp4
tmp6 = tl_math.log(tmp5)
tmp7 = tmp0 - tmp6
tl.store(out_ptr0 + (x2 + (16*y3)), tmp7, xmask & ymask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
args.clear()
assert_size_stride(primals_1, (64, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_2, (64, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (256, 64, 1, 1), (64, 1, 1, 1))
assert_size_stride(primals_5, (256, ), (1, ))
assert_size_stride(primals_6, (128, 256, 1, 1), (256, 1, 1, 1))
assert_size_stride(primals_7, (128, ), (1, ))
assert_size_stride(primals_8, (2, 128, 1, 1), (128, 1, 1, 1))
assert_size_stride(primals_9, (2, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
# Unsorted Source Nodes: [], Original ATen: []
stream0 = get_raw_stream(0)
triton_poi_fused_0.run(primals_3, buf0, 16, 16, grid=grid(16, 16), stream=stream0)
del primals_3
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 64, 4, 4), (1024, 1, 256, 64))
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_1.run(buf2, primals_2, 4096, grid=grid(4096), stream=stream0)
del primals_2
# Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [conv2d_1, x_1], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_2.run(buf4, primals_5, 16384, grid=grid(16384), stream=stream0)
del primals_5
# Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 128, 4, 4), (2048, 1, 512, 128))
buf6 = buf5; del buf5 # reuse
# Topologically Sorted Source Nodes: [conv2d_2, x_2], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_3.run(buf6, primals_7, 8192, grid=grid(8192), stream=stream0)
del primals_7
# Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
buf7 = extern_kernels.convolution(buf6, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 2, 4, 4), (32, 1, 8, 2))
buf8 = empty_strided_cuda((4, 2, 4, 4), (32, 1, 8, 2), torch.float32)
buf10 = empty_strided_cuda((4, 2, 4, 4), (32, 1, 8, 2), torch.bool)
# Topologically Sorted Source Nodes: [conv2d_3, x_3, x_4], Original ATen: [aten.convolution, aten.relu, aten._log_softmax, aten.threshold_backward]
triton_poi_fused__log_softmax_convolution_relu_threshold_backward_4.run(buf7, primals_9, buf8, buf10, 128, grid=grid(128), stream=stream0)
del primals_9
buf9 = reinterpret_tensor(buf7, (4, 2, 4, 4), (32, 16, 4, 1), 0); del buf7 # reuse
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten._log_softmax]
triton_poi_fused__log_softmax_5.run(buf8, buf9, 8, 16, grid=grid(8, 16), stream=stream0)
del buf8
return (buf9, primals_1, buf0, primals_4, primals_6, primals_8, buf2, buf4, buf6, buf9, buf10, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((64, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((256, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((128, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((2, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 4
y1 = yindex // 4
tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 256
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 128
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused__log_softmax_convolution_relu_threshold_backward_4(in_ptr0
, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 2
x1 = xindex // 2
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr1 + 0)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp10 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last'
)
tmp11 = tl.load(in_ptr1 + 1)
tmp12 = tl.broadcast_to(tmp11, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp8 = tmp5 + tmp7
tmp9 = triton_helpers.maximum(tmp3, tmp8)
tmp13 = tmp10 + tmp12
tmp14 = triton_helpers.maximum(tmp3, tmp13)
tmp15 = triton_helpers.maximum(tmp9, tmp14)
tmp16 = tmp4 - tmp15
tmp17 = 0.0
tmp18 = tmp4 <= tmp17
tl.store(out_ptr0 + x2, tmp16, xmask)
tl.store(out_ptr1 + x2, tmp18, xmask)
@triton.jit
def triton_poi_fused__log_softmax_5(in_ptr0, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 8
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 2
y1 = yindex // 2
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 2 * x2 + 32 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (2 * x2 + 32 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 2 * x2 + 32 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp2 = tl_math.exp(tmp1)
tmp4 = tl_math.exp(tmp3)
tmp5 = tmp2 + tmp4
tmp6 = tl_math.log(tmp5)
tmp7 = tmp0 - tmp6
tl.store(out_ptr0 + (x2 + 16 * y3), tmp7, xmask & ymask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9) = args
args.clear()
assert_size_stride(primals_1, (64, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (256, 64, 1, 1), (64, 1, 1, 1))
assert_size_stride(primals_5, (256,), (1,))
assert_size_stride(primals_6, (128, 256, 1, 1), (256, 1, 1, 1))
assert_size_stride(primals_7, (128,), (1,))
assert_size_stride(primals_8, (2, 128, 1, 1), (128, 1, 1, 1))
assert_size_stride(primals_9, (2,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(16, 16)](primals_3, buf0, 16, 16, XBLOCK=16,
YBLOCK=16, num_warps=4, num_stages=1)
del primals_3
buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 64, 4, 4), (1024, 1, 256, 64))
buf2 = buf1
del buf1
triton_poi_fused_convolution_relu_1[grid(4096)](buf2, primals_2,
4096, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 256, 4, 4), (4096, 1, 1024, 256))
buf4 = buf3
del buf3
triton_poi_fused_convolution_relu_2[grid(16384)](buf4, primals_5,
16384, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 128, 4, 4), (2048, 1, 512, 128))
buf6 = buf5
del buf5
triton_poi_fused_convolution_relu_3[grid(8192)](buf6, primals_7,
8192, XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
buf7 = extern_kernels.convolution(buf6, primals_8, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 2, 4, 4), (32, 1, 8, 2))
buf8 = empty_strided_cuda((4, 2, 4, 4), (32, 1, 8, 2), torch.float32)
buf10 = empty_strided_cuda((4, 2, 4, 4), (32, 1, 8, 2), torch.bool)
triton_poi_fused__log_softmax_convolution_relu_threshold_backward_4[
grid(128)](buf7, primals_9, buf8, buf10, 128, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_9
buf9 = reinterpret_tensor(buf7, (4, 2, 4, 4), (32, 16, 4, 1), 0)
del buf7
triton_poi_fused__log_softmax_5[grid(8, 16)](buf8, buf9, 8, 16,
XBLOCK=16, YBLOCK=8, num_warps=4, num_stages=1)
del buf8
return (buf9, primals_1, buf0, primals_4, primals_6, primals_8, buf2,
buf4, buf6, buf9, buf10)
class net2New(nn.Module):
"""
"""
def __init__(self, n_classes=2):
super(net2New, self).__init__()
if torch.cuda.is_available():
torch.device('cuda')
else:
torch.device('cpu')
self.n_classes = n_classes
self.conv1 = nn.Conv2d(4, 64, 1)
self.conv2 = nn.Conv2d(64, 256, 1)
self.conv3 = nn.Conv2d(256, 128, 1)
self.conv4 = nn.Conv2d(128, self.n_classes, 1)
self.logsoftmax = nn.LogSoftmax(dim=1)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.conv3.weight
primals_7 = self.conv3.bias
primals_8 = self.conv4.weight
primals_9 = self.conv4.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9])
return output[0]
|
luisesanmartin/dwelling-recognition
|
net2
| false | 7,139 |
[
"MIT"
] | 1 |
b2437b64088a26746947c1c88077c96332e7b9c6
|
https://github.com/luisesanmartin/dwelling-recognition/tree/b2437b64088a26746947c1c88077c96332e7b9c6
|
qy
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/jf/cjfr4sfsrvydqmccowtorseagqzcsymbondn73vnpv23l35q756r.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# x_1 => cat_1
# Graph fragment:
# %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%cat, %view_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 96
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = (xindex // 6)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 5, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.full([1], 4, tl.int64)
tmp6 = tmp0 < tmp5
tmp7 = tmp6 & tmp4
tmp8 = tl.load(in_ptr0 + ((4*(x1 // 4)) + x0), tmp7 & xmask, eviction_policy='evict_last', other=0.0)
tmp9 = 0.0
tmp10 = tmp8 > tmp9
tmp11 = 0.01
tmp12 = tmp8 * tmp11
tmp13 = tl.where(tmp10, tmp8, tmp12)
tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
tmp15 = tl.where(tmp7, tmp13, tmp14)
tmp16 = tmp0 >= tmp5
tmp17 = tmp16 & tmp4
tmp18 = tl.load(in_ptr1 + (x1), tmp17 & xmask, eviction_policy='evict_last', other=0.0)
tmp19 = tl.where(tmp6, tmp15, tmp18)
tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
tmp21 = tl.where(tmp4, tmp19, tmp20)
tmp22 = tmp0 >= tmp3
tmp23 = tl.full([1], 6, tl.int64)
tmp24 = tmp0 < tmp23
tmp25 = tl.load(in_ptr2 + (x1), tmp22 & xmask, eviction_policy='evict_last', other=0.0)
tmp26 = tl.where(tmp4, tmp21, tmp25)
tl.store(out_ptr0 + (x2), tmp26, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/bh/cbhiwirufn4xgjlassc2kuz5i566zrr37i3sefu2ywwzuzkthdsl.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
# x_3 => gt_1, mul_1, where_1
# Graph fragment:
# %add_tensor_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_5), kwargs = {})
# %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_tensor_2, 0), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_tensor_2, 0.01), kwargs = {})
# %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %add_tensor_2, %mul_1), kwargs = {})
triton_poi_fused_leaky_relu_1 = async_compile.triton('triton_poi_fused_leaky_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8192],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 8192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 512
tmp0 = tl.load(in_out_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(in_out_ptr0 + (x2), tmp7, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/qd/cqdja7yao6mcn2im3ot77wimzd5degbo24upnzgxqbv2njv5uffs.py
# Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.cos]
# Source node to ATen node mapping:
# x_9 => cos
# Graph fragment:
# %cos : [num_users=2] = call_function[target=torch.ops.aten.cos.default](args = (%addmm_3,), kwargs = {})
triton_poi_fused_cos_2 = async_compile.triton('triton_poi_fused_cos_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cos_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cos_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), None)
tmp1 = tl_math.cos(tmp0)
tl.store(out_ptr0 + (x0), tmp1, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/vw/cvwcjxztifu7pseky77dsket6nm7izi3xqkbtqtxa6b7we5vdmtx.py
# Topologically Sorted Source Nodes: [h], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
# h => gt, mul, where
# Graph fragment:
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%primals_1, 0), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, 0.01), kwargs = {})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %primals_1, %mul), kwargs = {})
# %copy_ : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%primals_1, %where), kwargs = {})
triton_poi_fused_leaky_relu_3 = async_compile.triton('triton_poi_fused_leaky_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_3', 'mutated_arg_names': ['in_ptr0', 'out_ptr1'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_3(in_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 0.01
tmp4 = tmp0 * tmp3
tmp5 = tl.where(tmp2, tmp0, tmp4)
tl.store(out_ptr1 + (x0), tmp5, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (512, 6), (6, 1))
assert_size_stride(primals_5, (512, ), (1, ))
assert_size_stride(primals_6, (512, 512), (512, 1))
assert_size_stride(primals_7, (512, ), (1, ))
assert_size_stride(primals_8, (512, 512), (512, 1))
assert_size_stride(primals_9, (512, ), (1, ))
assert_size_stride(primals_10, (256, 512), (512, 1))
assert_size_stride(primals_11, (256, ), (1, ))
assert_size_stride(primals_12, (4, 256), (256, 1))
assert_size_stride(primals_13, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 6), (6, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_1, primals_3, primals_2, buf0, 96, grid=grid(96), stream=stream0)
del primals_2
del primals_3
buf1 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf0, reinterpret_tensor(primals_4, (6, 512), (1, 6), 0), out=buf1)
del primals_4
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.leaky_relu]
triton_poi_fused_leaky_relu_1.run(buf2, primals_5, 8192, grid=grid(8192), stream=stream0)
del primals_5
buf3 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf2, reinterpret_tensor(primals_6, (512, 512), (1, 512), 0), out=buf3)
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.leaky_relu]
triton_poi_fused_leaky_relu_1.run(buf4, primals_7, 8192, grid=grid(8192), stream=stream0)
del primals_7
buf5 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf4, reinterpret_tensor(primals_8, (512, 512), (1, 512), 0), out=buf5)
buf6 = buf5; del buf5 # reuse
# Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.leaky_relu]
triton_poi_fused_leaky_relu_1.run(buf6, primals_9, 8192, grid=grid(8192), stream=stream0)
del primals_9
buf7 = empty_strided_cuda((16, 256), (256, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_11, buf6, reinterpret_tensor(primals_10, (512, 256), (1, 512), 0), alpha=1, beta=1, out=buf7)
del primals_11
buf8 = empty_strided_cuda((16, 256), (256, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.cos]
triton_poi_fused_cos_2.run(buf7, buf8, 4096, grid=grid(4096), stream=stream0)
buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_13, buf8, reinterpret_tensor(primals_12, (256, 4), (1, 256), 0), alpha=1, beta=1, out=buf9)
del primals_13
# Topologically Sorted Source Nodes: [h], Original ATen: [aten.leaky_relu]
triton_poi_fused_leaky_relu_3.run(primals_1, primals_1, 16, grid=grid(16), stream=stream0)
del primals_1
return (reinterpret_tensor(buf9, (16, 2), (4, 1), 0), reinterpret_tensor(buf9, (16, 2), (4, 1), 2), buf9, buf0, buf2, buf4, buf6, buf7, buf8, primals_12, primals_10, primals_8, primals_6, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((512, 6), (6, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((512, 512), (512, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((512, 512), (512, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((256, 512), (512, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((4, 256), (256, 1), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 96
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 5, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.full([1], 4, tl.int64)
tmp6 = tmp0 < tmp5
tmp7 = tmp6 & tmp4
tmp8 = tl.load(in_ptr0 + (4 * (x1 // 4) + x0), tmp7 & xmask,
eviction_policy='evict_last', other=0.0)
tmp9 = 0.0
tmp10 = tmp8 > tmp9
tmp11 = 0.01
tmp12 = tmp8 * tmp11
tmp13 = tl.where(tmp10, tmp8, tmp12)
tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
tmp15 = tl.where(tmp7, tmp13, tmp14)
tmp16 = tmp0 >= tmp5
tmp17 = tmp16 & tmp4
tmp18 = tl.load(in_ptr1 + x1, tmp17 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp19 = tl.where(tmp6, tmp15, tmp18)
tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
tmp21 = tl.where(tmp4, tmp19, tmp20)
tmp22 = tmp0 >= tmp3
tl.full([1], 6, tl.int64)
tmp25 = tl.load(in_ptr2 + x1, tmp22 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp26 = tl.where(tmp4, tmp21, tmp25)
tl.store(out_ptr0 + x2, tmp26, xmask)
@triton.jit
def triton_poi_fused_leaky_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 512
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(in_out_ptr0 + x2, tmp7, None)
@triton.jit
def triton_poi_fused_cos_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, None)
tmp1 = tl_math.cos(tmp0)
tl.store(out_ptr0 + x0, tmp1, None)
@triton.jit
def triton_poi_fused_leaky_relu_3(in_ptr0, out_ptr1, xnumel, XBLOCK: tl.
constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 0.01
tmp4 = tmp0 * tmp3
tmp5 = tl.where(tmp2, tmp0, tmp4)
tl.store(out_ptr1 + x0, tmp5, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (512, 6), (6, 1))
assert_size_stride(primals_5, (512,), (1,))
assert_size_stride(primals_6, (512, 512), (512, 1))
assert_size_stride(primals_7, (512,), (1,))
assert_size_stride(primals_8, (512, 512), (512, 1))
assert_size_stride(primals_9, (512,), (1,))
assert_size_stride(primals_10, (256, 512), (512, 1))
assert_size_stride(primals_11, (256,), (1,))
assert_size_stride(primals_12, (4, 256), (256, 1))
assert_size_stride(primals_13, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 6), (6, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(96)](primals_1, primals_3, primals_2,
buf0, 96, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
del primals_3
buf1 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
extern_kernels.mm(buf0, reinterpret_tensor(primals_4, (6, 512), (1,
6), 0), out=buf1)
del primals_4
buf2 = buf1
del buf1
triton_poi_fused_leaky_relu_1[grid(8192)](buf2, primals_5, 8192,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf3 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
extern_kernels.mm(buf2, reinterpret_tensor(primals_6, (512, 512), (
1, 512), 0), out=buf3)
buf4 = buf3
del buf3
triton_poi_fused_leaky_relu_1[grid(8192)](buf4, primals_7, 8192,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
buf5 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
extern_kernels.mm(buf4, reinterpret_tensor(primals_8, (512, 512), (
1, 512), 0), out=buf5)
buf6 = buf5
del buf5
triton_poi_fused_leaky_relu_1[grid(8192)](buf6, primals_9, 8192,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_9
buf7 = empty_strided_cuda((16, 256), (256, 1), torch.float32)
extern_kernels.addmm(primals_11, buf6, reinterpret_tensor(
primals_10, (512, 256), (1, 512), 0), alpha=1, beta=1, out=buf7)
del primals_11
buf8 = empty_strided_cuda((16, 256), (256, 1), torch.float32)
triton_poi_fused_cos_2[grid(4096)](buf7, buf8, 4096, XBLOCK=128,
num_warps=4, num_stages=1)
buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_13, buf8, reinterpret_tensor(
primals_12, (256, 4), (1, 256), 0), alpha=1, beta=1, out=buf9)
del primals_13
triton_poi_fused_leaky_relu_3[grid(16)](primals_1, primals_1, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del primals_1
return (reinterpret_tensor(buf9, (16, 2), (4, 1), 0),
reinterpret_tensor(buf9, (16, 2), (4, 1), 2), buf9, buf0, buf2,
buf4, buf6, buf7, buf8, primals_12, primals_10, primals_8, primals_6)
def weights_init(m):
if isinstance(m, nn.Conv2d):
nn.init.kaiming_uniform_(m.weight, a=0, mode='fan_in', nonlinearity
='leaky_relu')
try:
nn.init.constant_(m.bias, 0.01)
except:
pass
if isinstance(m, nn.Linear):
nn.init.kaiming_uniform_(m.weight, a=0, mode='fan_in', nonlinearity
='leaky_relu')
nn.init.constant_(m.bias, 0.01)
class Generator(nn.Module):
def __init__(self, input_dim=8, output_dim=2):
super(Generator, self).__init__()
self.linear1 = nn.Linear(input_dim, 512)
self.linear2 = nn.Linear(512, 512)
self.linear3 = nn.Linear(512, 512)
self.linear4 = nn.Linear(512, 256)
self.linear5 = nn.Linear(256, output_dim)
self.apply(weights_init)
def forward(self, condition, v0, t):
x = torch.cat([condition, v0], dim=1)
x = torch.cat([x, t], dim=1)
x = self.linear1(x)
x = F.leaky_relu(x, inplace=True)
x = self.linear2(x)
x = F.leaky_relu(x, inplace=True)
x = self.linear3(x)
x = F.leaky_relu(x, inplace=True)
x = self.linear4(x)
x = torch.cos(x)
x = self.linear5(x)
return x
class qyNew(nn.Module):
def __init__(self, zy_dim):
super(qyNew, self).__init__()
self.trajectory_generation = Generator(input_dim=1 + 1 + zy_dim,
output_dim=4)
def forward(self, input_0, input_1, input_2):
primals_4 = self.trajectory_generation.linear1.weight
primals_5 = self.trajectory_generation.linear1.bias
primals_6 = self.trajectory_generation.linear2.weight
primals_7 = self.trajectory_generation.linear2.bias
primals_8 = self.trajectory_generation.linear3.weight
primals_9 = self.trajectory_generation.linear3.bias
primals_10 = self.trajectory_generation.linear4.weight
primals_11 = self.trajectory_generation.linear4.bias
primals_12 = self.trajectory_generation.linear5.weight
primals_13 = self.trajectory_generation.linear5.bias
primals_1 = input_0
primals_2 = input_1
primals_3 = input_2
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13])
return output[0], output[1]
|
IamWangYunKai/DG-TrajGen
|
qy
| false | 8,303 |
[
"MIT"
] | 31 |
0a8aab7e1c05111a5afe43d53801c55942e9ff56
|
https://github.com/IamWangYunKai/DG-TrajGen/tree/0a8aab7e1c05111a5afe43d53801c55942e9ff56
|
LabelPredictor
|
import torch
from torch import nn
class LabelPredictor(nn.Module):
def __init__(self, nz_feat, classify_rot=True):
super(LabelPredictor, self).__init__()
self.pred_layer = nn.Linear(nz_feat, 1)
def forward(self, feat):
pred = self.pred_layer.forward(feat)
pred = torch.sigmoid(pred)
return pred
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'nz_feat': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_sigmoid_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tmp4 = tl.sigmoid(tmp3)
tl.store(in_out_ptr0 + x0, tmp4, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (1, 4), (4, 1))
assert_size_stride(primals_2, (1,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_sigmoid_0[grid(64)](buf1, primals_2, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_2
return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1
class LabelPredictorNew(nn.Module):
def __init__(self, nz_feat, classify_rot=True):
super(LabelPredictorNew, self).__init__()
self.pred_layer = nn.Linear(nz_feat, 1)
def forward(self, input_0):
primals_1 = self.pred_layer.weight
primals_2 = self.pred_layer.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
JasonQSY/Associative3D
|
LabelPredictor
| false | 8,359 |
[
"MIT"
] | 25 |
c50818b593ec48c38ed7ee3e109c23531089da32
|
https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32
|
SequentialCNNNet
|
import torch
import torch.nn as nn
class SequentialCNNNet(nn.Module):
def __init__(self):
super(SequentialCNNNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(64, 128, 5)
self.fc1 = nn.Linear(128 * 5 * 5, 1024)
self.fc2 = nn.Linear(1024, 84)
self.fc3 = nn.Linear(84, 50)
self.features = nn.Sequential(self.conv1, nn.ReLU(), self.pool,
self.conv2, nn.ReLU(), self.pool)
self.classifier = nn.Sequential(self.fc1, self.fc2, self.fc3)
def forward(self, x):
x = self.features(x)
x = x.view(-1, 128 * 5 * 5)
x = self.classifier(x)
return x
def get_inputs():
return [torch.rand([4, 3, 32, 32])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 192
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 3
y1 = yindex // 3
tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask & ymask, eviction_policy
='evict_last')
tl.store(out_ptr0 + (y0 + 3 * x2 + 75 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 12
xnumel = 1024
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 3
y1 = yindex // 3
tmp0 = tl.load(in_ptr0 + (x2 + 1024 * y3), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (y0 + 3 * x2 + 3072 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 64
y1 = yindex // 64
tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 64 * x2 + 1600 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_4(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 50176
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 64
x1 = xindex // 64 % 14
x2 = xindex // 896
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1 + 3584 * x2), xmask)
tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 3584 * x2), xmask)
tmp3 = tl.load(in_ptr0 + (1792 + x0 + 128 * x1 + 3584 * x2), xmask)
tmp5 = tl.load(in_ptr0 + (1856 + x0 + 128 * x1 + 3584 * x2), xmask)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x3, tmp6, xmask)
tl.store(out_ptr1 + x3, tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_5(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 128
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_6(in_ptr0, out_ptr0, out_ptr1,
ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 100
xnumel = 128
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 5
y1 = yindex // 5
y5 = yindex
y4 = yindex // 25
y6 = yindex % 25
tmp0 = tl.load(in_ptr0 + (x2 + 256 * y0 + 2560 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (128 + x2 + 256 * y0 + 2560 * y1), xmask &
ymask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (1280 + x2 + 256 * y0 + 2560 * y1), xmask &
ymask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (1408 + x2 + 256 * y0 + 2560 * y1), xmask &
ymask, eviction_policy='evict_last')
tmp2 = tmp1 > tmp0
tmp3 = tl.full([1, 1], 1, tl.int8)
tmp4 = tl.full([1, 1], 0, tl.int8)
tmp5 = tl.where(tmp2, tmp3, tmp4)
tmp6 = triton_helpers.maximum(tmp1, tmp0)
tmp8 = tmp7 > tmp6
tmp9 = tl.full([1, 1], 2, tl.int8)
tmp10 = tl.where(tmp8, tmp9, tmp5)
tmp11 = triton_helpers.maximum(tmp7, tmp6)
tmp13 = tmp12 > tmp11
tmp14 = tl.full([1, 1], 3, tl.int8)
tmp15 = tl.where(tmp13, tmp14, tmp10)
tmp16 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x2 + 128 * y5), tmp15, xmask & ymask)
tl.store(out_ptr1 + (y6 + 25 * x2 + 3200 * y4), tmp16, xmask & ymask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (64, 3, 5, 5), (75, 25, 5, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 3, 32, 32), (3072, 1024, 32, 1))
assert_size_stride(primals_4, (128, 64, 5, 5), (1600, 25, 5, 1))
assert_size_stride(primals_5, (128,), (1,))
assert_size_stride(primals_6, (1024, 3200), (3200, 1))
assert_size_stride(primals_7, (1024,), (1,))
assert_size_stride(primals_8, (84, 1024), (1024, 1))
assert_size_stride(primals_9, (84,), (1,))
assert_size_stride(primals_10, (50, 84), (84, 1))
assert_size_stride(primals_11, (50,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 3, 5, 5), (75, 1, 15, 3), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(192, 25)](primals_1, buf0, 192, 25, XBLOCK=
32, YBLOCK=32, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 3, 32, 32), (3072, 1, 96, 3), torch.
float32)
triton_poi_fused_1[grid(12, 1024)](primals_3, buf1, 12, 1024,
XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((128, 64, 5, 5), (1600, 1, 320, 64),
torch.float32)
triton_poi_fused_2[grid(8192, 25)](primals_4, buf2, 8192, 25,
XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
del primals_4
buf3 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 64, 28, 28), (50176, 1, 1792, 64))
buf4 = buf3
del buf3
triton_poi_fused_convolution_relu_3[grid(200704)](buf4, primals_2,
200704, XBLOCK=512, num_warps=8, num_stages=1)
del primals_2
buf5 = empty_strided_cuda((4, 64, 14, 14), (12544, 1, 896, 64),
torch.float32)
buf6 = empty_strided_cuda((4, 64, 14, 14), (12544, 1, 896, 64),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_4[grid(50176)](buf4, buf5,
buf6, 50176, XBLOCK=512, num_warps=4, num_stages=1)
buf7 = extern_kernels.convolution(buf5, buf2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 128, 10, 10), (12800, 1, 1280, 128))
buf8 = buf7
del buf7
triton_poi_fused_convolution_relu_5[grid(51200)](buf8, primals_5,
51200, XBLOCK=512, num_warps=4, num_stages=1)
del primals_5
buf9 = empty_strided_cuda((4, 128, 5, 5), (3200, 1, 640, 128),
torch.int8)
buf10 = empty_strided_cuda((4, 128, 5, 5), (3200, 25, 5, 1), torch.
float32)
triton_poi_fused_max_pool2d_with_indices_6[grid(100, 128)](buf8,
buf9, buf10, 100, 128, XBLOCK=128, YBLOCK=2, num_warps=4,
num_stages=1)
buf11 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf10, (4, 3200),
(3200, 1), 0), reinterpret_tensor(primals_6, (3200, 1024), (1,
3200), 0), alpha=1, beta=1, out=buf11)
del primals_7
buf12 = empty_strided_cuda((4, 84), (84, 1), torch.float32)
extern_kernels.addmm(primals_9, buf11, reinterpret_tensor(primals_8,
(1024, 84), (1, 1024), 0), alpha=1, beta=1, out=buf12)
del primals_9
buf13 = empty_strided_cuda((4, 50), (50, 1), torch.float32)
extern_kernels.addmm(primals_11, buf12, reinterpret_tensor(
primals_10, (84, 50), (1, 84), 0), alpha=1, beta=1, out=buf13)
del primals_11
return (buf13, buf0, buf1, buf2, buf4, buf5, buf6, buf8, buf9,
reinterpret_tensor(buf10, (4, 3200), (3200, 1), 0), buf11, buf12,
primals_10, primals_8, primals_6)
class SequentialCNNNetNew(nn.Module):
def __init__(self):
super(SequentialCNNNetNew, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(64, 128, 5)
self.fc1 = nn.Linear(128 * 5 * 5, 1024)
self.fc2 = nn.Linear(1024, 84)
self.fc3 = nn.Linear(84, 50)
self.features = nn.Sequential(self.conv1, nn.ReLU(), self.pool,
self.conv2, nn.ReLU(), self.pool)
self.classifier = nn.Sequential(self.fc1, self.fc2, self.fc3)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.fc1.weight
primals_7 = self.fc1.bias
primals_8 = self.fc2.weight
primals_9 = self.fc2.bias
primals_10 = self.fc3.weight
primals_11 = self.fc3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0]
|
fangkaimin/pytorch_classification_new
|
SequentialCNNNet
| false | 10,122 |
[
"MIT"
] | 0 |
21032e7ab91f0f3106ba07aa97657a023b1cc717
|
https://github.com/fangkaimin/pytorch_classification_new/tree/21032e7ab91f0f3106ba07aa97657a023b1cc717
|
ConvNorm
|
import torch
import torch.utils.data
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
assert kernel_size % 2 == 1
padding = int(dilation * (kernel_size - 1) / 2)
self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size=
kernel_size, stride=stride, padding=padding, dilation=dilation,
bias=bias)
torch.nn.init.xavier_uniform_(self.conv.weight, gain=torch.nn.init.
calculate_gain(w_init_gain))
def forward(self, signal):
conv_signal = self.conv(signal)
return conv_signal
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x2, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 1), (4, 1, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
4, 4), (16, 4, 1), 0), primals_1, stride=(1,), padding=(0,),
dilation=(1,), transposed=False, output_padding=(0,), groups=1,
bias=None)
assert_size_stride(buf0, (1, 4, 4), (16, 4, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(16)](buf1, primals_2, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del primals_2
return reinterpret_tensor(buf1, (4, 4), (4, 1), 0
), primals_1, reinterpret_tensor(primals_3, (1, 4, 4), (16, 4, 1), 0)
class ConvNormNew(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNormNew, self).__init__()
if padding is None:
assert kernel_size % 2 == 1
padding = int(dilation * (kernel_size - 1) / 2)
self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size=
kernel_size, stride=stride, padding=padding, dilation=dilation,
bias=bias)
torch.nn.init.xavier_uniform_(self.conv.weight, gain=torch.nn.init.
calculate_gain(w_init_gain))
def forward(self, input_0):
primals_1 = self.conv.weight
primals_2 = self.conv.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
AeroXi/Tacotron2-Mandarin
|
ConvNorm
| false | 13,246 |
[
"MIT"
] | 67 |
b7bc213d1c1a9c3e2f2e11f69f586c2582010668
|
https://github.com/AeroXi/Tacotron2-Mandarin/tree/b7bc213d1c1a9c3e2f2e11f69f586c2582010668
|
NasPathBranch
|
import torch
import torch.nn as nn
import torch.utils.data
def conv1x1(in_channels, out_channels, stride=1, bias=False):
"""
Convolution 1x1 layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
stride : int or tuple/list of 2 int, default 1
Strides of the convolution.
bias : bool, default False
Whether the layer uses a bias vector.
"""
return nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
kernel_size=1, stride=stride, bias=bias)
def nasnet_avgpool1x1_s2():
"""
NASNet specific 1x1 Average pooling layer with stride 2.
"""
return nn.AvgPool2d(kernel_size=1, stride=2, count_include_pad=False)
class NasPathBranch(nn.Module):
"""
NASNet specific `path` branch (auxiliary block).
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
extra_padding : bool, default False
Whether to use extra padding.
"""
def __init__(self, in_channels, out_channels, extra_padding=False):
super(NasPathBranch, self).__init__()
self.extra_padding = extra_padding
self.avgpool = nasnet_avgpool1x1_s2()
self.conv = conv1x1(in_channels=in_channels, out_channels=out_channels)
if self.extra_padding:
self.pad = nn.ZeroPad2d(padding=(0, 1, 0, 1))
def forward(self, x):
if self.extra_padding:
x = self.pad(x)
x = x[:, :, 1:, 1:].contiguous()
x = self.avgpool(x)
x = self.conv(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_avg_pool2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 2
x1 = xindex // 2
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy=
'evict_last')
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x2, tmp2, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_avg_pool2d_0[grid(64)](primals_1, buf0, 64, XBLOCK
=64, num_warps=1, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 2, 2), (16, 4, 2, 1))
return buf1, primals_2, buf0
def conv1x1(in_channels, out_channels, stride=1, bias=False):
"""
Convolution 1x1 layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
stride : int or tuple/list of 2 int, default 1
Strides of the convolution.
bias : bool, default False
Whether the layer uses a bias vector.
"""
return nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
kernel_size=1, stride=stride, bias=bias)
def nasnet_avgpool1x1_s2():
"""
NASNet specific 1x1 Average pooling layer with stride 2.
"""
return nn.AvgPool2d(kernel_size=1, stride=2, count_include_pad=False)
class NasPathBranchNew(nn.Module):
"""
NASNet specific `path` branch (auxiliary block).
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
extra_padding : bool, default False
Whether to use extra padding.
"""
def __init__(self, in_channels, out_channels, extra_padding=False):
super(NasPathBranchNew, self).__init__()
self.extra_padding = extra_padding
self.avgpool = nasnet_avgpool1x1_s2()
self.conv = conv1x1(in_channels=in_channels, out_channels=out_channels)
if self.extra_padding:
self.pad = nn.ZeroPad2d(padding=(0, 1, 0, 1))
def forward(self, input_0):
primals_2 = self.conv.weight
primals_1 = input_0
output = call([primals_1, primals_2])
return output[0]
|
earhian/imgclsmob
|
NasPathBranch
| false | 6,628 |
[
"MIT"
] | 1 |
c87c0942420876941868c016211073dec4392e4d
|
https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d
|
ConvAE
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_7/inductor_cache/td/ctdv3m5a33kovvtng5iilth4k6mtnyfcota6hhwoiqm34iumu7wi.py
# Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
# input_1 => constant_pad_nd
# Graph fragment:
# %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [1, 1, 1, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 6) % 6
x0 = xindex % 6
x2 = (xindex // 36)
x4 = xindex
tmp0 = (-1) + x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = (-1) + x0
tmp6 = tmp5 >= tmp1
tmp7 = tmp5 < tmp3
tmp8 = tmp2 & tmp4
tmp9 = tmp8 & tmp6
tmp10 = tmp9 & tmp7
tmp11 = tl.load(in_ptr0 + ((-5) + x0 + (4*x1) + (16*x2)), tmp10 & xmask, other=0.0)
tl.store(out_ptr0 + (x4), tmp11, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_7/inductor_cache/i3/ci3nuuurbsrmcufle642yc7udhwn4itsu6aptfssij5nzrnylpne.py
# Topologically Sorted Source Nodes: [input_2, input_3], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# input_2 => convolution
# input_3 => relu
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_2, %primals_3, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 4) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_7/inductor_cache/my/cmykveslman44cmgx6n67zhhjovkcupjikam4mujghfvxablzzpx.py
# Topologically Sorted Source Nodes: [input_4, input_6], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# input_4 => convolution_1
# input_6 => relu_1
# Graph fragment:
# %convolution_1 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [2, 2], [0, 0], [1, 1], True, [0, 0], 1), kwargs = {})
# %relu_1 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%slice_4,), kwargs = {})
# %slice_scatter_default : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_tensor, %relu_1, 3, -3, 8), kwargs = {})
# %slice_scatter_default_1 : [num_users=2] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%convolution_1, %slice_scatter_default, 2, -3, 9), kwargs = {})
triton_poi_fused_convolution_relu_2 = async_compile.triton('triton_poi_fused_convolution_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 6) % 6
x0 = xindex % 6
x4 = xindex
x2 = (xindex // 36) % 4
tmp19 = tl.load(in_out_ptr0 + (x4), xmask)
tmp20 = tl.load(in_ptr0 + (x2), xmask, eviction_policy='evict_last')
tmp0 = x1
tmp1 = tl.full([1], 3, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = x0
tmp4 = tmp3 >= tmp1
tmp5 = tmp4 & tmp2
tmp6 = tl.load(in_out_ptr0 + (x4), tmp5 & xmask, other=0.0)
tmp7 = tl.load(in_ptr0 + (x2), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
tmp12 = tl.where(tmp5, tmp10, tmp11)
tmp13 = tl.load(in_out_ptr0 + (x4), tmp2 & xmask, other=0.0)
tmp14 = tl.load(in_ptr0 + (x2), tmp2 & xmask, eviction_policy='evict_last', other=0.0)
tmp15 = tmp13 + tmp14
tmp16 = tl.where(tmp4, tmp12, tmp15)
tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
tmp18 = tl.where(tmp2, tmp16, tmp17)
tmp21 = tmp19 + tmp20
tmp22 = tl.where(tmp2, tmp18, tmp21)
tl.store(in_out_ptr0 + (x4), tmp22, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_7/inductor_cache/xy/cxy7c463ocont2eh2rauxmeskuwjwuxxuxekyemvjiwq562uy7br.py
# Topologically Sorted Source Nodes: [], Original ATen: [aten.threshold_backward]
# Source node to ATen node mapping:
# Graph fragment:
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%slice_27, 0), kwargs = {})
triton_poi_fused_threshold_backward_3 = async_compile.triton('triton_poi_fused_threshold_backward_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_threshold_backward_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_threshold_backward_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 144
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 3
x1 = (xindex // 3) % 3
x2 = (xindex // 9)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (21 + x0 + (6*x1) + (36*x2)), xmask)
tmp1 = 0.0
tmp2 = tmp0 <= tmp1
tl.store(out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.constant_pad_nd]
stream0 = get_raw_stream(0)
triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 576, grid=grid(576), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 2, 2), (16, 4, 2, 1))
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [input_2, input_3], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_1.run(buf2, primals_3, 64, grid=grid(64), stream=stream0)
del primals_3
# Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 6, 6), (144, 36, 6, 1))
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [input_4, input_6], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_2.run(buf4, primals_5, 576, grid=grid(576), stream=stream0)
del primals_5
buf5 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.bool)
# Topologically Sorted Source Nodes: [], Original ATen: [aten.threshold_backward]
triton_poi_fused_threshold_backward_3.run(buf4, buf5, 144, grid=grid(144), stream=stream0)
return (reinterpret_tensor(buf4, (4, 4, 3, 3), (144, 36, 6, 1), 21), primals_2, primals_4, buf0, buf2, buf5, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 6 % 6
x0 = xindex % 6
x2 = xindex // 36
x4 = xindex
tmp0 = -1 + x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = -1 + x0
tmp6 = tmp5 >= tmp1
tmp7 = tmp5 < tmp3
tmp8 = tmp2 & tmp4
tmp9 = tmp8 & tmp6
tmp10 = tmp9 & tmp7
tmp11 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask,
other=0.0)
tl.store(out_ptr0 + x4, tmp11, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 4 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 6 % 6
x0 = xindex % 6
x4 = xindex
x2 = xindex // 36 % 4
tmp19 = tl.load(in_out_ptr0 + x4, xmask)
tmp20 = tl.load(in_ptr0 + x2, xmask, eviction_policy='evict_last')
tmp0 = x1
tmp1 = tl.full([1], 3, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = x0
tmp4 = tmp3 >= tmp1
tmp5 = tmp4 & tmp2
tmp6 = tl.load(in_out_ptr0 + x4, tmp5 & xmask, other=0.0)
tmp7 = tl.load(in_ptr0 + x2, tmp5 & xmask, eviction_policy='evict_last',
other=0.0)
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
tmp12 = tl.where(tmp5, tmp10, tmp11)
tmp13 = tl.load(in_out_ptr0 + x4, tmp2 & xmask, other=0.0)
tmp14 = tl.load(in_ptr0 + x2, tmp2 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp15 = tmp13 + tmp14
tmp16 = tl.where(tmp4, tmp12, tmp15)
tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
tmp18 = tl.where(tmp2, tmp16, tmp17)
tmp21 = tmp19 + tmp20
tmp22 = tl.where(tmp2, tmp18, tmp21)
tl.store(in_out_ptr0 + x4, tmp22, xmask)
@triton.jit
def triton_poi_fused_threshold_backward_3(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 144
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 3
x1 = xindex // 3 % 3
x2 = xindex // 9
x3 = xindex
tmp0 = tl.load(in_ptr0 + (21 + x0 + 6 * x1 + 36 * x2), xmask)
tmp1 = 0.0
tmp2 = tmp0 <= tmp1
tl.store(out_ptr0 + x3, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_constant_pad_nd_0[grid(576)](primals_1, buf0, 576,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(2, 2),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 2, 2), (16, 4, 2, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_relu_1[grid(64)](buf2, primals_3, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_3
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(2, 2),
padding=(0, 0), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 6, 6), (144, 36, 6, 1))
buf4 = buf3
del buf3
triton_poi_fused_convolution_relu_2[grid(576)](buf4, primals_5, 576,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf5 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.bool)
triton_poi_fused_threshold_backward_3[grid(144)](buf4, buf5, 144,
XBLOCK=128, num_warps=4, num_stages=1)
return reinterpret_tensor(buf4, (4, 4, 3, 3), (144, 36, 6, 1), 21
), primals_2, primals_4, buf0, buf2, buf5
class Conv2dSamePad(nn.Module):
"""
Implement Tensorflow's 'SAME' padding mode in Conv2d.
When an odd number, say `m`, of pixels are need to pad, Tensorflow will pad one more column at right or one more
row at bottom. But Pytorch will pad `m+1` pixels, i.e., Pytorch always pads in both sides.
So we can pad the tensor in the way of Tensorflow before call the Conv2d module.
"""
def __init__(self, kernel_size, stride):
super(Conv2dSamePad, self).__init__()
self.kernel_size = kernel_size if type(kernel_size) in [list, tuple
] else [kernel_size, kernel_size]
self.stride = stride if type(stride) in [list, tuple] else [stride,
stride]
def forward(self, x):
in_height = x.size(2)
in_width = x.size(3)
out_height = math.ceil(float(in_height) / float(self.stride[0]))
out_width = math.ceil(float(in_width) / float(self.stride[1]))
pad_along_height = (out_height - 1) * self.stride[0
] + self.kernel_size[0] - in_height
pad_along_width = (out_width - 1) * self.stride[1] + self.kernel_size[1
] - in_width
pad_top = math.floor(pad_along_height / 2)
pad_left = math.floor(pad_along_width / 2)
pad_bottom = pad_along_height - pad_top
pad_right = pad_along_width - pad_left
return F.pad(x, [pad_left, pad_right, pad_top, pad_bottom],
'constant', 0)
class ConvTranspose2dSamePad(nn.Module):
"""
This module implements the "SAME" padding mode for ConvTranspose2d as in Tensorflow.
A tensor with width w_in, feed it to ConvTranspose2d(ci, co, kernel, stride), the width of output tensor T_nopad:
w_nopad = (w_in - 1) * stride + kernel
If we use padding, i.e., ConvTranspose2d(ci, co, kernel, stride, padding, output_padding), the width of T_pad:
w_pad = (w_in - 1) * stride + kernel - (2*padding - output_padding) = w_nopad - (2*padding - output_padding)
Yes, in ConvTranspose2d, more padding, the resulting tensor is smaller, i.e., the padding is actually deleting row/col.
If `pad`=(2*padding - output_padding) is odd, Pytorch deletes more columns in the left, i.e., the first ceil(pad/2) and
last `pad - ceil(pad/2)` columns of T_nopad are deleted to get T_pad.
In contrast, Tensorflow deletes more columns in the right, i.e., the first floor(pad/2) and last `pad - floor(pad/2)`
columns are deleted.
For the height, Pytorch deletes more rows at top, while Tensorflow at bottom.
In practice, we usually want `w_pad = w_in * stride` or `w_pad = w_in * stride - 1`, i.e., the "SAME" padding mode
in Tensorflow. To determine the value of `w_pad`, we should pass it to this function.
So the number of columns to delete:
pad = 2*padding - output_padding = w_nopad - w_pad
If pad is even, we can directly set padding=pad/2 and output_padding=0 in ConvTranspose2d.
If pad is odd, we can use ConvTranspose2d to get T_nopad, and then delete `pad` rows/columns by
ourselves.
This module should be called after the ConvTranspose2d module with shared kernel_size and stride values.
"""
def __init__(self, output_size):
super(ConvTranspose2dSamePad, self).__init__()
self.output_size = output_size
def forward(self, x):
in_height = x.size(2)
in_width = x.size(3)
pad_height = in_height - self.output_size[0]
pad_width = in_width - self.output_size[1]
pad_top = pad_height // 2
pad_bottom = pad_height - pad_top
pad_left = pad_width // 2
pad_right = pad_width - pad_left
return x[:, :, pad_top:in_height - pad_bottom, pad_left:in_width -
pad_right]
class ConvAENew(nn.Module):
def __init__(self, channels, kernels):
"""
:param channels: a list containing all channels including the input image channel (1 for gray, 3 for RGB)
:param kernels: a list containing all kernel sizes, it should satisfy: len(kernels) = len(channels) - 1.
"""
super(ConvAENew, self).__init__()
assert isinstance(channels, list) and isinstance(kernels, list)
self.encoder = nn.Sequential()
for i in range(1, len(channels)):
self.encoder.add_module('pad%d' % i, Conv2dSamePad(kernels[i -
1], 2))
self.encoder.add_module('conv%d' % i, nn.Conv2d(channels[i - 1],
channels[i], kernel_size=kernels[i - 1], stride=2))
self.encoder.add_module('relu%d' % i, nn.ReLU(True))
self.decoder = nn.Sequential()
channels = list(reversed(channels))
kernels = list(reversed(kernels))
sizes = [[12, 11], [24, 21], [48, 42]]
for i in range(len(channels) - 1):
self.decoder.add_module('deconv%d' % (i + 1), nn.
ConvTranspose2d(channels[i], channels[i + 1], kernel_size=
kernels[i], stride=2))
self.decoder.add_module('padd%d' % i, ConvTranspose2dSamePad(
sizes[i]))
self.decoder.add_module('relud%d' % i, nn.ReLU(True))
def forward(self, input_0):
primals_1 = self.encoder.conv1.weight
primals_3 = self.encoder.conv1.bias
primals_2 = self.decoder.deconv1.weight
primals_5 = self.decoder.deconv1.bias
primals_4 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
qilinli/DSC-Net
|
ConvAE
| false | 4,154 |
[
"MIT"
] | 0 |
c0e7a3cae3e07c34b2989234f568c7007cf0fc55
|
https://github.com/qilinli/DSC-Net/tree/c0e7a3cae3e07c34b2989234f568c7007cf0fc55
|
CapsuleLoss
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class MarginLoss(nn.Module):
def __init__(self, size_average=False, loss_lambda=0.5):
"""
Margin loss for digit existence
Eq. (4): L_k = T_k * max(0, m+ - ||v_k||)^2 + lambda * (1 - T_k) * max(0, ||v_k|| - m-)^2
Args:
size_average: should the losses be averaged (True) or summed (False) over observations for each minibatch.
loss_lambda: parameter for down-weighting the loss for missing digits
"""
super(MarginLoss, self).__init__()
self.size_average = size_average
self.m_plus = 0.9
self.m_minus = 0.1
self.loss_lambda = loss_lambda
def forward(self, inputs, labels):
L_k = labels * F.relu(self.m_plus - inputs) ** 2 + self.loss_lambda * (
1 - labels) * F.relu(inputs - self.m_minus) ** 2
L_k = L_k.sum(dim=1)
if self.size_average:
return L_k.mean()
else:
return L_k.sum()
class CapsuleLoss(nn.Module):
def __init__(self, loss_lambda=0.5, recon_loss_scale=0.0005,
size_average=False):
"""
Combined margin loss and reconstruction loss. Margin loss see above.
Sum squared error (SSE) was used as a reconstruction loss.
Args:
recon_loss_scale: param for scaling down the the reconstruction loss
size_average: if True, reconstruction loss becomes MSE instead of SSE
"""
super(CapsuleLoss, self).__init__()
self.size_average = size_average
self.margin_loss = MarginLoss(size_average=size_average,
loss_lambda=loss_lambda)
self.reconstruction_loss = nn.MSELoss(size_average=size_average)
self.recon_loss_scale = recon_loss_scale
def forward(self, inputs, labels, images, reconstructions):
margin_loss = self.margin_loss(inputs, labels)
reconstruction_loss = self.reconstruction_loss(reconstructions, images)
caps_loss = margin_loss + self.recon_loss_scale * reconstruction_loss
return caps_loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0(in_ptr0, in_ptr1,
out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex % 16
r1 = rindex // 16
tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
tmp18 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
tmp19 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
tmp32 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
tmp33 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
tmp46 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
tmp47 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
tmp2 = 0.9
tmp3 = tmp2 - tmp1
tmp4 = tl.full([1, 1], 0, tl.int32)
tmp5 = triton_helpers.maximum(tmp4, tmp3)
tmp6 = tmp5 * tmp5
tmp7 = tmp0 * tmp6
tmp8 = 1.0
tmp9 = tmp8 - tmp0
tmp10 = 0.5
tmp11 = tmp9 * tmp10
tmp12 = 0.1
tmp13 = tmp1 - tmp12
tmp14 = triton_helpers.maximum(tmp4, tmp13)
tmp15 = tmp14 * tmp14
tmp16 = tmp11 * tmp15
tmp17 = tmp7 + tmp16
tmp20 = tmp2 - tmp19
tmp21 = triton_helpers.maximum(tmp4, tmp20)
tmp22 = tmp21 * tmp21
tmp23 = tmp18 * tmp22
tmp24 = tmp8 - tmp18
tmp25 = tmp24 * tmp10
tmp26 = tmp19 - tmp12
tmp27 = triton_helpers.maximum(tmp4, tmp26)
tmp28 = tmp27 * tmp27
tmp29 = tmp25 * tmp28
tmp30 = tmp23 + tmp29
tmp31 = tmp17 + tmp30
tmp34 = tmp2 - tmp33
tmp35 = triton_helpers.maximum(tmp4, tmp34)
tmp36 = tmp35 * tmp35
tmp37 = tmp32 * tmp36
tmp38 = tmp8 - tmp32
tmp39 = tmp38 * tmp10
tmp40 = tmp33 - tmp12
tmp41 = triton_helpers.maximum(tmp4, tmp40)
tmp42 = tmp41 * tmp41
tmp43 = tmp39 * tmp42
tmp44 = tmp37 + tmp43
tmp45 = tmp31 + tmp44
tmp48 = tmp2 - tmp47
tmp49 = triton_helpers.maximum(tmp4, tmp48)
tmp50 = tmp49 * tmp49
tmp51 = tmp46 * tmp50
tmp52 = tmp8 - tmp46
tmp53 = tmp52 * tmp10
tmp54 = tmp47 - tmp12
tmp55 = triton_helpers.maximum(tmp4, tmp54)
tmp56 = tmp55 * tmp55
tmp57 = tmp53 * tmp56
tmp58 = tmp51 + tmp57
tmp59 = tmp45 + tmp58
tmp60 = tl.broadcast_to(tmp59, [XBLOCK, RBLOCK])
tmp62 = tl.sum(tmp60, 1)[:, None]
tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp62, None)
@triton.jit
def triton_per_fused_add_mse_loss_mul_1(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp7 = tl.load(in_out_ptr0 + 0)
tmp8 = tl.broadcast_to(tmp7, [1])
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp9 = 0.0005
tmp10 = tmp6 * tmp9
tmp11 = tmp8 + tmp10
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp11, None)
def call(args):
arg0_1, arg1_1, arg2_1, arg3_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf1 = empty_strided_cuda((), (), torch.float32)
get_raw_stream(0)
triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0[grid(1)](arg1_1,
arg0_1, buf1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
buf3 = buf1
del buf1
triton_per_fused_add_mse_loss_mul_1[grid(1)](buf3, arg3_1, arg2_1,
1, 256, num_warps=2, num_stages=1)
del arg2_1
del arg3_1
return buf3,
class MarginLoss(nn.Module):
def __init__(self, size_average=False, loss_lambda=0.5):
"""
Margin loss for digit existence
Eq. (4): L_k = T_k * max(0, m+ - ||v_k||)^2 + lambda * (1 - T_k) * max(0, ||v_k|| - m-)^2
Args:
size_average: should the losses be averaged (True) or summed (False) over observations for each minibatch.
loss_lambda: parameter for down-weighting the loss for missing digits
"""
super(MarginLoss, self).__init__()
self.size_average = size_average
self.m_plus = 0.9
self.m_minus = 0.1
self.loss_lambda = loss_lambda
def forward(self, inputs, labels):
L_k = labels * F.relu(self.m_plus - inputs) ** 2 + self.loss_lambda * (
1 - labels) * F.relu(inputs - self.m_minus) ** 2
L_k = L_k.sum(dim=1)
if self.size_average:
return L_k.mean()
else:
return L_k.sum()
class CapsuleLossNew(nn.Module):
def __init__(self, loss_lambda=0.5, recon_loss_scale=0.0005,
size_average=False):
"""
Combined margin loss and reconstruction loss. Margin loss see above.
Sum squared error (SSE) was used as a reconstruction loss.
Args:
recon_loss_scale: param for scaling down the the reconstruction loss
size_average: if True, reconstruction loss becomes MSE instead of SSE
"""
super(CapsuleLossNew, self).__init__()
self.size_average = size_average
self.margin_loss = MarginLoss(size_average=size_average,
loss_lambda=loss_lambda)
self.reconstruction_loss = nn.MSELoss(size_average=size_average)
self.recon_loss_scale = recon_loss_scale
def forward(self, input_0, input_1, input_2, input_3):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
arg3_1 = input_3
output = call([arg0_1, arg1_1, arg2_1, arg3_1])
return output[0]
|
richardsun-voyager/capsule-network
|
CapsuleLoss
| false | 7,554 |
[
"MIT"
] | 1 |
349cec1caa9ab95ff4b3333c33d04b1bdb442f67
|
https://github.com/richardsun-voyager/capsule-network/tree/349cec1caa9ab95ff4b3333c33d04b1bdb442f67
|
Vgg16
|
import torch
from torch import nn
import torch.nn.functional as F
class Vgg16(nn.Module):
def __init__(self):
super(Vgg16, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
def forward(self, X):
h = F.relu(self.conv1_1(X), inplace=True)
h = F.relu(self.conv1_2(h), inplace=True)
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv2_1(h), inplace=True)
h = F.relu(self.conv2_2(h), inplace=True)
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv3_1(h), inplace=True)
h = F.relu(self.conv3_2(h), inplace=True)
h = F.relu(self.conv3_3(h), inplace=True)
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv4_1(h), inplace=True)
h = F.relu(self.conv4_2(h), inplace=True)
h = F.relu(self.conv4_3(h), inplace=True)
h = F.relu(self.conv5_1(h), inplace=True)
h = F.relu(self.conv5_2(h), inplace=True)
h = F.relu(self.conv5_3(h), inplace=True)
relu5_3 = h
return relu5_3
def get_inputs():
return [torch.rand([4, 3, 64, 64])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 192
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 3
y1 = yindex // 3
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 3 * x2 + 27 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 12
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
x2 = xindex
y3 = yindex
y0 = yindex % 3
y1 = yindex // 3
tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp0, ymask)
@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 64
y1 = yindex // 64
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 64
y1 = yindex // 64
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 128
y1 = yindex // 128
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 128
y1 = yindex // 128
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 128 * x2 + 1152 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 256
y1 = yindex // 256
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_7(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 256
y1 = yindex // 256
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_8(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 512
y1 = yindex // 512
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 512 * x2 + 4608 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_9(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_10(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 64
x1 = xindex // 64 % 32
x2 = xindex // 2048
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1 + 8192 * x2), None)
tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 8192 * x2), None)
tmp3 = tl.load(in_ptr0 + (4096 + x0 + 128 * x1 + 8192 * x2), None)
tmp5 = tl.load(in_ptr0 + (4160 + x0 + 128 * x1 + 8192 * x2), None)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x3, tmp6, None)
tl.store(out_ptr1 + x3, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_11(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 128
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_12(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 128
x1 = xindex // 128 % 16
x2 = xindex // 2048
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1 + 8192 * x2), None)
tmp1 = tl.load(in_ptr0 + (128 + x0 + 256 * x1 + 8192 * x2), None)
tmp3 = tl.load(in_ptr0 + (4096 + x0 + 256 * x1 + 8192 * x2), None)
tmp5 = tl.load(in_ptr0 + (4224 + x0 + 256 * x1 + 8192 * x2), None)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x3, tmp6, None)
tl.store(out_ptr1 + x3, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_13(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 256
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_14(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 256
x1 = xindex // 256 % 8
x2 = xindex // 2048
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 512 * x1 + 8192 * x2), None)
tmp1 = tl.load(in_ptr0 + (256 + x0 + 512 * x1 + 8192 * x2), None)
tmp3 = tl.load(in_ptr0 + (4096 + x0 + 512 * x1 + 8192 * x2), None)
tmp5 = tl.load(in_ptr0 + (4352 + x0 + 512 * x1 + 8192 * x2), None)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x3, tmp6, None)
tl.store(out_ptr1 + x3, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_15(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 512
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0,
in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr,
XBLOCK: tl.constexpr):
xnumel = 64
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 512
y1 = yindex // 512
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 512 * x2 + 32768 * y1), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1, 1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x2 + 64 * y3), tmp4, xmask)
tl.store(out_ptr1 + (y0 + 512 * x2 + 32768 * y1), tmp6, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17,
primals_18, primals_19, primals_20, primals_21, primals_22,
primals_23, primals_24, primals_25, primals_26, primals_27) = args
args.clear()
assert_size_stride(primals_1, (64, 3, 3, 3), (27, 9, 3, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_7, (128,), (1,))
assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_9, (128,), (1,))
assert_size_stride(primals_10, (256, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_11, (256,), (1,))
assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_13, (256,), (1,))
assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_15, (256,), (1,))
assert_size_stride(primals_16, (512, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_17, (512,), (1,))
assert_size_stride(primals_18, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_19, (512,), (1,))
assert_size_stride(primals_20, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_21, (512,), (1,))
assert_size_stride(primals_22, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_23, (512,), (1,))
assert_size_stride(primals_24, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_25, (512,), (1,))
assert_size_stride(primals_26, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_27, (512,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 3, 3, 3), (27, 1, 9, 3), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(192, 9)](primals_1, buf0, 192, 9, XBLOCK=16,
YBLOCK=64, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
.float32)
triton_poi_fused_1[grid(12, 4096)](primals_3, buf1, 12, 4096,
XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
float32)
triton_poi_fused_2[grid(4096, 9)](primals_4, buf2, 4096, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch
.float32)
triton_poi_fused_3[grid(8192, 9)](primals_6, buf3, 8192, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_6
buf4 = empty_strided_cuda((128, 128, 3, 3), (1152, 1, 384, 128),
torch.float32)
triton_poi_fused_4[grid(16384, 9)](primals_8, buf4, 16384, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_8
buf5 = empty_strided_cuda((256, 128, 3, 3), (1152, 1, 384, 128),
torch.float32)
triton_poi_fused_5[grid(32768, 9)](primals_10, buf5, 32768, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_10
buf6 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_6[grid(65536, 9)](primals_12, buf6, 65536, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_12
buf7 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_6[grid(65536, 9)](primals_14, buf7, 65536, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_14
buf8 = empty_strided_cuda((512, 256, 3, 3), (2304, 1, 768, 256),
torch.float32)
triton_poi_fused_7[grid(131072, 9)](primals_16, buf8, 131072, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_16
buf9 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
torch.float32)
triton_poi_fused_8[grid(262144, 9)](primals_18, buf9, 262144, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_18
buf10 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
torch.float32)
triton_poi_fused_8[grid(262144, 9)](primals_20, buf10, 262144, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_20
buf11 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
torch.float32)
triton_poi_fused_8[grid(262144, 9)](primals_22, buf11, 262144, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_22
buf12 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
torch.float32)
triton_poi_fused_8[grid(262144, 9)](primals_24, buf12, 262144, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_24
buf13 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
torch.float32)
triton_poi_fused_8[grid(262144, 9)](primals_26, buf13, 262144, 9,
XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_26
buf14 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf14, (4, 64, 64, 64), (262144, 1, 4096, 64))
buf15 = buf14
del buf14
triton_poi_fused_convolution_relu_9[grid(1048576)](buf15, primals_2,
1048576, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_2
buf16 = extern_kernels.convolution(buf15, buf2, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf16, (4, 64, 64, 64), (262144, 1, 4096, 64))
buf17 = buf16
del buf16
triton_poi_fused_convolution_relu_9[grid(1048576)](buf17, primals_5,
1048576, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_5
buf18 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64),
torch.float32)
buf19 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_10[grid(262144)](buf17,
buf18, buf19, 262144, XBLOCK=512, num_warps=8, num_stages=1)
buf20 = extern_kernels.convolution(buf18, buf3, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf20, (4, 128, 32, 32), (131072, 1, 4096, 128))
buf21 = buf20
del buf20
triton_poi_fused_convolution_relu_11[grid(524288)](buf21, primals_7,
524288, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_7
buf22 = extern_kernels.convolution(buf21, buf4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf22, (4, 128, 32, 32), (131072, 1, 4096, 128))
buf23 = buf22
del buf22
triton_poi_fused_convolution_relu_11[grid(524288)](buf23, primals_9,
524288, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_9
buf24 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128),
torch.float32)
buf25 = empty_strided_cuda((4, 128, 16, 16), (32768, 1, 2048, 128),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_12[grid(131072)](buf23,
buf24, buf25, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
buf26 = extern_kernels.convolution(buf24, buf5, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf26, (4, 256, 16, 16), (65536, 1, 4096, 256))
buf27 = buf26
del buf26
triton_poi_fused_convolution_relu_13[grid(262144)](buf27,
primals_11, 262144, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_11
buf28 = extern_kernels.convolution(buf27, buf6, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf28, (4, 256, 16, 16), (65536, 1, 4096, 256))
buf29 = buf28
del buf28
triton_poi_fused_convolution_relu_13[grid(262144)](buf29,
primals_13, 262144, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_13
buf30 = extern_kernels.convolution(buf29, buf7, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf30, (4, 256, 16, 16), (65536, 1, 4096, 256))
buf31 = buf30
del buf30
triton_poi_fused_convolution_relu_13[grid(262144)](buf31,
primals_15, 262144, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_15
buf32 = empty_strided_cuda((4, 256, 8, 8), (16384, 1, 2048, 256),
torch.float32)
buf33 = empty_strided_cuda((4, 256, 8, 8), (16384, 1, 2048, 256),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_14[grid(65536)](buf31,
buf32, buf33, 65536, XBLOCK=512, num_warps=4, num_stages=1)
buf34 = extern_kernels.convolution(buf32, buf8, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf34, (4, 512, 8, 8), (32768, 1, 4096, 512))
buf35 = buf34
del buf34
triton_poi_fused_convolution_relu_15[grid(131072)](buf35,
primals_17, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_17
buf36 = extern_kernels.convolution(buf35, buf9, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf36, (4, 512, 8, 8), (32768, 1, 4096, 512))
buf37 = buf36
del buf36
triton_poi_fused_convolution_relu_15[grid(131072)](buf37,
primals_19, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_19
buf38 = extern_kernels.convolution(buf37, buf10, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf38, (4, 512, 8, 8), (32768, 1, 4096, 512))
buf39 = buf38
del buf38
triton_poi_fused_convolution_relu_15[grid(131072)](buf39,
primals_21, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_21
buf40 = extern_kernels.convolution(buf39, buf11, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf40, (4, 512, 8, 8), (32768, 1, 4096, 512))
buf41 = buf40
del buf40
triton_poi_fused_convolution_relu_15[grid(131072)](buf41,
primals_23, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_23
buf42 = extern_kernels.convolution(buf41, buf12, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf42, (4, 512, 8, 8), (32768, 1, 4096, 512))
buf43 = buf42
del buf42
triton_poi_fused_convolution_relu_15[grid(131072)](buf43,
primals_25, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_25
buf44 = extern_kernels.convolution(buf43, buf13, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf44, (4, 512, 8, 8), (32768, 1, 4096, 512))
buf45 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch
.float32)
buf46 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_16[grid(2048, 64)
](buf44, primals_27, buf45, buf46, 2048, 64, XBLOCK=32, YBLOCK=
32, num_warps=4, num_stages=1)
del buf44
del primals_27
return (buf45, buf0, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8,
buf9, buf10, buf11, buf12, buf13, buf15, buf17, buf18, buf19, buf21,
buf23, buf24, buf25, buf27, buf29, buf31, buf32, buf33, buf35,
buf37, buf39, buf41, buf43, buf46)
class Vgg16New(nn.Module):
def __init__(self):
super(Vgg16New, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
def forward(self, input_0):
primals_1 = self.conv1_1.weight
primals_2 = self.conv1_1.bias
primals_4 = self.conv1_2.weight
primals_5 = self.conv1_2.bias
primals_6 = self.conv2_1.weight
primals_7 = self.conv2_1.bias
primals_8 = self.conv2_2.weight
primals_9 = self.conv2_2.bias
primals_10 = self.conv3_1.weight
primals_11 = self.conv3_1.bias
primals_12 = self.conv3_2.weight
primals_13 = self.conv3_2.bias
primals_14 = self.conv3_3.weight
primals_15 = self.conv3_3.bias
primals_16 = self.conv4_1.weight
primals_17 = self.conv4_1.bias
primals_18 = self.conv4_2.weight
primals_19 = self.conv4_2.bias
primals_20 = self.conv4_3.weight
primals_21 = self.conv4_3.bias
primals_22 = self.conv5_1.weight
primals_23 = self.conv5_1.bias
primals_24 = self.conv5_2.weight
primals_25 = self.conv5_2.bias
primals_26 = self.conv5_3.weight
primals_27 = self.conv5_3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17, primals_18, primals_19,
primals_20, primals_21, primals_22, primals_23, primals_24,
primals_25, primals_26, primals_27])
return output[0]
|
YueZHOU0926/MUNIT_3D
|
Vgg16
| false | 12,055 |
[
"MIT"
] | 0 |
5cb22b5f3cb127d5b2c4eea038254a7881bab372
|
https://github.com/YueZHOU0926/MUNIT_3D/tree/5cb22b5f3cb127d5b2c4eea038254a7881bab372
|
WScaleLayer
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/ow/cowfee35kpe4fozqjkhe365j7x54f6lbx5ziwwqb3k3xg2ha4t5j.py
# Topologically Sorted Source Nodes: [mul, x], Original ATen: [aten.mul, aten.add]
# Source node to ATen node mapping:
# mul => mul
# x => add
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %primals_2), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %expand), kwargs = {})
triton_poi_fused_add_mul_0 = async_compile.triton('triton_poi_fused_add_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (0))
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tmp0 * tmp2
tmp5 = tmp3 + tmp4
tl.store(out_ptr0 + (x3), tmp5, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1, ), (1, ))
assert_size_stride(primals_3, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mul, x], Original ATen: [aten.mul, aten.add]
stream0 = get_raw_stream(0)
triton_poi_fused_add_mul_0.run(primals_1, primals_2, primals_3, buf0, 256, grid=grid(256), stream=stream0)
del primals_2
del primals_3
return (buf0, primals_1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp3 = tmp0 * tmp2
tmp5 = tmp3 + tmp4
tl.store(out_ptr0 + x3, tmp5, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1,), (1,))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_mul_0[grid(256)](primals_1, primals_2,
primals_3, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
del primals_3
return buf0, primals_1
class WScaleLayerNew(nn.Module):
def __init__(self, size):
super(WScaleLayerNew, self).__init__()
self.scale = nn.Parameter(torch.randn([1]))
self.b = nn.Parameter(torch.randn(size))
self.size = size
def forward(self, input_0):
primals_2 = self.scale
primals_3 = self.b
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors
|
WScaleLayer
| false | 7,848 |
[
"MIT"
] | 24 |
4198bd2d325a32ffc4e714c486540e63440ab110
|
https://github.com/ChandreyeeB/Blind-Image-Deconvolution-using-Deep-Generative-Priors/tree/4198bd2d325a32ffc4e714c486540e63440ab110
|
ChannelAttention_avg
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_7/inductor_cache/l3/cl35tzbhrd24dhunkbb6gjs54aklpyr46oikqhoylcgmkcmhujil.py
# Topologically Sorted Source Nodes: [adaptive_avg_pool2d], Original ATen: [aten.mean]
# Source node to ATen node mapping:
# adaptive_avg_pool2d => mean
# Graph fragment:
# %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [-1, -2], True), kwargs = {})
triton_per_fused_mean_0 = async_compile.triton('triton_per_fused_mean_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 16],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 16.0
tmp6 = tmp4 / tmp5
tl.debug_barrier()
tl.store(in_out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_7/inductor_cache/zy/czyrimvsfdgehav6rdabpeydp2eeevjo6pbh4uvgqw77qulgvho5.py
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# relu => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_relu_1 = async_compile.triton('triton_poi_fused_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(in_out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_7/inductor_cache/of/cofcgw47ymoiqc63s7satzahuexl2awjmsq4wmifftjukyl7ayjq.py
# Topologically Sorted Source Nodes: [y_sum], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# y_sum => cat
# Graph fragment:
# %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%expand, %expand_1], 1), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 64
x1 = (xindex // 64)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 32, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (2*x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tl.sigmoid(tmp5)
tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
tmp8 = tl.where(tmp4, tmp6, tmp7)
tmp9 = tmp0 >= tmp3
tmp10 = tl.full([1], 64, tl.int64)
tmp11 = tmp0 < tmp10
tmp12 = tl.load(in_ptr0 + (1 + (2*x1)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
tmp13 = tl.sigmoid(tmp12)
tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
tmp15 = tl.where(tmp9, tmp13, tmp14)
tmp16 = tl.where(tmp4, tmp8, tmp15)
tl.store(out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (8, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (2, 8, 1, 1), (8, 1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0); del buf0 # reuse
# Topologically Sorted Source Nodes: [adaptive_avg_pool2d], Original ATen: [aten.mean]
stream0 = get_raw_stream(0)
triton_per_fused_mean_0.run(buf1, primals_1, 16, 16, grid=grid(16), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 8, 1, 1), (8, 1, 1, 1))
buf3 = buf2; del buf2 # reuse
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu]
triton_poi_fused_relu_1.run(buf3, 32, grid=grid(32), stream=stream0)
# Topologically Sorted Source Nodes: [avg_out], Original ATen: [aten.convolution]
buf4 = extern_kernels.convolution(buf3, primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 2, 1, 1), (2, 1, 1, 1))
buf5 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 1, 1), torch.float32)
# Topologically Sorted Source Nodes: [y_sum], Original ATen: [aten.cat]
triton_poi_fused_cat_2.run(buf4, buf5, 256, grid=grid(256), stream=stream0)
return (buf5, primals_2, primals_3, buf1, buf3, buf4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((8, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((2, 8, 1, 1), (8, 1, 1, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 16.0
tmp6 = tmp4 / tmp5
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp6, xmask)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(in_out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_cat_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 64
x1 = xindex // 64
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 32, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + 2 * x1, tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tl.sigmoid(tmp5)
tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
tmp8 = tl.where(tmp4, tmp6, tmp7)
tmp9 = tmp0 >= tmp3
tl.full([1], 64, tl.int64)
tmp12 = tl.load(in_ptr0 + (1 + 2 * x1), tmp9 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp13 = tl.sigmoid(tmp12)
tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
tmp15 = tl.where(tmp9, tmp13, tmp14)
tmp16 = tl.where(tmp4, tmp8, tmp15)
tl.store(out_ptr0 + x2, tmp16, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (8, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (2, 8, 1, 1), (8, 1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
del buf0
get_raw_stream(0)
triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=1,
num_warps=2, num_stages=1)
del primals_1
buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 8, 1, 1), (8, 1, 1, 1))
buf3 = buf2
del buf2
triton_poi_fused_relu_1[grid(32)](buf3, 32, XBLOCK=32, num_warps=1,
num_stages=1)
buf4 = extern_kernels.convolution(buf3, primals_3, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 2, 1, 1), (2, 1, 1, 1))
buf5 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 1, 1), torch.float32)
triton_poi_fused_cat_2[grid(256)](buf4, buf5, 256, XBLOCK=128,
num_warps=4, num_stages=1)
return buf5, primals_2, primals_3, buf1, buf3, buf4
class ChannelAttention_avgNew(nn.Module):
def __init__(self, in_planes, ratio=8):
super(ChannelAttention_avgNew, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(in_planes, ratio, 1, bias=False)
self.relu1 = nn.ReLU()
self.fc2 = nn.Conv2d(ratio, 2, 1, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, input_0):
primals_2 = self.fc1.weight
primals_3 = self.fc2.weight
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
HT-hlf/mmdetection_miner-2.22.0
|
ChannelAttention_avg
| false | 2,318 |
[
"Apache-2.0"
] | 0 |
76eb94d6547f9f95cd58f41bb5c91941e82322b9
|
https://github.com/HT-hlf/mmdetection_miner-2.22.0/tree/76eb94d6547f9f95cd58f41bb5c91941e82322b9
|
LN_self
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_2/inductor_cache/yw/cywoaswdwrpxhidv5azgjhhzgbcbci44yowv4c42f5bnzwh5u3sl.py
# Topologically Sorted Source Nodes: [var_mean, sub, add, sqrt, X, mul, add_1], Original ATen: [aten.var_mean, aten.sub, aten.add, aten.sqrt, aten.div, aten.mul]
# Source node to ATen node mapping:
# X => div
# add => add
# add_1 => add_1
# mul => mul
# sqrt => sqrt
# sub => sub
# var_mean => var_mean
# Graph fragment:
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_1, [1, 2, 3]), kwargs = {correction: 0, keepdim: True})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %getitem_1), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %sqrt : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %sqrt), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %div), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_3), kwargs = {})
triton_per_fused_add_div_mul_sqrt_sub_var_mean_0 = async_compile.triton('triton_per_fused_add_div_mul_sqrt_sub_var_mean_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[4, 64],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 7), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mul_sqrt_sub_var_mean_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_div_mul_sqrt_sub_var_mean_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 4
rnumel = 64
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
r3 = (rindex // 16)
tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
tmp22 = tl.load(in_ptr1 + (r3), None, eviction_policy='evict_last')
tmp26 = tl.load(in_ptr2 + (r3), None, eviction_policy='evict_last')
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 64, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = 64.0
tmp18 = tmp16 / tmp17
tmp19 = 1e-05
tmp20 = tmp18 + tmp19
tmp21 = libdevice.sqrt(tmp20)
tmp23 = tmp0 - tmp10
tmp24 = tmp23 / tmp21
tmp25 = tmp22 * tmp24
tmp27 = tmp25 + tmp26
tl.debug_barrier()
tl.store(in_out_ptr0 + (x0), tmp21, xmask)
tl.store(out_ptr1 + (r1 + (64*x0)), tmp27, xmask)
tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 1, 1, 1), torch.float32)
buf1 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
buf3 = reinterpret_tensor(buf1, (4, 1, 1, 1), (1, 1, 1, 1), 0); del buf1 # reuse
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [var_mean, sub, add, sqrt, X, mul, add_1], Original ATen: [aten.var_mean, aten.sub, aten.add, aten.sqrt, aten.div, aten.mul]
stream0 = get_raw_stream(0)
triton_per_fused_add_div_mul_sqrt_sub_var_mean_0.run(buf3, primals_1, primals_2, primals_3, buf0, buf4, 4, 64, grid=grid(4), stream=stream0)
del primals_2
del primals_3
return (buf4, primals_1, buf0, buf3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_add_div_mul_sqrt_sub_var_mean_0(in_out_ptr0, in_ptr0,
in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr
):
xnumel = 4
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
r3 = rindex // 16
tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
tmp22 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
tmp26 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tl.where(xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 64, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = 64.0
tmp18 = tmp16 / tmp17
tmp19 = 1e-05
tmp20 = tmp18 + tmp19
tmp21 = libdevice.sqrt(tmp20)
tmp23 = tmp0 - tmp10
tmp24 = tmp23 / tmp21
tmp25 = tmp22 * tmp24
tmp27 = tmp25 + tmp26
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp21, xmask)
tl.store(out_ptr1 + (r1 + 64 * x0), tmp27, xmask)
tl.store(out_ptr0 + x0, tmp10, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 1, 1, 1), torch.float32)
buf1 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
buf3 = reinterpret_tensor(buf1, (4, 1, 1, 1), (1, 1, 1, 1), 0)
del buf1
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_per_fused_add_div_mul_sqrt_sub_var_mean_0[grid(4)](buf3,
primals_1, primals_2, primals_3, buf0, buf4, 4, 64, XBLOCK=1,
num_warps=2, num_stages=1)
del primals_2
del primals_3
return buf4, primals_1, buf0, buf3
class LN_selfNew(nn.Module):
def __init__(self, num_features):
super().__init__()
shape = 1, num_features, 1, 1
self.gamma = nn.Parameter(torch.ones(shape))
self.beta = nn.Parameter(torch.zeros(shape))
def forward(self, input_0):
primals_2 = self.gamma
primals_3 = self.beta
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
EkdeepSLubana/BeyondBatchNorm
|
LN_self
| false | 17,240 |
[
"MIT"
] | 10 |
2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196
|
https://github.com/EkdeepSLubana/BeyondBatchNorm/tree/2ab1626a1ebfdfe55f0a4bc6ac24c8bbdd4e0196
|
TwoMLPHead
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_6/inductor_cache/tf/ctfobpckmiv3kkga3a6gzs6unuclcnxpb4xc2h5r3udgxgix4ip5.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# x_1 => relu
# Graph fragment:
# %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_3), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_0 = async_compile.triton('triton_poi_fused_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_6/inductor_cache/wq/cwqkfc7efcgiuv6rsa3stkinyzeft7fq5wl4uyfa53emahjnunte.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# x_2 => relu_1
# Graph fragment:
# %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_5), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
del primals_2
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu]
stream0 = get_raw_stream(0)
triton_poi_fused_relu_0.run(buf1, primals_3, 16, grid=grid(16), stream=stream0)
del primals_3
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
buf3 = buf2; del buf2 # reuse
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu, aten.threshold_backward]
triton_poi_fused_relu_threshold_backward_1.run(buf3, primals_5, buf4, 16, grid=grid(16), stream=stream0)
del primals_5
return (buf3, primals_1, buf1, buf4, primals_4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
(1, 4), 0), out=buf0)
del primals_2
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_relu_0[grid(16)](buf1, primals_3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4
), 0), out=buf2)
buf3 = buf2
del buf2
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_1[grid(16)](buf3,
primals_5, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_5
return buf3, primals_1, buf1, buf4, primals_4
class TwoMLPHeadNew(nn.Module):
"""
Heads for FPN for classification
"""
def __init__(self, in_channels, representation_size):
super(TwoMLPHeadNew, self).__init__()
self.fc6 = nn.Linear(in_channels, representation_size)
self.fc7 = nn.Linear(representation_size, representation_size)
def forward(self, input_0):
primals_1 = self.fc6.weight
primals_3 = self.fc6.bias
primals_2 = self.fc7.weight
primals_5 = self.fc7.bias
primals_4 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
billchenxi/vision
|
TwoMLPHead
| false | 1,548 |
[
"BSD-3-Clause"
] | 0 |
5608b06a6fbc9c84284dad9f07a16df61da10f85
|
https://github.com/billchenxi/vision/tree/5608b06a6fbc9c84284dad9f07a16df61da10f85
|
ResidualBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_8/inductor_cache/v6/cv6oewqqnsshd7he7ylh2kikzu4smtrhj2dmv6nb5csosp7g6vw5.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
# out => _unsafe_index, _unsafe_index_1
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = (xindex // 6) % 6
x2 = (xindex // 36)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/t6/ct6syu6rq3n7yx3zuog2yujcrfreefdccraqz7zj2m3c5xhvp5vl.py
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
# out_1 => convolution
# out_2 => add, rsqrt, var_mean
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_1 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 16],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_convolution_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_1(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
x3 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (r2 + (16*x3)), xmask, other=0.0)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = tl.where(xmask, tmp3, 0)
tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp8 = tl.where(xmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp11 = tmp10.to(tl.float32)
tmp12 = tmp9 / tmp11
tmp13 = tmp3 - tmp12
tmp14 = tmp13 * tmp13
tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
tmp17 = tl.where(xmask, tmp15, 0)
tmp18 = tl.sum(tmp17, 1)[:, None]
tmp19 = 16.0
tmp20 = tmp18 / tmp19
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(in_out_ptr0 + (r2 + (16*x3)), tmp2, xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + (x3), tmp23, xmask)
tl.store(out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/tv/ctvgdstglzbrzudd6bkrrqy4btcmsc7pfbqrlqw6ojd4t5dl7bkd.py
# Topologically Sorted Source Nodes: [out_3, out_4], Original ATen: [aten._prelu_kernel, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_3 => gt, mul_1, where
# out_4 => _unsafe_index_2, _unsafe_index_3
# Graph fragment:
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %view_1), kwargs = {})
# %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_1, %mul_1), kwargs = {})
# %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_3 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused__prelu_kernel_reflection_pad2d_2 = async_compile.triton('triton_poi_fused__prelu_kernel_reflection_pad2d_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_reflection_pad2d_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__prelu_kernel_reflection_pad2d_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = (xindex // 6) % 6
x2 = (xindex // 36)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr3 + (0))
tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp5 = 0.0
tmp6 = tmp4 > tmp5
tmp9 = tmp8 * tmp4
tmp10 = tl.where(tmp6, tmp4, tmp9)
tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/kw/ckw2ihgfnmmk4dhle4jn6wn67uv4od7ypr3h4aqj7caqddj4urj3.py
# Topologically Sorted Source Nodes: [out_5, out_6, out_8], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten._prelu_kernel]
# Source node to ATen node mapping:
# out_5 => convolution_1
# out_6 => add_1, rsqrt_1, var_mean_1
# out_8 => gt_1, mul_3, where_1
# Graph fragment:
# %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_3, %primals_5, %primals_6, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_3, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
# %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
# %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_6, 0), kwargs = {})
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %view_6), kwargs = {})
# %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %view_6, %mul_3), kwargs = {})
triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_3 = async_compile.triton('triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 16],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_3', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_3(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
x3 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (r2 + (16*x3)), xmask, other=0.0)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp26 = tl.load(in_ptr1 + (r2 + (16*x3)), xmask, other=0.0)
tmp30 = tl.load(in_ptr2 + (0))
tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = tl.where(xmask, tmp3, 0)
tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp8 = tl.where(xmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp11 = tmp10.to(tl.float32)
tmp12 = tmp9 / tmp11
tmp13 = tmp3 - tmp12
tmp14 = tmp13 * tmp13
tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
tmp17 = tl.where(xmask, tmp15, 0)
tmp18 = tl.sum(tmp17, 1)[:, None]
tmp19 = 16.0
tmp20 = tmp18 / tmp19
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tmp24 = tmp2 - tmp12
tmp25 = tmp24 * tmp23
tmp27 = tmp25 + tmp26
tmp28 = 0.0
tmp29 = tmp27 > tmp28
tmp32 = tmp31 * tmp27
tmp33 = tl.where(tmp29, tmp27, tmp32)
tl.store(in_out_ptr0 + (r2 + (16*x3)), tmp2, xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + (x3), tmp23, xmask)
tl.store(out_ptr1 + (r2 + (16*x3)), tmp33, xmask)
tl.store(out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (1, ), (1, ))
assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_6, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 576, grid=grid(576), stream=stream0)
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = buf1; del buf1 # reuse
buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf4 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
buf6 = reinterpret_tensor(buf4, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf4 # reuse
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
triton_per_fused__native_batch_norm_legit_convolution_1.run(buf2, buf6, primals_3, buf3, 16, 16, grid=grid(16), stream=stream0)
del primals_3
buf7 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_3, out_4], Original ATen: [aten._prelu_kernel, aten.reflection_pad2d]
triton_poi_fused__prelu_kernel_reflection_pad2d_2.run(buf2, buf3, buf6, primals_4, buf7, 576, grid=grid(576), stream=stream0)
# Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.convolution]
buf8 = extern_kernels.convolution(buf7, primals_5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
buf9 = buf8; del buf8 # reuse
buf10 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf11 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
buf13 = reinterpret_tensor(buf11, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf11 # reuse
buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_5, out_6, out_8], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten._prelu_kernel]
triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_3.run(buf9, buf13, primals_6, primals_1, primals_4, buf10, buf14, 16, 16, grid=grid(16), stream=stream0)
del primals_6
return (buf14, primals_1, primals_2, primals_4, primals_5, buf0, buf2, buf3, buf6, buf7, buf9, buf10, buf13, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_1(in_out_ptr0,
in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
x3 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tl.where(xmask, tmp3, 0)
tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp8 = tl.where(xmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp11 = tmp10.to(tl.float32)
tmp12 = tmp9 / tmp11
tmp13 = tmp3 - tmp12
tmp14 = tmp13 * tmp13
tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
tmp17 = tl.where(xmask, tmp15, 0)
tmp18 = tl.sum(tmp17, 1)[:, None]
tmp19 = 16.0
tmp20 = tmp18 / tmp19
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + x3, tmp23, xmask)
tl.store(out_ptr0 + x3, tmp12, xmask)
@triton.jit
def triton_poi_fused__prelu_kernel_reflection_pad2d_2(in_ptr0, in_ptr1,
in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr3 + 0)
tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp5 = 0.0
tmp6 = tmp4 > tmp5
tmp9 = tmp8 * tmp4
tmp10 = tl.where(tmp6, tmp4, tmp9)
tl.store(out_ptr0 + x3, tmp10, xmask)
@triton.jit
def triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_3(
in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
x3 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp26 = tl.load(in_ptr1 + (r2 + 16 * x3), xmask, other=0.0)
tmp30 = tl.load(in_ptr2 + 0)
tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tl.where(xmask, tmp3, 0)
tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp8 = tl.where(xmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp11 = tmp10.to(tl.float32)
tmp12 = tmp9 / tmp11
tmp13 = tmp3 - tmp12
tmp14 = tmp13 * tmp13
tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
tmp17 = tl.where(xmask, tmp15, 0)
tmp18 = tl.sum(tmp17, 1)[:, None]
tmp19 = 16.0
tmp20 = tmp18 / tmp19
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tmp24 = tmp2 - tmp12
tmp25 = tmp24 * tmp23
tmp27 = tmp25 + tmp26
tmp28 = 0.0
tmp29 = tmp27 > tmp28
tmp32 = tmp31 * tmp27
tmp33 = tl.where(tmp29, tmp27, tmp32)
tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + x3, tmp23, xmask)
tl.store(out_ptr1 + (r2 + 16 * x3), tmp33, xmask)
tl.store(out_ptr0 + x3, tmp12, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (1,), (1,))
assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_6, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(576)](primals_1, buf0, 576,
XBLOCK=256, num_warps=4, num_stages=1)
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = buf1
del buf1
buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf4 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
)
buf6 = reinterpret_tensor(buf4, (1, 16, 1, 1), (16, 1, 1, 1), 0)
del buf4
triton_per_fused__native_batch_norm_legit_convolution_1[grid(16)](buf2,
buf6, primals_3, buf3, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
del primals_3
buf7 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
triton_poi_fused__prelu_kernel_reflection_pad2d_2[grid(576)](buf2,
buf3, buf6, primals_4, buf7, 576, XBLOCK=256, num_warps=4,
num_stages=1)
buf8 = extern_kernels.convolution(buf7, primals_5, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
buf9 = buf8
del buf8
buf10 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf11 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
float32)
buf13 = reinterpret_tensor(buf11, (1, 16, 1, 1), (16, 1, 1, 1), 0)
del buf11
buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_3[
grid(16)](buf9, buf13, primals_6, primals_1, primals_4, buf10,
buf14, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
del primals_6
return (buf14, primals_1, primals_2, primals_4, primals_5, buf0, buf2,
buf3, buf6, buf7, buf9, buf10, buf13)
class ResidualBlockNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, padding=1,
stride=1):
super(ResidualBlockNew, self).__init__()
self.padding1 = nn.ReflectionPad2d(padding)
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=
kernel_size, padding=0, stride=stride)
self.bn1 = nn.InstanceNorm2d(out_channels)
self.prelu = nn.PReLU()
self.padding2 = nn.ReflectionPad2d(padding)
self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=
kernel_size, padding=0, stride=stride)
self.bn2 = nn.InstanceNorm2d(out_channels)
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = self.conv1.bias
primals_4 = self.prelu.weight
primals_5 = self.conv2.weight
primals_6 = self.conv2.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
qiyuqianxai/debvc
|
ResidualBlock
| false | 10,784 |
[
"MIT"
] | 0 |
1d919019a3191d1c6a7da9b8f16e47bca6b3aef9
|
https://github.com/qiyuqianxai/debvc/tree/1d919019a3191d1c6a7da9b8f16e47bca6b3aef9
|
ConvLayer
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_2/inductor_cache/zo/czoefxnecwpok2nmimcnrjk6tumldvtx7xkzpmzgmnay3tkwhjwg.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
# out => _unsafe_index, _unsafe_index_1
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8) % 8
x2 = (xindex // 64)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-2) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-2) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_2/inductor_cache/7x/c7xksf46hekvm7qrrnxlo34nyt2yrkxxbfiyydgamwacppezvp4w.py
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# out_1 => convolution
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 25) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 1024, grid=grid(1024), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 5, 5), (100, 25, 5, 1))
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf2, primals_3, 400, grid=grid(400), stream=stream0)
del primals_3
return (buf2, primals_2, buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8 % 8
x2 = xindex // 64
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-2 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-2 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 25 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(1024)](primals_1, buf0,
1024, XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 5, 5), (100, 25, 5, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_1[grid(400)](buf2, primals_3, 400,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
return buf2, primals_2, buf0
class ConvLayerNew(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayerNew, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv2d(in_channels, out_channels,
kernel_size, stride)
def forward(self, input_0):
primals_1 = self.conv2d.weight
primals_3 = self.conv2d.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
JEF1056/Reconstruction-Style
|
ConvLayer
| false | 17,447 |
[
"MIT"
] | 6 |
3430d9e9f05c6980ae251cf15b619148a2c899d6
|
https://github.com/JEF1056/Reconstruction-Style/tree/3430d9e9f05c6980ae251cf15b619148a2c899d6
|
DenseCrossEntropy
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_0/inductor_cache/nr/cnrkptzsuv7qm3ss6i6xgoxkou23z76h2vmwqkwz2zkgpdbxhedc.py
# Topologically Sorted Source Nodes: [logprobs], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
# logprobs => amax, sub
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [-1], True), kwargs = {})
# %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax), kwargs = {})
triton_poi_fused__log_softmax_0 = async_compile.triton('triton_poi_fused__log_softmax_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_0/inductor_cache/nb/cnbdxy34iv6vkig4bfuqrxbegug3ek6lhyugevz3qctt7efdvtge.py
# Topologically Sorted Source Nodes: [logprobs, neg, loss, loss_1, mean], Original ATen: [aten._log_softmax, aten.neg, aten.mul, aten.sum, aten.mean]
# Source node to ATen node mapping:
# logprobs => exp, log, sub_1, sum_1
# loss => mul
# loss_1 => sum_2
# mean => mean
# neg => neg
# Graph fragment:
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
# %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
# %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sub_1,), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, %arg1_1), kwargs = {})
# %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), kwargs = {})
triton_per_fused__log_softmax_mean_mul_neg_sum_1 = async_compile.triton('triton_per_fused__log_softmax_mean_mul_neg_sum_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 64],
reduction_hint=ReductionHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__log_softmax_mean_mul_neg_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__log_softmax_mean_mul_neg_sum_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 64
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
tmp18 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
tmp23 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
tmp28 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
tmp1 = tl_math.exp(tmp0)
tmp3 = tl_math.exp(tmp2)
tmp4 = tmp1 + tmp3
tmp6 = tl_math.exp(tmp5)
tmp7 = tmp4 + tmp6
tmp9 = tl_math.exp(tmp8)
tmp10 = tmp7 + tmp9
tmp11 = tl_math.log(tmp10)
tmp12 = tmp0 - tmp11
tmp13 = -tmp12
tmp15 = tmp13 * tmp14
tmp16 = tmp2 - tmp11
tmp17 = -tmp16
tmp19 = tmp17 * tmp18
tmp20 = tmp15 + tmp19
tmp21 = tmp5 - tmp11
tmp22 = -tmp21
tmp24 = tmp22 * tmp23
tmp25 = tmp20 + tmp24
tmp26 = tmp8 - tmp11
tmp27 = -tmp26
tmp29 = tmp27 * tmp28
tmp30 = tmp25 + tmp29
tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
tmp33 = tl.sum(tmp31, 1)[:, None]
tmp34 = 64.0
tmp35 = tmp33 / tmp34
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp35, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [logprobs], Original ATen: [aten._log_softmax]
stream0 = get_raw_stream(0)
triton_poi_fused__log_softmax_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
buf2 = empty_strided_cuda((), (), torch.float32)
buf3 = buf2; del buf2 # reuse
# Topologically Sorted Source Nodes: [logprobs, neg, loss, loss_1, mean], Original ATen: [aten._log_softmax, aten.neg, aten.mul, aten.sum, aten.mean]
triton_per_fused__log_softmax_mean_mul_neg_sum_1.run(buf3, buf0, arg1_1, 1, 64, grid=grid(1), stream=stream0)
del arg1_1
del buf0
return (buf3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_per_fused__log_softmax_mean_mul_neg_sum_1(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
tmp18 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
tmp23 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
tmp28 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
tmp1 = tl_math.exp(tmp0)
tmp3 = tl_math.exp(tmp2)
tmp4 = tmp1 + tmp3
tmp6 = tl_math.exp(tmp5)
tmp7 = tmp4 + tmp6
tmp9 = tl_math.exp(tmp8)
tmp10 = tmp7 + tmp9
tmp11 = tl_math.log(tmp10)
tmp12 = tmp0 - tmp11
tmp13 = -tmp12
tmp15 = tmp13 * tmp14
tmp16 = tmp2 - tmp11
tmp17 = -tmp16
tmp19 = tmp17 * tmp18
tmp20 = tmp15 + tmp19
tmp21 = tmp5 - tmp11
tmp22 = -tmp21
tmp24 = tmp22 * tmp23
tmp25 = tmp20 + tmp24
tmp26 = tmp8 - tmp11
tmp27 = -tmp26
tmp29 = tmp27 * tmp28
tmp30 = tmp25 + tmp29
tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
tmp33 = tl.sum(tmp31, 1)[:, None]
tmp34 = 64.0
tmp35 = tmp33 / tmp34
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp35, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__log_softmax_0[grid(256)](arg0_1, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
buf2 = empty_strided_cuda((), (), torch.float32)
buf3 = buf2
del buf2
triton_per_fused__log_softmax_mean_mul_neg_sum_1[grid(1)](buf3,
buf0, arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
del arg1_1
del buf0
return buf3,
class DenseCrossEntropyNew(nn.Module):
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
i-pan/kaggle-melanoma
|
DenseCrossEntropy
| false | 15,569 |
[
"MIT"
] | 68 |
caaec0d7e9cafc7b405eb86e7fdf00107d89e1d9
|
https://github.com/i-pan/kaggle-melanoma/tree/caaec0d7e9cafc7b405eb86e7fdf00107d89e1d9
|
MidNet4
|
import torch
import torch.nn as nn
class MidNet4(nn.Module):
def forward(self, x_in):
"""Network with dilation rate 4
:param x_in: input convolutional features
:returns: processed convolutional features
:rtype: Tensor
"""
x = self.lrelu(self.conv1(x_in))
x = self.lrelu(self.conv2(x))
x = self.lrelu(self.conv3(x))
x = self.conv4(x)
return x
def __init__(self, in_channels=16):
"""FIXME! briefly describe function
:param in_channels: Input channels
:returns: N/A
:rtype: N/A
"""
super(MidNet4, self).__init__()
self.lrelu = nn.LeakyReLU()
self.conv1 = nn.Conv2d(in_channels, 64, 3, 1, 4, 4)
self.conv2 = nn.Conv2d(64, 64, 3, 1, 4, 4)
self.conv3 = nn.Conv2d(64, 64, 3, 1, 4, 4)
self.conv4 = nn.Conv2d(64, 64, 3, 1, 4, 4)
def get_inputs():
return [torch.rand([4, 16, 64, 64])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 4096 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, None)
tl.store(out_ptr1 + x3, tmp7, None)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 4096 % 64
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, None)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9) = args
args.clear()
assert_size_stride(primals_1, (64, 16, 3, 3), (144, 9, 3, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 16, 64, 64), (65536, 4096, 64, 1))
assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_7, (64,), (1,))
assert_size_stride(primals_8, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_9, (64,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(4, 4), dilation=(4, 4), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf1 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.bool)
buf2 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0[grid(1048576)](buf0,
primals_2, buf1, buf2, 1048576, XBLOCK=1024, num_warps=4,
num_stages=1)
del primals_2
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(4, 4), dilation=(4, 4), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf4 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.bool)
buf5 = buf0
del buf0
triton_poi_fused_convolution_leaky_relu_0[grid(1048576)](buf3,
primals_5, buf4, buf5, 1048576, XBLOCK=1024, num_warps=4,
num_stages=1)
del primals_5
buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1),
padding=(4, 4), dilation=(4, 4), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf7 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.bool)
buf8 = buf3
del buf3
triton_poi_fused_convolution_leaky_relu_0[grid(1048576)](buf6,
primals_7, buf7, buf8, 1048576, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf6
del primals_7
buf9 = extern_kernels.convolution(buf8, primals_8, stride=(1, 1),
padding=(4, 4), dilation=(4, 4), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf10 = buf9
del buf9
triton_poi_fused_convolution_1[grid(1048576)](buf10, primals_9,
1048576, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_9
return (buf10, primals_1, primals_3, primals_4, primals_6, primals_8,
buf1, buf2, buf4, buf5, buf7, buf8)
class MidNet4New(nn.Module):
def __init__(self, in_channels=16):
"""FIXME! briefly describe function
:param in_channels: Input channels
:returns: N/A
:rtype: N/A
"""
super(MidNet4New, self).__init__()
self.lrelu = nn.LeakyReLU()
self.conv1 = nn.Conv2d(in_channels, 64, 3, 1, 4, 4)
self.conv2 = nn.Conv2d(64, 64, 3, 1, 4, 4)
self.conv3 = nn.Conv2d(64, 64, 3, 1, 4, 4)
self.conv4 = nn.Conv2d(64, 64, 3, 1, 4, 4)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.conv3.weight
primals_7 = self.conv3.bias
primals_8 = self.conv4.weight
primals_9 = self.conv4.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9])
return output[0]
|
deshwalmahesh/CURL---cpu-gpu
|
MidNet4
| false | 3,415 |
[
"BSD-3-Clause"
] | 0 |
f4e87275b6cce556b9e04a188cf7ae13d810d82a
|
https://github.com/deshwalmahesh/CURL---cpu-gpu/tree/f4e87275b6cce556b9e04a188cf7ae13d810d82a
|
GateContextSelectionLayer
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_4/inductor_cache/2f/c2fcasureqlcqroneun3t4p4fi3ty77g34yxduqsb7qua2qbyhta.py
# Topologically Sorted Source Nodes: [add, update, sub, mul, mul_1, out], Original ATen: [aten.add, aten.sigmoid, aten.rsub, aten.mul, aten.sigmoid_backward]
# Source node to ATen node mapping:
# add => add
# mul => mul
# mul_1 => mul_1
# out => add_1
# sub => sub
# update => sigmoid
# Graph fragment:
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %view_3), kwargs = {})
# %sigmoid : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add,), kwargs = {})
# %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %primals_3), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %primals_6), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
# %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %sub), kwargs = {})
triton_poi_fused_add_mul_rsub_sigmoid_sigmoid_backward_0 = async_compile.triton('triton_poi_fused_add_mul_rsub_sigmoid_sigmoid_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_rsub_sigmoid_sigmoid_backward_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_sigmoid_backward_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x2), xmask)
tmp4 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + (x2), xmask)
tmp12 = tl.load(in_ptr5 + (x2), xmask)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp7 = tl.sigmoid(tmp6)
tmp8 = 1.0
tmp9 = tmp8 - tmp7
tmp11 = tmp9 * tmp10
tmp13 = tmp7 * tmp12
tmp14 = tmp11 + tmp13
tmp15 = tmp7 * tmp9
tl.store(out_ptr0 + (x2), tmp14, xmask)
tl.store(out_ptr1 + (x2), tmp15, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
del primals_4
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [add, update, sub, mul, mul_1, out], Original ATen: [aten.add, aten.sigmoid, aten.rsub, aten.mul, aten.sigmoid_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_add_mul_rsub_sigmoid_sigmoid_backward_0.run(buf0, primals_2, buf1, primals_5, primals_3, primals_6, buf2, buf3, 256, grid=grid(256), stream=stream0)
del buf0
del buf1
del primals_2
del primals_5
return (buf2, primals_3, primals_6, buf3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_sigmoid_backward_0(in_ptr0,
in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, out_ptr1, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x2, xmask)
tmp4 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + x2, xmask)
tmp12 = tl.load(in_ptr5 + x2, xmask)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp7 = tl.sigmoid(tmp6)
tmp8 = 1.0
tmp9 = tmp8 - tmp7
tmp11 = tmp9 * tmp10
tmp13 = tmp7 * tmp12
tmp14 = tmp11 + tmp13
tmp15 = tmp7 * tmp9
tl.store(out_ptr0 + x2, tmp14, xmask)
tl.store(out_ptr1 + x2, tmp15, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
del primals_4
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_mul_rsub_sigmoid_sigmoid_backward_0[grid(256)](
buf0, primals_2, buf1, primals_5, primals_3, primals_6, buf2,
buf3, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf0
del buf1
del primals_2
del primals_5
return buf2, primals_3, primals_6, buf3
class GateContextSelectionLayerNew(nn.Module):
def __init__(self, dim_model, dim_ff, prob_dropout):
super(GateContextSelectionLayerNew, self).__init__()
self.source = nn.Linear(dim_model, dim_model)
self.context = nn.Linear(dim_model, dim_model)
def forward(self, input_0, input_1):
primals_1 = self.source.weight
primals_2 = self.source.bias
primals_4 = self.context.weight
primals_5 = self.context.bias
primals_3 = input_0
primals_6 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
KirkGuo/HCN
|
GateContextSelectionLayer
| false | 5,437 |
[
"MIT"
] | 1 |
7d8020c8d76413b6ca3a359fb2e9b34652949e17
|
https://github.com/KirkGuo/HCN/tree/7d8020c8d76413b6ca3a359fb2e9b34652949e17
|
Critic
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_4/inductor_cache/ms/cmsuzohbg5nq52jnvirovzkvykrzzko5xomu7zyu5e5u2lhegppw.py
# Topologically Sorted Source Nodes: [state_action], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# state_action => cat
# Graph fragment:
# %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_4/inductor_cache/ac/cacdwifxdru2eihx3n66wqfym5hjpdo6yxk3gsol5t54xroplkwv.py
# Topologically Sorted Source Nodes: [q], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# q => relu
# Graph fragment:
# %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_4), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_1 = async_compile.triton('triton_poi_fused_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[2048],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 400
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_4/inductor_cache/vp/cvpyw2bjgo55x2ne47dmpi2vmqu5t4eb3wcvjgjcnove3xyj7bcr.py
# Topologically Sorted Source Nodes: [q_1], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# q_1 => relu_1
# Graph fragment:
# %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_6), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_relu_2 = async_compile.triton('triton_poi_fused_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[2048],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 300
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (400, 8), (8, 1))
assert_size_stride(primals_4, (400, ), (1, ))
assert_size_stride(primals_5, (300, 400), (400, 1))
assert_size_stride(primals_6, (300, ), (1, ))
assert_size_stride(primals_7, (1, 300), (300, 1))
assert_size_stride(primals_8, (1, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
# Topologically Sorted Source Nodes: [state_action], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 32, grid=grid(32), stream=stream0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 400), (1, 8), 0), out=buf1)
del primals_3
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [q], Original ATen: [aten.relu]
triton_poi_fused_relu_1.run(buf2, primals_4, 1600, grid=grid(1600), stream=stream0)
del primals_4
buf3 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (400, 300), (1, 400), 0), out=buf3)
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [q_1], Original ATen: [aten.relu]
triton_poi_fused_relu_2.run(buf4, primals_6, 1200, grid=grid(1200), stream=stream0)
del primals_6
buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
# Topologically Sorted Source Nodes: [q_2], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7, (300, 1), (1, 300), 0), alpha=1, beta=1, out=buf6)
del primals_8
return (buf6, buf0, buf2, buf4, primals_7, primals_5, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((400, 8), (8, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((300, 400), (400, 1), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((300, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((1, 300), (300, 1), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x2, tmp10, xmask)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 400
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 1200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 300
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (400, 8), (8, 1))
assert_size_stride(primals_4, (400,), (1,))
assert_size_stride(primals_5, (300, 400), (400, 1))
assert_size_stride(primals_6, (300,), (1,))
assert_size_stride(primals_7, (1, 300), (300, 1))
assert_size_stride(primals_8, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
XBLOCK=32, num_warps=1, num_stages=1)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 400), (1,
8), 0), out=buf1)
del primals_3
buf2 = buf1
del buf1
triton_poi_fused_relu_1[grid(1600)](buf2, primals_4, 1600, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (400, 300), (
1, 400), 0), out=buf3)
buf4 = buf3
del buf3
triton_poi_fused_relu_2[grid(1200)](buf4, primals_6, 1200, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_6
buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7,
(300, 1), (1, 300), 0), alpha=1, beta=1, out=buf6)
del primals_8
return buf6, buf0, buf2, buf4, primals_7, primals_5
class CriticNew(nn.Module):
def __init__(self, state_dim, action_dim):
super(CriticNew, self).__init__()
self.fc1 = nn.Linear(state_dim + action_dim, 400)
self.fc2 = nn.Linear(400, 300)
self.fc3 = nn.Linear(300, 1)
def forward(self, input_0, input_1):
primals_3 = self.fc1.weight
primals_4 = self.fc1.bias
primals_5 = self.fc2.weight
primals_6 = self.fc2.bias
primals_7 = self.fc3.weight
primals_8 = self.fc3.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
Chris0919/Deep-reinforcement-learning-with-pytorch
|
Critic
| false | 4,999 |
[
"MIT"
] | 1 |
a4f458dde7659654fcae4635d25f6bd05a5d2d6c
|
https://github.com/Chris0919/Deep-reinforcement-learning-with-pytorch/tree/a4f458dde7659654fcae4635d25f6bd05a5d2d6c
|
ScaledDotProductAttentionMemory
|
import torch
import numpy as np
from torch import nn
class ScaledDotProductAttentionMemory(nn.Module):
"""
Scaled dot-product attention with memory
"""
def __init__(self, d_model, d_k, d_v, h, m):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries and keys
:param d_v: Dimensionality of values
:param h: Number of heads
:param m: Number of memory slots
"""
super(ScaledDotProductAttentionMemory, self).__init__()
self.fc_q = nn.Linear(d_model, h * d_k)
self.fc_k = nn.Linear(d_model, h * d_k)
self.fc_v = nn.Linear(d_model, h * d_v)
self.fc_o = nn.Linear(h * d_v, d_model)
self.m_k = nn.Parameter(torch.FloatTensor(1, m, h * d_k))
self.m_v = nn.Parameter(torch.FloatTensor(1, m, h * d_v))
self.d_model = d_model
self.d_k = d_k
self.d_v = d_v
self.h = h
self.m = m
self.init_weights()
def init_weights(self):
nn.init.xavier_uniform_(self.fc_q.weight)
nn.init.xavier_uniform_(self.fc_k.weight)
nn.init.xavier_uniform_(self.fc_v.weight)
nn.init.xavier_uniform_(self.fc_o.weight)
nn.init.normal_(self.m_k, 0, 1 / self.d_k)
nn.init.normal_(self.m_v, 0, 1 / self.m)
nn.init.constant_(self.fc_q.bias, 0)
nn.init.constant_(self.fc_k.bias, 0)
nn.init.constant_(self.fc_v.bias, 0)
nn.init.constant_(self.fc_o.bias, 0)
def forward(self, queries, keys, values, attention_mask=None,
attention_weights=None):
"""
Computes
:param queries: Queries (b_s, nq, d_model)
:param keys: Keys (b_s, nk, d_model)
:param values: Values (b_s, nk, d_model)
:param attention_mask: Mask over attention values (b_s, h, nq, nk). True indicates masking.
:param attention_weights: Multiplicative weights for attention values (b_s, h, nq, nk).
:return:
"""
b_s, nq = queries.shape[:2]
nk = keys.shape[1]
m_k = np.sqrt(self.d_k) * self.m_k.expand(b_s, self.m, self.h *
self.d_k)
m_v = np.sqrt(self.m) * self.m_v.expand(b_s, self.m, self.h * self.d_v)
q = self.fc_q(queries).view(b_s, nq, self.h, self.d_k).permute(0, 2,
1, 3)
k = torch.cat([self.fc_k(keys), m_k], 1).view(b_s, nk + self.m,
self.h, self.d_k).permute(0, 2, 3, 1)
v = torch.cat([self.fc_v(values), m_v], 1).view(b_s, nk + self.m,
self.h, self.d_v).permute(0, 2, 1, 3)
att = torch.matmul(q, k) / np.sqrt(self.d_k)
if attention_weights is not None:
att = torch.cat([att[:, :, :, :nk] * attention_weights, att[:,
:, :, nk:]], -1)
if attention_mask is not None:
att[:, :, :, :nk] = att[:, :, :, :nk].masked_fill(attention_mask,
-np.inf)
att = torch.softmax(att, -1)
out = torch.matmul(att, v).permute(0, 2, 1, 3).contiguous().view(b_s,
nq, self.h * self.d_v)
out = self.fc_o(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4])
]
def get_init_inputs():
return [[], {'d_model': 4, 'd_k': 4, 'd_v': 4, 'h': 4, 'm': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_clone_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16 % 4
x3 = xindex // 64
x4 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + x4, tmp2, xmask)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8 % 16
x2 = xindex // 128
x3 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x1 + 16 * x0 + 64 * x2), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (x1 + 16 * (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = 2.0
tmp11 = tmp10 * tmp9
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp6, tmp11, tmp12)
tmp14 = tl.where(tmp4, tmp5, tmp13)
tl.store(out_ptr0 + x3, tmp14, xmask)
@triton.jit
def triton_per_fused__softmax_sqrt_2(in_ptr0, out_ptr2, xnumel, rnumel,
XBLOCK: tl.constexpr):
xnumel = 64
RBLOCK: tl.constexpr = 8
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 8 * x0), xmask, other=0.0)
tmp1 = tl.full([1, 1], 2.0, tl.float64)
tmp2 = tl.full([1, 1], 0.0, tl.float64)
tmp3 = tmp1 >= tmp2
tmp4 = 1.0
tmp5 = -1.0
tmp6 = tl.where(tmp3, tmp4, tmp5)
tmp7 = tmp0 * tmp6
tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
tmp10 = tl.where(xmask, tmp8, float('-inf'))
tmp11 = triton_helpers.max2(tmp10, 1)[:, None]
tmp12 = tmp7 - tmp11
tmp13 = tmp6.to(tl.float64)
tmp14 = tmp13 * tmp1
tmp15 = tmp14.to(tl.float32)
tmp16 = tmp12 / tmp15
tmp17 = tl_math.exp(tmp16)
tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
tmp20 = tl.where(xmask, tmp18, 0)
tmp21 = tl.sum(tmp20, 1)[:, None]
tmp22 = tmp17 / tmp21
tl.store(out_ptr2 + (r1 + 8 * x0), tmp22, xmask)
@triton.jit
def triton_poi_fused_clone_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 8
x0 = xindex % 4
x2 = xindex // 32 % 4
x3 = xindex // 128
x4 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp4 &
xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (x0 + 4 * x2 + 16 * (-4 + x1)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = 2.0
tmp11 = tmp10 * tmp9
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp6, tmp11, tmp12)
tmp14 = tl.where(tmp4, tmp5, tmp13)
tl.store(out_ptr0 + x4, tmp14, xmask)
@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16 % 4
x3 = xindex // 64
x4 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
tl.store(out_ptr0 + x4, tmp0, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (1, 4, 16), (64, 16, 1))
assert_size_stride(primals_4, (1, 4, 16), (64, 16, 1))
assert_size_stride(primals_5, (16, 4), (4, 1))
assert_size_stride(primals_6, (16,), (1,))
assert_size_stride(primals_7, (16, 4), (4, 1))
assert_size_stride(primals_8, (16,), (1,))
assert_size_stride(primals_9, (16, 4), (4, 1))
assert_size_stride(primals_10, (16,), (1,))
assert_size_stride(primals_11, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_12, (4, 16), (16, 1))
assert_size_stride(primals_13, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_5, (4, 16), (1, 4), 0), out=buf0)
del primals_5
buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.addmm(primals_8, reinterpret_tensor(primals_2, (16,
4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 16), (1, 4),
0), alpha=1, beta=1, out=buf1)
del primals_7
del primals_8
buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
extern_kernels.addmm(primals_10, reinterpret_tensor(primals_11, (16,
4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 16), (1, 4),
0), alpha=1, beta=1, out=buf2)
del primals_10
del primals_9
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(256)](buf0, primals_6, buf3, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del buf0
del primals_6
buf4 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
triton_poi_fused_clone_1[grid(512)](buf1, primals_3, buf4, 512,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
buf5 = empty_strided_cuda((16, 4, 8), (32, 8, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf4, (16, 4, 8), (32, 8, 1), 0), out=buf5)
buf8 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
triton_per_fused__softmax_sqrt_2[grid(64)](buf5, buf8, 64, 8,
XBLOCK=32, num_warps=2, num_stages=1)
buf9 = reinterpret_tensor(buf5, (4, 4, 8, 4), (128, 32, 4, 1), 0)
del buf5
triton_poi_fused_clone_3[grid(512)](buf2, primals_4, buf9, 512,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_4
buf10 = reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0)
del buf2
extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 8), (32, 8, 1),
0), reinterpret_tensor(buf9, (16, 8, 4), (32, 4, 1), 0), out=buf10)
buf11 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf1
triton_poi_fused_clone_4[grid(256)](buf10, buf11, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del buf10
buf12 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_13, reinterpret_tensor(buf11, (16, 16),
(16, 1), 0), reinterpret_tensor(primals_12, (16, 4), (1, 16), 0
), alpha=1, beta=1, out=buf12)
del primals_13
return reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
), reinterpret_tensor(primals_11, (16, 4), (4, 1), 0
), buf8, reinterpret_tensor(buf11, (16, 16), (16, 1), 0
), primals_12, reinterpret_tensor(buf9, (16, 4, 8), (32, 1, 4), 0
), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0
), reinterpret_tensor(buf4, (16, 8, 4), (32, 1, 8), 0)
class ScaledDotProductAttentionMemoryNew(nn.Module):
"""
Scaled dot-product attention with memory
"""
def __init__(self, d_model, d_k, d_v, h, m):
"""
:param d_model: Output dimensionality of the model
:param d_k: Dimensionality of queries and keys
:param d_v: Dimensionality of values
:param h: Number of heads
:param m: Number of memory slots
"""
super(ScaledDotProductAttentionMemoryNew, self).__init__()
self.fc_q = nn.Linear(d_model, h * d_k)
self.fc_k = nn.Linear(d_model, h * d_k)
self.fc_v = nn.Linear(d_model, h * d_v)
self.fc_o = nn.Linear(h * d_v, d_model)
self.m_k = nn.Parameter(torch.FloatTensor(1, m, h * d_k))
self.m_v = nn.Parameter(torch.FloatTensor(1, m, h * d_v))
self.d_model = d_model
self.d_k = d_k
self.d_v = d_v
self.h = h
self.m = m
self.init_weights()
def init_weights(self):
nn.init.xavier_uniform_(self.fc_q.weight)
nn.init.xavier_uniform_(self.fc_k.weight)
nn.init.xavier_uniform_(self.fc_v.weight)
nn.init.xavier_uniform_(self.fc_o.weight)
nn.init.normal_(self.m_k, 0, 1 / self.d_k)
nn.init.normal_(self.m_v, 0, 1 / self.m)
nn.init.constant_(self.fc_q.bias, 0)
nn.init.constant_(self.fc_k.bias, 0)
nn.init.constant_(self.fc_v.bias, 0)
nn.init.constant_(self.fc_o.bias, 0)
def forward(self, input_0, input_1, input_2):
primals_3 = self.m_k
primals_4 = self.m_v
primals_5 = self.fc_q.weight
primals_6 = self.fc_q.bias
primals_7 = self.fc_k.weight
primals_8 = self.fc_k.bias
primals_9 = self.fc_v.weight
primals_10 = self.fc_v.bias
primals_12 = self.fc_o.weight
primals_13 = self.fc_o.bias
primals_1 = input_0
primals_2 = input_1
primals_11 = input_2
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13])
return output[0]
|
Kilichbek/artemis-m2-transformer
|
ScaledDotProductAttentionMemory
| false | 17,550 |
[
"MIT"
] | 8 |
99f7e797965710bf2565283d6b5028a6fe32664c
|
https://github.com/Kilichbek/artemis-m2-transformer/tree/99f7e797965710bf2565283d6b5028a6fe32664c
|
DenseGraphConv
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_6/inductor_cache/3i/c3iibarqokkpdvrjfwijmnifcbse5v3c5vj2oqjzbj6rok54jxf5.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.clone]
# Source node to ATen node mapping:
# out => clone
# Graph fragment:
# %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 64
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_6/inductor_cache/w2/cw2bwqpq3dkexeyqz25khcvdcedkdcrcwpb7zrtd6eayijd5lgez.py
# Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.add]
# Source node to ATen node mapping:
# out_2 => add
# Graph fragment:
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_4, %view_6), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x2), xmask)
tmp2 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.clone]
stream0 = get_raw_stream(0)
triton_poi_fused_clone_0.run(primals_2, buf0, 256, grid=grid(256), stream=stream0)
del primals_2
buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_1, (16, 4, 4), (16, 4, 1), 0), out=buf1)
buf2 = reinterpret_tensor(buf0, (64, 4), (4, 1), 0); del buf0 # reuse
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), primals_3, out=buf2)
del primals_3
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
del primals_4
buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2 # reuse
# Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.add]
triton_poi_fused_add_1.run(buf4, buf3, primals_5, 256, grid=grid(256), stream=stream0)
del buf3
del primals_5
return (buf4, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (4, 64), (1, 4), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch.nn import Parameter
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 64
x2 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x2, tmp0, xmask)
@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x2, xmask)
tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(256)](primals_2, buf0, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_2
buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(primals_1, (16, 4, 4), (16, 4, 1), 0),
out=buf1)
buf2 = reinterpret_tensor(buf0, (64, 4), (4, 1), 0)
del buf0
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
primals_3, out=buf2)
del primals_3
buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
del primals_4
buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf2
triton_poi_fused_add_1[grid(256)](buf4, buf3, primals_5, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del buf3
del primals_5
return buf4, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (4, 64), (1, 4), 0)
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
class DenseGraphConvNew(torch.nn.Module):
"""See :class:`torch_geometric.nn.conv.GraphConv`.
"""
def __init__(self, in_channels, out_channels, aggr='add', bias=True):
assert aggr in ['add', 'mean', 'max']
super(DenseGraphConvNew, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.aggr = aggr
self.weight = Parameter(torch.Tensor(in_channels, out_channels))
self.lin = torch.nn.Linear(in_channels, out_channels, bias=bias)
self.reset_parameters()
def reset_parameters(self):
uniform(self.in_channels, self.weight)
self.lin.reset_parameters()
def __repr__(self):
return '{}({}, {})'.format(self.__class__.__name__, self.
in_channels, self.out_channels)
def forward(self, input_0, input_1):
primals_3 = self.weight
primals_4 = self.lin.weight
primals_5 = self.lin.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
cshjin/pytorch_geometric
|
DenseGraphConv
| false | 1,763 |
[
"MIT"
] | 0 |
8dd0e76beb72135949a275edd851f80f7b97648f
|
https://github.com/cshjin/pytorch_geometric/tree/8dd0e76beb72135949a275edd851f80f7b97648f
|
Unet
|
import torch
import torch.nn as nn
def crop(image, new_shape):
plus_h, plus_w = 0, 0
if new_shape[2] % 2 != 0:
plus_h = 1
if new_shape[3] % 2 != 0:
plus_w = 1
middle_height = image.shape[2] // 2
middle_weight = image.shape[3] // 2
go_height = new_shape[2] // 2
go_weight = new_shape[3] // 2
cropped_image = image[:, :, middle_height - go_height:middle_height +
go_height + plus_h, middle_weight - go_weight:middle_weight +
go_weight + plus_w]
return cropped_image
class ContractingBlock(nn.Module):
def __init__(self, input_channel):
super(ContractingBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=input_channel, out_channels=
input_channel * 2, kernel_size=(3, 3))
self.conv2 = nn.Conv2d(input_channel * 2, input_channel * 2,
kernel_size=(3, 3))
self.maxpool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))
self.activation = nn.ReLU()
def forward(self, x):
x = self.activation(self.conv1(x))
x = self.activation(self.conv2(x))
return self.maxpool(x)
class ExpandingBlock(nn.Module):
def __init__(self, input_channels):
super(ExpandingBlock, self).__init__()
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear',
align_corners=True)
self.conv1 = nn.Conv2d(input_channels, input_channels // 2,
kernel_size=(2, 2))
self.conv2 = nn.Conv2d(input_channels, input_channels // 2,
kernel_size=(3, 3))
self.conv3 = nn.Conv2d(input_channels // 2, input_channels // 2,
kernel_size=(3, 3))
self.activation = nn.ReLU()
def forward(self, skip_con_x, x):
x = self.upsample(x)
x = self.conv1(x)
skip_con_x = crop(skip_con_x, x.shape)
x = torch.cat([skip_con_x, x], axis=1)
x = self.conv2(x)
x = self.activation(x)
x = self.conv3(x)
return self.activation(x)
class FeatureMapBlock(nn.Module):
def __init__(self, input_channels, output_channels):
super(FeatureMapBlock, self).__init__()
self.conv = nn.Conv2d(input_channels, output_channels, kernel_size=
(1, 1))
def forward(self, x):
return self.conv(x)
class Unet(nn.Module):
def __init__(self, input_channels, output_channels, hidden_channels=64):
super(Unet, self).__init__()
self.upfeature = FeatureMapBlock(input_channels, hidden_channels)
self.contract1 = ContractingBlock(hidden_channels)
self.contract2 = ContractingBlock(hidden_channels * 2)
self.contract3 = ContractingBlock(hidden_channels * 4)
self.contract4 = ContractingBlock(hidden_channels * 8)
self.expanding1 = ExpandingBlock(hidden_channels * 16)
self.expanding2 = ExpandingBlock(hidden_channels * 8)
self.expanding3 = ExpandingBlock(hidden_channels * 4)
self.expanding4 = ExpandingBlock(hidden_channels * 2)
self.downfeature = FeatureMapBlock(hidden_channels, output_channels)
def forward(self, x):
x0 = self.upfeature(x)
x1 = self.contract1(x0)
x2 = self.contract2(x1)
x3 = self.contract3(x2)
x4 = self.contract4(x3)
x5 = self.expanding1(skip_con_x=x3, x=x4)
x6 = self.expanding2(skip_con_x=x2, x=x5)
x7 = self.expanding3(skip_con_x=x1, x=x6)
x8 = self.expanding4(skip_con_x=x0, x=x7)
return self.downfeature(x8)
def get_inputs():
return [torch.rand([4, 4, 256, 256])]
def get_init_inputs():
return [[], {'input_channels': 4, 'output_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 65536 % 64
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, None)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 64516 % 128
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 63504 % 128
x0 = xindex % 63504
x4 = xindex // 63504
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x0 + 63520 * x4), tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 126
x1 = xindex // 126 % 126
x2 = xindex // 15876
x3 = xindex % 15876
tmp0 = tl.load(in_ptr0 + (2 * x0 + 504 * x1 + 63520 * x2), None,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 504 * x1 + 63520 * x2), None,
eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (252 + 2 * x0 + 504 * x1 + 63520 * x2), None,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (253 + 2 * x0 + 504 * x1 + 63520 * x2), None,
eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + (x3 + 15904 * x2), tmp6, None)
tl.store(out_ptr1 + (x3 + 16000 * x2), tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 15376 % 256
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_relu_5(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 14884 % 256
x0 = xindex % 14884
x4 = xindex // 14884
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x0 + 14912 * x4), tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_6(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 3810304
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 61
x1 = xindex // 61 % 61
x2 = xindex // 3721
x3 = xindex % 3721
tmp0 = tl.load(in_ptr0 + (2 * x0 + 244 * x1 + 14912 * x2), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 244 * x1 + 14912 * x2), xmask,
eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (122 + 2 * x0 + 244 * x1 + 14912 * x2), xmask,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (123 + 2 * x0 + 244 * x1 + 14912 * x2), xmask,
eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + (x3 + 3744 * x2), tmp6, xmask)
tl.store(out_ptr1 + (x3 + 3840 * x2), tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_7(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 3481 % 512
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_relu_8(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 3249 % 512
x0 = xindex % 3249
x4 = xindex // 3249
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x0 + 3264 * x4), tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_9(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 28
x1 = xindex // 28 % 28
x2 = xindex // 784
x3 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 114 * x1 + 3264 * x2), None,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 114 * x1 + 3264 * x2), None,
eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (57 + 2 * x0 + 114 * x1 + 3264 * x2), None,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (58 + 2 * x0 + 114 * x1 + 3264 * x2), None,
eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x3, tmp6, None)
tl.store(out_ptr1 + x3, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_10(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 676 % 1024
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_relu_11(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 576 % 1024
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_12(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 12
x1 = xindex // 12
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 48 * x1), None, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 48 * x1), None, eviction_policy=
'evict_last')
tmp7 = tl.load(in_ptr0 + (24 + 2 * x0 + 48 * x1), None, eviction_policy
='evict_last')
tmp12 = tl.load(in_ptr0 + (25 + 2 * x0 + 48 * x1), None,
eviction_policy='evict_last')
tmp2 = tmp1 > tmp0
tmp3 = tl.full([1], 1, tl.int8)
tmp4 = tl.full([1], 0, tl.int8)
tmp5 = tl.where(tmp2, tmp3, tmp4)
tmp6 = triton_helpers.maximum(tmp1, tmp0)
tmp8 = tmp7 > tmp6
tmp9 = tl.full([1], 2, tl.int8)
tmp10 = tl.where(tmp8, tmp9, tmp5)
tmp11 = triton_helpers.maximum(tmp7, tmp6)
tmp13 = tmp12 > tmp11
tmp14 = tl.full([1], 3, tl.int8)
tmp15 = tl.where(tmp13, tmp14, tmp10)
triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x2, tmp15, None)
@triton.jit
def triton_poi_fused__to_copy_13(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 24
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.4782608695652174
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tl.store(out_ptr0 + x0, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_clamp_14(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 24
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.4782608695652174
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tmp7 = tl.full([1], 1, tl.int64)
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 11, tl.int64)
tmp10 = triton_helpers.minimum(tmp8, tmp9)
tl.store(out_ptr0 + x0, tmp10, xmask)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_15(out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 24
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.4782608695652174
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tmp7 = tmp6.to(tl.float32)
tmp8 = tmp5 - tmp7
tmp9 = triton_helpers.maximum(tmp8, tmp4)
tmp10 = 1.0
tmp11 = triton_helpers.minimum(tmp9, tmp10)
tl.store(out_ptr0 + x0, tmp11, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_max_pool2d_with_indices_mul_sub_16(
in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5,
in_ptr6, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 24 % 24
x0 = xindex % 24
x2 = xindex // 576
x4 = xindex
tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last')
tmp27 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
tmp39 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
tmp53 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 12, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr2 + (2 * tmp8 + 48 * tmp4 + 576 * x2), None,
eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + (1 + 2 * tmp8 + 48 * tmp4 + 576 * x2), None,
eviction_policy='evict_last')
tmp11 = triton_helpers.maximum(tmp10, tmp9)
tmp12 = tl.load(in_ptr2 + (24 + 2 * tmp8 + 48 * tmp4 + 576 * x2), None,
eviction_policy='evict_last')
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tmp14 = tl.load(in_ptr2 + (25 + 2 * tmp8 + 48 * tmp4 + 576 * x2), None,
eviction_policy='evict_last')
tmp15 = triton_helpers.maximum(tmp14, tmp13)
tmp17 = tmp16 + tmp1
tmp18 = tmp16 < 0
tmp19 = tl.where(tmp18, tmp17, tmp16)
tmp20 = tl.load(in_ptr2 + (2 * tmp8 + 48 * tmp19 + 576 * x2), None,
eviction_policy='evict_last')
tmp21 = tl.load(in_ptr2 + (1 + 2 * tmp8 + 48 * tmp19 + 576 * x2), None,
eviction_policy='evict_last')
tmp22 = triton_helpers.maximum(tmp21, tmp20)
tmp23 = tl.load(in_ptr2 + (24 + 2 * tmp8 + 48 * tmp19 + 576 * x2), None,
eviction_policy='evict_last')
tmp24 = triton_helpers.maximum(tmp23, tmp22)
tmp25 = tl.load(in_ptr2 + (25 + 2 * tmp8 + 48 * tmp19 + 576 * x2), None,
eviction_policy='evict_last')
tmp26 = triton_helpers.maximum(tmp25, tmp24)
tmp28 = tmp27 + tmp1
tmp29 = tmp27 < 0
tmp30 = tl.where(tmp29, tmp28, tmp27)
tmp31 = tl.load(in_ptr2 + (2 * tmp30 + 48 * tmp19 + 576 * x2), None,
eviction_policy='evict_last')
tmp32 = tl.load(in_ptr2 + (1 + 2 * tmp30 + 48 * tmp19 + 576 * x2), None,
eviction_policy='evict_last')
tmp33 = triton_helpers.maximum(tmp32, tmp31)
tmp34 = tl.load(in_ptr2 + (24 + 2 * tmp30 + 48 * tmp19 + 576 * x2),
None, eviction_policy='evict_last')
tmp35 = triton_helpers.maximum(tmp34, tmp33)
tmp36 = tl.load(in_ptr2 + (25 + 2 * tmp30 + 48 * tmp19 + 576 * x2),
None, eviction_policy='evict_last')
tmp37 = triton_helpers.maximum(tmp36, tmp35)
tmp38 = tmp37 - tmp26
tmp40 = tmp38 * tmp39
tmp41 = tmp26 + tmp40
tmp42 = tl.load(in_ptr2 + (2 * tmp30 + 48 * tmp4 + 576 * x2), None,
eviction_policy='evict_last')
tmp43 = tl.load(in_ptr2 + (1 + 2 * tmp30 + 48 * tmp4 + 576 * x2), None,
eviction_policy='evict_last')
tmp44 = triton_helpers.maximum(tmp43, tmp42)
tmp45 = tl.load(in_ptr2 + (24 + 2 * tmp30 + 48 * tmp4 + 576 * x2), None,
eviction_policy='evict_last')
tmp46 = triton_helpers.maximum(tmp45, tmp44)
tmp47 = tl.load(in_ptr2 + (25 + 2 * tmp30 + 48 * tmp4 + 576 * x2), None,
eviction_policy='evict_last')
tmp48 = triton_helpers.maximum(tmp47, tmp46)
tmp49 = tmp48 - tmp15
tmp50 = tmp49 * tmp39
tmp51 = tmp15 + tmp50
tmp52 = tmp51 - tmp41
tmp54 = tmp52 * tmp53
tmp55 = tmp41 + tmp54
tl.store(in_out_ptr1 + x4, tmp55, None)
@triton.jit
def triton_poi_fused_cat_17(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex // 529 % 1024
x0 = xindex % 23
x1 = xindex // 23 % 23
x3 = xindex // 541696
x4 = xindex % 529
x5 = xindex
tmp0 = x2
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 512, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (87 + x0 + 28 * x1 + 784 * x2 + 401408 * x3),
tmp4, other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 1024, tl.int64)
tmp9 = tl.load(in_ptr1 + (x4 + 529 * (-512 + x2) + 270848 * x3), tmp6,
other=0.0)
tmp10 = tl.load(in_ptr2 + (-512 + x2), tmp6, eviction_policy=
'evict_last', other=0.0)
tmp11 = tmp9 + tmp10
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp6, tmp11, tmp12)
tmp14 = tl.where(tmp4, tmp5, tmp13)
tl.store(out_ptr0 + x5, tmp14, None)
@triton.jit
def triton_poi_fused_convolution_relu_18(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 441 % 512
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused__to_copy_19(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 38
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.4864864864864865
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tl.store(out_ptr0 + x0, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_clamp_20(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 38
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.4864864864864865
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tmp7 = tl.full([1], 1, tl.int64)
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 18, tl.int64)
tmp10 = triton_helpers.minimum(tmp8, tmp9)
tl.store(out_ptr0 + x0, tmp10, xmask)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_21(out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 38
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.4864864864864865
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tmp7 = tmp6.to(tl.float32)
tmp8 = tmp5 - tmp7
tmp9 = triton_helpers.maximum(tmp8, tmp4)
tmp10 = 1.0
tmp11 = triton_helpers.minimum(tmp9, tmp10)
tl.store(out_ptr0 + x0, tmp11, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_22(in_ptr0,
in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr2,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 38 % 38
x0 = xindex % 38
x5 = xindex // 1444
x2 = xindex // 1444 % 512
xindex % 1444
x6 = xindex
tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
tmp39 = tl.load(in_ptr7 + x1, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 19, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr2 + (tmp8 + 19 * tmp4 + 361 * x5), None,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tmp15 = tmp14 + tmp1
tmp16 = tmp14 < 0
tmp17 = tl.where(tmp16, tmp15, tmp14)
tmp18 = tl.load(in_ptr2 + (tmp17 + 19 * tmp4 + 361 * x5), None,
eviction_policy='evict_last')
tmp19 = tmp18 + tmp10
tmp20 = triton_helpers.maximum(tmp12, tmp19)
tmp21 = tmp20 - tmp13
tmp23 = tmp21 * tmp22
tmp24 = tmp13 + tmp23
tmp26 = tmp25 + tmp1
tmp27 = tmp25 < 0
tmp28 = tl.where(tmp27, tmp26, tmp25)
tmp29 = tl.load(in_ptr2 + (tmp8 + 19 * tmp28 + 361 * x5), None,
eviction_policy='evict_last')
tmp30 = tmp29 + tmp10
tmp31 = triton_helpers.maximum(tmp12, tmp30)
tmp32 = tl.load(in_ptr2 + (tmp17 + 19 * tmp28 + 361 * x5), None,
eviction_policy='evict_last')
tmp33 = tmp32 + tmp10
tmp34 = triton_helpers.maximum(tmp12, tmp33)
tmp35 = tmp34 - tmp31
tmp36 = tmp35 * tmp22
tmp37 = tmp31 + tmp36
tmp38 = tmp37 - tmp24
tmp40 = tmp38 * tmp39
tmp41 = tmp24 + tmp40
tl.store(out_ptr2 + x6, tmp41, None)
@triton.jit
def triton_poi_fused_cat_23(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex // 1369 % 512
x0 = xindex % 37
x1 = xindex // 37 % 37
x3 = xindex // 700928
x4 = xindex % 1369
x5 = xindex
tmp0 = x2
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 256, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (744 + x0 + 61 * x1 + 3744 * x2 + 958464 * x3),
tmp4, other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 512, tl.int64)
tmp9 = tl.load(in_ptr1 + (x4 + 1369 * (-256 + x2) + 350464 * x3), tmp6,
other=0.0)
tmp10 = tl.load(in_ptr2 + (-256 + x2), tmp6, eviction_policy=
'evict_last', other=0.0)
tmp11 = tmp9 + tmp10
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp6, tmp11, tmp12)
tmp14 = tl.where(tmp4, tmp5, tmp13)
tl.store(out_ptr0 + x5, tmp14, None)
@triton.jit
def triton_poi_fused_convolution_relu_24(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 1254400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 1225 % 256
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused__to_copy_25(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 66
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.49230769230769234
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tl.store(out_ptr0 + x0, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_clamp_26(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 66
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.49230769230769234
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tmp7 = tl.full([1], 1, tl.int64)
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 32, tl.int64)
tmp10 = triton_helpers.minimum(tmp8, tmp9)
tl.store(out_ptr0 + x0, tmp10, xmask)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_27(out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 66
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.49230769230769234
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tmp7 = tmp6.to(tl.float32)
tmp8 = tmp5 - tmp7
tmp9 = triton_helpers.maximum(tmp8, tmp4)
tmp10 = 1.0
tmp11 = triton_helpers.minimum(tmp9, tmp10)
tl.store(out_ptr0 + x0, tmp11, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_28(in_ptr0,
in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr2,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 66 % 66
x0 = xindex % 66
x5 = xindex // 4356
x2 = xindex // 4356 % 256
xindex % 4356
x6 = xindex
tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
tmp39 = tl.load(in_ptr7 + x1, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 33, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr2 + (tmp8 + 33 * tmp4 + 1089 * x5), None,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tmp15 = tmp14 + tmp1
tmp16 = tmp14 < 0
tmp17 = tl.where(tmp16, tmp15, tmp14)
tmp18 = tl.load(in_ptr2 + (tmp17 + 33 * tmp4 + 1089 * x5), None,
eviction_policy='evict_last')
tmp19 = tmp18 + tmp10
tmp20 = triton_helpers.maximum(tmp12, tmp19)
tmp21 = tmp20 - tmp13
tmp23 = tmp21 * tmp22
tmp24 = tmp13 + tmp23
tmp26 = tmp25 + tmp1
tmp27 = tmp25 < 0
tmp28 = tl.where(tmp27, tmp26, tmp25)
tmp29 = tl.load(in_ptr2 + (tmp8 + 33 * tmp28 + 1089 * x5), None,
eviction_policy='evict_last')
tmp30 = tmp29 + tmp10
tmp31 = triton_helpers.maximum(tmp12, tmp30)
tmp32 = tl.load(in_ptr2 + (tmp17 + 33 * tmp28 + 1089 * x5), None,
eviction_policy='evict_last')
tmp33 = tmp32 + tmp10
tmp34 = triton_helpers.maximum(tmp12, tmp33)
tmp35 = tmp34 - tmp31
tmp36 = tmp35 * tmp22
tmp37 = tmp31 + tmp36
tmp38 = tmp37 - tmp24
tmp40 = tmp38 * tmp39
tmp41 = tmp24 + tmp40
tl.store(out_ptr2 + x6, tmp41, None)
@triton.jit
def triton_poi_fused_cat_29(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 4326400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex // 4225 % 256
x0 = xindex % 65
x1 = xindex // 65 % 65
x3 = xindex // 1081600
x4 = xindex % 4225
x5 = xindex
tmp0 = x2
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 128, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (3937 + x0 + 126 * x1 + 15904 * x2 + 2035712 *
x3), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 256, tl.int64)
tmp9 = tl.load(in_ptr1 + (x4 + 4225 * (-128 + x2) + 540800 * x3), tmp6 &
xmask, other=0.0)
tmp10 = tl.load(in_ptr2 + (-128 + x2), tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp11 = tmp9 + tmp10
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp6, tmp11, tmp12)
tmp14 = tl.where(tmp4, tmp5, tmp13)
tl.store(out_ptr0 + x5, tmp14, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_30(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 2032128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 3969 % 128
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused__to_copy_31(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 122
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.49586776859504134
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tl.store(out_ptr0 + x0, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_clamp_32(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 122
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.49586776859504134
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tmp7 = tl.full([1], 1, tl.int64)
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 60, tl.int64)
tmp10 = triton_helpers.minimum(tmp8, tmp9)
tl.store(out_ptr0 + x0, tmp10, xmask)
@triton.jit
def triton_poi_fused__to_copy_arange_clamp_mul_sub_33(out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 122
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.49586776859504134
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tmp7 = tmp6.to(tl.float32)
tmp8 = tmp5 - tmp7
tmp9 = triton_helpers.maximum(tmp8, tmp4)
tmp10 = 1.0
tmp11 = triton_helpers.minimum(tmp9, tmp10)
tl.store(out_ptr0 + x0, tmp11, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_34(in_ptr0,
in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr2,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 122 % 122
x0 = xindex % 122
x5 = xindex // 14884
x2 = xindex // 14884 % 128
xindex % 14884
x6 = xindex
tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr5 + x0, None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last')
tmp39 = tl.load(in_ptr7 + x1, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 61, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr2 + (tmp8 + 61 * tmp4 + 3721 * x5), None,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tmp15 = tmp14 + tmp1
tmp16 = tmp14 < 0
tmp17 = tl.where(tmp16, tmp15, tmp14)
tmp18 = tl.load(in_ptr2 + (tmp17 + 61 * tmp4 + 3721 * x5), None,
eviction_policy='evict_last')
tmp19 = tmp18 + tmp10
tmp20 = triton_helpers.maximum(tmp12, tmp19)
tmp21 = tmp20 - tmp13
tmp23 = tmp21 * tmp22
tmp24 = tmp13 + tmp23
tmp26 = tmp25 + tmp1
tmp27 = tmp25 < 0
tmp28 = tl.where(tmp27, tmp26, tmp25)
tmp29 = tl.load(in_ptr2 + (tmp8 + 61 * tmp28 + 3721 * x5), None,
eviction_policy='evict_last')
tmp30 = tmp29 + tmp10
tmp31 = triton_helpers.maximum(tmp12, tmp30)
tmp32 = tl.load(in_ptr2 + (tmp17 + 61 * tmp28 + 3721 * x5), None,
eviction_policy='evict_last')
tmp33 = tmp32 + tmp10
tmp34 = triton_helpers.maximum(tmp12, tmp33)
tmp35 = tmp34 - tmp31
tmp36 = tmp35 * tmp22
tmp37 = tmp31 + tmp36
tmp38 = tmp37 - tmp24
tmp40 = tmp38 * tmp39
tmp41 = tmp24 + tmp40
tl.store(out_ptr2 + x6, tmp41, None)
@triton.jit
def triton_poi_fused_cat_35(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 7496192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex // 14641 % 128
x0 = xindex % 121
x1 = xindex // 121 % 121
x3 = xindex // 1874048
x4 = xindex % 14641
x5 = xindex
tmp0 = x2
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 64, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (17476 + x0 + 256 * x1 + 65536 * x2 + 4194304 *
x3), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 128, tl.int64)
tmp9 = tl.load(in_ptr1 + (x4 + 14641 * (-64 + x2) + 937024 * x3), tmp6 &
xmask, other=0.0)
tmp10 = tl.load(in_ptr2 + (-64 + x2), tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp11 = tmp9 + tmp10
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp6, tmp11, tmp12)
tmp14 = tl.where(tmp4, tmp5, tmp13)
tl.store(out_ptr0 + x5, tmp14, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_36(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 3625216
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 14161 % 64
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_37(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 3504384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 13689 % 64
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_convolution_38(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 219024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 13689 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_39(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 1905152
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 3721 % 128
x0 = xindex % 3721
x4 = xindex // 3721
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x0 + 3840 * x4), tmp6, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_40(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 1115136
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 1089 % 256
x0 = xindex % 1089
x4 = xindex // 1089
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x0 + 1152 * x4), tmp6, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_41(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 361 % 512
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17,
primals_18, primals_19, primals_20, primals_21, primals_22,
primals_23, primals_24, primals_25, primals_26, primals_27,
primals_28, primals_29, primals_30, primals_31, primals_32,
primals_33, primals_34, primals_35, primals_36, primals_37,
primals_38, primals_39, primals_40, primals_41, primals_42,
primals_43, primals_44, primals_45) = args
args.clear()
assert_size_stride(primals_1, (64, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 4, 256, 256), (262144, 65536, 256, 1))
assert_size_stride(primals_4, (128, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (128,), (1,))
assert_size_stride(primals_6, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_7, (128,), (1,))
assert_size_stride(primals_8, (256, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_9, (256,), (1,))
assert_size_stride(primals_10, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_11, (256,), (1,))
assert_size_stride(primals_12, (512, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_13, (512,), (1,))
assert_size_stride(primals_14, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_15, (512,), (1,))
assert_size_stride(primals_16, (1024, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_17, (1024,), (1,))
assert_size_stride(primals_18, (1024, 1024, 3, 3), (9216, 9, 3, 1))
assert_size_stride(primals_19, (1024,), (1,))
assert_size_stride(primals_20, (512, 1024, 2, 2), (4096, 4, 2, 1))
assert_size_stride(primals_21, (512,), (1,))
assert_size_stride(primals_22, (512, 1024, 3, 3), (9216, 9, 3, 1))
assert_size_stride(primals_23, (512,), (1,))
assert_size_stride(primals_24, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_25, (512,), (1,))
assert_size_stride(primals_26, (256, 512, 2, 2), (2048, 4, 2, 1))
assert_size_stride(primals_27, (256,), (1,))
assert_size_stride(primals_28, (256, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_29, (256,), (1,))
assert_size_stride(primals_30, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_31, (256,), (1,))
assert_size_stride(primals_32, (128, 256, 2, 2), (1024, 4, 2, 1))
assert_size_stride(primals_33, (128,), (1,))
assert_size_stride(primals_34, (128, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_35, (128,), (1,))
assert_size_stride(primals_36, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_37, (128,), (1,))
assert_size_stride(primals_38, (64, 128, 2, 2), (512, 4, 2, 1))
assert_size_stride(primals_39, (64,), (1,))
assert_size_stride(primals_40, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_41, (64,), (1,))
assert_size_stride(primals_42, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_43, (64,), (1,))
assert_size_stride(primals_44, (4, 64, 1, 1), (64, 1, 1, 1))
assert_size_stride(primals_45, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 64, 256, 256), (4194304, 65536, 256, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(16777216)](buf1, primals_2,
16777216, XBLOCK=512, num_warps=8, num_stages=1)
del primals_2
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 128, 254, 254), (8258048, 64516, 254, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_relu_1[grid(33032192)](buf3, primals_5,
33032192, XBLOCK=512, num_warps=8, num_stages=1)
del primals_5
buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 128, 252, 252), (8128512, 63504, 252, 1))
buf5 = empty_strided_cuda((4, 128, 252, 252), (8130560, 63520, 252,
1), torch.float32)
triton_poi_fused_convolution_relu_2[grid(32514048)](buf4, primals_7,
buf5, 32514048, XBLOCK=512, num_warps=8, num_stages=1)
del buf4
del primals_7
buf6 = empty_strided_cuda((4, 128, 126, 126), (2035712, 15904, 126,
1), torch.float32)
buf7 = empty_strided_cuda((4, 128, 126, 126), (2048000, 16000, 126,
1), torch.int8)
triton_poi_fused_max_pool2d_with_indices_3[grid(8128512)](buf5,
buf6, buf7, 8128512, XBLOCK=1024, num_warps=4, num_stages=1)
buf8 = extern_kernels.convolution(buf6, primals_8, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 256, 124, 124), (3936256, 15376, 124, 1))
buf9 = buf8
del buf8
triton_poi_fused_convolution_relu_4[grid(15745024)](buf9, primals_9,
15745024, XBLOCK=512, num_warps=8, num_stages=1)
del primals_9
buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 256, 122, 122), (3810304, 14884, 122, 1))
buf11 = empty_strided_cuda((4, 256, 122, 122), (3817472, 14912, 122,
1), torch.float32)
triton_poi_fused_convolution_relu_5[grid(15241216)](buf10,
primals_11, buf11, 15241216, XBLOCK=1024, num_warps=4, num_stages=1
)
del buf10
del primals_11
buf12 = empty_strided_cuda((4, 256, 61, 61), (958464, 3744, 61, 1),
torch.float32)
buf13 = empty_strided_cuda((4, 256, 61, 61), (983040, 3840, 61, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_6[grid(3810304)](buf11,
buf12, buf13, 3810304, XBLOCK=512, num_warps=8, num_stages=1)
buf14 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf14, (4, 512, 59, 59), (1782272, 3481, 59, 1))
buf15 = buf14
del buf14
triton_poi_fused_convolution_relu_7[grid(7129088)](buf15,
primals_13, 7129088, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_13
buf16 = extern_kernels.convolution(buf15, primals_14, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf16, (4, 512, 57, 57), (1663488, 3249, 57, 1))
buf17 = empty_strided_cuda((4, 512, 57, 57), (1671168, 3264, 57, 1),
torch.float32)
triton_poi_fused_convolution_relu_8[grid(6653952)](buf16,
primals_15, buf17, 6653952, XBLOCK=512, num_warps=8, num_stages=1)
del buf16
del primals_15
buf18 = empty_strided_cuda((4, 512, 28, 28), (401408, 784, 28, 1),
torch.float32)
buf19 = empty_strided_cuda((4, 512, 28, 28), (401408, 784, 28, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_9[grid(1605632)](buf17,
buf18, buf19, 1605632, XBLOCK=512, num_warps=8, num_stages=1)
buf20 = extern_kernels.convolution(buf18, primals_16, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf20, (4, 1024, 26, 26), (692224, 676, 26, 1))
buf21 = buf20
del buf20
triton_poi_fused_convolution_relu_10[grid(2768896)](buf21,
primals_17, 2768896, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_17
buf22 = extern_kernels.convolution(buf21, primals_18, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf22, (4, 1024, 24, 24), (589824, 576, 24, 1))
buf23 = buf22
del buf22
triton_poi_fused_convolution_relu_11[grid(2359296)](buf23,
primals_19, 2359296, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_19
buf24 = empty_strided_cuda((4, 1024, 12, 12), (147456, 144, 12, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_12[grid(589824)](buf23,
buf24, 589824, XBLOCK=512, num_warps=8, num_stages=1)
buf25 = empty_strided_cuda((24, 1), (1, 1), torch.int64)
triton_poi_fused__to_copy_13[grid(24)](buf25, 24, XBLOCK=32,
num_warps=1, num_stages=1)
buf26 = empty_strided_cuda((24, 1), (1, 1), torch.int64)
triton_poi_fused_add_clamp_14[grid(24)](buf26, 24, XBLOCK=32,
num_warps=1, num_stages=1)
buf27 = empty_strided_cuda((24,), (1,), torch.int64)
triton_poi_fused__to_copy_13[grid(24)](buf27, 24, XBLOCK=32,
num_warps=1, num_stages=1)
buf28 = empty_strided_cuda((24,), (1,), torch.int64)
triton_poi_fused_add_clamp_14[grid(24)](buf28, 24, XBLOCK=32,
num_warps=1, num_stages=1)
buf31 = empty_strided_cuda((24,), (1,), torch.float32)
triton_poi_fused__to_copy_arange_clamp_mul_sub_15[grid(24)](buf31,
24, XBLOCK=32, num_warps=1, num_stages=1)
buf33 = empty_strided_cuda((24, 1), (1, 1), torch.float32)
triton_poi_fused__to_copy_arange_clamp_mul_sub_15[grid(24)](buf33,
24, XBLOCK=32, num_warps=1, num_stages=1)
buf30 = empty_strided_cuda((4, 1024, 24, 24), (589824, 576, 24, 1),
torch.float32)
buf34 = buf30
del buf30
buf35 = buf34
del buf34
triton_poi_fused__unsafe_index_add_max_pool2d_with_indices_mul_sub_16[
grid(2359296)](buf35, buf26, buf27, buf23, buf25, buf28, buf31,
buf33, 2359296, XBLOCK=512, num_warps=8, num_stages=1)
buf36 = extern_kernels.convolution(buf35, primals_20, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf36, (4, 512, 23, 23), (270848, 529, 23, 1))
buf37 = empty_strided_cuda((4, 1024, 23, 23), (541696, 529, 23, 1),
torch.float32)
triton_poi_fused_cat_17[grid(2166784)](buf18, buf36, primals_21,
buf37, 2166784, XBLOCK=1024, num_warps=4, num_stages=1)
del buf36
del primals_21
buf38 = extern_kernels.convolution(buf37, primals_22, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf38, (4, 512, 21, 21), (225792, 441, 21, 1))
buf39 = buf38
del buf38
triton_poi_fused_convolution_relu_18[grid(903168)](buf39,
primals_23, 903168, XBLOCK=512, num_warps=8, num_stages=1)
del primals_23
buf40 = extern_kernels.convolution(buf39, primals_24, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf40, (4, 512, 19, 19), (184832, 361, 19, 1))
buf41 = empty_strided_cuda((38, 1), (1, 1), torch.int64)
triton_poi_fused__to_copy_19[grid(38)](buf41, 38, XBLOCK=64,
num_warps=1, num_stages=1)
buf42 = empty_strided_cuda((38, 1), (1, 1), torch.int64)
triton_poi_fused_add_clamp_20[grid(38)](buf42, 38, XBLOCK=64,
num_warps=1, num_stages=1)
buf43 = empty_strided_cuda((38,), (1,), torch.int64)
triton_poi_fused__to_copy_19[grid(38)](buf43, 38, XBLOCK=64,
num_warps=1, num_stages=1)
buf44 = empty_strided_cuda((38,), (1,), torch.int64)
triton_poi_fused_add_clamp_20[grid(38)](buf44, 38, XBLOCK=64,
num_warps=1, num_stages=1)
buf45 = empty_strided_cuda((38,), (1,), torch.float32)
triton_poi_fused__to_copy_arange_clamp_mul_sub_21[grid(38)](buf45,
38, XBLOCK=64, num_warps=1, num_stages=1)
buf47 = empty_strided_cuda((38, 1), (1, 1), torch.float32)
triton_poi_fused__to_copy_arange_clamp_mul_sub_21[grid(38)](buf47,
38, XBLOCK=64, num_warps=1, num_stages=1)
buf49 = empty_strided_cuda((4, 512, 38, 38), (739328, 1444, 38, 1),
torch.float32)
triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_22[grid
(2957312)](buf41, buf43, buf40, primals_25, buf44, buf45, buf42,
buf47, buf49, 2957312, XBLOCK=1024, num_warps=4, num_stages=1)
buf50 = extern_kernels.convolution(buf49, primals_26, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf50, (4, 256, 37, 37), (350464, 1369, 37, 1))
buf51 = empty_strided_cuda((4, 512, 37, 37), (700928, 1369, 37, 1),
torch.float32)
triton_poi_fused_cat_23[grid(2803712)](buf12, buf50, primals_27,
buf51, 2803712, XBLOCK=1024, num_warps=4, num_stages=1)
del buf50
del primals_27
buf52 = extern_kernels.convolution(buf51, primals_28, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf52, (4, 256, 35, 35), (313600, 1225, 35, 1))
buf53 = buf52
del buf52
triton_poi_fused_convolution_relu_24[grid(1254400)](buf53,
primals_29, 1254400, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_29
buf54 = extern_kernels.convolution(buf53, primals_30, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf54, (4, 256, 33, 33), (278784, 1089, 33, 1))
buf55 = empty_strided_cuda((66, 1), (1, 1), torch.int64)
triton_poi_fused__to_copy_25[grid(66)](buf55, 66, XBLOCK=128,
num_warps=4, num_stages=1)
buf56 = empty_strided_cuda((66, 1), (1, 1), torch.int64)
triton_poi_fused_add_clamp_26[grid(66)](buf56, 66, XBLOCK=128,
num_warps=4, num_stages=1)
buf57 = empty_strided_cuda((66,), (1,), torch.int64)
triton_poi_fused__to_copy_25[grid(66)](buf57, 66, XBLOCK=128,
num_warps=4, num_stages=1)
buf58 = empty_strided_cuda((66,), (1,), torch.int64)
triton_poi_fused_add_clamp_26[grid(66)](buf58, 66, XBLOCK=128,
num_warps=4, num_stages=1)
buf59 = empty_strided_cuda((66,), (1,), torch.float32)
triton_poi_fused__to_copy_arange_clamp_mul_sub_27[grid(66)](buf59,
66, XBLOCK=128, num_warps=4, num_stages=1)
buf61 = empty_strided_cuda((66, 1), (1, 1), torch.float32)
triton_poi_fused__to_copy_arange_clamp_mul_sub_27[grid(66)](buf61,
66, XBLOCK=128, num_warps=4, num_stages=1)
buf63 = empty_strided_cuda((4, 256, 66, 66), (1115136, 4356, 66, 1),
torch.float32)
triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_28[grid
(4460544)](buf55, buf57, buf54, primals_31, buf58, buf59, buf56,
buf61, buf63, 4460544, XBLOCK=1024, num_warps=4, num_stages=1)
buf64 = extern_kernels.convolution(buf63, primals_32, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf64, (4, 128, 65, 65), (540800, 4225, 65, 1))
buf65 = empty_strided_cuda((4, 256, 65, 65), (1081600, 4225, 65, 1),
torch.float32)
triton_poi_fused_cat_29[grid(4326400)](buf6, buf64, primals_33,
buf65, 4326400, XBLOCK=512, num_warps=8, num_stages=1)
del buf64
del primals_33
buf66 = extern_kernels.convolution(buf65, primals_34, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf66, (4, 128, 63, 63), (508032, 3969, 63, 1))
buf67 = buf66
del buf66
triton_poi_fused_convolution_relu_30[grid(2032128)](buf67,
primals_35, 2032128, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_35
buf68 = extern_kernels.convolution(buf67, primals_36, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf68, (4, 128, 61, 61), (476288, 3721, 61, 1))
buf69 = empty_strided_cuda((122, 1), (1, 1), torch.int64)
triton_poi_fused__to_copy_31[grid(122)](buf69, 122, XBLOCK=128,
num_warps=4, num_stages=1)
buf70 = empty_strided_cuda((122, 1), (1, 1), torch.int64)
triton_poi_fused_add_clamp_32[grid(122)](buf70, 122, XBLOCK=128,
num_warps=4, num_stages=1)
buf71 = empty_strided_cuda((122,), (1,), torch.int64)
triton_poi_fused__to_copy_31[grid(122)](buf71, 122, XBLOCK=128,
num_warps=4, num_stages=1)
buf72 = empty_strided_cuda((122,), (1,), torch.int64)
triton_poi_fused_add_clamp_32[grid(122)](buf72, 122, XBLOCK=128,
num_warps=4, num_stages=1)
buf73 = empty_strided_cuda((122,), (1,), torch.float32)
triton_poi_fused__to_copy_arange_clamp_mul_sub_33[grid(122)](buf73,
122, XBLOCK=128, num_warps=4, num_stages=1)
buf75 = empty_strided_cuda((122, 1), (1, 1), torch.float32)
triton_poi_fused__to_copy_arange_clamp_mul_sub_33[grid(122)](buf75,
122, XBLOCK=128, num_warps=4, num_stages=1)
buf77 = empty_strided_cuda((4, 128, 122, 122), (1905152, 14884, 122,
1), torch.float32)
triton_poi_fused__unsafe_index_add_convolution_mul_relu_sub_34[grid
(7620608)](buf69, buf71, buf68, primals_37, buf72, buf73, buf70,
buf75, buf77, 7620608, XBLOCK=1024, num_warps=4, num_stages=1)
buf78 = extern_kernels.convolution(buf77, primals_38, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf78, (4, 64, 121, 121), (937024, 14641, 121, 1))
buf79 = empty_strided_cuda((4, 128, 121, 121), (1874048, 14641, 121,
1), torch.float32)
triton_poi_fused_cat_35[grid(7496192)](buf1, buf78, primals_39,
buf79, 7496192, XBLOCK=1024, num_warps=4, num_stages=1)
del buf78
del primals_39
buf80 = extern_kernels.convolution(buf79, primals_40, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf80, (4, 64, 119, 119), (906304, 14161, 119, 1))
buf81 = buf80
del buf80
triton_poi_fused_convolution_relu_36[grid(3625216)](buf81,
primals_41, 3625216, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_41
buf82 = extern_kernels.convolution(buf81, primals_42, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf82, (4, 64, 117, 117), (876096, 13689, 117, 1))
buf83 = buf82
del buf82
triton_poi_fused_convolution_relu_37[grid(3504384)](buf83,
primals_43, 3504384, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_43
buf84 = extern_kernels.convolution(buf83, primals_44, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf84, (4, 4, 117, 117), (54756, 13689, 117, 1))
buf85 = buf84
del buf84
triton_poi_fused_convolution_38[grid(219024)](buf85, primals_45,
219024, XBLOCK=512, num_warps=8, num_stages=1)
del primals_45
buf86 = empty_strided_cuda((4, 128, 61, 61), (491520, 3840, 61, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_39[grid(1905152)](
buf68, primals_37, buf86, 1905152, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf68
del primals_37
buf87 = empty_strided_cuda((4, 256, 33, 33), (294912, 1152, 33, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_40[grid(1115136)](
buf54, primals_31, buf87, 1115136, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf54
del primals_31
buf88 = empty_strided_cuda((4, 512, 19, 19), (184832, 361, 19, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_41[grid(739328)](
buf40, primals_25, buf88, 739328, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf40
del primals_25
return (buf85, primals_1, primals_3, primals_4, primals_6, primals_8,
primals_10, primals_12, primals_14, primals_16, primals_18,
primals_20, primals_22, primals_24, primals_26, primals_28,
primals_30, primals_32, primals_34, primals_36, primals_38,
primals_40, primals_42, primals_44, buf1, buf3, buf5, buf6, buf7,
buf9, buf11, buf12, buf13, buf15, buf17, buf18, buf19, buf21, buf23,
buf24, buf25, buf26, buf27, buf28, buf31, buf33, buf35, buf37,
buf39, buf41, buf42, buf43, buf44, buf45, buf47, buf49, buf51,
buf53, buf55, buf56, buf57, buf58, buf59, buf61, buf63, buf65,
buf67, buf69, buf70, buf71, buf72, buf73, buf75, buf77, buf79,
buf81, buf83, buf86, buf87, buf88)
def crop(image, new_shape):
plus_h, plus_w = 0, 0
if new_shape[2] % 2 != 0:
plus_h = 1
if new_shape[3] % 2 != 0:
plus_w = 1
middle_height = image.shape[2] // 2
middle_weight = image.shape[3] // 2
go_height = new_shape[2] // 2
go_weight = new_shape[3] // 2
cropped_image = image[:, :, middle_height - go_height:middle_height +
go_height + plus_h, middle_weight - go_weight:middle_weight +
go_weight + plus_w]
return cropped_image
class ContractingBlock(nn.Module):
def __init__(self, input_channel):
super(ContractingBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=input_channel, out_channels=
input_channel * 2, kernel_size=(3, 3))
self.conv2 = nn.Conv2d(input_channel * 2, input_channel * 2,
kernel_size=(3, 3))
self.maxpool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))
self.activation = nn.ReLU()
def forward(self, x):
x = self.activation(self.conv1(x))
x = self.activation(self.conv2(x))
return self.maxpool(x)
class ExpandingBlock(nn.Module):
def __init__(self, input_channels):
super(ExpandingBlock, self).__init__()
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear',
align_corners=True)
self.conv1 = nn.Conv2d(input_channels, input_channels // 2,
kernel_size=(2, 2))
self.conv2 = nn.Conv2d(input_channels, input_channels // 2,
kernel_size=(3, 3))
self.conv3 = nn.Conv2d(input_channels // 2, input_channels // 2,
kernel_size=(3, 3))
self.activation = nn.ReLU()
def forward(self, skip_con_x, x):
x = self.upsample(x)
x = self.conv1(x)
skip_con_x = crop(skip_con_x, x.shape)
x = torch.cat([skip_con_x, x], axis=1)
x = self.conv2(x)
x = self.activation(x)
x = self.conv3(x)
return self.activation(x)
class FeatureMapBlock(nn.Module):
def __init__(self, input_channels, output_channels):
super(FeatureMapBlock, self).__init__()
self.conv = nn.Conv2d(input_channels, output_channels, kernel_size=
(1, 1))
def forward(self, x):
return self.conv(x)
class UnetNew(nn.Module):
def __init__(self, input_channels, output_channels, hidden_channels=64):
super(UnetNew, self).__init__()
self.upfeature = FeatureMapBlock(input_channels, hidden_channels)
self.contract1 = ContractingBlock(hidden_channels)
self.contract2 = ContractingBlock(hidden_channels * 2)
self.contract3 = ContractingBlock(hidden_channels * 4)
self.contract4 = ContractingBlock(hidden_channels * 8)
self.expanding1 = ExpandingBlock(hidden_channels * 16)
self.expanding2 = ExpandingBlock(hidden_channels * 8)
self.expanding3 = ExpandingBlock(hidden_channels * 4)
self.expanding4 = ExpandingBlock(hidden_channels * 2)
self.downfeature = FeatureMapBlock(hidden_channels, output_channels)
def forward(self, input_0):
primals_1 = self.upfeature.conv.weight
primals_2 = self.upfeature.conv.bias
primals_4 = self.contract1.conv1.weight
primals_5 = self.contract1.conv1.bias
primals_6 = self.contract1.conv2.weight
primals_7 = self.contract1.conv2.bias
primals_8 = self.contract2.conv1.weight
primals_9 = self.contract2.conv1.bias
primals_10 = self.contract2.conv2.weight
primals_11 = self.contract2.conv2.bias
primals_12 = self.contract3.conv1.weight
primals_13 = self.contract3.conv1.bias
primals_14 = self.contract3.conv2.weight
primals_15 = self.contract3.conv2.bias
primals_16 = self.contract4.conv1.weight
primals_17 = self.contract4.conv1.bias
primals_18 = self.contract4.conv2.weight
primals_19 = self.contract4.conv2.bias
primals_20 = self.expanding1.conv1.weight
primals_21 = self.expanding1.conv1.bias
primals_22 = self.expanding1.conv2.weight
primals_23 = self.expanding1.conv2.bias
primals_24 = self.expanding1.conv3.weight
primals_25 = self.expanding1.conv3.bias
primals_26 = self.expanding2.conv1.weight
primals_27 = self.expanding2.conv1.bias
primals_28 = self.expanding2.conv2.weight
primals_29 = self.expanding2.conv2.bias
primals_30 = self.expanding2.conv3.weight
primals_31 = self.expanding2.conv3.bias
primals_32 = self.expanding3.conv1.weight
primals_33 = self.expanding3.conv1.bias
primals_34 = self.expanding3.conv2.weight
primals_35 = self.expanding3.conv2.bias
primals_36 = self.expanding3.conv3.weight
primals_37 = self.expanding3.conv3.bias
primals_38 = self.expanding4.conv1.weight
primals_39 = self.expanding4.conv1.bias
primals_40 = self.expanding4.conv2.weight
primals_41 = self.expanding4.conv2.bias
primals_42 = self.expanding4.conv3.weight
primals_43 = self.expanding4.conv3.bias
primals_44 = self.downfeature.conv.weight
primals_45 = self.downfeature.conv.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17, primals_18, primals_19,
primals_20, primals_21, primals_22, primals_23, primals_24,
primals_25, primals_26, primals_27, primals_28, primals_29,
primals_30, primals_31, primals_32, primals_33, primals_34,
primals_35, primals_36, primals_37, primals_38, primals_39,
primals_40, primals_41, primals_42, primals_43, primals_44,
primals_45])
return output[0]
|
furkannturkmen/pytorch-CNN-architecture
|
Unet
| false | 10,222 |
[
"MIT"
] | 0 |
6a864811f51409c1526224c288fe608010e0c888
|
https://github.com/furkannturkmen/pytorch-CNN-architecture/tree/6a864811f51409c1526224c288fe608010e0c888
|
InverseDepthSmoothnessLoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_8/inductor_cache/xo/cxopkxo47z5lmvyq2gx2n4exgeyhweclzftd73pco236dokn54dm.py
# Topologically Sorted Source Nodes: [image_dx, abs_1, mean, neg, weights_x], Original ATen: [aten.sub, aten.abs, aten.mean, aten.neg, aten.exp]
# Source node to ATen node mapping:
# abs_1 => abs_1
# image_dx => sub_2
# mean => mean
# neg => neg
# weights_x => exp
# Graph fragment:
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_20, %slice_24), kwargs = {})
# %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_2,), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%abs_1, [1], True), kwargs = {})
# %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean,), kwargs = {})
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {})
triton_poi_fused_abs_exp_mean_neg_sub_0 = async_compile.triton('triton_poi_fused_abs_exp_mean_neg_sub_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_exp_mean_neg_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_abs_exp_mean_neg_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 48
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 3
x1 = (xindex // 3) % 4
x2 = (xindex // 12)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (4*x1) + (64*x2)), xmask)
tmp1 = tl.load(in_ptr0 + (1 + x0 + (4*x1) + (64*x2)), xmask)
tmp4 = tl.load(in_ptr0 + (16 + x0 + (4*x1) + (64*x2)), xmask)
tmp5 = tl.load(in_ptr0 + (17 + x0 + (4*x1) + (64*x2)), xmask)
tmp9 = tl.load(in_ptr0 + (32 + x0 + (4*x1) + (64*x2)), xmask)
tmp10 = tl.load(in_ptr0 + (33 + x0 + (4*x1) + (64*x2)), xmask)
tmp14 = tl.load(in_ptr0 + (48 + x0 + (4*x1) + (64*x2)), xmask)
tmp15 = tl.load(in_ptr0 + (49 + x0 + (4*x1) + (64*x2)), xmask)
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp6 = tmp4 - tmp5
tmp7 = tl_math.abs(tmp6)
tmp8 = tmp3 + tmp7
tmp11 = tmp9 - tmp10
tmp12 = tl_math.abs(tmp11)
tmp13 = tmp8 + tmp12
tmp16 = tmp14 - tmp15
tmp17 = tl_math.abs(tmp16)
tmp18 = tmp13 + tmp17
tmp19 = 4.0
tmp20 = tmp18 / tmp19
tmp21 = -tmp20
tmp22 = tl_math.exp(tmp21)
tl.store(out_ptr0 + (x3), tmp22, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/wl/cwl6yd7l3sndnb22swumccpruxx46plry27po4wlv5ktz7r5sl5b.py
# Topologically Sorted Source Nodes: [image_dy, abs_2, mean_1, neg_1, weights_y], Original ATen: [aten.sub, aten.abs, aten.mean, aten.neg, aten.exp]
# Source node to ATen node mapping:
# abs_2 => abs_2
# image_dy => sub_3
# mean_1 => mean_1
# neg_1 => neg_1
# weights_y => exp_1
# Graph fragment:
# %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_27, %slice_31), kwargs = {})
# %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_3,), kwargs = {})
# %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%abs_2, [1], True), kwargs = {})
# %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean_1,), kwargs = {})
# %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_1,), kwargs = {})
triton_poi_fused_abs_exp_mean_neg_sub_1 = async_compile.triton('triton_poi_fused_abs_exp_mean_neg_sub_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_exp_mean_neg_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_abs_exp_mean_neg_sub_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 48
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 12
x1 = (xindex // 12)
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
tmp1 = tl.load(in_ptr0 + (4 + x0 + (64*x1)), xmask)
tmp4 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
tmp5 = tl.load(in_ptr0 + (20 + x0 + (64*x1)), xmask)
tmp9 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
tmp10 = tl.load(in_ptr0 + (36 + x0 + (64*x1)), xmask)
tmp14 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
tmp15 = tl.load(in_ptr0 + (52 + x0 + (64*x1)), xmask)
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp6 = tmp4 - tmp5
tmp7 = tl_math.abs(tmp6)
tmp8 = tmp3 + tmp7
tmp11 = tmp9 - tmp10
tmp12 = tl_math.abs(tmp11)
tmp13 = tmp8 + tmp12
tmp16 = tmp14 - tmp15
tmp17 = tl_math.abs(tmp16)
tmp18 = tmp13 + tmp17
tmp19 = 4.0
tmp20 = tmp18 / tmp19
tmp21 = -tmp20
tmp22 = tl_math.exp(tmp21)
tl.store(out_ptr0 + (x2), tmp22, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/o7/co77hkmtzy5ubvbrmxqqwu5n4mggkr3ljkpuheg7zy4q2bdaspun.py
# Topologically Sorted Source Nodes: [idepth_dx, image_dx, abs_1, mean, neg, weights_x, mul, smoothness_x, mean_2, idepth_dy, image_dy, abs_2, mean_1, neg_1, weights_y, mul_1, smoothness_y, mean_3, add], Original ATen: [aten.sub, aten.abs, aten.mean, aten.neg, aten.exp, aten.mul, aten.add]
# Source node to ATen node mapping:
# abs_1 => abs_1
# abs_2 => abs_2
# add => add
# idepth_dx => sub
# idepth_dy => sub_1
# image_dx => sub_2
# image_dy => sub_3
# mean => mean
# mean_1 => mean_1
# mean_2 => mean_2
# mean_3 => mean_3
# mul => mul
# mul_1 => mul_1
# neg => neg
# neg_1 => neg_1
# smoothness_x => abs_3
# smoothness_y => abs_4
# weights_x => exp
# weights_y => exp_1
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_4, %slice_8), kwargs = {})
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_20, %slice_24), kwargs = {})
# %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_2,), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%abs_1, [1], True), kwargs = {})
# %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean,), kwargs = {})
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %exp), kwargs = {})
# %abs_3 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%mul,), kwargs = {})
# %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_3,), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_11, %slice_15), kwargs = {})
# %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_27, %slice_31), kwargs = {})
# %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_3,), kwargs = {})
# %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%abs_2, [1], True), kwargs = {})
# %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean_1,), kwargs = {})
# %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_1,), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %exp_1), kwargs = {})
# %abs_4 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%mul_1,), kwargs = {})
# %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_4,), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_2, %mean_3), kwargs = {})
triton_per_fused_abs_add_exp_mean_mul_neg_sub_2 = async_compile.triton('triton_per_fused_abs_add_exp_mean_mul_neg_sub_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_exp_mean_mul_neg_sub_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_abs_add_exp_mean_mul_neg_sub_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 192
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = rindex < rnumel
r0 = rindex % 3
r5 = (rindex // 3)
r3 = (rindex // 48)
r4 = rindex % 12
r6 = (rindex // 12)
tmp0 = tl.load(in_ptr0 + (r0 + (4*r5)), rmask, other=0.0)
tmp1 = tl.load(in_ptr0 + (1 + r0 + (4*r5)), rmask, other=0.0)
tmp3 = tl.load(in_ptr1 + (r4 + (12*r3)), rmask, eviction_policy='evict_last', other=0.0)
tmp10 = tl.load(in_ptr0 + (r4 + (16*r6)), rmask, other=0.0)
tmp11 = tl.load(in_ptr0 + (4 + r4 + (16*r6)), rmask, other=0.0)
tmp13 = tl.load(in_ptr2 + (r4 + (12*r3)), rmask, eviction_policy='evict_last', other=0.0)
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp5 = tl_math.abs(tmp4)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
tmp8 = tl.where(rmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp12 = tmp10 - tmp11
tmp14 = tmp12 * tmp13
tmp15 = tl_math.abs(tmp14)
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp18 = tl.where(rmask, tmp16, 0)
tmp19 = tl.sum(tmp18, 1)[:, None]
tmp20 = 192.0
tmp21 = tmp9 / tmp20
tmp22 = tmp19 / tmp20
tmp23 = tmp21 + tmp22
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp23, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 4, 3), (12, 48, 3, 1), torch.float32)
# Topologically Sorted Source Nodes: [image_dx, abs_1, mean, neg, weights_x], Original ATen: [aten.sub, aten.abs, aten.mean, aten.neg, aten.exp]
stream0 = get_raw_stream(0)
triton_poi_fused_abs_exp_mean_neg_sub_0.run(arg1_1, buf0, 48, grid=grid(48), stream=stream0)
buf2 = empty_strided_cuda((4, 1, 3, 4), (12, 48, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [image_dy, abs_2, mean_1, neg_1, weights_y], Original ATen: [aten.sub, aten.abs, aten.mean, aten.neg, aten.exp]
triton_poi_fused_abs_exp_mean_neg_sub_1.run(arg1_1, buf2, 48, grid=grid(48), stream=stream0)
del arg1_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf4 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [idepth_dx, image_dx, abs_1, mean, neg, weights_x, mul, smoothness_x, mean_2, idepth_dy, image_dy, abs_2, mean_1, neg_1, weights_y, mul_1, smoothness_y, mean_3, add], Original ATen: [aten.sub, aten.abs, aten.mean, aten.neg, aten.exp, aten.mul, aten.add]
triton_per_fused_abs_add_exp_mean_mul_neg_sub_2.run(buf4, arg0_1, buf0, buf2, 1, 192, grid=grid(1), stream=stream0)
del arg0_1
del buf0
del buf2
return (buf4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_abs_exp_mean_neg_sub_0(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 48
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 3
x1 = xindex // 3 % 4
x2 = xindex // 12
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x1 + 64 * x2), xmask)
tmp1 = tl.load(in_ptr0 + (1 + x0 + 4 * x1 + 64 * x2), xmask)
tmp4 = tl.load(in_ptr0 + (16 + x0 + 4 * x1 + 64 * x2), xmask)
tmp5 = tl.load(in_ptr0 + (17 + x0 + 4 * x1 + 64 * x2), xmask)
tmp9 = tl.load(in_ptr0 + (32 + x0 + 4 * x1 + 64 * x2), xmask)
tmp10 = tl.load(in_ptr0 + (33 + x0 + 4 * x1 + 64 * x2), xmask)
tmp14 = tl.load(in_ptr0 + (48 + x0 + 4 * x1 + 64 * x2), xmask)
tmp15 = tl.load(in_ptr0 + (49 + x0 + 4 * x1 + 64 * x2), xmask)
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp6 = tmp4 - tmp5
tmp7 = tl_math.abs(tmp6)
tmp8 = tmp3 + tmp7
tmp11 = tmp9 - tmp10
tmp12 = tl_math.abs(tmp11)
tmp13 = tmp8 + tmp12
tmp16 = tmp14 - tmp15
tmp17 = tl_math.abs(tmp16)
tmp18 = tmp13 + tmp17
tmp19 = 4.0
tmp20 = tmp18 / tmp19
tmp21 = -tmp20
tmp22 = tl_math.exp(tmp21)
tl.store(out_ptr0 + x3, tmp22, xmask)
@triton.jit
def triton_poi_fused_abs_exp_mean_neg_sub_1(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 48
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 12
x1 = xindex // 12
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tmp1 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp5 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
tmp9 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp10 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
tmp14 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp15 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp6 = tmp4 - tmp5
tmp7 = tl_math.abs(tmp6)
tmp8 = tmp3 + tmp7
tmp11 = tmp9 - tmp10
tmp12 = tl_math.abs(tmp11)
tmp13 = tmp8 + tmp12
tmp16 = tmp14 - tmp15
tmp17 = tl_math.abs(tmp16)
tmp18 = tmp13 + tmp17
tmp19 = 4.0
tmp20 = tmp18 / tmp19
tmp21 = -tmp20
tmp22 = tl_math.exp(tmp21)
tl.store(out_ptr0 + x2, tmp22, xmask)
@triton.jit
def triton_per_fused_abs_add_exp_mean_mul_neg_sub_2(in_out_ptr0, in_ptr0,
in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
rnumel = 192
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
rmask = rindex < rnumel
r0 = rindex % 3
r5 = rindex // 3
r3 = rindex // 48
r4 = rindex % 12
r6 = rindex // 12
tmp0 = tl.load(in_ptr0 + (r0 + 4 * r5), rmask, other=0.0)
tmp1 = tl.load(in_ptr0 + (1 + r0 + 4 * r5), rmask, other=0.0)
tmp3 = tl.load(in_ptr1 + (r4 + 12 * r3), rmask, eviction_policy=
'evict_last', other=0.0)
tmp10 = tl.load(in_ptr0 + (r4 + 16 * r6), rmask, other=0.0)
tmp11 = tl.load(in_ptr0 + (4 + r4 + 16 * r6), rmask, other=0.0)
tmp13 = tl.load(in_ptr2 + (r4 + 12 * r3), rmask, eviction_policy=
'evict_last', other=0.0)
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp5 = tl_math.abs(tmp4)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
tmp8 = tl.where(rmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp12 = tmp10 - tmp11
tmp14 = tmp12 * tmp13
tmp15 = tl_math.abs(tmp14)
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp18 = tl.where(rmask, tmp16, 0)
tmp19 = tl.sum(tmp18, 1)[:, None]
tmp20 = 192.0
tmp21 = tmp9 / tmp20
tmp22 = tmp19 / tmp20
tmp23 = tmp21 + tmp22
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp23, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 4, 3), (12, 48, 3, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_abs_exp_mean_neg_sub_0[grid(48)](arg1_1, buf0, 48,
XBLOCK=64, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 1, 3, 4), (12, 48, 4, 1), torch.float32)
triton_poi_fused_abs_exp_mean_neg_sub_1[grid(48)](arg1_1, buf2, 48,
XBLOCK=64, num_warps=1, num_stages=1)
del arg1_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf4 = buf1
del buf1
triton_per_fused_abs_add_exp_mean_mul_neg_sub_2[grid(1)](buf4,
arg0_1, buf0, buf2, 1, 192, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del buf0
del buf2
return buf4,
def _gradient_x(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :, :-1] - img[:, :, :, 1:]
def _gradient_y(img: 'torch.Tensor') ->torch.Tensor:
assert len(img.shape) == 4, img.shape
return img[:, :, :-1, :] - img[:, :, 1:, :]
def inverse_depth_smoothness_loss(idepth: 'torch.Tensor', image: 'torch.Tensor'
) ->torch.Tensor:
"""Computes image-aware inverse depth smoothness loss.
See :class:`~kornia.losses.InvDepthSmoothnessLoss` for details.
"""
if not torch.is_tensor(idepth):
raise TypeError('Input idepth type is not a torch.Tensor. Got {}'.
format(type(idepth)))
if not torch.is_tensor(image):
raise TypeError('Input image type is not a torch.Tensor. Got {}'.
format(type(image)))
if not len(idepth.shape) == 4:
raise ValueError('Invalid idepth shape, we expect BxCxHxW. Got: {}'
.format(idepth.shape))
if not len(image.shape) == 4:
raise ValueError('Invalid image shape, we expect BxCxHxW. Got: {}'.
format(image.shape))
if not idepth.shape[-2:] == image.shape[-2:]:
raise ValueError('idepth and image shapes must be the same. Got: {}'
.format(idepth.shape))
if not idepth.device == image.device:
raise ValueError('idepth and image must be in the same device. Got: {}'
.format(idepth.device))
if not idepth.dtype == image.dtype:
raise ValueError('idepth and image must be in the same dtype. Got: {}'
.format(idepth.dtype))
idepth_dx: 'torch.Tensor' = _gradient_x(idepth)
idepth_dy: 'torch.Tensor' = _gradient_y(idepth)
image_dx: 'torch.Tensor' = _gradient_x(image)
image_dy: 'torch.Tensor' = _gradient_y(image)
weights_x: 'torch.Tensor' = torch.exp(-torch.mean(torch.abs(image_dx),
dim=1, keepdim=True))
weights_y: 'torch.Tensor' = torch.exp(-torch.mean(torch.abs(image_dy),
dim=1, keepdim=True))
smoothness_x: 'torch.Tensor' = torch.abs(idepth_dx * weights_x)
smoothness_y: 'torch.Tensor' = torch.abs(idepth_dy * weights_y)
return torch.mean(smoothness_x) + torch.mean(smoothness_y)
class InverseDepthSmoothnessLossNew(nn.Module):
"""Criterion that computes image-aware inverse depth smoothness loss.
.. math::
\\text{loss} = \\left | \\partial_x d_{ij} \\right | e^{-\\left \\|
\\partial_x I_{ij} \\right \\|} + \\left |
\\partial_y d_{ij} \\right | e^{-\\left \\| \\partial_y I_{ij} \\right \\|}
Shape:
- Inverse Depth: :math:`(N, 1, H, W)`
- Image: :math:`(N, 3, H, W)`
- Output: scalar
Examples::
>>> idepth = torch.rand(1, 1, 4, 5)
>>> image = torch.rand(1, 3, 4, 5)
>>> smooth = kornia.losses.DepthSmoothnessLoss()
>>> loss = smooth(idepth, image)
"""
def __init__(self) ->None:
super(InverseDepthSmoothnessLossNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
IEM-Computer-Vision/kornia
|
InverseDepthSmoothnessLoss
| false | 9,297 |
[
"ECL-2.0",
"Apache-2.0"
] | 0 |
f98bd9a2158a6e59cda076d55d476acf13f4e0af
|
https://github.com/IEM-Computer-Vision/kornia/tree/f98bd9a2158a6e59cda076d55d476acf13f4e0af
|
PlanarFlow
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_0/inductor_cache/il/cil5hpoi7ygpsypgim2u7imeqozhugxjb3qehifbgnc6wf4rjrsp.py
# Topologically Sorted Source Nodes: [uw, softplus, muw, sub, mul, pow_1, sum_1, truediv, uhat, mv, zwb, psi, psi_u, add_4, abs_1, add_5, logdet_jacobian], Original ATen: [aten.dot, aten.softplus, aten.add, aten.sub, aten.mul, aten.pow, aten.sum, aten.div, aten.mv, aten.abs, aten.log]
# Source node to ATen node mapping:
# abs_1 => abs_1
# add_4 => add_4
# add_5 => add_5
# logdet_jacobian => log
# mul => mul_1
# muw => add
# mv => mul_2, sum_3
# pow_1 => pow_1
# psi => mul_4
# psi_u => mul_5, sum_4
# softplus => exp, gt, log1p, where
# sub => sub
# sum_1 => sum_2
# truediv => div
# uhat => add_1
# uw => mul, sum_1
# zwb => add_2
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %primals_2), kwargs = {})
# %sum_1 : [num_users=4] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sum_1,), kwargs = {})
# %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {})
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sum_1, 20), kwargs = {})
# %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %sum_1, %log1p), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%where, -1), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %sum_1), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %primals_2), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_2, 2), kwargs = {})
# %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_1,), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %sum_2), kwargs = {})
# %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %div), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %primals_2), kwargs = {})
# %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [1]), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_3, %primals_4), kwargs = {})
# %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %view_3), kwargs = {})
# %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %add_1), kwargs = {})
# %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_5, [1]), kwargs = {})
# %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_4, 1), kwargs = {})
# %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%add_4,), kwargs = {})
# %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%abs_1, 1e-08), kwargs = {})
# %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_5,), kwargs = {})
triton_per_fused_abs_add_div_dot_log_mul_mv_pow_softplus_sub_sum_0 = async_compile.triton('triton_per_fused_abs_add_div_dot_log_mul_mv_pow_softplus_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 4],
reduction_hint=ReductionHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {8: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=(8,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_div_dot_log_mul_mv_pow_softplus_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 15, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_abs_add_div_dot_log_mul_mv_pow_softplus_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 4
RBLOCK: tl.constexpr = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp1 = tl.load(in_ptr1 + (r0), None)
tmp10 = tl.load(in_ptr2 + (4*r0), None, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr1 + (0))
tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
tmp14 = tl.load(in_ptr2 + (1 + (4*r0)), None, eviction_policy='evict_last')
tmp15 = tl.load(in_ptr1 + (1))
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp19 = tl.load(in_ptr2 + (2 + (4*r0)), None, eviction_policy='evict_last')
tmp20 = tl.load(in_ptr1 + (2))
tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
tmp24 = tl.load(in_ptr2 + (3 + (4*r0)), None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr1 + (3))
tmp26 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
tmp29 = tl.load(in_ptr3 + (0))
tmp30 = tl.broadcast_to(tmp29, [XBLOCK, RBLOCK])
tmp37 = tl.load(in_ptr0 + (0))
tmp38 = tl.broadcast_to(tmp37, [XBLOCK, RBLOCK])
tmp52 = tl.load(in_ptr0 + (1))
tmp53 = tl.broadcast_to(tmp52, [XBLOCK, RBLOCK])
tmp60 = tl.load(in_ptr0 + (2))
tmp61 = tl.broadcast_to(tmp60, [XBLOCK, RBLOCK])
tmp68 = tl.load(in_ptr0 + (3))
tmp69 = tl.broadcast_to(tmp68, [XBLOCK, RBLOCK])
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = tl.sum(tmp3, 1)[:, None]
tmp6 = tmp1 * tmp1
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp9 = tl.sum(tmp7, 1)[:, None]
tmp13 = tmp10 * tmp12
tmp17 = tmp14 * tmp16
tmp18 = tmp13 + tmp17
tmp22 = tmp19 * tmp21
tmp23 = tmp18 + tmp22
tmp27 = tmp24 * tmp26
tmp28 = tmp23 + tmp27
tmp31 = tmp28 + tmp30
tmp32 = libdevice.tanh(tmp31)
tmp33 = tmp32 * tmp32
tmp34 = 1.0
tmp35 = tmp34 - tmp33
tmp36 = tmp35 * tmp12
tmp39 = 20.0
tmp40 = tmp5 > tmp39
tmp41 = tl_math.exp(tmp5)
tmp42 = libdevice.log1p(tmp41)
tmp43 = tl.where(tmp40, tmp5, tmp42)
tmp44 = -1.0
tmp45 = tmp43 + tmp44
tmp46 = tmp45 - tmp5
tmp47 = tmp46 * tmp12
tmp48 = tmp47 / tmp9
tmp49 = tmp38 + tmp48
tmp50 = tmp36 * tmp49
tmp51 = tmp35 * tmp16
tmp54 = tmp46 * tmp16
tmp55 = tmp54 / tmp9
tmp56 = tmp53 + tmp55
tmp57 = tmp51 * tmp56
tmp58 = tmp50 + tmp57
tmp59 = tmp35 * tmp21
tmp62 = tmp46 * tmp21
tmp63 = tmp62 / tmp9
tmp64 = tmp61 + tmp63
tmp65 = tmp59 * tmp64
tmp66 = tmp58 + tmp65
tmp67 = tmp35 * tmp26
tmp70 = tmp46 * tmp26
tmp71 = tmp70 / tmp9
tmp72 = tmp69 + tmp71
tmp73 = tmp67 * tmp72
tmp74 = tmp66 + tmp73
tmp75 = tmp74 + tmp34
tmp76 = tl_math.abs(tmp75)
tmp77 = 1e-08
tmp78 = tmp76 + tmp77
tmp79 = tl_math.log(tmp78)
tl.store(out_ptr2 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp31, None)
tl.store(in_out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp79, None)
tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp5, None)
tl.store(out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp9, None)
''', device_str='cuda')
# kernel path: runs/run_shard_0/inductor_cache/kk/ckkz4nmmavawr3cwhzxvsdtcrjydick2txrs43laojgo3e6wua2r.py
# Topologically Sorted Source Nodes: [mul_1, f_z], Original ATen: [aten.mul, aten.add]
# Source node to ATen node mapping:
# f_z => add_3
# mul_1 => mul_3
# Graph fragment:
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view_1), kwargs = {})
# %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %mul_3), kwargs = {})
triton_poi_fused_add_mul_1 = async_compile.triton('triton_poi_fused_add_mul_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + (0))
tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
tmp12 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr4 + (0))
tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
tmp18 = tl.load(in_ptr5 + (x1), xmask, eviction_policy='evict_last')
tmp4 = 20.0
tmp5 = tmp3 > tmp4
tmp6 = tl_math.exp(tmp3)
tmp7 = libdevice.log1p(tmp6)
tmp8 = tl.where(tmp5, tmp3, tmp7)
tmp9 = -1.0
tmp10 = tmp8 + tmp9
tmp11 = tmp10 - tmp3
tmp13 = tmp11 * tmp12
tmp16 = tmp13 / tmp15
tmp17 = tmp1 + tmp16
tmp19 = libdevice.tanh(tmp18)
tmp20 = tmp17 * tmp19
tmp21 = tmp0 + tmp20
tl.store(out_ptr0 + (x2), tmp21, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, ), (1, ))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (1, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = empty_strided_cuda((4, ), (1, ), torch.float32)
buf4 = empty_strided_cuda((4, ), (1, ), torch.float32)
buf5 = buf4; del buf4 # reuse
# Topologically Sorted Source Nodes: [uw, softplus, muw, sub, mul, pow_1, sum_1, truediv, uhat, mv, zwb, psi, psi_u, add_4, abs_1, add_5, logdet_jacobian], Original ATen: [aten.dot, aten.softplus, aten.add, aten.sub, aten.mul, aten.pow, aten.sum, aten.div, aten.mv, aten.abs, aten.log]
stream0 = get_raw_stream(0)
triton_per_fused_abs_add_div_dot_log_mul_mv_pow_softplus_sub_sum_0.run(buf5, primals_1, primals_2, primals_3, primals_4, buf0, buf1, buf2, 1, 4, grid=grid(1), stream=stream0)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mul_1, f_z], Original ATen: [aten.mul, aten.add]
triton_poi_fused_add_mul_1.run(primals_3, primals_1, buf0, primals_2, buf1, buf2, buf3, 16, grid=grid(16), stream=stream0)
del buf0
del buf1
del buf2
return (buf3, buf5, primals_1, primals_2, primals_3, primals_4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_abs_add_div_dot_log_mul_mv_pow_softplus_sub_sum_0(
in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1,
out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 4
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp10 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr1 + 0)
tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
tmp14 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
tmp15 = tl.load(in_ptr1 + 1)
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp19 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
tmp20 = tl.load(in_ptr1 + 2)
tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
tmp24 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr1 + 3)
tmp26 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
tmp29 = tl.load(in_ptr3 + 0)
tmp30 = tl.broadcast_to(tmp29, [XBLOCK, RBLOCK])
tmp37 = tl.load(in_ptr0 + 0)
tmp38 = tl.broadcast_to(tmp37, [XBLOCK, RBLOCK])
tmp52 = tl.load(in_ptr0 + 1)
tmp53 = tl.broadcast_to(tmp52, [XBLOCK, RBLOCK])
tmp60 = tl.load(in_ptr0 + 2)
tmp61 = tl.broadcast_to(tmp60, [XBLOCK, RBLOCK])
tmp68 = tl.load(in_ptr0 + 3)
tmp69 = tl.broadcast_to(tmp68, [XBLOCK, RBLOCK])
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = tl.sum(tmp3, 1)[:, None]
tmp6 = tmp1 * tmp1
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp9 = tl.sum(tmp7, 1)[:, None]
tmp13 = tmp10 * tmp12
tmp17 = tmp14 * tmp16
tmp18 = tmp13 + tmp17
tmp22 = tmp19 * tmp21
tmp23 = tmp18 + tmp22
tmp27 = tmp24 * tmp26
tmp28 = tmp23 + tmp27
tmp31 = tmp28 + tmp30
tmp32 = libdevice.tanh(tmp31)
tmp33 = tmp32 * tmp32
tmp34 = 1.0
tmp35 = tmp34 - tmp33
tmp36 = tmp35 * tmp12
tmp39 = 20.0
tmp40 = tmp5 > tmp39
tmp41 = tl_math.exp(tmp5)
tmp42 = libdevice.log1p(tmp41)
tmp43 = tl.where(tmp40, tmp5, tmp42)
tmp44 = -1.0
tmp45 = tmp43 + tmp44
tmp46 = tmp45 - tmp5
tmp47 = tmp46 * tmp12
tmp48 = tmp47 / tmp9
tmp49 = tmp38 + tmp48
tmp50 = tmp36 * tmp49
tmp51 = tmp35 * tmp16
tmp54 = tmp46 * tmp16
tmp55 = tmp54 / tmp9
tmp56 = tmp53 + tmp55
tmp57 = tmp51 * tmp56
tmp58 = tmp50 + tmp57
tmp59 = tmp35 * tmp21
tmp62 = tmp46 * tmp21
tmp63 = tmp62 / tmp9
tmp64 = tmp61 + tmp63
tmp65 = tmp59 * tmp64
tmp66 = tmp58 + tmp65
tmp67 = tmp35 * tmp26
tmp70 = tmp46 * tmp26
tmp71 = tmp70 / tmp9
tmp72 = tmp69 + tmp71
tmp73 = tmp67 * tmp72
tmp74 = tmp66 + tmp73
tmp75 = tmp74 + tmp34
tmp76 = tl_math.abs(tmp75)
tmp77 = 1e-08
tmp78 = tmp76 + tmp77
tmp79 = tl_math.log(tmp78)
tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp31, None)
tl.store(in_out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp79, None)
tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp5, None)
tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp9, None)
@triton.jit
def triton_poi_fused_add_mul_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + 0)
tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
tmp12 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr4 + 0)
tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
tmp18 = tl.load(in_ptr5 + x1, xmask, eviction_policy='evict_last')
tmp4 = 20.0
tmp5 = tmp3 > tmp4
tmp6 = tl_math.exp(tmp3)
tmp7 = libdevice.log1p(tmp6)
tmp8 = tl.where(tmp5, tmp3, tmp7)
tmp9 = -1.0
tmp10 = tmp8 + tmp9
tmp11 = tmp10 - tmp3
tmp13 = tmp11 * tmp12
tmp16 = tmp13 / tmp15
tmp17 = tmp1 + tmp16
tmp19 = libdevice.tanh(tmp18)
tmp20 = tmp17 * tmp19
tmp21 = tmp0 + tmp20
tl.store(out_ptr0 + x2, tmp21, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4,), (1,))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = empty_strided_cuda((4,), (1,), torch.float32)
buf4 = empty_strided_cuda((4,), (1,), torch.float32)
buf5 = buf4
del buf4
get_raw_stream(0)
triton_per_fused_abs_add_div_dot_log_mul_mv_pow_softplus_sub_sum_0[grid
(1)](buf5, primals_1, primals_2, primals_3, primals_4, buf0,
buf1, buf2, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_add_mul_1[grid(16)](primals_3, primals_1, buf0,
primals_2, buf1, buf2, buf3, 16, XBLOCK=16, num_warps=1,
num_stages=1)
del buf0
del buf1
del buf2
return buf3, buf5, primals_1, primals_2, primals_3, primals_4
class PlanarFlowNew(nn.Module):
"""Planar normalizing flow [Rezende & Mohamed 2015].
Provides a tighter bound on the ELBO by giving more expressive
power to the approximate distribution, such as by introducing
covariance between terms.
@param in_features: integer
number of input dimensions. this is often
the dimensionality of the latent variables
"""
def __init__(self, in_features):
super(PlanarFlowNew, self).__init__()
self.u = nn.Parameter(torch.randn(in_features))
self.w = nn.Parameter(torch.randn(in_features))
self.b = nn.Parameter(torch.ones(1))
def forward(self, input_0):
primals_1 = self.u
primals_2 = self.w
primals_4 = self.b
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0], output[1]
|
BratChar/variational-item-response-theory-public
|
PlanarFlow
| false | 13,420 |
[
"MIT"
] | 52 |
12862157e99506a0ed7018f1b8a485d4e61fb5bf
|
https://github.com/BratChar/variational-item-response-theory-public/tree/12862157e99506a0ed7018f1b8a485d4e61fb5bf
|
NaiveGate
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class NaiveGate(nn.Module):
"""
A naive gate implementation that defines the standard behavior of the gate
which determines which experts the tokens are going to.
Both the indecies and the score, or confidence, are output to the parent
module.
The load-balance strategies are also designed to be implemented within the
`Gate` module.
"""
def __init__(self, d_model, num_expert, world_size, top_k=2):
super().__init__()
self.gate = nn.Linear(d_model, num_expert * world_size)
self.top_k = top_k
def forward(self, inp):
"""
The naive implementation simply calculates the top-k of a linear layer's
output.
"""
gate = self.gate(inp)
gate_top_k_val, gate_top_k_idx = torch.topk(gate, k=self.top_k, dim
=-1, largest=True, sorted=False)
gate_top_k_val = gate_top_k_val.view(-1, self.top_k)
gate_score = F.softmax(gate_top_k_val, dim=-1).unsqueeze(1)
gate_top_k_idx = gate_top_k_idx.view(-1)
return gate_top_k_idx, gate_score
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'d_model': 4, 'num_expert': 4, 'world_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 2
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp4 = tmp0 - tmp3
tmp5 = tl_math.exp(tmp4)
tmp6 = tmp1 - tmp3
tmp7 = tl_math.exp(tmp6)
tmp8 = tmp2 - tmp3
tmp9 = tl_math.exp(tmp8)
tmp10 = tmp7 + tmp9
tmp11 = tmp5 / tmp10
tl.store(out_ptr0 + x2, tmp11, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (16, 4), (4, 1))
assert_size_stride(primals_2, (16,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 16), (1, 4),
0), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = torch.ops.aten.topk.default(reinterpret_tensor(buf0, (4, 4,
4, 16), (256, 64, 16, 1), 0), 2, -1, True, False)
del buf0
buf2 = buf1[0]
buf3 = buf1[1]
del buf1
buf4 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__softmax_0[grid(128)](buf2, buf4, 128, XBLOCK=128,
num_warps=4, num_stages=1)
del buf2
return reinterpret_tensor(buf3, (128,), (1,), 0), reinterpret_tensor(buf4,
(64, 1, 2), (2, 2, 1), 0), reinterpret_tensor(primals_3, (64, 4), (
4, 1), 0), buf3, buf4
class NaiveGateNew(nn.Module):
"""
A naive gate implementation that defines the standard behavior of the gate
which determines which experts the tokens are going to.
Both the indecies and the score, or confidence, are output to the parent
module.
The load-balance strategies are also designed to be implemented within the
`Gate` module.
"""
def __init__(self, d_model, num_expert, world_size, top_k=2):
super().__init__()
self.gate = nn.Linear(d_model, num_expert * world_size)
self.top_k = top_k
def forward(self, input_0):
primals_1 = self.gate.weight
primals_2 = self.gate.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0], output[1]
|
whn09/fastmoe
|
NaiveGate
| false | 4,533 |
[
"Apache-2.0"
] | 0 |
d0ffaffc6431abcd3ea6d0287dbf09f8cd727a0a
|
https://github.com/whn09/fastmoe/tree/d0ffaffc6431abcd3ea6d0287dbf09f8cd727a0a
|
NormLoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_9/inductor_cache/tx/ctxgfdomqe65rl3biiiivn2iqu44cw3hqgbqow2ioi4jjo2s4zi6.py
# Topologically Sorted Source Nodes: [linalg_norm, ny, linalg_norm_1, nY, diff, getitem, square, getitem_1, mul, mean], Original ATen: [aten.linalg_vector_norm, aten.div, aten.sub, aten.index, aten.pow, aten.mul, aten.mean]
# Source node to ATen node mapping:
# diff => sub
# getitem => index
# getitem_1 => index_1
# linalg_norm => pow_1, pow_2, sum_1
# linalg_norm_1 => pow_3, pow_4, sum_2
# mean => mean
# mul => mul
# nY => div_1
# ny => div
# square => pow_5
# Graph fragment:
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 2.0), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1]), kwargs = {})
# %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%pow_2, 5.0), kwargs = {})
# %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg1_1, 2.0), kwargs = {})
# %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, [1]), kwargs = {})
# %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%pow_4, 5.0), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %div_1), kwargs = {})
# %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%sub, [%arg2_1]), kwargs = {})
# %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%index, 2), kwargs = {})
# %index_1 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg3_1, [%arg2_1]), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_5, %index_1), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul,), kwargs = {})
triton_per_fused_div_index_linalg_vector_norm_mean_mul_pow_sub_0 = async_compile.triton('triton_per_fused_div_index_linalg_vector_norm_mean_mul_pow_sub_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 64],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*fp32', 3: '*fp32', 4: '*i64', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_index_linalg_vector_norm_mean_mul_pow_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_div_index_linalg_vector_norm_mean_mul_pow_sub_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 64
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = (rindex // 16)
r0 = rindex % 16
r4 = rindex
r2 = rindex % 4
tmp0 = tl.load(in_ptr0 + (r1), None, eviction_policy='evict_last')
tmp35 = tl.load(in_ptr0 + (r2), None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tl.device_assert((0 <= tmp4) & (tmp4 < 4), "index out of bounds: 0 <= tmp4 < 4")
tmp6 = tl.load(in_ptr1 + (r0 + (64*tmp4)), None)
tmp7 = tmp6 * tmp6
tmp8 = tl.load(in_ptr1 + (16 + r0 + (64*tmp4)), None)
tmp9 = tmp8 * tmp8
tmp10 = tmp7 + tmp9
tmp11 = tl.load(in_ptr1 + (32 + r0 + (64*tmp4)), None)
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tl.load(in_ptr1 + (48 + r0 + (64*tmp4)), None)
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = libdevice.sqrt(tmp16)
tmp18 = 0.2
tmp19 = tmp17 * tmp18
tmp20 = tl.load(in_ptr2 + (r0 + (64*tmp4)), None)
tmp21 = tmp20 * tmp20
tmp22 = tl.load(in_ptr2 + (16 + r0 + (64*tmp4)), None)
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tl.load(in_ptr2 + (32 + r0 + (64*tmp4)), None)
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = tl.load(in_ptr2 + (48 + r0 + (64*tmp4)), None)
tmp29 = tmp28 * tmp28
tmp30 = tmp27 + tmp29
tmp31 = libdevice.sqrt(tmp30)
tmp32 = tmp31 * tmp18
tmp33 = tmp19 - tmp32
tmp34 = tmp33 * tmp33
tmp36 = tmp35 + tmp1
tmp37 = tmp35 < 0
tmp38 = tl.where(tmp37, tmp36, tmp35)
tl.device_assert((0 <= tmp38) & (tmp38 < 4), "index out of bounds: 0 <= tmp38 < 4")
tmp40 = tl.load(in_ptr3 + (tmp38), None, eviction_policy='evict_last')
tmp41 = tmp40.to(tl.float32)
tmp42 = tmp34 * tmp41
tmp43 = tl.broadcast_to(tmp42, [XBLOCK, RBLOCK])
tmp45 = tl.sum(tmp43, 1)[:, None]
tmp46 = 64.0
tmp47 = tmp45 / tmp46
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp47, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1, arg2_1, arg3_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, ), (1, ))
assert_size_stride(arg3_1, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [linalg_norm, ny, linalg_norm_1, nY, diff, getitem, square, getitem_1, mul, mean], Original ATen: [aten.linalg_vector_norm, aten.div, aten.sub, aten.index, aten.pow, aten.mul, aten.mean]
stream0 = get_raw_stream(0)
triton_per_fused_div_index_linalg_vector_norm_mean_mul_pow_sub_0.run(buf2, arg2_1, arg0_1, arg1_1, arg3_1, 1, 64, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
del arg2_1
del arg3_1
return (buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg2_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
arg3_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_div_index_linalg_vector_norm_mean_mul_pow_sub_0(
in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel, XBLOCK:
tl.constexpr):
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex // 16
r0 = rindex % 16
r2 = rindex % 4
tmp0 = tl.load(in_ptr0 + r1, None, eviction_policy='evict_last')
tmp35 = tl.load(in_ptr0 + r2, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tl.device_assert((0 <= tmp4) & (tmp4 < 4),
'index out of bounds: 0 <= tmp4 < 4')
tmp6 = tl.load(in_ptr1 + (r0 + 64 * tmp4), None)
tmp7 = tmp6 * tmp6
tmp8 = tl.load(in_ptr1 + (16 + r0 + 64 * tmp4), None)
tmp9 = tmp8 * tmp8
tmp10 = tmp7 + tmp9
tmp11 = tl.load(in_ptr1 + (32 + r0 + 64 * tmp4), None)
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tl.load(in_ptr1 + (48 + r0 + 64 * tmp4), None)
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = libdevice.sqrt(tmp16)
tmp18 = 0.2
tmp19 = tmp17 * tmp18
tmp20 = tl.load(in_ptr2 + (r0 + 64 * tmp4), None)
tmp21 = tmp20 * tmp20
tmp22 = tl.load(in_ptr2 + (16 + r0 + 64 * tmp4), None)
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tl.load(in_ptr2 + (32 + r0 + 64 * tmp4), None)
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = tl.load(in_ptr2 + (48 + r0 + 64 * tmp4), None)
tmp29 = tmp28 * tmp28
tmp30 = tmp27 + tmp29
tmp31 = libdevice.sqrt(tmp30)
tmp32 = tmp31 * tmp18
tmp33 = tmp19 - tmp32
tmp34 = tmp33 * tmp33
tmp36 = tmp35 + tmp1
tmp37 = tmp35 < 0
tmp38 = tl.where(tmp37, tmp36, tmp35)
tl.device_assert((0 <= tmp38) & (tmp38 < 4),
'index out of bounds: 0 <= tmp38 < 4')
tmp40 = tl.load(in_ptr3 + tmp38, None, eviction_policy='evict_last')
tmp41 = tmp40.to(tl.float32)
tmp42 = tmp34 * tmp41
tmp43 = tl.broadcast_to(tmp42, [XBLOCK, RBLOCK])
tmp45 = tl.sum(tmp43, 1)[:, None]
tmp46 = 64.0
tmp47 = tmp45 / tmp46
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp47, None)
def call(args):
arg0_1, arg1_1, arg2_1, arg3_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4,), (1,))
assert_size_stride(arg3_1, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = buf1
del buf1
get_raw_stream(0)
triton_per_fused_div_index_linalg_vector_norm_mean_mul_pow_sub_0[grid
(1)](buf2, arg2_1, arg0_1, arg1_1, arg3_1, 1, 64, XBLOCK=1,
num_warps=2, num_stages=1)
del arg0_1
del arg1_1
del arg2_1
del arg3_1
return buf2,
class NormLossNew(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, input_0, input_1, input_2, input_3):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
arg3_1 = input_3
output = call([arg0_1, arg1_1, arg2_1, arg3_1])
return output[0]
|
acycliq/cellpose
|
NormLoss
| false | 12,045 |
[
"BSD-3-Clause"
] | 0 |
6d7a3f692206bf791e3ea7bd9524ee6df628ed8a
|
https://github.com/acycliq/cellpose/tree/6d7a3f692206bf791e3ea7bd9524ee6df628ed8a
|
ConvReLUNorm
|
import torch
import torch.cuda
import torch.distributed
import torch.utils.data
import torch.optim
class ConvReLUNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, dropout=0.0):
super(ConvReLUNorm, self).__init__()
self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size=
kernel_size, padding=kernel_size // 2)
self.norm = torch.nn.LayerNorm(out_channels)
self.dropout = torch.nn.Dropout(dropout)
def forward(self, signal):
out = torch.nn.functional.relu(self.conv(signal))
out = self.norm(out.transpose(1, 2)).transpose(1, 2)
return self.dropout(out)
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.cuda
import torch.distributed
import torch.utils.data
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 4 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
tmp6 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
tmp9 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp1, tmp3)
tmp5 = tmp2 + tmp4
tmp7 = triton_helpers.maximum(tmp1, tmp6)
tmp8 = tmp5 + tmp7
tmp10 = triton_helpers.maximum(tmp1, tmp9)
tmp11 = tmp8 + tmp10
tmp12 = 4.0
tmp13 = tmp11 / tmp12
tmp14 = tmp2 - tmp13
tmp15 = tmp14 * tmp14
tmp16 = tmp4 - tmp13
tmp17 = tmp16 * tmp16
tmp18 = tmp15 + tmp17
tmp19 = tmp7 - tmp13
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp10 - tmp13
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp24 / tmp12
tmp26 = 1e-05
tmp27 = tmp25 + tmp26
tmp28 = libdevice.rsqrt(tmp27)
tl.store(out_ptr0 + x2, tmp13, xmask)
tl.store(out_ptr1 + x2, tmp28, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + y3, ymask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr2 + y3, ymask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
tmp1 = tl.full([1, 1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = tmp2 - tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 * tmp7
tmp10 = tmp8 + tmp9
tl.store(out_ptr0 + (x2 + 4 * y3), tmp10, xmask & ymask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 1), (4, 1, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4), (16, 4, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(64)](buf1, primals_2, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf3 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(16)](buf1, buf2, buf3, 16,
XBLOCK=16, num_warps=1, num_stages=1)
buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_2[grid(16, 4)](buf1, buf2, buf3,
primals_4, primals_5, buf4, 16, 4, XBLOCK=4, YBLOCK=8,
num_warps=1, num_stages=1)
del buf2
del buf3
del primals_5
return reinterpret_tensor(buf4, (4, 4, 4), (16, 1, 4), 0
), primals_1, primals_3, primals_4, buf1
class ConvReLUNormNew(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, dropout=0.0):
super(ConvReLUNormNew, self).__init__()
self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size=
kernel_size, padding=kernel_size // 2)
self.norm = torch.nn.LayerNorm(out_channels)
self.dropout = torch.nn.Dropout(dropout)
def forward(self, input_0):
primals_1 = self.conv.weight
primals_2 = self.conv.bias
primals_4 = self.norm.weight
primals_5 = self.norm.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
Oreoluwa1234/NeMo
|
ConvReLUNorm
| false | 9,707 |
[
"Apache-2.0"
] | 0 |
b01e3ceed34efe31fd43866685dbdd19a6b30928
|
https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928
|
RNNCell
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_0/inductor_cache/3l/c3lo77c7wjxasxrhtr6wesb72ods2d2rxnxhbfieun7j2wukm3wn.py
# Topologically Sorted Source Nodes: [combined], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# combined => cat
# Graph fragment:
# %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 2), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_0/inductor_cache/ey/cey6dsgmzj2byupf73e6nwt5fetf5ne2sa57kzcmy7ejvaqhqb72.py
# Topologically Sorted Source Nodes: [hidden], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# hidden => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, 8), (8, 1))
assert_size_stride(primals_4, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
# Topologically Sorted Source Nodes: [combined], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 128, grid=grid(128), stream=stream0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf0, (16, 8), (8, 1), 0), reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf1)
del primals_3
buf2 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0); del buf1 # reuse
buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [hidden], Original ATen: [aten.relu, aten.threshold_backward]
triton_poi_fused_relu_threshold_backward_1.run(buf2, primals_4, buf3, 64, grid=grid(64), stream=stream0)
del primals_4
return (buf2, reinterpret_tensor(buf0, (16, 8), (8, 1), 0), buf3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x2, tmp10, xmask)
@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, 8), (8, 1))
assert_size_stride(primals_4, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(128)](primals_1, primals_2, buf0, 128,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
del primals_2
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 8), (8, 1), 0),
reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf1)
del primals_3
buf2 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
del buf1
buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_1[grid(64)](buf2,
primals_4, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
del primals_4
return buf2, reinterpret_tensor(buf0, (16, 8), (8, 1), 0), buf3
class RNNCellNew(nn.Module):
def __init__(self, embed_dim, hidden_size, vocab_dim):
super().__init__()
self.hidden_size = hidden_size
self.input2hidden = nn.Linear(embed_dim + hidden_size, hidden_size)
def init_hidden(self, batch_size):
return torch.zeros(1, batch_size, self.hidden_size)
def forward(self, input_0, input_1):
primals_3 = self.input2hidden.weight
primals_4 = self.input2hidden.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
sdhnshu/HandsOnDeepLearningWithPytorch
|
RNNCell
| false | 16,379 |
[
"MIT"
] | 87 |
2292a952a4cb112b03d5db4048c78bc503eb858d
|
https://github.com/sdhnshu/HandsOnDeepLearningWithPytorch/tree/2292a952a4cb112b03d5db4048c78bc503eb858d
|
LogSumPenalty
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_8/inductor_cache/7q/c7qpomy3et3phhwkqc7t644bmcri5fm4hhrdq75vw4jwojia65zu.py
# Topologically Sorted Source Nodes: [abs_1, add, log, sum_1], Original ATen: [aten.abs, aten.add, aten.log, aten.sum]
# Source node to ATen node mapping:
# abs_1 => abs_1
# add => add
# log => log
# sum_1 => sum_1
# Graph fragment:
# %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%abs_1, 1), kwargs = {})
# %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%log,), kwargs = {})
triton_per_fused_abs_add_log_sum_0 = async_compile.triton('triton_per_fused_abs_add_log_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_log_sum_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_abs_add_log_sum_0(in_ptr0, out_ptr0, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp1 = tl_math.abs(tmp0)
tmp2 = 1.0
tmp3 = tmp1 + tmp2
tmp4 = tl_math.log(tmp3)
tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp7, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
# Topologically Sorted Source Nodes: [abs_1, add, log, sum_1], Original ATen: [aten.abs, aten.add, aten.log, aten.sum]
stream0 = get_raw_stream(0)
triton_per_fused_abs_add_log_sum_0.run(arg0_1, buf0, 1, 256, grid=grid(1), stream=stream0)
del arg0_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_abs_add_log_sum_0(in_ptr0, out_ptr0, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl_math.abs(tmp0)
tmp2 = 1.0
tmp3 = tmp1 + tmp2
tmp4 = tl_math.log(tmp3)
tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp7, None)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
get_raw_stream(0)
triton_per_fused_abs_add_log_sum_0[grid(1)](arg0_1, buf0, 1, 256,
num_warps=2, num_stages=1)
del arg0_1
return buf0,
class LogSumPenaltyNew(Module):
def __init__(self, epsilon=1):
super(LogSumPenaltyNew, self).__init__()
self.epsilon = epsilon
def eta_hat(self, w):
w = torch.abs(w)
return w * (w + self.epsilon)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
dlej/adaptive-dropout
|
LogSumPenalty
| false | 10,067 |
[
"MIT"
] | 0 |
0540b2d06f1f97eb5861c6917eec6c086d33dfa8
|
https://github.com/dlej/adaptive-dropout/tree/0540b2d06f1f97eb5861c6917eec6c086d33dfa8
|
GAT
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, self).__init__()
self.dropout = dropout
self.in_features = in_features
self.out_features = out_features
self.alpha = alpha
self.concat = concat
self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
nn.init.xavier_uniform_(self.W.data, gain=1.414)
self.a = nn.Parameter(torch.zeros(size=(2 * out_features, 1)))
nn.init.xavier_uniform_(self.a.data, gain=1.414)
self.leakyrelu = nn.LeakyReLU(self.alpha)
def forward(self, input, adj):
h = torch.matmul(input, self.W)
N = h.size()[1]
batch_size = h.size(0)
a_input = torch.cat([h.repeat(1, 1, N).view(batch_size, N * N, -1),
h.repeat(1, N, 1)], dim=2).view(batch_size, N, -1, 2 * self.
out_features)
e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(3))
zero_vec = -9000000000000000.0 * torch.ones_like(e)
attention = torch.where(adj > 0, e, zero_vec)
attention = F.softmax(attention, dim=1)
attention = F.dropout(attention, self.dropout, training=self.training)
h_prime = torch.matmul(attention, h)
if self.concat:
return F.elu(h_prime)
else:
return h_prime
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
class GAT(nn.Module):
def __init__(self, nfeat, nhid, dropout, alpha, nheads):
super(GAT, self).__init__()
self.dropout = dropout
self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout,
alpha=alpha, concat=True) for _ in range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
def forward(self, x, adj):
x = F.dropout(x, self.dropout, training=self.training)
x = torch.cat([att(x, adj) for att in self.attentions], dim=1)
x = F.dropout(x, self.dropout, training=self.training)
return x
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'nfeat': 4, 'nhid': 4, 'dropout': 0.5, 'alpha': 4,
'nheads': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8 % 16
x2 = xindex // 128
x3 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * ((4 * x1 + x0) // 16 % 4) + 16 * ((4 * x1 +
64 * x2 + x0) // 64 % 4) + (4 * x1 + x0) % 16 % 4), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr0 + (4 * (x1 % 4) + 16 * x2 + (-4 + x0)), tmp6 &
xmask, eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x3, tmp10, xmask)
@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_gt_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_3(in_ptr0, in_ptr1,
in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, out_ptr0,
out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, out_ptr6, out_ptr7,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask).to(tl.int1)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask).to(tl.int1)
tmp9 = tl.load(in_ptr1 + (16 + x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp10 = tl.load(in_ptr2 + (16 + x0), xmask, eviction_policy='evict_last')
tmp15 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask).to(tl.int1)
tmp16 = tl.load(in_ptr1 + (32 + x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp17 = tl.load(in_ptr2 + (32 + x0), xmask, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask).to(tl.int1)
tmp23 = tl.load(in_ptr1 + (48 + x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp24 = tl.load(in_ptr2 + (48 + x0), xmask, eviction_policy='evict_last')
tmp40 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp41 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp45 = tl.load(in_ptr3 + (16 + x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp46 = tl.load(in_ptr4 + (16 + x0), xmask, eviction_policy='evict_last')
tmp51 = tl.load(in_ptr3 + (32 + x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp52 = tl.load(in_ptr4 + (32 + x0), xmask, eviction_policy='evict_last')
tmp57 = tl.load(in_ptr3 + (48 + x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp58 = tl.load(in_ptr4 + (48 + x0), xmask, eviction_policy='evict_last')
tmp74 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp75 = tl.load(in_ptr6 + x0, xmask, eviction_policy='evict_last')
tmp79 = tl.load(in_ptr5 + (16 + x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp80 = tl.load(in_ptr6 + (16 + x0), xmask, eviction_policy='evict_last')
tmp85 = tl.load(in_ptr5 + (32 + x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp86 = tl.load(in_ptr6 + (32 + x0), xmask, eviction_policy='evict_last')
tmp91 = tl.load(in_ptr5 + (48 + x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp92 = tl.load(in_ptr6 + (48 + x0), xmask, eviction_policy='evict_last')
tmp108 = tl.load(in_ptr7 + x0, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp109 = tl.load(in_ptr8 + x0, xmask, eviction_policy='evict_last')
tmp113 = tl.load(in_ptr7 + (16 + x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp114 = tl.load(in_ptr8 + (16 + x0), xmask, eviction_policy='evict_last')
tmp119 = tl.load(in_ptr7 + (32 + x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp120 = tl.load(in_ptr8 + (32 + x0), xmask, eviction_policy='evict_last')
tmp125 = tl.load(in_ptr7 + (48 + x0), xmask, eviction_policy='evict_last'
).to(tl.int1)
tmp126 = tl.load(in_ptr8 + (48 + x0), xmask, eviction_policy='evict_last')
tmp3 = 4.0
tmp4 = tmp2 * tmp3
tmp5 = tl.where(tmp1, tmp2, tmp4)
tmp6 = -8999999815811072.0
tmp7 = tl.where(tmp0, tmp5, tmp6)
tmp11 = tmp10 * tmp3
tmp12 = tl.where(tmp9, tmp10, tmp11)
tmp13 = tl.where(tmp8, tmp12, tmp6)
tmp14 = triton_helpers.maximum(tmp7, tmp13)
tmp18 = tmp17 * tmp3
tmp19 = tl.where(tmp16, tmp17, tmp18)
tmp20 = tl.where(tmp15, tmp19, tmp6)
tmp21 = triton_helpers.maximum(tmp14, tmp20)
tmp25 = tmp24 * tmp3
tmp26 = tl.where(tmp23, tmp24, tmp25)
tmp27 = tl.where(tmp22, tmp26, tmp6)
tmp28 = triton_helpers.maximum(tmp21, tmp27)
tmp29 = tmp7 - tmp28
tmp30 = tl_math.exp(tmp29)
tmp31 = tmp13 - tmp28
tmp32 = tl_math.exp(tmp31)
tmp33 = tmp30 + tmp32
tmp34 = tmp20 - tmp28
tmp35 = tl_math.exp(tmp34)
tmp36 = tmp33 + tmp35
tmp37 = tmp27 - tmp28
tmp38 = tl_math.exp(tmp37)
tmp39 = tmp36 + tmp38
tmp42 = tmp41 * tmp3
tmp43 = tl.where(tmp40, tmp41, tmp42)
tmp44 = tl.where(tmp0, tmp43, tmp6)
tmp47 = tmp46 * tmp3
tmp48 = tl.where(tmp45, tmp46, tmp47)
tmp49 = tl.where(tmp8, tmp48, tmp6)
tmp50 = triton_helpers.maximum(tmp44, tmp49)
tmp53 = tmp52 * tmp3
tmp54 = tl.where(tmp51, tmp52, tmp53)
tmp55 = tl.where(tmp15, tmp54, tmp6)
tmp56 = triton_helpers.maximum(tmp50, tmp55)
tmp59 = tmp58 * tmp3
tmp60 = tl.where(tmp57, tmp58, tmp59)
tmp61 = tl.where(tmp22, tmp60, tmp6)
tmp62 = triton_helpers.maximum(tmp56, tmp61)
tmp63 = tmp44 - tmp62
tmp64 = tl_math.exp(tmp63)
tmp65 = tmp49 - tmp62
tmp66 = tl_math.exp(tmp65)
tmp67 = tmp64 + tmp66
tmp68 = tmp55 - tmp62
tmp69 = tl_math.exp(tmp68)
tmp70 = tmp67 + tmp69
tmp71 = tmp61 - tmp62
tmp72 = tl_math.exp(tmp71)
tmp73 = tmp70 + tmp72
tmp76 = tmp75 * tmp3
tmp77 = tl.where(tmp74, tmp75, tmp76)
tmp78 = tl.where(tmp0, tmp77, tmp6)
tmp81 = tmp80 * tmp3
tmp82 = tl.where(tmp79, tmp80, tmp81)
tmp83 = tl.where(tmp8, tmp82, tmp6)
tmp84 = triton_helpers.maximum(tmp78, tmp83)
tmp87 = tmp86 * tmp3
tmp88 = tl.where(tmp85, tmp86, tmp87)
tmp89 = tl.where(tmp15, tmp88, tmp6)
tmp90 = triton_helpers.maximum(tmp84, tmp89)
tmp93 = tmp92 * tmp3
tmp94 = tl.where(tmp91, tmp92, tmp93)
tmp95 = tl.where(tmp22, tmp94, tmp6)
tmp96 = triton_helpers.maximum(tmp90, tmp95)
tmp97 = tmp78 - tmp96
tmp98 = tl_math.exp(tmp97)
tmp99 = tmp83 - tmp96
tmp100 = tl_math.exp(tmp99)
tmp101 = tmp98 + tmp100
tmp102 = tmp89 - tmp96
tmp103 = tl_math.exp(tmp102)
tmp104 = tmp101 + tmp103
tmp105 = tmp95 - tmp96
tmp106 = tl_math.exp(tmp105)
tmp107 = tmp104 + tmp106
tmp110 = tmp109 * tmp3
tmp111 = tl.where(tmp108, tmp109, tmp110)
tmp112 = tl.where(tmp0, tmp111, tmp6)
tmp115 = tmp114 * tmp3
tmp116 = tl.where(tmp113, tmp114, tmp115)
tmp117 = tl.where(tmp8, tmp116, tmp6)
tmp118 = triton_helpers.maximum(tmp112, tmp117)
tmp121 = tmp120 * tmp3
tmp122 = tl.where(tmp119, tmp120, tmp121)
tmp123 = tl.where(tmp15, tmp122, tmp6)
tmp124 = triton_helpers.maximum(tmp118, tmp123)
tmp127 = tmp126 * tmp3
tmp128 = tl.where(tmp125, tmp126, tmp127)
tmp129 = tl.where(tmp22, tmp128, tmp6)
tmp130 = triton_helpers.maximum(tmp124, tmp129)
tmp131 = tmp112 - tmp130
tmp132 = tl_math.exp(tmp131)
tmp133 = tmp117 - tmp130
tmp134 = tl_math.exp(tmp133)
tmp135 = tmp132 + tmp134
tmp136 = tmp123 - tmp130
tmp137 = tl_math.exp(tmp136)
tmp138 = tmp135 + tmp137
tmp139 = tmp129 - tmp130
tmp140 = tl_math.exp(tmp139)
tmp141 = tmp138 + tmp140
tl.store(out_ptr0 + x2, tmp28, xmask)
tl.store(out_ptr1 + x2, tmp39, xmask)
tl.store(out_ptr2 + x2, tmp62, xmask)
tl.store(out_ptr3 + x2, tmp73, xmask)
tl.store(out_ptr4 + x2, tmp96, xmask)
tl.store(out_ptr5 + x2, tmp107, xmask)
tl.store(out_ptr6 + x2, tmp130, xmask)
tl.store(out_ptr7 + x2, tmp141, xmask)
@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_4(in_ptr0, in_ptr1,
in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9,
in_ptr10, in_ptr11, in_ptr12, in_ptr13, in_ptr14, in_ptr15, in_ptr16,
out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x4 = xindex % 64
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask).to(tl.int1)
tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp2 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr3 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp11 = tl.load(in_ptr4 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp13 = tl.load(in_ptr5 + x4, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp14 = tl.load(in_ptr6 + x4, xmask, eviction_policy='evict_last')
tmp18 = tl.load(in_ptr7 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp21 = tl.load(in_ptr8 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp23 = tl.load(in_ptr9 + x4, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp24 = tl.load(in_ptr10 + x4, xmask, eviction_policy='evict_last')
tmp28 = tl.load(in_ptr11 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp31 = tl.load(in_ptr12 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp33 = tl.load(in_ptr13 + x4, xmask, eviction_policy='evict_last').to(tl
.int1)
tmp34 = tl.load(in_ptr14 + x4, xmask, eviction_policy='evict_last')
tmp38 = tl.load(in_ptr15 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp41 = tl.load(in_ptr16 + (x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = 4.0
tmp4 = tmp2 * tmp3
tmp5 = tl.where(tmp1, tmp2, tmp4)
tmp6 = -8999999815811072.0
tmp7 = tl.where(tmp0, tmp5, tmp6)
tmp9 = tmp7 - tmp8
tmp10 = tl_math.exp(tmp9)
tmp12 = tmp10 / tmp11
tmp15 = tmp14 * tmp3
tmp16 = tl.where(tmp13, tmp14, tmp15)
tmp17 = tl.where(tmp0, tmp16, tmp6)
tmp19 = tmp17 - tmp18
tmp20 = tl_math.exp(tmp19)
tmp22 = tmp20 / tmp21
tmp25 = tmp24 * tmp3
tmp26 = tl.where(tmp23, tmp24, tmp25)
tmp27 = tl.where(tmp0, tmp26, tmp6)
tmp29 = tmp27 - tmp28
tmp30 = tl_math.exp(tmp29)
tmp32 = tmp30 / tmp31
tmp35 = tmp34 * tmp3
tmp36 = tl.where(tmp33, tmp34, tmp35)
tmp37 = tl.where(tmp0, tmp36, tmp6)
tmp39 = tmp37 - tmp38
tmp40 = tl_math.exp(tmp39)
tmp42 = tmp40 / tmp41
tl.store(out_ptr0 + x3, tmp12, xmask)
tl.store(out_ptr1 + x3, tmp22, xmask)
tl.store(out_ptr2 + x3, tmp32, xmask)
tl.store(out_ptr3 + x3, tmp42, xmask)
@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 64
x2 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x2, tmp0, xmask)
@triton.jit
def triton_poi_fused_cat_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16 % 16
x0 = xindex % 16
x2 = xindex // 256
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
tmp6 = 0.0
tmp7 = tmp5 > tmp6
tmp8 = 1.0
tmp9 = tmp5 * tmp8
tmp10 = libdevice.expm1(tmp9)
tmp11 = tmp10 * tmp8
tmp12 = tl.where(tmp7, tmp9, tmp11)
tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
tmp14 = tl.where(tmp4, tmp12, tmp13)
tmp15 = tmp0 >= tmp3
tmp16 = tl.full([1], 8, tl.int64)
tmp17 = tmp0 < tmp16
tmp18 = tmp15 & tmp17
tmp19 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp18 &
xmask, other=0.0)
tmp20 = tmp19 > tmp6
tmp21 = tmp19 * tmp8
tmp22 = libdevice.expm1(tmp21)
tmp23 = tmp22 * tmp8
tmp24 = tl.where(tmp20, tmp21, tmp23)
tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
tmp26 = tl.where(tmp18, tmp24, tmp25)
tmp27 = tmp0 >= tmp16
tmp28 = tl.full([1], 12, tl.int64)
tmp29 = tmp0 < tmp28
tmp30 = tmp27 & tmp29
tmp31 = tl.load(in_ptr2 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp30 &
xmask, other=0.0)
tmp32 = tmp31 > tmp6
tmp33 = tmp31 * tmp8
tmp34 = libdevice.expm1(tmp33)
tmp35 = tmp34 * tmp8
tmp36 = tl.where(tmp32, tmp33, tmp35)
tmp37 = tl.full(tmp36.shape, 0.0, tmp36.dtype)
tmp38 = tl.where(tmp30, tmp36, tmp37)
tmp39 = tmp0 >= tmp28
tl.full([1], 16, tl.int64)
tmp42 = tl.load(in_ptr3 + (x0 + 16 * (-12 + x1) + 64 * x2), tmp39 &
xmask, other=0.0)
tmp43 = tmp42 > tmp6
tmp44 = tmp42 * tmp8
tmp45 = libdevice.expm1(tmp44)
tmp46 = tmp45 * tmp8
tmp47 = tl.where(tmp43, tmp44, tmp46)
tmp48 = tl.full(tmp47.shape, 0.0, tmp47.dtype)
tmp49 = tl.where(tmp39, tmp47, tmp48)
tmp50 = tl.where(tmp30, tmp38, tmp49)
tmp51 = tl.where(tmp18, tmp26, tmp50)
tmp52 = tl.where(tmp4, tmp14, tmp51)
tl.store(out_ptr0 + x3, tmp52, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (8, 1), (1, 1))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (8, 1), (1, 1))
assert_size_stride(primals_7, (4, 4), (4, 1))
assert_size_stride(primals_8, (8, 1), (1, 1))
assert_size_stride(primals_9, (4, 4), (4, 1))
assert_size_stride(primals_10, (8, 1), (1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
primals_2, out=buf0)
del primals_2
buf1 = empty_strided_cuda((4, 16, 8), (128, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(512)](buf0, buf1, 512, XBLOCK=256,
num_warps=4, num_stages=1)
buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 8), (8, 1), 0),
primals_3, out=buf2)
buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(64)](buf2, buf3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_gt_2[grid(256)](primals_4, buf4, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del primals_4
buf10 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
primals_5, out=buf10)
del primals_5
buf11 = empty_strided_cuda((4, 16, 8), (128, 8, 1), torch.float32)
triton_poi_fused_cat_0[grid(512)](buf10, buf11, 512, XBLOCK=256,
num_warps=4, num_stages=1)
buf12 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf11, (64, 8), (8, 1), 0),
primals_6, out=buf12)
buf13 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(64)](buf12, buf13, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf19 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
primals_7, out=buf19)
del primals_7
buf20 = empty_strided_cuda((4, 16, 8), (128, 8, 1), torch.float32)
triton_poi_fused_cat_0[grid(512)](buf19, buf20, 512, XBLOCK=256,
num_warps=4, num_stages=1)
buf21 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf20, (64, 8), (8, 1), 0),
primals_8, out=buf21)
buf22 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(64)](buf21, buf22, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf28 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
primals_9, out=buf28)
del primals_9
buf29 = empty_strided_cuda((4, 16, 8), (128, 8, 1), torch.float32)
triton_poi_fused_cat_0[grid(512)](buf28, buf29, 512, XBLOCK=256,
num_warps=4, num_stages=1)
buf30 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf29, (64, 8), (8, 1), 0),
primals_10, out=buf30)
buf31 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_leaky_relu_1[grid(64)](buf30, buf31, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf5 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
buf6 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
buf14 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
buf15 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
buf23 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
buf24 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
buf32 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
buf33 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
triton_poi_fused__softmax_leaky_relu_mul_where_3[grid(64)](buf4,
buf3, buf2, buf13, buf12, buf22, buf21, buf31, buf30, buf5,
buf6, buf14, buf15, buf23, buf24, buf32, buf33, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf16 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf25 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf34 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_leaky_relu_mul_where_4[grid(256)](buf4,
buf3, buf2, buf5, buf6, buf13, buf12, buf14, buf15, buf22,
buf21, buf23, buf24, buf31, buf30, buf32, buf33, buf7, buf16,
buf25, buf34, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf12
del buf14
del buf15
del buf2
del buf21
del buf23
del buf24
del buf30
del buf32
del buf33
del buf5
del buf6
buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_5[grid(256)](buf0, buf8, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf0
buf9 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0), out=buf9)
buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_5[grid(256)](buf10, buf17, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf10
buf18 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf16, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf17, (16, 4, 4), (16, 4, 1), 0), out=buf18
)
buf26 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_5[grid(256)](buf19, buf26, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf19
buf27 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf25, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf26, (16, 4, 4), (16, 4, 1), 0), out=buf27
)
buf35 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_5[grid(256)](buf28, buf35, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf28
buf36 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf34, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf35, (16, 4, 4), (16, 4, 1), 0), out=buf36
)
buf37 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.
float32)
triton_poi_fused_cat_6[grid(1024)](buf9, buf18, buf27, buf36, buf37,
1024, XBLOCK=128, num_warps=4, num_stages=1)
return (buf37, buf3, buf4, buf7, buf9, buf13, buf16, buf18, buf22,
buf25, buf27, buf31, buf34, buf36, reinterpret_tensor(buf35, (16, 4,
4), (16, 1, 4), 0), reinterpret_tensor(buf29, (8, 64), (1, 8), 0),
reinterpret_tensor(primals_10, (1, 8), (1, 1), 0),
reinterpret_tensor(primals_1, (4, 16), (1, 4), 0),
reinterpret_tensor(buf26, (16, 4, 4), (16, 1, 4), 0),
reinterpret_tensor(buf20, (8, 64), (1, 8), 0), reinterpret_tensor(
primals_8, (1, 8), (1, 1), 0), reinterpret_tensor(buf17, (16, 4, 4),
(16, 1, 4), 0), reinterpret_tensor(buf11, (8, 64), (1, 8), 0),
reinterpret_tensor(primals_6, (1, 8), (1, 1), 0),
reinterpret_tensor(buf8, (16, 4, 4), (16, 1, 4), 0),
reinterpret_tensor(buf1, (8, 64), (1, 8), 0), reinterpret_tensor(
primals_3, (1, 8), (1, 1), 0))
class GraphAttentionLayer(nn.Module):
"""
Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
"""
def __init__(self, in_features, out_features, dropout, alpha, concat=True):
super(GraphAttentionLayer, self).__init__()
self.dropout = dropout
self.in_features = in_features
self.out_features = out_features
self.alpha = alpha
self.concat = concat
self.W = nn.Parameter(torch.zeros(size=(in_features, out_features)))
nn.init.xavier_uniform_(self.W.data, gain=1.414)
self.a = nn.Parameter(torch.zeros(size=(2 * out_features, 1)))
nn.init.xavier_uniform_(self.a.data, gain=1.414)
self.leakyrelu = nn.LeakyReLU(self.alpha)
def forward(self, input, adj):
h = torch.matmul(input, self.W)
N = h.size()[1]
batch_size = h.size(0)
a_input = torch.cat([h.repeat(1, 1, N).view(batch_size, N * N, -1),
h.repeat(1, N, 1)], dim=2).view(batch_size, N, -1, 2 * self.
out_features)
e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(3))
zero_vec = -9000000000000000.0 * torch.ones_like(e)
attention = torch.where(adj > 0, e, zero_vec)
attention = F.softmax(attention, dim=1)
attention = F.dropout(attention, self.dropout, training=self.training)
h_prime = torch.matmul(attention, h)
if self.concat:
return F.elu(h_prime)
else:
return h_prime
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
class GATNew(nn.Module):
def __init__(self, nfeat, nhid, dropout, alpha, nheads):
super(GATNew, self).__init__()
self.dropout = dropout
self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout,
alpha=alpha, concat=True) for _ in range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
def forward(self, input_0, input_1):
primals_2 = self.attention_0.W
primals_3 = self.attention_0.a
primals_5 = self.attention_1.W
primals_6 = self.attention_1.a
primals_7 = self.attention_2.W
primals_8 = self.attention_2.a
primals_9 = self.attention_3.W
primals_10 = self.attention_3.a
primals_1 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
return output[0]
|
daiki-kimura/commonsense-rl
|
GAT
| false | 12,256 |
[
"Apache-2.0"
] | 0 |
5513926957b6501ce9cfa46f77f8f2c1c4892fa5
|
https://github.com/daiki-kimura/commonsense-rl/tree/5513926957b6501ce9cfa46f77f8f2c1c4892fa5
|
Normalize
|
from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional as F
class Normalize(Module):
"""Performs :math:`L_p` normalization of inputs over specified dimension.
Does:
.. math::
v = \\frac{v}{\\max(\\lVert v \\rVert_p, \\epsilon)}
for each subtensor v over dimension dim of input. Each subtensor is
flattened into a vector, i.e. :math:`\\lVert v \\rVert_p` is not a matrix
norm.
With default arguments normalizes over the second dimension with Euclidean
norm.
Args:
p (float): the exponent value in the norm formulation. Default: 2
dim (int): the dimension to reduce. Default: 1
"""
def __init__(self, p=2, dim=1):
super(Normalize, self).__init__()
self.p = p
self.dim = dim
def forward(self, x):
return F.normalize(x, self.p, self.dim, eps=1e-08)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-08
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + x3, tmp15, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_div_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class NormalizeNew(Module):
"""Performs :math:`L_p` normalization of inputs over specified dimension.
Does:
.. math::
v = \\frac{v}{\\max(\\lVert v \\rVert_p, \\epsilon)}
for each subtensor v over dimension dim of input. Each subtensor is
flattened into a vector, i.e. :math:`\\lVert v \\rVert_p` is not a matrix
norm.
With default arguments normalizes over the second dimension with Euclidean
norm.
Args:
p (float): the exponent value in the norm formulation. Default: 2
dim (int): the dimension to reduce. Default: 1
"""
def __init__(self, p=2, dim=1):
super(NormalizeNew, self).__init__()
self.p = p
self.dim = dim
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
RL-WWW/ISST
|
Normalize
| false | 17,820 |
[
"BSD-3-Clause"
] | 5 |
42b656686fa9660794007a0bc00a7177937410e9
|
https://github.com/RL-WWW/ISST/tree/42b656686fa9660794007a0bc00a7177937410e9
|
SEModule
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Hswish(nn.Module):
def __init__(self, inplace=True):
super(Hswish, self).__init__()
self.inplace = inplace
def forward(self, x):
return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(1.2 * x + 3.0, inplace=self.inplace) / 6.0
class Activation(nn.Module):
def __init__(self, act_type, inplace=True):
super(Activation, self).__init__()
act_type = act_type.lower()
if act_type == 'relu':
self.act = nn.ReLU(inplace=inplace)
elif act_type == 'relu6':
self.act = nn.ReLU6(inplace=inplace)
elif act_type == 'sigmoid':
raise NotImplementedError
elif act_type == 'hard_sigmoid':
self.act = Hsigmoid(inplace)
elif act_type == 'hard_swish':
self.act = Hswish(inplace=inplace)
elif act_type == 'leakyrelu':
self.act = nn.LeakyReLU(inplace=inplace)
else:
raise NotImplementedError
def forward(self, inputs):
return self.act(inputs)
class SEModule(nn.Module):
def __init__(self, in_channels, reduction=4, name=''):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=
in_channels // reduction, kernel_size=1, stride=1, padding=0,
bias=True)
self.relu1 = Activation(act_type='relu', inplace=True)
self.conv2 = nn.Conv2d(in_channels=in_channels // reduction,
out_channels=in_channels, kernel_size=1, stride=1, padding=0,
bias=True)
self.hard_sigmoid = Activation(act_type='hard_sigmoid', inplace=True)
def forward(self, inputs):
outputs = self.avg_pool(inputs)
outputs = self.conv1(outputs)
outputs = self.relu1(outputs)
outputs = self.conv2(outputs)
outputs = self.hard_sigmoid(outputs)
outputs = inputs * outputs
return outputs
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 16.0
tmp6 = tmp4 / tmp5
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp6, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tmp4 = tl.full([1], 0, tl.int32)
tmp5 = triton_helpers.maximum(tmp4, tmp3)
tl.store(in_out_ptr0 + x0, tmp5, xmask)
@triton.jit
def triton_poi_fused_add_convolution_div_hardtanh_mul_2(in_ptr0, in_ptr1,
in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x4 = xindex // 16
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = 1.2
tmp5 = tmp3 * tmp4
tmp6 = 3.0
tmp7 = tmp5 + tmp6
tmp8 = 0.0
tmp9 = triton_helpers.maximum(tmp7, tmp8)
tmp10 = 6.0
tmp11 = triton_helpers.minimum(tmp9, tmp10)
tmp12 = 0.16666666666666666
tmp13 = tmp11 * tmp12
tmp14 = tmp0 * tmp13
tl.store(out_ptr0 + x3, tmp14, xmask)
@triton.jit
def triton_poi_fused_add_convolution_hardtanh_backward_mul_3(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.2
tmp4 = tmp2 * tmp3
tmp5 = 3.0
tmp6 = tmp4 + tmp5
tmp7 = 0.0
tmp8 = tmp6 <= tmp7
tmp9 = 6.0
tmp10 = tmp6 >= tmp9
tmp11 = tmp8 | tmp10
tl.store(out_ptr0 + x2, tmp11, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (1,), (1,))
assert_size_stride(primals_4, (4, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
del buf0
get_raw_stream(0)
triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=1,
num_warps=2, num_stages=1)
buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 1, 1, 1), (1, 1, 1, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_relu_1[grid(4)](buf3, primals_3, 4,
XBLOCK=4, num_warps=1, num_stages=1)
del primals_3
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 4, 1, 1), (4, 1, 1, 1))
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_convolution_div_hardtanh_mul_2[grid(256)](
primals_1, buf4, primals_5, buf5, 256, XBLOCK=128, num_warps=4,
num_stages=1)
buf6 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
triton_poi_fused_add_convolution_hardtanh_backward_mul_3[grid(16)](buf4
, primals_5, buf6, 16, XBLOCK=16, num_warps=1, num_stages=1)
del buf4
del primals_5
return buf5, primals_1, primals_2, primals_4, buf1, buf3, buf6
class Hswish(nn.Module):
def __init__(self, inplace=True):
super(Hswish, self).__init__()
self.inplace = inplace
def forward(self, x):
return x * F.relu6(x + 3.0, inplace=self.inplace) / 6.0
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(1.2 * x + 3.0, inplace=self.inplace) / 6.0
class Activation(nn.Module):
def __init__(self, act_type, inplace=True):
super(Activation, self).__init__()
act_type = act_type.lower()
if act_type == 'relu':
self.act = nn.ReLU(inplace=inplace)
elif act_type == 'relu6':
self.act = nn.ReLU6(inplace=inplace)
elif act_type == 'sigmoid':
raise NotImplementedError
elif act_type == 'hard_sigmoid':
self.act = Hsigmoid(inplace)
elif act_type == 'hard_swish':
self.act = Hswish(inplace=inplace)
elif act_type == 'leakyrelu':
self.act = nn.LeakyReLU(inplace=inplace)
else:
raise NotImplementedError
def forward(self, inputs):
return self.act(inputs)
class SEModuleNew(nn.Module):
def __init__(self, in_channels, reduction=4, name=''):
super(SEModuleNew, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=
in_channels // reduction, kernel_size=1, stride=1, padding=0,
bias=True)
self.relu1 = Activation(act_type='relu', inplace=True)
self.conv2 = nn.Conv2d(in_channels=in_channels // reduction,
out_channels=in_channels, kernel_size=1, stride=1, padding=0,
bias=True)
self.hard_sigmoid = Activation(act_type='hard_sigmoid', inplace=True)
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
manjrekarom/PaddleOCR2Pytorch
|
SEModule
| false | 15,996 |
[
"Apache-2.0"
] | 364 |
6d98508f4c85b9dd3bf022924b0ecc5354ec8281
|
https://github.com/manjrekarom/PaddleOCR2Pytorch/tree/6d98508f4c85b9dd3bf022924b0ecc5354ec8281
|
GEGLU
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_7/inductor_cache/g3/cg3jxrez5zd4oyrbdxcja37kso4ls6az3z6q5iucel6jjkrpi3th.py
# Topologically Sorted Source Nodes: [gelu, mul], Original ATen: [aten.gelu, aten.mul]
# Source node to ATen node mapping:
# gelu => add, erf, mul, mul_1, mul_2
# mul => mul_3
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem_1, 0.5), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem_1, 0.7071067811865476), kwargs = {})
# %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_1,), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %add), kwargs = {})
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %getitem), kwargs = {})
triton_poi_fused_gelu_mul_0 = async_compile.triton('triton_poi_fused_gelu_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_gelu_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_gelu_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 2
x1 = (xindex // 2)
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 + x0 + (4*x1)), xmask)
tmp9 = tl.load(in_ptr0 + (x0 + (4*x1)), xmask)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = 0.7071067811865476
tmp4 = tmp0 * tmp3
tmp5 = libdevice.erf(tmp4)
tmp6 = 1.0
tmp7 = tmp5 + tmp6
tmp8 = tmp2 * tmp7
tmp10 = tmp8 * tmp9
tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 2), (32, 8, 2, 1), torch.float32)
# Topologically Sorted Source Nodes: [gelu, mul], Original ATen: [aten.gelu, aten.mul]
stream0 = get_raw_stream(0)
triton_poi_fused_gelu_mul_0.run(arg0_1, buf0, 128, grid=grid(128), stream=stream0)
del arg0_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_gelu_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 2
x1 = xindex // 2
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 + x0 + 4 * x1), xmask)
tmp9 = tl.load(in_ptr0 + (x0 + 4 * x1), xmask)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = 0.7071067811865476
tmp4 = tmp0 * tmp3
tmp5 = libdevice.erf(tmp4)
tmp6 = 1.0
tmp7 = tmp5 + tmp6
tmp8 = tmp2 * tmp7
tmp10 = tmp8 * tmp9
tl.store(out_ptr0 + x2, tmp10, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 2), (32, 8, 2, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_gelu_mul_0[grid(128)](arg0_1, buf0, 128, XBLOCK=
128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class GEGLUNew(nn.Module):
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
AlansBoyHeart/vit-pytorch
|
GEGLU
| false | 1,910 |
[
"MIT"
] | 0 |
1959adae0bdd7801475bba34d7d61bdc529b4616
|
https://github.com/AlansBoyHeart/vit-pytorch/tree/1959adae0bdd7801475bba34d7d61bdc529b4616
|
Accuracy
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_9/inductor_cache/wx/cwxwvlntewdrqi2r4caciy5ht4jdvafnhtiqncr4lo4aegcb4imz.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# softmax => amax, exp, sub
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [-1], True), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax), kwargs = {})
# %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/zj/czjdjyrflbcisam4biblebtuqyafkq32kfbtpbzsspl6b4xtphh2.py
# Topologically Sorted Source Nodes: [softmax, prediction], Original ATen: [aten._softmax, aten.argmax]
# Source node to ATen node mapping:
# prediction => argmax
# softmax => div, sum_1
# Graph fragment:
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
# %argmax : [num_users=1] = call_function[target=torch.ops.aten.argmax.default](args = (%div, -2), kwargs = {})
triton_poi_fused__softmax_argmax_1 = async_compile.triton('triton_poi_fused__softmax_argmax_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_argmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 20, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_argmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = (xindex // 4)
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
tmp1 = tl.load(in_ptr0 + (16*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (16*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (16*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (16*x1)), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
tmp10 = tl.load(in_ptr0 + (4 + (16*x1)), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (5 + (16*x1)), xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr0 + (6 + (16*x1)), xmask, eviction_policy='evict_last')
tmp15 = tl.load(in_ptr0 + (7 + (16*x1)), xmask, eviction_policy='evict_last')
tmp33 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
tmp34 = tl.load(in_ptr0 + (8 + (16*x1)), xmask, eviction_policy='evict_last')
tmp35 = tl.load(in_ptr0 + (9 + (16*x1)), xmask, eviction_policy='evict_last')
tmp37 = tl.load(in_ptr0 + (10 + (16*x1)), xmask, eviction_policy='evict_last')
tmp39 = tl.load(in_ptr0 + (11 + (16*x1)), xmask, eviction_policy='evict_last')
tmp56 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
tmp57 = tl.load(in_ptr0 + (12 + (16*x1)), xmask, eviction_policy='evict_last')
tmp58 = tl.load(in_ptr0 + (13 + (16*x1)), xmask, eviction_policy='evict_last')
tmp60 = tl.load(in_ptr0 + (14 + (16*x1)), xmask, eviction_policy='evict_last')
tmp62 = tl.load(in_ptr0 + (15 + (16*x1)), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tmp12 = tmp10 + tmp11
tmp14 = tmp12 + tmp13
tmp16 = tmp14 + tmp15
tmp17 = tmp9 / tmp16
tmp18 = tmp8 > tmp17
tmp19 = tmp8 == tmp17
tmp20 = tmp8 != tmp8
tmp21 = tmp17 != tmp17
tmp22 = tmp20 > tmp21
tmp23 = tmp18 | tmp22
tmp24 = tmp20 & tmp21
tmp25 = tmp19 | tmp24
tmp26 = tl.full([1], 0, tl.int64)
tmp27 = tl.full([1], 1, tl.int64)
tmp28 = tmp26 < tmp27
tmp29 = tmp25 & tmp28
tmp30 = tmp23 | tmp29
tmp31 = tl.where(tmp30, tmp8, tmp17)
tmp32 = tl.where(tmp30, tmp26, tmp27)
tmp36 = tmp34 + tmp35
tmp38 = tmp36 + tmp37
tmp40 = tmp38 + tmp39
tmp41 = tmp33 / tmp40
tmp42 = tmp31 > tmp41
tmp43 = tmp31 == tmp41
tmp44 = tmp31 != tmp31
tmp45 = tmp41 != tmp41
tmp46 = tmp44 > tmp45
tmp47 = tmp42 | tmp46
tmp48 = tmp44 & tmp45
tmp49 = tmp43 | tmp48
tmp50 = tl.full([1], 2, tl.int64)
tmp51 = tmp32 < tmp50
tmp52 = tmp49 & tmp51
tmp53 = tmp47 | tmp52
tmp54 = tl.where(tmp53, tmp31, tmp41)
tmp55 = tl.where(tmp53, tmp32, tmp50)
tmp59 = tmp57 + tmp58
tmp61 = tmp59 + tmp60
tmp63 = tmp61 + tmp62
tmp64 = tmp56 / tmp63
tmp65 = tmp54 > tmp64
tmp66 = tmp54 == tmp64
tmp67 = tmp54 != tmp54
tmp68 = tmp64 != tmp64
tmp69 = tmp67 > tmp68
tmp70 = tmp65 | tmp69
tmp71 = tmp67 & tmp68
tmp72 = tmp66 | tmp71
tmp73 = tl.full([1], 3, tl.int64)
tmp74 = tmp55 < tmp73
tmp75 = tmp72 & tmp74
tmp76 = tmp70 | tmp75
tmp77 = tl.where(tmp76, tmp54, tmp64)
tmp78 = tl.where(tmp76, tmp55, tmp73)
tl.store(out_ptr0 + (x2), tmp78, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/dp/cdpemgebi7is3mfuap3dlfrpijxckzdvrrnaly6xxb4yenoeidgc.py
# Topologically Sorted Source Nodes: [scores, sum_1, truediv], Original ATen: [aten.eq, aten.sum, aten.div]
# Source node to ATen node mapping:
# scores => eq
# sum_1 => sum_2
# truediv => div_1
# Graph fragment:
# %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%argmax, %arg1_1), kwargs = {})
# %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%eq,), kwargs = {})
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_2, 256.0), kwargs = {})
triton_per_fused_div_eq_sum_2 = async_compile.triton('triton_per_fused_div_eq_sum_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_eq_sum_2', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_div_eq_sum_2(in_ptr0, in_ptr1, out_ptr1, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex % 64
r2 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr1 + (r2), None)
tmp1 = tmp0.to(tl.float32)
tmp3 = tmp1 == tmp2
tmp4 = tmp3.to(tl.int64)
tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
tmp8 = tmp7.to(tl.float32)
tmp9 = 0.00390625
tmp10 = tmp8 * tmp9
tl.store(out_ptr1 + (tl.full([1], 0, tl.int32)), tmp10, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
stream0 = get_raw_stream(0)
triton_poi_fused__softmax_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
# Topologically Sorted Source Nodes: [softmax, prediction], Original ATen: [aten._softmax, aten.argmax]
triton_poi_fused__softmax_argmax_1.run(buf0, buf1, 64, grid=grid(64), stream=stream0)
del buf0
buf3 = empty_strided_cuda((), (), torch.float32)
# Topologically Sorted Source Nodes: [scores, sum_1, truediv], Original ATen: [aten.eq, aten.sum, aten.div]
triton_per_fused_div_eq_sum_2.run(buf1, arg1_1, buf3, 1, 256, grid=grid(1), stream=stream0)
del arg1_1
del buf1
return (buf3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_argmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
tmp1 = tl.load(in_ptr0 + 16 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 16 * x1), xmask, eviction_policy='evict_last'
)
tmp4 = tl.load(in_ptr0 + (2 + 16 * x1), xmask, eviction_policy='evict_last'
)
tmp6 = tl.load(in_ptr0 + (3 + 16 * x1), xmask, eviction_policy='evict_last'
)
tmp9 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
tmp10 = tl.load(in_ptr0 + (4 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp11 = tl.load(in_ptr0 + (5 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp13 = tl.load(in_ptr0 + (6 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp15 = tl.load(in_ptr0 + (7 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp33 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
tmp34 = tl.load(in_ptr0 + (8 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp35 = tl.load(in_ptr0 + (9 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp37 = tl.load(in_ptr0 + (10 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp39 = tl.load(in_ptr0 + (11 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp56 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
tmp57 = tl.load(in_ptr0 + (12 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp58 = tl.load(in_ptr0 + (13 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp60 = tl.load(in_ptr0 + (14 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp62 = tl.load(in_ptr0 + (15 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tmp12 = tmp10 + tmp11
tmp14 = tmp12 + tmp13
tmp16 = tmp14 + tmp15
tmp17 = tmp9 / tmp16
tmp18 = tmp8 > tmp17
tmp19 = tmp8 == tmp17
tmp20 = tmp8 != tmp8
tmp21 = tmp17 != tmp17
tmp22 = tmp20 > tmp21
tmp23 = tmp18 | tmp22
tmp24 = tmp20 & tmp21
tmp25 = tmp19 | tmp24
tmp26 = tl.full([1], 0, tl.int64)
tmp27 = tl.full([1], 1, tl.int64)
tmp28 = tmp26 < tmp27
tmp29 = tmp25 & tmp28
tmp30 = tmp23 | tmp29
tmp31 = tl.where(tmp30, tmp8, tmp17)
tmp32 = tl.where(tmp30, tmp26, tmp27)
tmp36 = tmp34 + tmp35
tmp38 = tmp36 + tmp37
tmp40 = tmp38 + tmp39
tmp41 = tmp33 / tmp40
tmp42 = tmp31 > tmp41
tmp43 = tmp31 == tmp41
tmp44 = tmp31 != tmp31
tmp45 = tmp41 != tmp41
tmp46 = tmp44 > tmp45
tmp47 = tmp42 | tmp46
tmp48 = tmp44 & tmp45
tmp49 = tmp43 | tmp48
tmp50 = tl.full([1], 2, tl.int64)
tmp51 = tmp32 < tmp50
tmp52 = tmp49 & tmp51
tmp53 = tmp47 | tmp52
tmp54 = tl.where(tmp53, tmp31, tmp41)
tmp55 = tl.where(tmp53, tmp32, tmp50)
tmp59 = tmp57 + tmp58
tmp61 = tmp59 + tmp60
tmp63 = tmp61 + tmp62
tmp64 = tmp56 / tmp63
tmp65 = tmp54 > tmp64
tmp66 = tmp54 == tmp64
tmp67 = tmp54 != tmp54
tmp68 = tmp64 != tmp64
tmp69 = tmp67 > tmp68
tmp70 = tmp65 | tmp69
tmp71 = tmp67 & tmp68
tmp72 = tmp66 | tmp71
tmp73 = tl.full([1], 3, tl.int64)
tmp74 = tmp55 < tmp73
tmp75 = tmp72 & tmp74
tmp76 = tmp70 | tmp75
tl.where(tmp76, tmp54, tmp64)
tmp78 = tl.where(tmp76, tmp55, tmp73)
tl.store(out_ptr0 + x2, tmp78, xmask)
@triton.jit
def triton_per_fused_div_eq_sum_2(in_ptr0, in_ptr1, out_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex % 64
r2 = rindex
tmp0 = tl.load(in_ptr0 + r0, None, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr1 + r2, None)
tmp1 = tmp0.to(tl.float32)
tmp3 = tmp1 == tmp2
tmp4 = tmp3.to(tl.int64)
tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
tmp8 = tmp7.to(tl.float32)
tmp9 = 0.00390625
tmp10 = tmp8 * tmp9
tl.store(out_ptr1 + tl.full([1], 0, tl.int32), tmp10, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__softmax_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del arg0_1
buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
triton_poi_fused__softmax_argmax_1[grid(64)](buf0, buf1, 64, XBLOCK
=64, num_warps=1, num_stages=1)
del buf0
buf3 = empty_strided_cuda((), (), torch.float32)
triton_per_fused_div_eq_sum_2[grid(1)](buf1, arg1_1, buf3, 1, 256,
num_warps=2, num_stages=1)
del arg1_1
del buf1
return buf3,
class AccuracyNew(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
BShennette/Pno-ai
|
Accuracy
| false | 11,228 |
[
"MIT"
] | 0 |
486434bfb40887d06e3d12a66831b9e0e7d020c2
|
https://github.com/BShennette/Pno-ai/tree/486434bfb40887d06e3d12a66831b9e0e7d020c2
|
BasicBlock1
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_8/inductor_cache/yc/cyc22ofksedv27pfbzelnb4w34yylhyqovnpjyfto4qrzov2wriv.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat => cat
# Graph fragment:
# %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_3, %relu], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 16) % 8
x0 = xindex % 16
x2 = (xindex // 128)
x3 = xindex
tmp0 = x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp6 & xmask, other=0.0)
tmp10 = tl.load(in_ptr2 + ((-4) + x1), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
tmp15 = tl.where(tmp6, tmp13, tmp14)
tmp16 = tl.where(tmp4, tmp5, tmp15)
tl.store(out_ptr0 + (x3), tmp16, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/as/casawnq4v3qkxvanufkeovwbmhrlvnzi55imfmscbraafjzufzyj.py
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out => convolution
# out_1 => relu
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_3, buf0, primals_2, buf1, 512, grid=grid(512), stream=stream0)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_1.run(buf0, primals_2, buf2, 256, grid=grid(256), stream=stream0)
del buf0
del primals_2
return (buf1, primals_1, primals_3, buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16 % 8
x0 = xindex % 16
x2 = xindex // 128
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
other=0.0)
tmp10 = tl.load(in_ptr2 + (-4 + x1), tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
tmp15 = tl.where(tmp6, tmp13, tmp14)
tmp16 = tl.where(tmp4, tmp5, tmp15)
tl.store(out_ptr0 + x3, tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_1(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(512)](primals_3, buf0, primals_2, buf1,
512, XBLOCK=128, num_warps=4, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_1[grid(256)](buf0,
primals_2, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
del buf0
del primals_2
return buf1, primals_1, primals_3, buf2
class BasicBlock1New(nn.Module):
def __init__(self, input_dim, output_dim):
super(BasicBlock1New, self).__init__()
self.ID = input_dim
self.conv = nn.Conv2d(in_channels=input_dim, out_channels=
output_dim, kernel_size=1, padding=0, stride=1)
self.relu = nn.ReLU()
def forward(self, input_0):
primals_1 = self.conv.weight
primals_2 = self.conv.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Houseqin/PytorchToCaffe
|
BasicBlock1
| false | 9,112 |
[
"MIT"
] | 0 |
e94224ba6414e76369f191e7e3d9731c12ce2bd7
|
https://github.com/Houseqin/PytorchToCaffe/tree/e94224ba6414e76369f191e7e3d9731c12ce2bd7
|
TargetContextGate
|
import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select the input from the target side context
(decoder state), from the source context (attention state) or both.
"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(ContextGate, self).__init__()
input_size = embeddings_size + decoder_size + attention_size
self.gate = nn.Linear(input_size, output_size, bias=True)
self.sig = nn.Sigmoid()
self.source_proj = nn.Linear(attention_size, output_size)
self.target_proj = nn.Linear(embeddings_size + decoder_size,
output_size)
def forward(self, prev_emb, dec_state, attn_state):
input_tensor = torch.cat((prev_emb, dec_state, attn_state), dim=1)
z = self.sig(self.gate(input_tensor))
proj_source = self.source_proj(attn_state)
proj_target = self.target_proj(torch.cat((prev_emb, dec_state), dim=1))
return z, proj_source, proj_target
class TargetContextGate(nn.Module):
"""Apply the context gate only to the target context"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(TargetContextGate, self).__init__()
self.context_gate = ContextGate(embeddings_size, decoder_size,
attention_size, output_size)
self.tanh = nn.Tanh()
def forward(self, prev_emb, dec_state, attn_state):
z, source, target = self.context_gate(prev_emb, dec_state, attn_state)
return self.tanh(z * target + source)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'embeddings_size': 4, 'decoder_size': 4, 'attention_size':
4, 'output_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.cuda
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 48
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 12
x1 = xindex // 12
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = tmp0 >= tmp7
tl.full([1], 12, tl.int64)
tmp14 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp11 & xmask,
eviction_policy='evict_last', other=0.0)
tmp15 = tl.where(tmp9, tmp10, tmp14)
tmp16 = tl.where(tmp4, tmp5, tmp15)
tl.store(out_ptr0 + x2, tmp16, xmask)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x2, tmp10, xmask)
@triton.jit
def triton_poi_fused_add_mul_sigmoid_tanh_2(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp2 = tl.load(in_ptr1 + x2, xmask)
tmp4 = tl.load(in_out_ptr0 + x2, xmask)
tmp5 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp1 = tl.sigmoid(tmp0)
tmp3 = tmp1 * tmp2
tmp6 = tmp4 + tmp5
tmp7 = tmp3 + tmp6
tmp8 = libdevice.tanh(tmp7)
tl.store(in_out_ptr0 + x2, tmp8, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4, 12), (12, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4, 8), (8, 1))
assert_size_stride(primals_9, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(48)](primals_1, primals_2, primals_3,
buf0, 48, XBLOCK=64, num_warps=1, num_stages=1)
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, buf0, reinterpret_tensor(primals_4,
(12, 4), (1, 12), 0), alpha=1, beta=1, out=buf1)
del primals_4
del primals_5
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, reinterpret_tensor(primals_6, (4, 4),
(1, 4), 0), out=buf2)
del primals_6
buf3 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
triton_poi_fused_cat_1[grid(32)](primals_1, primals_2, buf3, 32,
XBLOCK=32, num_warps=1, num_stages=1)
del primals_1
del primals_2
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_9, buf3, reinterpret_tensor(primals_8,
(8, 4), (1, 8), 0), alpha=1, beta=1, out=buf4)
del primals_8
del primals_9
buf5 = buf2
del buf2
triton_poi_fused_add_mul_sigmoid_tanh_2[grid(16)](buf5, buf1, buf4,
primals_7, 16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_7
return buf5, primals_3, buf0, buf1, buf3, buf4, buf5
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select the input from the target side context
(decoder state), from the source context (attention state) or both.
"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(ContextGate, self).__init__()
input_size = embeddings_size + decoder_size + attention_size
self.gate = nn.Linear(input_size, output_size, bias=True)
self.sig = nn.Sigmoid()
self.source_proj = nn.Linear(attention_size, output_size)
self.target_proj = nn.Linear(embeddings_size + decoder_size,
output_size)
def forward(self, prev_emb, dec_state, attn_state):
input_tensor = torch.cat((prev_emb, dec_state, attn_state), dim=1)
z = self.sig(self.gate(input_tensor))
proj_source = self.source_proj(attn_state)
proj_target = self.target_proj(torch.cat((prev_emb, dec_state), dim=1))
return z, proj_source, proj_target
class TargetContextGateNew(nn.Module):
"""Apply the context gate only to the target context"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(TargetContextGateNew, self).__init__()
self.context_gate = ContextGate(embeddings_size, decoder_size,
attention_size, output_size)
self.tanh = nn.Tanh()
def forward(self, input_0, input_1, input_2):
primals_4 = self.context_gate.gate.weight
primals_5 = self.context_gate.gate.bias
primals_1 = self.context_gate.source_proj.weight
primals_7 = self.context_gate.source_proj.bias
primals_8 = self.context_gate.target_proj.weight
primals_9 = self.context_gate.target_proj.bias
primals_2 = input_0
primals_3 = input_1
primals_6 = input_2
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9])
return output[0]
|
AndrewM1998/MultimodalNMT
|
TargetContextGate
| false | 7,710 |
[
"MIT"
] | 40 |
b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b
|
https://github.com/AndrewM1998/MultimodalNMT/tree/b66d3a40ac9bc5c11ae124f51d1a9abf7cd6a04b
|
TransformerEncoderLayer
|
import torch
from torch import Tensor
from typing import Optional
from torch import nn
def _get_activation_fn(activation: 'str'):
if activation == 'relu':
return nn.functional.relu
elif activation == 'gelu':
return nn.functional.gelu
raise RuntimeError('activation should be relu/gelu, not {}'.format(
activation))
class TransformerEncoderLayer(nn.Module):
"""
Modified from torch.nn.TransformerEncoderLayer. Add support of normalize_before,
i.e., use layer_norm before the first block.
Args:
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multiheadattention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
activation: the activation function of intermediate layer, relu or gelu (default=relu).
normalize_before: whether to use layer_norm before the first block.
Examples::
>>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8)
>>> src = torch.rand(10, 32, 512)
>>> out = encoder_layer(src)
"""
def __init__(self, d_model: 'int', nhead: 'int', dim_feedforward: 'int'
=2048, dropout: 'float'=0.1, activation: 'str'='relu',
normalize_before: 'bool'=True) ->None:
super(TransformerEncoderLayer, self).__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=0.0)
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
self.normalize_before = normalize_before
def __setstate__(self, state):
if 'activation' not in state:
state['activation'] = nn.functional.relu
super(TransformerEncoderLayer, self).__setstate__(state)
def forward(self, src: 'Tensor', src_mask: 'Optional[Tensor]'=None,
src_key_padding_mask: 'Optional[Tensor]'=None) ->Tensor:
"""
Pass the input through the encoder layer.
Args:
src: the sequence to the encoder layer (required).
src_mask: the mask for the src sequence (optional).
src_key_padding_mask: the mask for the src keys per batch (optional).
Shape:
src: (S, N, E).
src_mask: (S, S).
src_key_padding_mask: (N, S).
S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number
"""
residual = src
if self.normalize_before:
src = self.norm1(src)
src2 = self.self_attn(src, src, src, attn_mask=src_mask,
key_padding_mask=src_key_padding_mask)[0]
src = residual + self.dropout1(src2)
if not self.normalize_before:
src = self.norm1(src)
residual = src
if self.normalize_before:
src = self.norm2(src)
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
src = residual + self.dropout2(src2)
if not self.normalize_before:
src = self.norm2(src)
return src
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'d_model': 4, 'nhead': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_mul_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 4
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_6(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 + tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 + tmp12
tmp14 = tmp10 + tmp13
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp2 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp5 - tmp16
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp9 - tmp16
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp16
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = tmp27 / tmp15
tl.store(out_ptr0 + x0, tmp16, xmask)
tl.store(out_ptr1 + x0, tmp28, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 - tmp3
tmp6 = 1e-05
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + x2, tmp13, xmask)
@triton.jit
def triton_poi_fused_relu_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 2048
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_add_9(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel,
XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp3 = tl.load(in_out_ptr0 + x2, xmask)
tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tl.store(in_out_ptr0 + x2, tmp6, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (12, 4), (4, 1))
assert_size_stride(primals_5, (12,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (4,), (1,))
assert_size_stride(primals_10, (2048, 4), (4, 1))
assert_size_stride(primals_11, (2048,), (1,))
assert_size_stride(primals_12, (4, 2048), (2048, 1))
assert_size_stride(primals_13, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(4)](primals_1, buf0, buf1,
4, XBLOCK=4, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(16)](primals_1, buf0,
buf1, primals_2, primals_3, buf2, 16, XBLOCK=16, num_warps=1,
num_stages=1)
del primals_2
del primals_3
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4
), 0), out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(reinterpret_tensor(primals_5, (4,), (1,), 4),
buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 16), alpha=
1, beta=1, out=buf4)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(reinterpret_tensor(primals_5, (4,), (1,), 8),
buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 32), alpha=
1, beta=1, out=buf5)
buf6 = reinterpret_tensor(buf3, (4, 4, 1), (1, 4, 16), 0)
del buf3
triton_poi_fused_mul_2[grid(16)](buf6, primals_5, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_5
buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(buf6, reinterpret_tensor(buf4, (4, 1, 4), (1, 1,
4), 0), out=buf7)
buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__softmax_3[grid(64)](buf7, buf8, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf9 = buf7
del buf7
triton_poi_fused__softmax_4[grid(64)](buf8, buf9, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del buf8
buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
extern_kernels.bmm(buf9, reinterpret_tensor(buf5, (4, 4, 1), (1, 4,
1), 0), out=buf10)
buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
triton_poi_fused_clone_5[grid(4, 4)](buf10, buf11, 4, 4, XBLOCK=4,
YBLOCK=4, num_warps=1, num_stages=1)
buf12 = reinterpret_tensor(buf10, (4, 4), (4, 1), 0)
del buf10
extern_kernels.addmm(primals_7, reinterpret_tensor(buf11, (4, 4), (
4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf12)
del primals_7
buf13 = buf1
del buf1
buf14 = buf0
del buf0
triton_poi_fused_add_native_layer_norm_6[grid(4)](primals_1, buf12,
buf13, buf14, 4, XBLOCK=4, num_warps=1, num_stages=1)
buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_add_native_layer_norm_7[grid(16)](primals_1, buf12,
buf13, buf14, primals_8, primals_9, buf15, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf13
del buf14
del primals_9
buf16 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
extern_kernels.mm(buf15, reinterpret_tensor(primals_10, (4, 2048),
(1, 4), 0), out=buf16)
buf17 = buf16
del buf16
triton_poi_fused_relu_8[grid(8192)](buf17, primals_11, 8192, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_11
buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf17, reinterpret_tensor(primals_12, (2048, 4),
(1, 2048), 0), out=buf18)
buf19 = buf18
del buf18
triton_poi_fused_add_9[grid(16)](buf19, primals_1, buf12,
primals_13, 16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_13
return (buf19, primals_1, primals_8, buf2, buf9, reinterpret_tensor(
buf11, (4, 4), (4, 1), 0), buf12, buf15, buf17, primals_12,
primals_10, primals_6, reinterpret_tensor(buf5, (4, 1, 4), (1, 1, 4
), 0), reinterpret_tensor(buf6, (4, 1, 4), (1, 1, 4), 0),
reinterpret_tensor(buf4, (4, 4, 1), (1, 4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (4, 1), 32),
reinterpret_tensor(primals_4, (4, 4), (4, 1), 16),
reinterpret_tensor(primals_4, (4, 4), (4, 1), 0))
def _get_activation_fn(activation: 'str'):
if activation == 'relu':
return nn.functional.relu
elif activation == 'gelu':
return nn.functional.gelu
raise RuntimeError('activation should be relu/gelu, not {}'.format(
activation))
class TransformerEncoderLayerNew(nn.Module):
"""
Modified from torch.nn.TransformerEncoderLayer. Add support of normalize_before,
i.e., use layer_norm before the first block.
Args:
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multiheadattention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
activation: the activation function of intermediate layer, relu or gelu (default=relu).
normalize_before: whether to use layer_norm before the first block.
Examples::
>>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8)
>>> src = torch.rand(10, 32, 512)
>>> out = encoder_layer(src)
"""
def __init__(self, d_model: 'int', nhead: 'int', dim_feedforward: 'int'
=2048, dropout: 'float'=0.1, activation: 'str'='relu',
normalize_before: 'bool'=True) ->None:
super(TransformerEncoderLayerNew, self).__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=0.0)
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
self.normalize_before = normalize_before
def __setstate__(self, state):
if 'activation' not in state:
state['activation'] = nn.functional.relu
super(TransformerEncoderLayerNew, self).__setstate__(state)
def forward(self, input_0):
primals_4 = self.self_attn.in_proj_weight
primals_5 = self.self_attn.in_proj_bias
primals_1 = self.self_attn.out_proj.weight
primals_2 = self.self_attn.out_proj.bias
primals_10 = self.linear1.weight
primals_11 = self.linear1.bias
primals_12 = self.linear2.weight
primals_3 = self.linear2.bias
primals_7 = self.norm1.weight
primals_8 = self.norm1.bias
primals_9 = self.norm2.weight
primals_13 = self.norm2.bias
primals_6 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13])
return output[0]
|
johnjosephmorgan/snowfall
|
TransformerEncoderLayer
| false | 12,761 |
[
"Apache-2.0"
] | 0 |
604d789c0aed035626d6745e6d7a427168063cae
|
https://github.com/johnjosephmorgan/snowfall/tree/604d789c0aed035626d6745e6d7a427168063cae
|
RMSE_log
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class RMSE_log(nn.Module):
def __init__(self):
super(RMSE_log, self).__init__()
def forward(self, fake, real):
if not fake.shape == real.shape:
_, _, H, W = real.shape
fake = F.upsample(fake, size=(H, W), mode='bilinear')
loss = torch.sqrt(torch.mean(torch.abs(torch.log(real) - torch.log(
fake)) ** 2))
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_abs_log_mean_pow_sqrt_sub_0(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp2 = tl.load(in_ptr1 + r0, None)
tmp1 = tl_math.log(tmp0)
tmp3 = tl_math.log(tmp2)
tmp4 = tmp1 - tmp3
tmp5 = tl_math.abs(tmp4)
tmp6 = tmp5 * tmp5
tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
tmp10 = 256.0
tmp11 = tmp9 / tmp10
tmp12 = libdevice.sqrt(tmp11)
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp12, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_abs_log_mean_pow_sqrt_sub_0[grid(1)](buf1, arg1_1,
arg0_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class RMSE_logNew(nn.Module):
def __init__(self):
super(RMSE_logNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
d4l3k/crowds
|
RMSE_log
| false | 12,237 |
[
"MIT"
] | 0 |
a57eee80d66498474c86cec22dd77be9d627ad97
|
https://github.com/d4l3k/crowds/tree/a57eee80d66498474c86cec22dd77be9d627ad97
|
SimpleTanhModel
|
import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleTanhModel(torch.nn.Module):
def __init__(self, inplace=False):
super(SimpleTanhModel, self).__init__()
self.inplace = inplace
def forward(self, tensor):
tensor = tensor + tensor
return tensor.tanh_() if self.inplace else tensor.tanh()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_tanh_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tmp0 + tmp0
tmp2 = libdevice.tanh(tmp1)
tl.store(out_ptr0 + x0, tmp2, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_tanh_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class SimpleTanhModelNew(torch.nn.Module):
def __init__(self, inplace=False):
super(SimpleTanhModelNew, self).__init__()
self.inplace = inplace
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
andreas-hommel/glow
|
SimpleTanhModel
| false | 3,357 |
[
"Apache-2.0"
] | 0 |
2bbbf8188a2a941e85677c83f2146bbd076a262e
|
https://github.com/andreas-hommel/glow/tree/2bbbf8188a2a941e85677c83f2146bbd076a262e
|
VAE
|
import torch
from torch import nn
import torch.utils.data
from torch.nn import functional as F
import torch.cuda
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Linear(784, 400)
self.fc21 = nn.Linear(400, 20)
self.fc22 = nn.Linear(400, 20)
self.fc3 = nn.Linear(20, 400)
self.fc4 = nn.Linear(400, 784)
def encode(self, x):
h1 = F.relu(self.fc1(x))
return self.fc21(h1), self.fc22(h1)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z):
h3 = F.relu(self.fc3(z))
return torch.sigmoid(self.fc4(h3))
def forward(self, x):
mu, logvar = self.encode(x.view(-1, 784))
z = self.reparameterize(mu, logvar)
return self.decode(z), mu, logvar
def get_inputs():
return [torch.rand([4, 784])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.utils.data
from torch.nn import functional as F
import torch.cuda
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 400
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_add_exp_mul_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 80
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask)
tmp2 = tl.load(in_ptr2 + x0, xmask)
tmp3 = 0.5
tmp4 = tmp2 * tmp3
tmp5 = tl_math.exp(tmp4)
tmp6 = tmp1 * tmp5
tmp7 = tmp0 + tmp6
tl.store(out_ptr0 + x0, tmp7, xmask)
@triton.jit
def triton_poi_fused_sigmoid_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 3136
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 784
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.sigmoid(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (4, 784), (784, 1))
assert_size_stride(primals_2, (400, 784), (784, 1))
assert_size_stride(primals_3, (400,), (1,))
assert_size_stride(primals_4, (20, 400), (400, 1))
assert_size_stride(primals_5, (20,), (1,))
assert_size_stride(primals_6, (20, 400), (400, 1))
assert_size_stride(primals_7, (20,), (1,))
assert_size_stride(primals_8, (400, 20), (20, 1))
assert_size_stride(primals_9, (400,), (1,))
assert_size_stride(primals_10, (784, 400), (400, 1))
assert_size_stride(primals_11, (784,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (784,
400), (1, 784), 0), out=buf0)
del primals_2
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_relu_0[grid(1600)](buf1, primals_3, 1600, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
(400, 20), (1, 400), 0), alpha=1, beta=1, out=buf2)
del primals_5
buf3 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
extern_kernels.addmm(primals_7, buf1, reinterpret_tensor(primals_6,
(400, 20), (1, 400), 0), alpha=1, beta=1, out=buf3)
del primals_7
buf4 = torch.ops.aten.randn.default([4, 20], dtype=torch.float32,
device=device(type='cuda', index=0), pin_memory=False)
buf5 = buf4
del buf4
buf6 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
triton_poi_fused_add_exp_mul_1[grid(80)](buf2, buf5, buf3, buf6, 80,
XBLOCK=128, num_warps=4, num_stages=1)
buf7 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
extern_kernels.mm(buf6, reinterpret_tensor(primals_8, (20, 400), (1,
20), 0), out=buf7)
buf8 = buf7
del buf7
triton_poi_fused_relu_0[grid(1600)](buf8, primals_9, 1600, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_9
buf9 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
extern_kernels.mm(buf8, reinterpret_tensor(primals_10, (400, 784),
(1, 400), 0), out=buf9)
buf10 = buf9
del buf9
triton_poi_fused_sigmoid_2[grid(3136)](buf10, primals_11, 3136,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_11
return (buf10, buf2, buf3, primals_1, buf1, buf3, buf5, buf6, buf8,
buf10, primals_10, primals_8, primals_6, primals_4)
class VAENew(nn.Module):
def __init__(self):
super(VAENew, self).__init__()
self.fc1 = nn.Linear(784, 400)
self.fc21 = nn.Linear(400, 20)
self.fc22 = nn.Linear(400, 20)
self.fc3 = nn.Linear(20, 400)
self.fc4 = nn.Linear(400, 784)
def encode(self, x):
h1 = F.relu(self.fc1(x))
return self.fc21(h1), self.fc22(h1)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def decode(self, z):
h3 = F.relu(self.fc3(z))
return torch.sigmoid(self.fc4(h3))
def forward(self, input_0):
primals_2 = self.fc1.weight
primals_3 = self.fc1.bias
primals_4 = self.fc21.weight
primals_5 = self.fc21.bias
primals_6 = self.fc22.weight
primals_7 = self.fc22.bias
primals_8 = self.fc3.weight
primals_9 = self.fc3.bias
primals_10 = self.fc4.weight
primals_11 = self.fc4.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0], output[1], output[2]
|
Code-Cornelius/libraries
|
VAE
| false | 5,032 |
[
"MIT"
] | 1 |
2ebd5f78dcedfdce1416280d7d40de7691906951
|
https://github.com/Code-Cornelius/libraries/tree/2ebd5f78dcedfdce1416280d7d40de7691906951
|
Network
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Network(nn.Module):
def __init__(self, input_size, nb_action):
super(Network, self).__init__()
self.input_size = input_size
self.nb_action = nb_action
self.fc1 = nn.Linear(input_size, 30)
self.fc2 = nn.Linear(30, nb_action)
def forward(self, state):
x = F.relu(self.fc1(state))
q_values = self.fc2(x)
return q_values
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'nb_action': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 1920
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 30
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (30, 4), (4, 1))
assert_size_stride(primals_2, (30,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 30), (30, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 30), (30, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 30), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 30), (480, 120, 30, 1), 0)
del buf0
buf3 = empty_strided_cuda((4, 4, 4, 30), (480, 120, 30, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(1920)](buf1,
primals_2, buf3, 1920, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 30),
(30, 1), 0), reinterpret_tensor(primals_4, (30, 4), (1, 30), 0),
alpha=1, beta=1, out=buf2)
del primals_5
return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 30), (30, 1), 0), primals_4, buf3
class NetworkNew(nn.Module):
def __init__(self, input_size, nb_action):
super(NetworkNew, self).__init__()
self.input_size = input_size
self.nb_action = nb_action
self.fc1 = nn.Linear(input_size, 30)
self.fc2 = nn.Linear(30, nb_action)
def forward(self, input_0):
primals_1 = self.fc1.weight
primals_2 = self.fc1.bias
primals_4 = self.fc2.weight
primals_5 = self.fc2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
ExileExodus/Deep-Reinforcement-Learning
|
Network
| false | 9,014 |
[
"MIT"
] | 0 |
0007e5c4b74e920c250a15c18762966e1b55c17d
|
https://github.com/ExileExodus/Deep-Reinforcement-Learning/tree/0007e5c4b74e920c250a15c18762966e1b55c17d
|
BatchDHCN
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
import torch.utils.data
import torch.optim
class BatchDHCN(nn.Module):
"""docstring for BatchDHCN"""
def __init__(self, embed_size=512, output_size=512, num_channel=2,
conv_size=3, batch_norm=True):
super(BatchDHCN, self).__init__()
self.batch_norm = batch_norm
self.embed_size = embed_size
self.output_size = output_size
self.num_channel = num_channel
self.padding = nn.ZeroPad2d((0, conv_size - 1, conv_size - 1, 0))
self.conv_1 = nn.Conv2d(self.num_channel, self.output_size, (
conv_size, conv_size))
self.dropout = nn.Dropout(p=0.5)
def forward(self, x):
x_conv_1 = self.conv_1(self.padding(x))
x_conv_1 = F.relu(x_conv_1)
return x_conv_1
def get_inputs():
return [torch.rand([4, 2, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils
import torch.utils.data
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 2
y1 = yindex // 2
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 2 * x2 + 18 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_constant_pad_nd_1(in_ptr0, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 8
xnumel = 36
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x3 = xindex // 6
x2 = xindex % 6
y4 = yindex
x5 = xindex
y0 = yindex % 2
y1 = yindex // 2
tmp0 = -2 + x3
tmp1 = tl.full([1, 1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = x2
tmp4 = tl.full([1, 1], 4, tl.int64)
tmp5 = tmp3 < tmp4
tmp6 = tmp2 & tmp5
tmp7 = tl.load(in_ptr0 + (-8 + x2 + 4 * x3 + 16 * y4), tmp6 & xmask &
ymask, eviction_policy='evict_last', other=0.0)
tl.store(out_ptr0 + (y0 + 2 * x5 + 72 * y1), tmp7, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_ptr0, in_ptr1,
out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
constexpr):
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 512
y1 = yindex // 512
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 512 * x2 + 8192 * y1), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1, 1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x2 + 16 * y3), tmp4, xmask)
tl.store(out_ptr1 + (y0 + 512 * x2 + 8192 * y1), tmp6, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 2, 4, 4), (32, 16, 4, 1))
assert_size_stride(primals_2, (512, 2, 3, 3), (18, 9, 3, 1))
assert_size_stride(primals_3, (512,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((512, 2, 3, 3), (18, 1, 6, 2), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(1024, 9)](primals_2, buf0, 1024, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_2
buf1 = empty_strided_cuda((4, 2, 6, 6), (72, 1, 12, 2), torch.float32)
triton_poi_fused_constant_pad_nd_1[grid(8, 36)](primals_1, buf1, 8,
36, XBLOCK=32, YBLOCK=8, num_warps=4, num_stages=1)
del primals_1
buf2 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 512, 4, 4), (8192, 1, 2048, 512))
buf3 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.
float32)
buf4 = empty_strided_cuda((4, 512, 4, 4), (8192, 1, 2048, 512),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_2[grid(2048, 16)](
buf2, primals_3, buf3, buf4, 2048, 16, XBLOCK=16, YBLOCK=64,
num_warps=4, num_stages=1)
del buf2
del primals_3
return buf3, buf0, buf1, buf4
class BatchDHCNNew(nn.Module):
"""docstring for BatchDHCN"""
def __init__(self, embed_size=512, output_size=512, num_channel=2,
conv_size=3, batch_norm=True):
super(BatchDHCNNew, self).__init__()
self.batch_norm = batch_norm
self.embed_size = embed_size
self.output_size = output_size
self.num_channel = num_channel
self.padding = nn.ZeroPad2d((0, conv_size - 1, conv_size - 1, 0))
self.conv_1 = nn.Conv2d(self.num_channel, self.output_size, (
conv_size, conv_size))
self.dropout = nn.Dropout(p=0.5)
def forward(self, input_0):
primals_2 = self.conv_1.weight
primals_3 = self.conv_1.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
deeplearning2020/self
|
BatchDHCN
| false | 1,830 |
[
"MIT"
] | 0 |
cf0e6f9acdcfe17906c6327042d25ac9c8894885
|
https://github.com/deeplearning2020/self/tree/cf0e6f9acdcfe17906c6327042d25ac9c8894885
|
_MultipleInputNetwork
|
import torch
import torch.nn as _nn
class _MultipleInputNetwork(_nn.Module):
def __init__(self):
super(_MultipleInputNetwork, self).__init__()
self.conv = _nn.Conv2d(3, 16, 3)
def forward(self, inp1, inp2):
inp = inp1 * inp2
out = self.conv(inp)
return out
def get_inputs():
return [torch.rand([4, 3, 64, 64]), torch.rand([4, 3, 64, 64])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as _nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, None)
tmp1 = tl.load(in_ptr1 + x0, None)
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x0, tmp2, None)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 246016
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 3844 % 16
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_2, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_3, (16, 3, 3, 3), (27, 9, 3, 1))
assert_size_stride(primals_4, (16,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_0[grid(49152)](primals_1, primals_2, buf0,
49152, XBLOCK=256, num_warps=4, num_stages=1)
del primals_1
del primals_2
buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 16, 62, 62), (61504, 3844, 62, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_1[grid(246016)](buf2, primals_4,
246016, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_4
return buf2, primals_3, buf0
class _MultipleInputNetworkNew(_nn.Module):
def __init__(self):
super(_MultipleInputNetworkNew, self).__init__()
self.conv = _nn.Conv2d(3, 16, 3)
def forward(self, input_0, input_1):
primals_3 = self.conv.weight
primals_4 = self.conv.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
Yifanfanfanfan/torchutils
|
_MultipleInputNetwork
| false | 18,129 |
[
"MIT"
] | 9 |
939331d28fcee97bfb0a4b2eaab8e799877fb0dc
|
https://github.com/Yifanfanfanfan/torchutils/tree/939331d28fcee97bfb0a4b2eaab8e799877fb0dc
|
UpsampleConv
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_7/inductor_cache/hg/chgxzii65iyfdwuwk6btc2ievya3atcd4si6chvog45v2d5sekhj.py
# Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.pixel_shuffle]
# Source node to ATen node mapping:
# output_1 => clone_2
# Graph fragment:
# %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_pixel_shuffle_0 = async_compile.triton('triton_poi_fused_pixel_shuffle_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_pixel_shuffle_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_pixel_shuffle_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 2
x1 = (xindex // 2) % 4
x2 = (xindex // 8) % 2
x3 = (xindex // 16) % 4
x5 = (xindex // 256)
x6 = xindex
tmp0 = tl.load(in_ptr0 + (x1 + (4*x3) + (16*x0) + (32*x2) + (64*x5)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x6), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_7/inductor_cache/mt/cmt4roffhwfg6vw2odjfrgu4bjav3cztqx74kxjfq5igljucibfl.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# conv2d => convolution
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view_2, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 64) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 2, 4, 2), (256, 64, 16, 8, 2, 1), torch.float32)
# Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.pixel_shuffle]
stream0 = get_raw_stream(0)
triton_poi_fused_pixel_shuffle_0.run(primals_1, buf0, 1024, grid=grid(1024), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (4, 4, 8, 8), (256, 64, 8, 1), 0), primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1))
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf2, primals_3, 1024, grid=grid(1024), stream=stream0)
del primals_3
return (buf2, primals_2, reinterpret_tensor(buf0, (4, 4, 8, 8), (256, 64, 8, 1), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_pixel_shuffle_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 2
x1 = xindex // 2 % 4
x2 = xindex // 8 % 2
x3 = xindex // 16 % 4
x5 = xindex // 256
x6 = xindex
tmp0 = tl.load(in_ptr0 + (x1 + 4 * x3 + 16 * x0 + 32 * x2 + 64 * x5),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x6, tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 64 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 2, 4, 2), (256, 64, 16, 8, 2, 1
), torch.float32)
get_raw_stream(0)
triton_poi_fused_pixel_shuffle_0[grid(1024)](primals_1, buf0, 1024,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (4, 4, 8,
8), (256, 64, 8, 1), 0), primals_2, stride=(1, 1), padding=(1,
1), dilation=(1, 1), transposed=False, output_padding=(0, 0),
groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_1[grid(1024)](buf2, primals_3, 1024,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
return buf2, primals_2, reinterpret_tensor(buf0, (4, 4, 8, 8), (256, 64,
8, 1), 0)
def spectral_norm(layer, n_iters=1):
return torch.nn.utils.spectral_norm(layer, n_power_iterations=n_iters)
class UpsampleConvNew(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size=3, biases=True,
spec_norm=False):
super().__init__()
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
padding=kernel_size // 2, bias=biases)
if spec_norm:
self.conv = spectral_norm(self.conv)
self.pixelshuffle = nn.PixelShuffle(upscale_factor=2)
def forward(self, input_0):
primals_2 = self.conv.weight
primals_3 = self.conv.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Sriram-Ravula/Inverse_Meta
|
UpsampleConv
| false | 2,851 |
[
"MIT"
] | 0 |
c6c1e4ae0d670093156249c60d74373b22d61f01
|
https://github.com/Sriram-Ravula/Inverse_Meta/tree/c6c1e4ae0d670093156249c60d74373b22d61f01
|
CoralLayer
|
import torch
class CoralLayer(torch.nn.Module):
""" Implements CORAL layer described in
Cao, Mirjalili, and Raschka (2020)
*Rank Consistent Ordinal Regression for Neural Networks
with Application to Age Estimation*
Pattern Recognition Letters, https://doi.org/10.1016/j.patrec.2020.11.008
Parameters
-----------
size_in : int
Number of input features for the inputs to the forward method, which
are expected to have shape=(num_examples, num_features).
num_classes : int
Number of classes in the dataset.
"""
def __init__(self, size_in, num_classes):
super().__init__()
self.size_in, self.size_out = size_in, 1
self.coral_weights = torch.nn.Linear(self.size_in, 1, bias=False)
self.coral_bias = torch.nn.Parameter(torch.zeros(num_classes - 1).
float())
def forward(self, x):
"""
Computes forward pass.
Parameters
-----------
x : torch.tensor, shape=(num_examples, num_features)
Input features.
Returns
-----------
logits : torch.tensor, shape=(num_examples, num_classes-1)
"""
return self.coral_weights(x) + self.coral_bias
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'size_in': 4, 'num_classes': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 3
x0 = xindex % 3
x2 = xindex
tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + x2, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (1, 4), (4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (3,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4, 4, 3), (48, 12, 3, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_0[grid(192)](buf0, primals_3, buf1, 192,
XBLOCK=256, num_warps=4, num_stages=1)
del buf0
del primals_3
return buf1, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0)
class CoralLayerNew(torch.nn.Module):
""" Implements CORAL layer described in
Cao, Mirjalili, and Raschka (2020)
*Rank Consistent Ordinal Regression for Neural Networks
with Application to Age Estimation*
Pattern Recognition Letters, https://doi.org/10.1016/j.patrec.2020.11.008
Parameters
-----------
size_in : int
Number of input features for the inputs to the forward method, which
are expected to have shape=(num_examples, num_features).
num_classes : int
Number of classes in the dataset.
"""
def __init__(self, size_in, num_classes):
super().__init__()
self.size_in, self.size_out = size_in, 1
self.coral_weights = torch.nn.Linear(self.size_in, 1, bias=False)
self.coral_bias = torch.nn.Parameter(torch.zeros(num_classes - 1).
float())
def forward(self, input_0):
primals_3 = self.coral_bias
primals_1 = self.coral_weights.weight
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
CHNxindong/corn-ordinal-neuralnet
|
CoralLayer
| false | 4,933 |
[
"MIT"
] | 1 |
7f8a45614cb6488e9c019c5e9d3a5aee0d714e94
|
https://github.com/CHNxindong/corn-ordinal-neuralnet/tree/7f8a45614cb6488e9c019c5e9d3a5aee0d714e94
|
L2
|
import torch
import torch.utils.data
import torch
import torch.nn as nn
class L2(nn.Module):
def __init__(self):
super(L2, self).__init__()
def forward(self, output, target):
lossvalue = torch.norm(output - target, p=2, dim=1).mean()
return lossvalue
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_linalg_vector_norm_mean_sub_0(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex % 16
r1 = rindex // 16
tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
tmp4 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
tmp5 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
tmp9 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
tmp10 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
tmp14 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
tmp15 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp6 = tmp4 - tmp5
tmp7 = tmp6 * tmp6
tmp8 = tmp3 + tmp7
tmp11 = tmp9 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tmp8 + tmp12
tmp16 = tmp14 - tmp15
tmp17 = tmp16 * tmp16
tmp18 = tmp13 + tmp17
tmp19 = libdevice.sqrt(tmp18)
tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
tmp22 = tl.sum(tmp20, 1)[:, None]
tmp23 = 64.0
tmp24 = tmp22 / tmp23
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp24, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_linalg_vector_norm_mean_sub_0[grid(1)](buf1,
arg0_1, arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class L2New(nn.Module):
def __init__(self):
super(L2New, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
WeisiX/ITAS3D
|
L2
| false | 18,111 |
[
"MIT"
] | 4 |
fc861e0cb2d4516905bfadab5e5e880c2b021832
|
https://github.com/WeisiX/ITAS3D/tree/fc861e0cb2d4516905bfadab5e5e880c2b021832
|
Net
|
import torch
from torch import Tensor
from torch.functional import Tensor
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 60, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(60, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x: 'Tensor'):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def get_inputs():
return [torch.rand([4, 3, 32, 32])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 188160
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 784 % 60
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 47040
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 14
x3 = xindex // 14
x2 = xindex // 11760
x4 = xindex % 11760
tmp0 = tl.load(in_ptr0 + (2 * x0 + 56 * x3), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 56 * x3), xmask, eviction_policy
='evict_last')
tmp3 = tl.load(in_ptr0 + (28 + 2 * x0 + 56 * x3), xmask,
eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (29 + 2 * x0 + 56 * x3), xmask,
eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + (x4 + 11776 * x2), tmp6, xmask)
tl.store(out_ptr1 + (x4 + 11776 * x2), tmp16, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 6400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 100 % 16
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 5
x1 = xindex // 5
x2 = xindex
tmp0 = tl.load(in_ptr0 + (2 * x0 + 20 * x1), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 20 * x1), xmask, eviction_policy
='evict_last')
tmp7 = tl.load(in_ptr0 + (10 + 2 * x0 + 20 * x1), xmask,
eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (11 + 2 * x0 + 20 * x1), xmask,
eviction_policy='evict_last')
tmp2 = tmp1 > tmp0
tmp3 = tl.full([1], 1, tl.int8)
tmp4 = tl.full([1], 0, tl.int8)
tmp5 = tl.where(tmp2, tmp3, tmp4)
tmp6 = triton_helpers.maximum(tmp1, tmp0)
tmp8 = tmp7 > tmp6
tmp9 = tl.full([1], 2, tl.int8)
tmp10 = tl.where(tmp8, tmp9, tmp5)
tmp11 = triton_helpers.maximum(tmp7, tmp6)
tmp13 = tmp12 > tmp11
tmp14 = tl.full([1], 3, tl.int8)
tmp15 = tl.where(tmp13, tmp14, tmp10)
tmp16 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x2, tmp15, xmask)
tl.store(out_ptr1 + x2, tmp16, xmask)
@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 480
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 120
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 336
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 84
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (60, 3, 5, 5), (75, 25, 5, 1))
assert_size_stride(primals_2, (60,), (1,))
assert_size_stride(primals_3, (4, 3, 32, 32), (3072, 1024, 32, 1))
assert_size_stride(primals_4, (16, 60, 5, 5), (1500, 25, 5, 1))
assert_size_stride(primals_5, (16,), (1,))
assert_size_stride(primals_6, (120, 400), (400, 1))
assert_size_stride(primals_7, (120,), (1,))
assert_size_stride(primals_8, (84, 120), (120, 1))
assert_size_stride(primals_9, (84,), (1,))
assert_size_stride(primals_10, (10, 84), (84, 1))
assert_size_stride(primals_11, (10,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 60, 28, 28), (47040, 784, 28, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(188160)](buf1, primals_2,
188160, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((4, 60, 14, 14), (11776, 196, 14, 1),
torch.float32)
buf3 = empty_strided_cuda((4, 60, 14, 14), (11776, 196, 14, 1),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_1[grid(47040)](buf1, buf2,
buf3, 47040, XBLOCK=512, num_warps=4, num_stages=1)
buf4 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 16, 10, 10), (1600, 100, 10, 1))
buf5 = buf4
del buf4
triton_poi_fused_convolution_relu_2[grid(6400)](buf5, primals_5,
6400, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf6 = empty_strided_cuda((4, 16, 5, 5), (400, 25, 5, 1), torch.int8)
buf7 = empty_strided_cuda((4, 16, 5, 5), (400, 25, 5, 1), torch.float32
)
triton_poi_fused_max_pool2d_with_indices_3[grid(1600)](buf5, buf6,
buf7, 1600, XBLOCK=128, num_warps=4, num_stages=1)
buf8 = empty_strided_cuda((4, 120), (120, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf7, (4, 400), (400, 1), 0),
reinterpret_tensor(primals_6, (400, 120), (1, 400), 0), out=buf8)
buf9 = buf8
del buf8
triton_poi_fused_relu_4[grid(480)](buf9, primals_7, 480, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_7
buf10 = empty_strided_cuda((4, 84), (84, 1), torch.float32)
extern_kernels.mm(buf9, reinterpret_tensor(primals_8, (120, 84), (1,
120), 0), out=buf10)
buf11 = buf10
del buf10
triton_poi_fused_relu_5[grid(336)](buf11, primals_9, 336, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_9
buf12 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_11, buf11, reinterpret_tensor(
primals_10, (84, 10), (1, 84), 0), alpha=1, beta=1, out=buf12)
del primals_11
return (buf12, primals_1, primals_3, primals_4, buf1, buf2, buf3, buf5,
buf6, reinterpret_tensor(buf7, (4, 400), (400, 1), 0), buf9, buf11,
primals_10, primals_8, primals_6)
class NetNew(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 60, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(60, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.fc1.weight
primals_7 = self.fc1.bias
primals_8 = self.fc2.weight
primals_9 = self.fc2.bias
primals_10 = self.fc3.weight
primals_11 = self.fc3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0]
|
minister19/RL_pytorch_get_started
|
Net
| false | 4,014 |
[
"MIT"
] | 0 |
e444f524a14d329f9a25c53f102bc96c4ea36ad8
|
https://github.com/minister19/RL_pytorch_get_started/tree/e444f524a14d329f9a25c53f102bc96c4ea36ad8
|
NormScaleFeature
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_8/inductor_cache/z3/cz33rq7gb3mccgzwpt6uvrtpcvtka4pm6wvkq7bnb5vj2gtsqkxk.py
# Topologically Sorted Source Nodes: [pow_1, sum_1, sqrt, magnitudes, mul, output], Original ATen: [aten.pow, aten.sum, aten.sqrt, aten.add, aten.mul, aten.div]
# Source node to ATen node mapping:
# magnitudes => add
# mul => mul
# output => div
# pow_1 => pow_1
# sqrt => sqrt
# sum_1 => sum_1
# Graph fragment:
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_1, 2), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1], True), kwargs = {})
# %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_1,), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-06), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %primals_1), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add), kwargs = {})
triton_poi_fused_add_div_mul_pow_sqrt_sum_0 = async_compile.triton('triton_poi_fused_add_div_mul_pow_sqrt_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mul_pow_sqrt_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_mul_pow_sqrt_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = (xindex // 64)
tmp0 = tl.load(in_ptr0 + (0))
tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
tmp2 = tl.load(in_ptr1 + (x3), xmask)
tmp4 = tl.load(in_ptr1 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr1 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr1 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr1 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp3 = tmp1 * tmp2
tmp5 = tmp4 * tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp13 = tmp12 * tmp12
tmp14 = tmp11 + tmp13
tmp15 = libdevice.sqrt(tmp14)
tmp16 = 1e-06
tmp17 = tmp15 + tmp16
tmp18 = tmp3 / tmp17
tl.store(out_ptr0 + (x3), tmp18, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [pow_1, sum_1, sqrt, magnitudes, mul, output], Original ATen: [aten.pow, aten.sum, aten.sqrt, aten.add, aten.mul, aten.div]
stream0 = get_raw_stream(0)
triton_poi_fused_add_div_mul_pow_sqrt_sum_0.run(primals_2, primals_1, buf0, 256, grid=grid(256), stream=stream0)
del primals_2
return (buf0, primals_1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_div_mul_pow_sqrt_sum_0(in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + 0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
tmp2 = tl.load(in_ptr1 + x3, xmask)
tmp4 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp12 = tl.load(in_ptr1 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tmp1 * tmp2
tmp5 = tmp4 * tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp13 = tmp12 * tmp12
tmp14 = tmp11 + tmp13
tmp15 = libdevice.sqrt(tmp14)
tmp16 = 1e-06
tmp17 = tmp15 + tmp16
tmp18 = tmp3 / tmp17
tl.store(out_ptr0 + x3, tmp18, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_div_mul_pow_sqrt_sum_0[grid(256)](primals_2,
primals_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
return buf0, primals_1
class NormScaleFeatureNew(nn.Module):
def __init__(self, init_value=1):
super().__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input_0):
primals_2 = self.scale
primals_1 = input_0
output = call([primals_1, primals_2])
return output[0]
|
uncbiag/FeatureMapICON
|
NormScaleFeature
| false | 10,874 |
[
"Apache-2.0"
] | 0 |
04160d0ce4e8f7615e1c59a1be5c6b8340b5b6e5
|
https://github.com/uncbiag/FeatureMapICON/tree/04160d0ce4e8f7615e1c59a1be5c6b8340b5b6e5
|
BertOutput
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/hk/chkxni526sa46elz3ztn5esaetlclniw4nbm3ra7wq5sova5mkd5.py
# Topologically Sorted Source Nodes: [add, u], Original ATen: [aten.add, aten.mean]
# Source node to ATen node mapping:
# add => add
# u => mean
# Graph fragment:
# %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %primals_4), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add, [-1], True), kwargs = {})
triton_poi_fused_add_mean_0 = async_compile.triton('triton_poi_fused_add_mean_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mean_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mean_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (0))
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp4 = tl.load(in_ptr2 + (4*x0), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (1))
tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
tmp10 = tl.load(in_ptr2 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr1 + (2))
tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
tmp17 = tl.load(in_ptr2 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp20 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp21 = tl.load(in_ptr1 + (3))
tmp22 = tl.broadcast_to(tmp21, [XBLOCK])
tmp24 = tl.load(in_ptr2 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp3 = tmp0 + tmp2
tmp5 = tmp3 + tmp4
tmp9 = tmp6 + tmp8
tmp11 = tmp9 + tmp10
tmp12 = tmp5 + tmp11
tmp16 = tmp13 + tmp15
tmp18 = tmp16 + tmp17
tmp19 = tmp12 + tmp18
tmp23 = tmp20 + tmp22
tmp25 = tmp23 + tmp24
tmp26 = tmp19 + tmp25
tmp27 = 4.0
tmp28 = tmp26 / tmp27
tl.store(out_ptr0 + (x0), tmp28, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/2a/c2atmp2erpyl7if7c6pxfntsmuvqm7hau7e33v4asqtjrqk6cihx.py
# Topologically Sorted Source Nodes: [add, sub], Original ATen: [aten.add, aten.sub]
# Source node to ATen node mapping:
# add => add
# sub => sub
# Graph fragment:
# %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %primals_4), kwargs = {})
# %sub : [num_users=3] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mean), kwargs = {})
triton_poi_fused_add_sub_1 = async_compile.triton('triton_poi_fused_add_sub_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_sub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_sub_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
x1 = (xindex // 4)
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (x2), xmask)
tmp5 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 - tmp5
tl.store(in_out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/gb/cgbyxsu2ykccfnkicedqiouveki57g42dz4fqztnk7wx3624yuma.py
# Topologically Sorted Source Nodes: [pow_1, s, add_1, sqrt, x, mul, hidden_states_2], Original ATen: [aten.pow, aten.mean, aten.add, aten.sqrt, aten.div, aten.mul]
# Source node to ATen node mapping:
# add_1 => add_1
# hidden_states_2 => add_2
# mul => mul
# pow_1 => pow_1
# s => mean_1
# sqrt => sqrt
# x => div
# Graph fragment:
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [-1], True), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_1, 1), kwargs = {})
# %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add_1,), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %sqrt), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_5, %div), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_6), kwargs = {})
triton_poi_fused_add_div_mean_mul_pow_sqrt_2 = async_compile.triton('triton_poi_fused_add_div_mean_mul_pow_sqrt_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_mul_pow_sqrt_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_mean_mul_pow_sqrt_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), xmask)
tmp2 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp20 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
tmp3 = tmp2 * tmp2
tmp5 = tmp4 * tmp4
tmp6 = tmp3 + tmp5
tmp8 = tmp7 * tmp7
tmp9 = tmp6 + tmp8
tmp11 = tmp10 * tmp10
tmp12 = tmp9 + tmp11
tmp13 = 4.0
tmp14 = tmp12 / tmp13
tmp15 = 1.0
tmp16 = tmp14 + tmp15
tmp17 = libdevice.sqrt(tmp16)
tmp18 = tmp1 / tmp17
tmp19 = tmp0 * tmp18
tmp21 = tmp19 + tmp20
tl.store(out_ptr0 + (x2), tmp21, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
# Topologically Sorted Source Nodes: [add, u], Original ATen: [aten.add, aten.mean]
stream0 = get_raw_stream(0)
triton_poi_fused_add_mean_0.run(buf0, primals_2, primals_4, buf1, 64, grid=grid(64), stream=stream0)
buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse
# Topologically Sorted Source Nodes: [add, sub], Original ATen: [aten.add, aten.sub]
triton_poi_fused_add_sub_1.run(buf2, primals_2, primals_4, buf1, 256, grid=grid(256), stream=stream0)
del buf1
del primals_2
del primals_4
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [pow_1, s, add_1, sqrt, x, mul, hidden_states_2], Original ATen: [aten.pow, aten.mean, aten.add, aten.sqrt, aten.div, aten.mul]
triton_poi_fused_add_div_mean_mul_pow_sqrt_2.run(primals_5, buf2, primals_6, buf3, 256, grid=grid(256), stream=stream0)
del primals_6
return (buf3, primals_5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_mean_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp4 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + 1)
tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
tmp10 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp14 = tl.load(in_ptr1 + 2)
tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
tmp17 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp21 = tl.load(in_ptr1 + 3)
tmp22 = tl.broadcast_to(tmp21, [XBLOCK])
tmp24 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp3 = tmp0 + tmp2
tmp5 = tmp3 + tmp4
tmp9 = tmp6 + tmp8
tmp11 = tmp9 + tmp10
tmp12 = tmp5 + tmp11
tmp16 = tmp13 + tmp15
tmp18 = tmp16 + tmp17
tmp19 = tmp12 + tmp18
tmp23 = tmp20 + tmp22
tmp25 = tmp23 + tmp24
tmp26 = tmp19 + tmp25
tmp27 = 4.0
tmp28 = tmp26 / tmp27
tl.store(out_ptr0 + x0, tmp28, xmask)
@triton.jit
def triton_poi_fused_add_sub_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
x1 = xindex // 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x2, xmask)
tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 - tmp5
tl.store(in_out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_div_mean_mul_pow_sqrt_2(in_ptr0, in_ptr1, in_ptr2,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp20 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp2 * tmp2
tmp5 = tmp4 * tmp4
tmp6 = tmp3 + tmp5
tmp8 = tmp7 * tmp7
tmp9 = tmp6 + tmp8
tmp11 = tmp10 * tmp10
tmp12 = tmp9 + tmp11
tmp13 = 4.0
tmp14 = tmp12 / tmp13
tmp15 = 1.0
tmp16 = tmp14 + tmp15
tmp17 = libdevice.sqrt(tmp16)
tmp18 = tmp1 / tmp17
tmp19 = tmp0 * tmp18
tmp21 = tmp19 + tmp20
tl.store(out_ptr0 + x2, tmp21, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_mean_0[grid(64)](buf0, primals_2, primals_4,
buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
triton_poi_fused_add_sub_1[grid(256)](buf2, primals_2, primals_4,
buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
del buf1
del primals_2
del primals_4
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_div_mean_mul_pow_sqrt_2[grid(256)](primals_5,
buf2, primals_6, buf3, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_6
return buf3, primals_5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf2
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps
def forward(self, x):
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
return self.weight * x + self.bias
class BertOutputNew(nn.Module):
def __init__(self, config):
super(BertOutputNew, self).__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.
layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, input_0, input_1):
primals_1 = self.dense.weight
primals_2 = self.dense.bias
primals_5 = self.LayerNorm.weight
primals_6 = self.LayerNorm.bias
primals_3 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
IsmaelElsharkawi/new_pororo_repo
|
BertOutput
| false | 8,811 |
[
"MIT"
] | 19 |
4617083b420615b8a3eb0f44d02e4e91a8f407f7
|
https://github.com/IsmaelElsharkawi/new_pororo_repo/tree/4617083b420615b8a3eb0f44d02e4e91a8f407f7
|
TransposeMultiheadAttention
|
import torch
import torch.nn as nn
from typing import Optional
import torch.utils.data
import torch.nn
class TransposeMultiheadAttention(nn.Module):
"""
Wrapper for nn.MultiheadAttention which first transposes the input tensor
from (batch_size, seq_len, feature_dim) to (seq_length, batch_size, feature_dim),
then applies the attention and transposes the attention outputs back to the input
shape.
"""
def __init__(self, feature_dim: 'int', num_heads: 'int'=1):
"""
Args:
feature_dim (int): attention embedding dimension
num_heads (int): number of attention heads
"""
super().__init__()
self._attention = nn.MultiheadAttention(embed_dim=feature_dim,
num_heads=num_heads)
self._attention_weights = None
@property
def attention_weights(self) ->Optional[torch.Tensor]:
"""
Contains attention weights from last forward call.
"""
return self._attention_weights
def forward(self, x: 'torch.Tensor', mask: 'Optional[torch.Tensor]'=None
) ->torch.Tensor:
"""
Args:
x (torch.Tensor): tensor of shape (batch_size, seq_len, feature_dim)
mask (torch.Tensor): bool tensor with shape (batch_size, seq_len).
Sequence elements that are False are invalid.
Returns:
Tensor with shape (batch_size, seq_len, feature_dim)
"""
assert x.dim(
) == 3, 'Requires x shape (batch_size x seq_len x feature_dim)'
if mask is not None:
mask[:, 0] = True
mask = ~mask
x = x.transpose(0, 1)
attn_output, self._attention_weights = self._attention(x, x, x,
key_padding_mask=mask)
attn_output = attn_output.transpose(0, 1)
return attn_output
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'feature_dim': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from typing import Optional
import torch.utils.data
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 16
x2 = xindex // 64
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 12 * x1), xmask)
tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_mul_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_mean_4(in_ptr0, out_ptr0, out_ptr1, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tmp9 = 1.0
tmp10 = tmp8 / tmp9
tl.store(out_ptr0 + x2, tmp8, xmask)
tl.store(out_ptr1 + x2, tmp10, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (12,), (1,))
assert_size_stride(primals_3, (12, 4), (4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_3, (4, 12), (1, 4), 0), out=buf1)
del primals_3
buf2 = empty_strided_cuda((3, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_1[grid(192)](buf1, primals_2, buf2, 192,
XBLOCK=256, num_warps=4, num_stages=1)
del buf1
del primals_2
buf3 = empty_strided_cuda((4, 4, 4), (4, 16, 1), torch.float32)
triton_poi_fused_mul_2[grid(64)](buf2, buf3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(buf3, reinterpret_tensor(buf2, (4, 4, 4), (4, 1,
16), 64), out=buf4)
buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__softmax_3[grid(64)](buf4, buf5, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf6 = buf4
del buf4
buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__softmax_mean_4[grid(64)](buf5, buf6, buf10, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf7 = buf5
del buf5
extern_kernels.bmm(buf6, reinterpret_tensor(buf2, (4, 4, 4), (4, 16,
1), 128), out=buf7)
buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_clone_0[grid(64)](buf7, buf8, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0)
del buf7
extern_kernels.addmm(primals_5, reinterpret_tensor(buf8, (16, 4), (
4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf9)
del primals_5
return reinterpret_tensor(buf9, (4, 4, 4), (4, 16, 1), 0
), buf10, reinterpret_tensor(buf0, (16, 4), (4, 1), 0
), buf6, reinterpret_tensor(buf8, (16, 4), (4, 1), 0
), primals_4, reinterpret_tensor(buf2, (4, 4, 4), (4, 1, 16), 128
), reinterpret_tensor(buf3, (4, 4, 4), (4, 1, 16), 0
), reinterpret_tensor(buf2, (4, 4, 4), (4, 16, 1), 64)
class TransposeMultiheadAttentionNew(nn.Module):
"""
Wrapper for nn.MultiheadAttention which first transposes the input tensor
from (batch_size, seq_len, feature_dim) to (seq_length, batch_size, feature_dim),
then applies the attention and transposes the attention outputs back to the input
shape.
"""
def __init__(self, feature_dim: 'int', num_heads: 'int'=1):
"""
Args:
feature_dim (int): attention embedding dimension
num_heads (int): number of attention heads
"""
super().__init__()
self._attention = nn.MultiheadAttention(embed_dim=feature_dim,
num_heads=num_heads)
self._attention_weights = None
@property
def attention_weights(self) ->Optional[torch.Tensor]:
"""
Contains attention weights from last forward call.
"""
return self._attention_weights
def forward(self, input_0):
primals_3 = self._attention.in_proj_weight
primals_2 = self._attention.in_proj_bias
primals_4 = self._attention.out_proj.weight
primals_5 = self._attention.out_proj.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
TheShadow29/pytorchvideo
|
TransposeMultiheadAttention
| false | 9,701 |
[
"Apache-2.0"
] | 0 |
39a3e34e33fb0e1ec142288df08f6e8c3585961a
|
https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a
|
AdjustSigmoid
|
from torch.nn import Module
import torch
from torch import Tensor
from typing import Optional
def KORNIA_CHECK_IS_TENSOR(x, msg: 'Optional[str]'=None):
if not isinstance(x, Tensor):
raise TypeError(f'Not a Tensor type. Got: {type(x)}.\n{msg}')
def adjust_sigmoid(image: 'Tensor', cutoff: 'float'=0.5, gain: 'float'=10,
inv: 'bool'=False) ->Tensor:
"""Adjust sigmoid correction on the input image tensor.
The input image is expected to be in the range of [0, 1].
Reference:
[1]: Gustav J. Braun, "Image Lightness Rescaling Using Sigmoidal Contrast Enhancement Functions",
http://markfairchild.org/PDFs/PAP07.pdf
Args:
image: Image to be adjusted in the shape of :math:`(*, H, W)`.
cutoff: The cutoff of sigmoid function.
gain: The multiplier of sigmoid function.
inv: If is set to True the function will return the inverse sigmoid correction.
Returns:
Adjusted tensor in the shape of :math:`(*, H, W)`.
Example:
>>> x = torch.ones(1, 1, 2, 2)
>>> adjust_sigmoid(x, gain=0)
tensor([[[[0.5000, 0.5000],
[0.5000, 0.5000]]]])
"""
KORNIA_CHECK_IS_TENSOR(image, 'Expected shape (*, H, W)')
if inv:
img_adjust = 1 - 1 / (1 + (gain * (cutoff - image)).exp())
else:
img_adjust = 1 / (1 + (gain * (cutoff - image)).exp())
return img_adjust
class AdjustSigmoid(Module):
"""Adjust the contrast of an image tensor or performs sigmoid correction on the input image tensor.
The input image is expected to be in the range of [0, 1].
Reference:
[1]: Gustav J. Braun, "Image Lightness Rescaling Using Sigmoidal Contrast Enhancement Functions",
http://markfairchild.org/PDFs/PAP07.pdf
Args:
image: Image to be adjusted in the shape of :math:`(*, H, W)`.
cutoff: The cutoff of sigmoid function.
gain: The multiplier of sigmoid function.
inv: If is set to True the function will return the negative sigmoid correction.
Example:
>>> x = torch.ones(1, 1, 2, 2)
>>> AdjustSigmoid(gain=0)(x)
tensor([[[[0.5000, 0.5000],
[0.5000, 0.5000]]]])
"""
def __init__(self, cutoff: 'float'=0.5, gain: 'float'=10, inv: 'bool'=False
) ->None:
super().__init__()
self.cutoff: 'float' = cutoff
self.gain: 'float' = gain
self.inv: 'bool' = inv
def forward(self, image: 'Tensor') ->Tensor:
return adjust_sigmoid(image, cutoff=self.cutoff, gain=self.gain,
inv=self.inv)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch.nn import Module
from torch import Tensor
from typing import Optional
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_exp_mul_reciprocal_rsub_0(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.5
tmp2 = tmp1 - tmp0
tmp3 = 10.0
tmp4 = tmp2 * tmp3
tmp5 = tl_math.exp(tmp4)
tmp6 = 1.0
tmp7 = tmp5 + tmp6
tmp8 = tl.full([1], 1, tl.int32)
tmp9 = tmp8 / tmp7
tmp10 = tmp9 * tmp6
tl.store(out_ptr0 + x0, tmp10, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_exp_mul_reciprocal_rsub_0[grid(256)](arg0_1,
buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
def KORNIA_CHECK_IS_TENSOR(x, msg: 'Optional[str]'=None):
if not isinstance(x, Tensor):
raise TypeError(f'Not a Tensor type. Got: {type(x)}.\n{msg}')
def adjust_sigmoid(image: 'Tensor', cutoff: 'float'=0.5, gain: 'float'=10,
inv: 'bool'=False) ->Tensor:
"""Adjust sigmoid correction on the input image tensor.
The input image is expected to be in the range of [0, 1].
Reference:
[1]: Gustav J. Braun, "Image Lightness Rescaling Using Sigmoidal Contrast Enhancement Functions",
http://markfairchild.org/PDFs/PAP07.pdf
Args:
image: Image to be adjusted in the shape of :math:`(*, H, W)`.
cutoff: The cutoff of sigmoid function.
gain: The multiplier of sigmoid function.
inv: If is set to True the function will return the inverse sigmoid correction.
Returns:
Adjusted tensor in the shape of :math:`(*, H, W)`.
Example:
>>> x = torch.ones(1, 1, 2, 2)
>>> adjust_sigmoid(x, gain=0)
tensor([[[[0.5000, 0.5000],
[0.5000, 0.5000]]]])
"""
KORNIA_CHECK_IS_TENSOR(image, 'Expected shape (*, H, W)')
if inv:
img_adjust = 1 - 1 / (1 + (gain * (cutoff - image)).exp())
else:
img_adjust = 1 / (1 + (gain * (cutoff - image)).exp())
return img_adjust
class AdjustSigmoidNew(Module):
"""Adjust the contrast of an image tensor or performs sigmoid correction on the input image tensor.
The input image is expected to be in the range of [0, 1].
Reference:
[1]: Gustav J. Braun, "Image Lightness Rescaling Using Sigmoidal Contrast Enhancement Functions",
http://markfairchild.org/PDFs/PAP07.pdf
Args:
image: Image to be adjusted in the shape of :math:`(*, H, W)`.
cutoff: The cutoff of sigmoid function.
gain: The multiplier of sigmoid function.
inv: If is set to True the function will return the negative sigmoid correction.
Example:
>>> x = torch.ones(1, 1, 2, 2)
>>> AdjustSigmoid(gain=0)(x)
tensor([[[[0.5000, 0.5000],
[0.5000, 0.5000]]]])
"""
def __init__(self, cutoff: 'float'=0.5, gain: 'float'=10, inv: 'bool'=False
) ->None:
super().__init__()
self.cutoff: 'float' = cutoff
self.gain: 'float' = gain
self.inv: 'bool' = inv
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
YanivHollander/kornia
|
AdjustSigmoid
| false | 14,628 |
[
"ECL-2.0",
"Apache-2.0"
] | 418 |
ccd258d0956da89b1feca96448eff8e4969d405a
|
https://github.com/YanivHollander/kornia/tree/ccd258d0956da89b1feca96448eff8e4969d405a
|
AdaIN
|
import math
import torch
import torch.nn as nn
from numpy import prod
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = prod(size[1:])
return math.sqrt(2.0 / fan_in)
class ConstrainedLayer(nn.Module):
"""
A handy refactor that allows the user to:
- initialize one layer's bias to zero
- apply He's initialization at runtime
"""
def __init__(self, module, equalized=True, lrMul=1.0, initBiasToZero=True):
"""
equalized (bool): if true, the layer's weight should evolve within
the range (-1, 1)
initBiasToZero (bool): if true, bias will be initialized to zero
"""
super(ConstrainedLayer, self).__init__()
self.module = module
self.equalized = equalized
if initBiasToZero:
self.module.bias.data.fill_(0)
if self.equalized:
self.module.weight.data.normal_(0, 1)
self.module.weight.data /= lrMul
self.weight = getLayerNormalizationFactor(self.module) * lrMul
def forward(self, x):
x = self.module(x)
if self.equalized:
x *= self.weight
return x
class EqualizedLinear(ConstrainedLayer):
def __init__(self, nChannelsPrevious, nChannels, bias=True, **kwargs):
"""
A nn.Linear module with specific constraints
Args:
nChannelsPrevious (int): number of channels in the previous layer
nChannels (int): number of channels of the current layer
bias (bool): with bias ?
"""
ConstrainedLayer.__init__(self, nn.Linear(nChannelsPrevious,
nChannels, bias=bias), **kwargs)
class AdaIN(nn.Module):
def __init__(self, dimIn, dimOut, epsilon=1e-08):
super(AdaIN, self).__init__()
self.epsilon = epsilon
self.styleModulator = EqualizedLinear(dimIn, 2 * dimOut, equalized=
True, initBiasToZero=True)
self.dimOut = dimOut
def forward(self, x, y):
batchSize, nChannel, _width, _height = x.size()
tmpX = x.view(batchSize, nChannel, -1)
mux = tmpX.mean(dim=2).view(batchSize, nChannel, 1, 1)
varx = torch.clamp((tmpX * tmpX).mean(dim=2).view(batchSize,
nChannel, 1, 1) - mux * mux, min=0)
varx = torch.rsqrt(varx + self.epsilon)
x = (x - mux) * varx
styleY = self.styleModulator(y)
yA = styleY[:, :self.dimOut].view(batchSize, self.dimOut, 1, 1)
yB = styleY[:, self.dimOut:].view(batchSize, self.dimOut, 1, 1)
return yA * x + yB
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'dimIn': 4, 'dimOut': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.nn as nn
from numpy import prod
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_add_clamp_mean_mul_rsqrt_sub_0(in_out_ptr0,
in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel,
XBLOCK: tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
x2 = xindex % 4
x3 = xindex // 4
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp20 = tl.load(in_ptr1 + (x2 + 8 * x3), xmask, eviction_policy=
'evict_last')
tmp21 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp28 = tl.load(in_ptr1 + (4 + x2 + 8 * x3), xmask, eviction_policy=
'evict_last')
tmp29 = tl.load(in_ptr2 + (4 + x2), xmask, eviction_policy='evict_last')
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = tmp0 * tmp0
tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
tmp8 = tl.where(xmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp10 = 16.0
tmp11 = tmp4 / tmp10
tmp12 = tmp9 / tmp10
tmp13 = tmp11 * tmp11
tmp14 = tmp12 - tmp13
tmp15 = 0.0
tmp16 = triton_helpers.maximum(tmp14, tmp15)
tmp17 = 1e-08
tmp18 = tmp16 + tmp17
tmp19 = libdevice.rsqrt(tmp18)
tmp22 = tmp20 + tmp21
tmp23 = 0.7071067811865476
tmp24 = tmp22 * tmp23
tmp25 = tmp0 - tmp11
tmp26 = tmp25 * tmp19
tmp27 = tmp24 * tmp26
tmp30 = tmp28 + tmp29
tmp31 = tmp30 * tmp23
tmp32 = tmp27 + tmp31
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp11, xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + x0, tmp19, xmask)
tl.store(out_ptr0 + (r1 + 16 * x0), tmp32, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (8, 4), (4, 1))
assert_size_stride(primals_3, (8,), (1,))
assert_size_stride(primals_4, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf4 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
extern_kernels.mm(primals_4, reinterpret_tensor(primals_2, (4, 8),
(1, 4), 0), out=buf4)
del primals_2
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf1 = buf0
del buf0
buf3 = reinterpret_tensor(buf2, (4, 4, 1, 1), (4, 1, 1, 1), 0)
del buf2
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_per_fused_add_clamp_mean_mul_rsqrt_sub_0[grid(16)](buf1,
buf3, primals_1, buf4, primals_3, buf5, 16, 16, XBLOCK=8,
num_warps=2, num_stages=1)
del buf4
del primals_3
return buf5, primals_1, primals_4, reinterpret_tensor(buf1, (4, 4, 1, 1
), (4, 1, 1, 1), 0), buf3
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = prod(size[1:])
return math.sqrt(2.0 / fan_in)
class ConstrainedLayer(nn.Module):
"""
A handy refactor that allows the user to:
- initialize one layer's bias to zero
- apply He's initialization at runtime
"""
def __init__(self, module, equalized=True, lrMul=1.0, initBiasToZero=True):
"""
equalized (bool): if true, the layer's weight should evolve within
the range (-1, 1)
initBiasToZero (bool): if true, bias will be initialized to zero
"""
super(ConstrainedLayer, self).__init__()
self.module = module
self.equalized = equalized
if initBiasToZero:
self.module.bias.data.fill_(0)
if self.equalized:
self.module.weight.data.normal_(0, 1)
self.module.weight.data /= lrMul
self.weight = getLayerNormalizationFactor(self.module) * lrMul
def forward(self, x):
x = self.module(x)
if self.equalized:
x *= self.weight
return x
class EqualizedLinear(ConstrainedLayer):
def __init__(self, nChannelsPrevious, nChannels, bias=True, **kwargs):
"""
A nn.Linear module with specific constraints
Args:
nChannelsPrevious (int): number of channels in the previous layer
nChannels (int): number of channels of the current layer
bias (bool): with bias ?
"""
ConstrainedLayer.__init__(self, nn.Linear(nChannelsPrevious,
nChannels, bias=bias), **kwargs)
class AdaINNew(nn.Module):
def __init__(self, dimIn, dimOut, epsilon=1e-08):
super(AdaINNew, self).__init__()
self.epsilon = epsilon
self.styleModulator = EqualizedLinear(dimIn, 2 * dimOut, equalized=
True, initBiasToZero=True)
self.dimOut = dimOut
def forward(self, input_0, input_1):
primals_2 = self.styleModulator.module.weight
primals_3 = self.styleModulator.module.bias
primals_1 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
Andribi/pytorch_GAN_zoo
|
AdaIN
| false | 4,862 |
[
"BSD-3-Clause"
] | 1 |
b37c7268cbd4ec7dc61ba65a3ccf11af71247597
|
https://github.com/Andribi/pytorch_GAN_zoo/tree/b37c7268cbd4ec7dc61ba65a3ccf11af71247597
|
PolicyNetwork
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_9/inductor_cache/r3/cr3febcwm3t44fuoitsx3ou2p6xg4sk4f7unagmmrvffasxf47te.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# x => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
# %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/rm/crmfikkxblrhxfynyknfm2x3wwcwtibkjkkbyhzwmxqi4kmwkosl.py
# Topologically Sorted Source Nodes: [log_std_1], Original ATen: [aten.clamp, aten.ge, aten.le, aten.logical_and]
# Source node to ATen node mapping:
# log_std_1 => clamp_max, clamp_min
# Graph fragment:
# %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%view_9, -20), kwargs = {})
# %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 2), kwargs = {})
# %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%view_9, -20), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%view_9, 2), kwargs = {})
# %logical_and : [num_users=1] = call_function[target=torch.ops.aten.logical_and.default](args = (%ge, %le), kwargs = {})
triton_poi_fused_clamp_ge_le_logical_and_1 = async_compile.triton('triton_poi_fused_clamp_ge_le_logical_and_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_ge_le_logical_and_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_ge_le_logical_and_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = -20.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 2.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tmp7 = tmp2 >= tmp3
tmp8 = tmp2 <= tmp5
tmp9 = tmp7 & tmp8
tl.store(out_ptr0 + (x2), tmp6, xmask)
tl.store(out_ptr1 + (x2), tmp9, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4, ), (1, ))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (4, ), (1, ))
assert_size_stride(primals_10, (4, 4), (4, 1))
assert_size_stride(primals_11, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse
buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf12, 256, grid=grid(256), stream=stream0)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2 # reuse
buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf11, 256, grid=grid(256), stream=stream0)
del primals_5
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mean], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4)
del primals_7
buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf5)
buf6 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf5 # reuse
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [relu_2], Original ATen: [aten.relu, aten.threshold_backward]
triton_poi_fused_relu_threshold_backward_0.run(buf6, primals_9, buf10, 256, grid=grid(256), stream=stream0)
del primals_9
buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf7)
buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [log_std_1], Original ATen: [aten.clamp, aten.ge, aten.le, aten.logical_and]
triton_poi_fused_clamp_ge_le_logical_and_1.run(buf7, primals_11, buf8, buf9, 256, grid=grid(256), stream=stream0)
del buf7
del primals_11
return (reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf6, (64, 4), (4, 1), 0), buf9, primals_10, buf10, primals_8, primals_6, buf11, primals_4, buf12, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from torch.distributions import Normal
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_clamp_ge_le_logical_and_1(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = -20.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 2.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tmp7 = tmp2 >= tmp3
tmp8 = tmp2 <= tmp5
tmp9 = tmp7 & tmp8
tl.store(out_ptr0 + x2, tmp6, xmask)
tl.store(out_ptr1 + x2, tmp9, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (4,), (1,))
assert_size_stride(primals_10, (4, 4), (4, 1))
assert_size_stride(primals_11, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
primals_2, buf12, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf2
buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf3,
primals_5, buf11, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf4)
del primals_7
buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf5)
buf6 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf5
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf6,
primals_9, buf10, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_9
buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf6, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf7)
buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_clamp_ge_le_logical_and_1[grid(256)](buf7,
primals_11, buf8, buf9, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf7
del primals_11
return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
), buf8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(
buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf6, (64, 4), (4, 1), 0
), buf9, primals_10, buf10, primals_8, primals_6, buf11, primals_4, buf12
class PolicyNetworkNew(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_size, init_w=0.003,
log_std_min=-20, log_std_max=2):
super(PolicyNetworkNew, self).__init__()
self.log_std_min = log_std_min
self.log_std_max = log_std_max
self.linear1 = nn.Linear(num_inputs, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
self.mean_linear = nn.Linear(hidden_size, num_actions)
self.mean_linear.weight.data.uniform_(-init_w, init_w)
self.mean_linear.bias.data.uniform_(-init_w, init_w)
self.log_std_linear1 = nn.Linear(hidden_size, hidden_size)
self.log_std_linear2 = nn.Linear(hidden_size, num_actions)
self.log_std_linear2.weight.data.uniform_(-init_w, init_w)
self.log_std_linear2.bias.data.uniform_(-init_w, init_w)
def evaluate(self, state, epsilon=1e-06):
mean, log_std = self.forward(state)
std = log_std.exp()
normal = Normal(mean, std)
z = normal.rsample()
action = torch.tanh(z)
log_prob = normal.log_prob(z) - torch.log(1 - action.pow(2) + epsilon)
log_prob = log_prob.sum(-1, keepdim=True)
return action, log_prob, z, mean, log_std
def get_action(self, state):
state = torch.FloatTensor(state).unsqueeze(0)
mean, log_std = self.forward(state)
std = log_std.exp()
normal = Normal(mean, std)
z = normal.sample()
action = torch.tanh(z)
action = action.detach().cpu().numpy()
return action[0]
def forward(self, input_0):
primals_1 = self.linear1.weight
primals_2 = self.linear1.bias
primals_4 = self.linear2.weight
primals_5 = self.linear2.bias
primals_6 = self.mean_linear.weight
primals_7 = self.mean_linear.bias
primals_8 = self.log_std_linear1.weight
primals_9 = self.log_std_linear1.bias
primals_10 = self.log_std_linear2.weight
primals_11 = self.log_std_linear2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0], output[1]
|
watermeleon/spot_mini_mini
|
PolicyNetwork
| false | 13,101 |
[
"MIT"
] | 0 |
8622d3b0e0a95f7c548cacb6722a94f61a7e2b4b
|
https://github.com/watermeleon/spot_mini_mini/tree/8622d3b0e0a95f7c548cacb6722a94f61a7e2b4b
|
Model
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_6/inductor_cache/mb/cmb72vxh36b4k6lvmt4562lj3nrqtpyzst2qbon2yqx22gdjfa7x.py
# Topologically Sorted Source Nodes: [y_pred], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
# y_pred => sigmoid
# Graph fragment:
# %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%mm,), kwargs = {})
triton_poi_fused_sigmoid_0 = async_compile.triton('triton_poi_fused_sigmoid_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_sigmoid_0(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = tl.sigmoid(tmp0)
tl.store(in_out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mm], Original ATen: [aten.mm]
extern_kernels.mm(primals_1, primals_2, out=buf0)
del primals_2
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [y_pred], Original ATen: [aten.sigmoid]
stream0 = get_raw_stream(0)
triton_poi_fused_sigmoid_0.run(buf1, 16, grid=grid(16), stream=stream0)
return (buf1, buf1, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_sigmoid_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.sigmoid(tmp0)
tl.store(in_out_ptr0 + x0, tmp1, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, primals_2, out=buf0)
del primals_2
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_sigmoid_0[grid(16)](buf1, 16, XBLOCK=16, num_warps
=1, num_stages=1)
return buf1, buf1, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0)
class ModelNew(torch.nn.Module):
def __init__(self, D_in, D_out):
super(ModelNew, self).__init__()
self.w1 = torch.nn.Parameter(torch.randn(D_in, D_out),
requires_grad=True)
self.sig = torch.nn.Sigmoid()
def forward(self, input_0):
primals_1 = self.w1
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
Saran-nns/gradients
|
Model
| false | 1,018 |
[
"MIT"
] | 0 |
67f9ff92589047828563dbbe30f225dca7ad47fd
|
https://github.com/Saran-nns/gradients/tree/67f9ff92589047828563dbbe30f225dca7ad47fd
|
MSE_disc
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_7/inductor_cache/4s/c4s3xowwp3oadled6o7j4eugj44hmnk7o4jpxawswyucmffhmpdw.py
# Topologically Sorted Source Nodes: [sub, loss, mean], Original ATen: [aten.sub, aten.pow, aten.mean]
# Source node to ATen node mapping:
# loss => pow_1
# mean => mean
# sub => sub
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [0]), kwargs = {})
triton_poi_fused_mean_pow_sub_0 = async_compile.triton('triton_poi_fused_mean_pow_sub_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_pow_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mean_pow_sub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = tl.load(in_ptr1 + (x0), xmask)
tmp4 = tl.load(in_ptr0 + (64 + x0), xmask)
tmp5 = tl.load(in_ptr1 + (64 + x0), xmask)
tmp9 = tl.load(in_ptr0 + (128 + x0), xmask)
tmp10 = tl.load(in_ptr1 + (128 + x0), xmask)
tmp14 = tl.load(in_ptr0 + (192 + x0), xmask)
tmp15 = tl.load(in_ptr1 + (192 + x0), xmask)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp6 = tmp4 - tmp5
tmp7 = tmp6 * tmp6
tmp8 = tmp3 + tmp7
tmp11 = tmp9 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tmp8 + tmp12
tmp16 = tmp14 - tmp15
tmp17 = tmp16 * tmp16
tmp18 = tmp13 + tmp17
tmp19 = 4.0
tmp20 = tmp18 / tmp19
tl.store(out_ptr0 + (x0), tmp20, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [sub, loss, mean], Original ATen: [aten.sub, aten.pow, aten.mean]
stream0 = get_raw_stream(0)
triton_poi_fused_mean_pow_sub_0.run(arg0_1, arg1_1, buf0, 64, grid=grid(64), stream=stream0)
del arg0_1
del arg1_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_mean_pow_sub_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask)
tmp4 = tl.load(in_ptr0 + (64 + x0), xmask)
tmp5 = tl.load(in_ptr1 + (64 + x0), xmask)
tmp9 = tl.load(in_ptr0 + (128 + x0), xmask)
tmp10 = tl.load(in_ptr1 + (128 + x0), xmask)
tmp14 = tl.load(in_ptr0 + (192 + x0), xmask)
tmp15 = tl.load(in_ptr1 + (192 + x0), xmask)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp6 = tmp4 - tmp5
tmp7 = tmp6 * tmp6
tmp8 = tmp3 + tmp7
tmp11 = tmp9 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tmp8 + tmp12
tmp16 = tmp14 - tmp15
tmp17 = tmp16 * tmp16
tmp18 = tmp13 + tmp17
tmp19 = 4.0
tmp20 = tmp18 / tmp19
tl.store(out_ptr0 + x0, tmp20, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mean_pow_sub_0[grid(64)](arg0_1, arg1_1, buf0, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del arg0_1
del arg1_1
return buf0,
class MSE_discNew(nn.Module):
def __init__(self, weight_list=None):
super(MSE_discNew, self).__init__()
self.weight_list = weight_list
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
Sampson-Lee/SIB-Net
|
MSE_disc
| false | 2,809 |
[
"MIT"
] | 0 |
650399082e9237327fa38168ccfc7d48153a1db5
|
https://github.com/Sampson-Lee/SIB-Net/tree/650399082e9237327fa38168ccfc7d48153a1db5
|
SphericalBesselBasis
|
import math
import torch
import numpy as np
class SphericalBesselBasis(torch.nn.Module):
"""
1D spherical Bessel basis
Parameters
----------
num_radial: int
Controls maximum frequency.
cutoff: float
Cutoff distance in Angstrom.
"""
def __init__(self, num_radial: 'int', cutoff: 'float'):
super().__init__()
self.norm_const = math.sqrt(2 / cutoff ** 3)
self.frequencies = torch.nn.Parameter(data=torch.tensor(np.pi * np.
arange(1, num_radial + 1, dtype=np.float32)), requires_grad=True)
def forward(self, d_scaled):
return self.norm_const / d_scaled[:, None] * torch.sin(self.
frequencies * d_scaled[:, None])
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_radial': 4, 'cutoff': 4}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import numpy as np
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_mul_reciprocal_sin_0(in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp5 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp1 = tl.full([1], 1, tl.int32)
tmp2 = tmp1 / tmp0
tmp3 = 0.1767766952966369
tmp4 = tmp2 * tmp3
tmp6 = tmp5 * tmp0
tmp7 = tl_math.sin(tmp6)
tmp8 = tmp4 * tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 64, 16, 4, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_reciprocal_sin_0[grid(256)](primals_1,
primals_2, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
return buf0, primals_1, primals_2
class SphericalBesselBasisNew(torch.nn.Module):
"""
1D spherical Bessel basis
Parameters
----------
num_radial: int
Controls maximum frequency.
cutoff: float
Cutoff distance in Angstrom.
"""
def __init__(self, num_radial: 'int', cutoff: 'float'):
super().__init__()
self.norm_const = math.sqrt(2 / cutoff ** 3)
self.frequencies = torch.nn.Parameter(data=torch.tensor(np.pi * np.
arange(1, num_radial + 1, dtype=np.float32)), requires_grad=True)
def forward(self, input_0):
primals_2 = self.frequencies
primals_1 = input_0
output = call([primals_1, primals_2])
return output[0]
|
RolnickLab/ocp
|
SphericalBesselBasis
| false | 2,781 |
[
"MIT"
] | 0 |
e120c3b90203a46f5fc7626f0b5c8979e4944765
|
https://github.com/RolnickLab/ocp/tree/e120c3b90203a46f5fc7626f0b5c8979e4944765
|
ISAB
|
import math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor of shape [B, M, d_v].
presence: None or tensor of shape [B, M].
Returns:
Tensor of shape [B, N, d_v]
"""
d_k = queries.shape[-1]
routing = torch.matmul(queries, keys.transpose(1, 2))
if presence is not None:
routing -= (1.0 - presence.unsqueeze(-2)) * 1e+32
routing = F.softmax(routing / np.sqrt(d_k), -1)
return torch.matmul(routing, values)
class MultiHeadQKVAttention(nn.Module):
"""Multi-head version of Transformer-like attention."""
def __init__(self, d_k, d_v, n_heads):
super().__init__()
self.d_k = d_k
self.d_v = d_v
self.n_heads = n_heads
d_k_p = int(math.ceil(d_k / n_heads)) * n_heads
d_v_p = int(math.ceil(d_v / n_heads)) * n_heads
self.q_projector = nn.Linear(d_k, d_k_p)
self.k_projector = nn.Linear(d_k, d_k_p)
self.v_projector = nn.Linear(d_v, d_v_p)
self.o_projector = nn.Linear(d_v_p, d_v)
def forward(self, queries, keys, values, presence=None):
"""
Multi-head transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor of shape [B, M, d_v].
presence: None or tensor of shape [B, M].
Returns:
Tensor of shape [B, N, d_v]
"""
assert queries.shape[2] == keys.shape[2]
assert keys.shape[1] == values.shape[1]
if presence is not None:
assert values.shape[:2] == presence.shape
B, N, _d_k = queries.shape
M, _d_v = values.shape[1:]
H = self.n_heads
q_p = self.q_projector(queries)
k_p = self.k_projector(keys)
v_p = self.v_projector(values)
del queries, keys, values
q = q_p.view(B, N, H, -1).permute(2, 0, 1, 3).contiguous().view(H *
B, N, -1)
k = k_p.view(B, M, H, -1).permute(2, 0, 1, 3).contiguous().view(H *
B, M, -1)
v = v_p.view(B, M, H, -1).permute(2, 0, 1, 3).contiguous().view(H *
B, M, -1)
if presence is not None:
presence = presence.repeat(self.n_heads, 1)
o = qkv_attention(q, k, v, presence)
o = o.view(H, B, N, -1).permute(1, 2, 0, 3).contiguous().view(B, N, -1)
return self.o_projector(o)
class MAB(nn.Module):
def __init__(self, d, n_heads, layer_norm=False):
super().__init__()
self.layer_norm = layer_norm
self.mqkv = MultiHeadQKVAttention(d_k=d, d_v=d, n_heads=n_heads)
if layer_norm:
self.ln0 = nn.LayerNorm(d)
self.ln1 = nn.LayerNorm(d)
self.fc = nn.Linear(d, d)
def forward(self, queries, keys, presence=None):
h = self.mqkv(queries, keys, keys, presence)
h = h + queries
if presence is not None:
assert presence.shape[1] == queries.shape[1] == keys.shape[1]
h = h * presence.unsqueeze(-1)
if self.layer_norm:
h = self.ln0(h)
h = h + F.relu(self.fc(h))
if self.layer_norm:
h = self.ln1(h)
return h
class ISAB(nn.Module):
def __init__(self, d, n_heads, n_inducing_points, layer_norm=False):
super().__init__()
self.mab0 = MAB(d=d, n_heads=n_heads, layer_norm=layer_norm)
self.mab1 = MAB(d=d, n_heads=n_heads, layer_norm=layer_norm)
self.I = nn.Parameter(torch.zeros(1, n_inducing_points, d),
requires_grad=True)
with torch.no_grad():
nn.init.xavier_uniform_(self.I)
def forward(self, x, presence=None):
batch_size = x.shape[0]
h = self.mab0(self.I.repeat(batch_size, 1, 1), x, presence)
return self.mab1(x, h)
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'d': 4, 'n_heads': 4, 'n_inducing_points': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_repeat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x2, tmp0, xmask)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 4
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x1 + 16 * y0), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x1), xmask & ymask, eviction_policy
='evict_last')
tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_add_5(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_add_relu_threshold_backward_6(in_ptr0, in_ptr1,
in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tl.full([1], 0, tl.int32)
tmp5 = triton_helpers.maximum(tmp4, tmp3)
tmp6 = tmp0 + tmp5
tmp7 = 0.0
tmp8 = tmp5 <= tmp7
tl.store(out_ptr0 + x2, tmp6, xmask)
tl.store(out_ptr1 + x2, tmp8, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17,
primals_18, primals_19, primals_20, primals_21, primals_22) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (1, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (4, 4), (4, 1))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (4, 4), (4, 1))
assert_size_stride(primals_10, (4,), (1,))
assert_size_stride(primals_11, (4, 4), (4, 1))
assert_size_stride(primals_12, (4,), (1,))
assert_size_stride(primals_13, (4, 4), (4, 1))
assert_size_stride(primals_14, (4,), (1,))
assert_size_stride(primals_15, (4, 4), (4, 1))
assert_size_stride(primals_16, (4,), (1,))
assert_size_stride(primals_17, (4, 4), (4, 1))
assert_size_stride(primals_18, (4,), (1,))
assert_size_stride(primals_19, (4, 4), (4, 1))
assert_size_stride(primals_20, (4,), (1,))
assert_size_stride(primals_21, (4, 4), (4, 1))
assert_size_stride(primals_22, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_repeat_0[grid(64)](primals_2, buf0, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_2
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
del primals_5
buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf3)
del primals_7
buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
triton_poi_fused_clone_1[grid(4, 16)](buf1, primals_4, buf4, 4, 16,
XBLOCK=16, YBLOCK=2, num_warps=1, num_stages=1)
del primals_4
buf5 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf1
triton_poi_fused_clone_1[grid(4, 16)](buf2, primals_6, buf5, 4, 16,
XBLOCK=16, YBLOCK=2, num_warps=1, num_stages=1)
del primals_6
buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf5, (16, 1, 4), (4, 0, 1), 0), out=buf6)
buf7 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__softmax_2[grid(256)](buf6, buf7, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf8 = buf6
del buf6
triton_poi_fused__softmax_3[grid(256)](buf7, buf8, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf9 = reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf2
triton_poi_fused_clone_1[grid(4, 16)](buf3, primals_8, buf9, 4, 16,
XBLOCK=16, YBLOCK=2, num_warps=1, num_stages=1)
del primals_8
buf10 = reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 1), 0)
del buf3
extern_kernels.bmm(buf8, reinterpret_tensor(buf9, (16, 4, 1), (4, 1,
0), 0), out=buf10)
buf11 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
triton_poi_fused_clone_4[grid(16, 4)](buf10, buf11, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf12 = reinterpret_tensor(buf10, (16, 4), (4, 1), 0)
del buf10
extern_kernels.mm(reinterpret_tensor(buf11, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf12)
buf13 = reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0)
del buf12
triton_poi_fused_add_5[grid(64)](buf13, primals_10, buf0, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_10
buf14 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf13, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), out=buf14)
buf15 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_13, (4, 4), (1, 4), 0), out=buf15)
del primals_13
buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
buf32 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_add_relu_threshold_backward_6[grid(64)](buf13,
buf14, primals_12, buf16, buf32, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del primals_12
buf17 = buf14
del buf14
extern_kernels.mm(reinterpret_tensor(buf16, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_15, (4, 4), (1, 4), 0), out=buf17)
buf18 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf16, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_17, (4, 4), (1, 4), 0), out=buf18)
buf19 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
triton_poi_fused_clone_1[grid(4, 16)](buf15, primals_14, buf19, 4,
16, XBLOCK=16, YBLOCK=2, num_warps=1, num_stages=1)
del primals_14
buf20 = reinterpret_tensor(buf15, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf15
triton_poi_fused_clone_1[grid(4, 16)](buf17, primals_16, buf20, 4,
16, XBLOCK=16, YBLOCK=2, num_warps=1, num_stages=1)
del primals_16
buf21 = buf7
del buf7
extern_kernels.bmm(reinterpret_tensor(buf19, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf20, (16, 1, 4), (4, 0, 1), 0), out=buf21)
buf22 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__softmax_2[grid(256)](buf21, buf22, 256, XBLOCK=
256, num_warps=4, num_stages=1)
buf23 = buf21
del buf21
triton_poi_fused__softmax_3[grid(256)](buf22, buf23, 256, XBLOCK=
256, num_warps=4, num_stages=1)
del buf22
buf24 = reinterpret_tensor(buf17, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf17
triton_poi_fused_clone_1[grid(4, 16)](buf18, primals_18, buf24, 4,
16, XBLOCK=16, YBLOCK=2, num_warps=1, num_stages=1)
del primals_18
buf25 = reinterpret_tensor(buf18, (16, 4, 1), (4, 1, 1), 0)
del buf18
extern_kernels.bmm(buf23, reinterpret_tensor(buf24, (16, 4, 1), (4,
1, 0), 0), out=buf25)
buf26 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
triton_poi_fused_clone_4[grid(16, 4)](buf25, buf26, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf27 = reinterpret_tensor(buf25, (16, 4), (4, 1), 0)
del buf25
extern_kernels.mm(reinterpret_tensor(buf26, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_19, (4, 4), (1, 4), 0), out=buf27)
buf28 = reinterpret_tensor(buf27, (4, 4, 4), (16, 4, 1), 0)
del buf27
triton_poi_fused_add_5[grid(64)](buf28, primals_20, primals_1, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_20
buf29 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf28, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_21, (4, 4), (1, 4), 0), out=buf29)
buf30 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
buf31 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
triton_poi_fused_add_relu_threshold_backward_6[grid(64)](buf28,
buf29, primals_22, buf30, buf31, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del buf29
del primals_22
return (buf30, reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf8,
reinterpret_tensor(buf11, (16, 4), (4, 1), 0), reinterpret_tensor(
buf13, (16, 4), (4, 1), 0), reinterpret_tensor(buf16, (16, 4), (4,
1), 0), buf23, reinterpret_tensor(buf26, (16, 4), (4, 1), 0),
reinterpret_tensor(buf28, (16, 4), (4, 1), 0), buf31, primals_21,
primals_19, reinterpret_tensor(buf24, (16, 1, 4), (4, 1, 1), 0),
reinterpret_tensor(buf19, (16, 1, 4), (4, 1, 1), 0),
reinterpret_tensor(buf20, (16, 4, 1), (4, 1, 1), 0), primals_17,
primals_15, buf32, primals_11, primals_9, reinterpret_tensor(buf9,
(16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf4, (16, 1, 4), (4,
1, 1), 0), reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 1), 0),
primals_3)
def qkv_attention(queries, keys, values, presence=None):
"""
Transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor of shape [B, M, d_v].
presence: None or tensor of shape [B, M].
Returns:
Tensor of shape [B, N, d_v]
"""
d_k = queries.shape[-1]
routing = torch.matmul(queries, keys.transpose(1, 2))
if presence is not None:
routing -= (1.0 - presence.unsqueeze(-2)) * 1e+32
routing = F.softmax(routing / np.sqrt(d_k), -1)
return torch.matmul(routing, values)
class MultiHeadQKVAttention(nn.Module):
"""Multi-head version of Transformer-like attention."""
def __init__(self, d_k, d_v, n_heads):
super().__init__()
self.d_k = d_k
self.d_v = d_v
self.n_heads = n_heads
d_k_p = int(math.ceil(d_k / n_heads)) * n_heads
d_v_p = int(math.ceil(d_v / n_heads)) * n_heads
self.q_projector = nn.Linear(d_k, d_k_p)
self.k_projector = nn.Linear(d_k, d_k_p)
self.v_projector = nn.Linear(d_v, d_v_p)
self.o_projector = nn.Linear(d_v_p, d_v)
def forward(self, queries, keys, values, presence=None):
"""
Multi-head transformer-like self-attention.
Args:
queries: Tensor of shape [B, N, d_k].
keys: Tensor of shape [B, M, d_k].
values: : Tensor of shape [B, M, d_v].
presence: None or tensor of shape [B, M].
Returns:
Tensor of shape [B, N, d_v]
"""
assert queries.shape[2] == keys.shape[2]
assert keys.shape[1] == values.shape[1]
if presence is not None:
assert values.shape[:2] == presence.shape
B, N, _d_k = queries.shape
M, _d_v = values.shape[1:]
H = self.n_heads
q_p = self.q_projector(queries)
k_p = self.k_projector(keys)
v_p = self.v_projector(values)
del queries, keys, values
q = q_p.view(B, N, H, -1).permute(2, 0, 1, 3).contiguous().view(H *
B, N, -1)
k = k_p.view(B, M, H, -1).permute(2, 0, 1, 3).contiguous().view(H *
B, M, -1)
v = v_p.view(B, M, H, -1).permute(2, 0, 1, 3).contiguous().view(H *
B, M, -1)
if presence is not None:
presence = presence.repeat(self.n_heads, 1)
o = qkv_attention(q, k, v, presence)
o = o.view(H, B, N, -1).permute(1, 2, 0, 3).contiguous().view(B, N, -1)
return self.o_projector(o)
class MAB(nn.Module):
def __init__(self, d, n_heads, layer_norm=False):
super().__init__()
self.layer_norm = layer_norm
self.mqkv = MultiHeadQKVAttention(d_k=d, d_v=d, n_heads=n_heads)
if layer_norm:
self.ln0 = nn.LayerNorm(d)
self.ln1 = nn.LayerNorm(d)
self.fc = nn.Linear(d, d)
def forward(self, queries, keys, presence=None):
h = self.mqkv(queries, keys, keys, presence)
h = h + queries
if presence is not None:
assert presence.shape[1] == queries.shape[1] == keys.shape[1]
h = h * presence.unsqueeze(-1)
if self.layer_norm:
h = self.ln0(h)
h = h + F.relu(self.fc(h))
if self.layer_norm:
h = self.ln1(h)
return h
class ISABNew(nn.Module):
def __init__(self, d, n_heads, n_inducing_points, layer_norm=False):
super().__init__()
self.mab0 = MAB(d=d, n_heads=n_heads, layer_norm=layer_norm)
self.mab1 = MAB(d=d, n_heads=n_heads, layer_norm=layer_norm)
self.I = nn.Parameter(torch.zeros(1, n_inducing_points, d),
requires_grad=True)
with torch.no_grad():
nn.init.xavier_uniform_(self.I)
def forward(self, input_0):
primals_2 = self.I
primals_3 = self.mab0.mqkv.q_projector.weight
primals_4 = self.mab0.mqkv.q_projector.bias
primals_5 = self.mab0.mqkv.k_projector.weight
primals_6 = self.mab0.mqkv.k_projector.bias
primals_7 = self.mab0.mqkv.v_projector.weight
primals_8 = self.mab0.mqkv.v_projector.bias
primals_9 = self.mab0.mqkv.o_projector.weight
primals_10 = self.mab0.mqkv.o_projector.bias
primals_11 = self.mab0.fc.weight
primals_12 = self.mab0.fc.bias
primals_13 = self.mab1.mqkv.q_projector.weight
primals_14 = self.mab1.mqkv.q_projector.bias
primals_15 = self.mab1.mqkv.k_projector.weight
primals_16 = self.mab1.mqkv.k_projector.bias
primals_17 = self.mab1.mqkv.v_projector.weight
primals_18 = self.mab1.mqkv.v_projector.bias
primals_19 = self.mab1.mqkv.o_projector.weight
primals_20 = self.mab1.mqkv.o_projector.bias
primals_21 = self.mab1.fc.weight
primals_22 = self.mab1.fc.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17, primals_18, primals_19,
primals_20, primals_21, primals_22])
return output[0]
|
karayanni/torch-scae
|
ISAB
| false | 10,442 |
[
"Apache-2.0"
] | 0 |
e044662d8942d8d1923d13d071f375144cf4a1e8
|
https://github.com/karayanni/torch-scae/tree/e044662d8942d8d1923d13d071f375144cf4a1e8
|
KlCriterion
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_4/inductor_cache/wx/cwxwvlntewdrqi2r4caciy5ht4jdvafnhtiqncr4lo4aegcb4imz.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# softmax => amax_1, exp_1, sub_2
# Graph fragment:
# %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg1_1, [-1], True), kwargs = {})
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %amax_1), kwargs = {})
# %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_4/inductor_cache/g5/cg5f2rptqnpi2mrqpqc4tujqpbrrrjrse6plhgftx425znsffpfv.py
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
# log_softmax => amax, sub
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [-1], True), kwargs = {})
# %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax), kwargs = {})
triton_poi_fused__log_softmax_1 = async_compile.triton('triton_poi_fused__log_softmax_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_4/inductor_cache/uf/cufuuxvelrltzwxjbbkikdu25p6qhp4n3izwy7jc75fb6567imxf.py
# Topologically Sorted Source Nodes: [softmax, loss, log_softmax, loss_1], Original ATen: [aten._softmax, aten.xlogy, aten._log_softmax, aten.mul, aten.sub, aten.sum, aten.div]
# Source node to ATen node mapping:
# log_softmax => exp, log, sub_1, sum_1
# loss => div_1, eq, full_default, full_default_1, isnan, log_1, mul, mul_1, sub_3, sum_3, where, where_1
# loss_1 => mul_2
# softmax => div, sum_2
# Graph fragment:
# %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [-1], True), kwargs = {})
# %div : [num_users=5] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_1, %sum_2), kwargs = {})
# %isnan : [num_users=1] = call_function[target=torch.ops.aten.isnan.default](args = (%div,), kwargs = {})
# %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], nan), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
# %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%div, 0), kwargs = {})
# %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
# %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div,), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %log_1), kwargs = {})
# %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %mul_1), kwargs = {})
# %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%isnan, %full_default_1, %where), kwargs = {})
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
# %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %sub_1), kwargs = {})
# %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_1, %mul), kwargs = {})
# %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sub_3,), kwargs = {})
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, 4), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_1, 1.0), kwargs = {})
triton_red_fused__log_softmax__softmax_div_mul_sub_sum_xlogy_2 = async_compile.triton('triton_red_fused__log_softmax__softmax_div_mul_sub_sum_xlogy_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__log_softmax__softmax_div_mul_sub_sum_xlogy_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 10, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused__log_softmax__softmax_div_mul_sub_sum_xlogy_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
xnumel = 1
rnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rbase = tl.arange(0, RBLOCK)[None, :]
_tmp34 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = rindex
r1 = (rindex // 4)
tmp0 = tl.load(in_ptr0 + (r2), rmask, eviction_policy='evict_first', other=0.0)
tmp1 = tl.load(in_ptr0 + (4*r1), rmask, eviction_policy='evict_last', other=0.0)
tmp2 = tl.load(in_ptr0 + (1 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
tmp4 = tl.load(in_ptr0 + (2 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
tmp6 = tl.load(in_ptr0 + (3 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
tmp17 = tl.load(in_ptr1 + (r2), rmask, eviction_policy='evict_first', other=0.0)
tmp18 = tl.load(in_ptr1 + (4*r1), rmask, eviction_policy='evict_last', other=0.0)
tmp20 = tl.load(in_ptr1 + (1 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
tmp23 = tl.load(in_ptr1 + (2 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
tmp26 = tl.load(in_ptr1 + (3 + (4*r1)), rmask, eviction_policy='evict_last', other=0.0)
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tmp9 = libdevice.isnan(tmp8).to(tl.int1)
tmp10 = 0.0
tmp11 = tmp8 == tmp10
tmp12 = tl_math.log(tmp8)
tmp13 = tmp8 * tmp12
tmp14 = tl.where(tmp11, tmp10, tmp13)
tmp15 = float("nan")
tmp16 = tl.where(tmp9, tmp15, tmp14)
tmp19 = tl_math.exp(tmp18)
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp27 = tl_math.exp(tmp26)
tmp28 = tmp25 + tmp27
tmp29 = tl_math.log(tmp28)
tmp30 = tmp17 - tmp29
tmp31 = tmp8 * tmp30
tmp32 = tmp16 - tmp31
tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
tmp35 = _tmp34 + tmp33
_tmp34 = tl.where(rmask, tmp35, _tmp34)
tmp34 = tl.sum(_tmp34, 1)[:, None]
tmp36 = 0.25
tmp37 = tmp34 * tmp36
tmp38 = 1.0
tmp39 = tmp37 * tmp38
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp39, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
stream0 = get_raw_stream(0)
triton_poi_fused__softmax_0.run(arg1_1, buf0, 256, grid=grid(256), stream=stream0)
del arg1_1
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
triton_poi_fused__log_softmax_1.run(arg0_1, buf2, 256, grid=grid(256), stream=stream0)
del arg0_1
buf3 = empty_strided_cuda((), (), torch.float32)
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [softmax, loss, log_softmax, loss_1], Original ATen: [aten._softmax, aten.xlogy, aten._log_softmax, aten.mul, aten.sub, aten.sum, aten.div]
triton_red_fused__log_softmax__softmax_div_mul_sub_sum_xlogy_2.run(buf4, buf0, buf2, 1, 256, grid=grid(1), stream=stream0)
del buf0
del buf2
return (buf4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_red_fused__log_softmax__softmax_div_mul_sub_sum_xlogy_2(in_out_ptr0,
in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.
constexpr):
rnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rbase = tl.arange(0, RBLOCK)[None, :]
_tmp34 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = rindex
r1 = rindex // 4
tmp0 = tl.load(in_ptr0 + r2, rmask, eviction_policy='evict_first',
other=0.0)
tmp1 = tl.load(in_ptr0 + 4 * r1, rmask, eviction_policy=
'evict_last', other=0.0)
tmp2 = tl.load(in_ptr0 + (1 + 4 * r1), rmask, eviction_policy=
'evict_last', other=0.0)
tmp4 = tl.load(in_ptr0 + (2 + 4 * r1), rmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tl.load(in_ptr0 + (3 + 4 * r1), rmask, eviction_policy=
'evict_last', other=0.0)
tmp17 = tl.load(in_ptr1 + r2, rmask, eviction_policy='evict_first',
other=0.0)
tmp18 = tl.load(in_ptr1 + 4 * r1, rmask, eviction_policy=
'evict_last', other=0.0)
tmp20 = tl.load(in_ptr1 + (1 + 4 * r1), rmask, eviction_policy=
'evict_last', other=0.0)
tmp23 = tl.load(in_ptr1 + (2 + 4 * r1), rmask, eviction_policy=
'evict_last', other=0.0)
tmp26 = tl.load(in_ptr1 + (3 + 4 * r1), rmask, eviction_policy=
'evict_last', other=0.0)
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tmp9 = libdevice.isnan(tmp8).to(tl.int1)
tmp10 = 0.0
tmp11 = tmp8 == tmp10
tmp12 = tl_math.log(tmp8)
tmp13 = tmp8 * tmp12
tmp14 = tl.where(tmp11, tmp10, tmp13)
tmp15 = float('nan')
tmp16 = tl.where(tmp9, tmp15, tmp14)
tmp19 = tl_math.exp(tmp18)
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp27 = tl_math.exp(tmp26)
tmp28 = tmp25 + tmp27
tmp29 = tl_math.log(tmp28)
tmp30 = tmp17 - tmp29
tmp31 = tmp8 * tmp30
tmp32 = tmp16 - tmp31
tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
tmp35 = _tmp34 + tmp33
_tmp34 = tl.where(rmask, tmp35, _tmp34)
tmp34 = tl.sum(_tmp34, 1)[:, None]
tmp36 = 0.25
tmp37 = tmp34 * tmp36
tmp38 = 1.0
tmp39 = tmp37 * tmp38
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp39, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__softmax_0[grid(256)](arg1_1, buf0, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del arg1_1
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__log_softmax_1[grid(256)](arg0_1, buf2, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
buf3 = empty_strided_cuda((), (), torch.float32)
buf4 = buf3
del buf3
triton_red_fused__log_softmax__softmax_div_mul_sub_sum_xlogy_2[grid(1)
](buf4, buf0, buf2, 1, 256, XBLOCK=1, RBLOCK=256, num_warps=8,
num_stages=1)
del buf0
del buf2
return buf4,
class Criterion(_Loss):
def __init__(self, alpha=1.0, name='criterion'):
super().__init__()
"""Alpha is used to weight each loss term
"""
self.alpha = alpha
self.name = name
def forward(self, input, target, weight=None, ignore_index=-1):
"""weight: sample weight
"""
return
class KlCriterionNew(Criterion):
def __init__(self, alpha=1.0, name='KL Div Criterion'):
super().__init__()
self.alpha = alpha
self.name = name
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
kiminh/mt-dnn
|
KlCriterion
| false | 7,029 |
[
"MIT"
] | 1 |
133884b380244dbe74acc4d7507e551b2c5035b3
|
https://github.com/kiminh/mt-dnn/tree/133884b380244dbe74acc4d7507e551b2c5035b3
|
VarianceNorm2d
|
import torch
import torch.nn as nn
class VarianceNorm2d(nn.Module):
def __init__(self, num_features, bias=False):
super().__init__()
self.num_features = num_features
self.bias = bias
self.alpha = nn.Parameter(torch.zeros(num_features))
self.alpha.data.normal_(1, 0.02)
def forward(self, x):
vars = torch.var(x, dim=(2, 3), keepdim=True)
h = x / torch.sqrt(vars + 1e-05)
out = self.alpha.view(-1, self.num_features, 1, 1) * h
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_features': 4}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_add_div_mul_sqrt_var_0(in_out_ptr0, in_ptr0, in_ptr1,
out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
x2 = xindex % 4
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp22 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tl.where(xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = 15.0
tmp18 = tmp16 / tmp17
tmp19 = 1e-05
tmp20 = tmp18 + tmp19
tmp21 = libdevice.sqrt(tmp20)
tmp23 = tmp0 / tmp21
tmp24 = tmp22 * tmp23
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp21, xmask)
tl.store(out_ptr0 + (r1 + 16 * x0), tmp24, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf3 = reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0)
del buf1
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_per_fused_add_div_mul_sqrt_var_0[grid(16)](buf3, primals_1,
primals_2, buf4, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
del primals_2
return buf4, primals_1, buf3
class VarianceNorm2dNew(nn.Module):
def __init__(self, num_features, bias=False):
super().__init__()
self.num_features = num_features
self.bias = bias
self.alpha = nn.Parameter(torch.zeros(num_features))
self.alpha.data.normal_(1, 0.02)
def forward(self, input_0):
primals_2 = self.alpha
primals_1 = input_0
output = call([primals_1, primals_2])
return output[0]
|
DeepTitan/PNDM
|
VarianceNorm2d
| false | 13,568 |
[
"Apache-2.0"
] | 61 |
4037a4f40011c9a0d47b92303e64d47fcc7ed56a
|
https://github.com/DeepTitan/PNDM/tree/4037a4f40011c9a0d47b92303e64d47fcc7ed56a
|
Downsample
|
import torch
import torch.nn as nn
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f'unsupported dimensions: {dims}')
def avg_pool_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D average pooling module.
"""
if dims == 1:
return nn.AvgPool1d(*args, **kwargs)
elif dims == 2:
return nn.AvgPool2d(*args, **kwargs)
elif dims == 3:
return nn.AvgPool3d(*args, **kwargs)
raise ValueError(f'unsupported dimensions: {dims}')
class Downsample(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2):
super().__init__()
self.channels = channels
self.use_conv = use_conv
self.dims = dims
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(dims, channels, channels, 3, stride=stride,
padding=1)
else:
self.op = avg_pool_nd(stride)
def forward(self, x):
assert x.shape[1] == self.channels
return self.op(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'channels': 4, 'use_conv': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 4 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(2,
2), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(64)](buf1, primals_3, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_3
return buf1, primals_1, primals_2
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f'unsupported dimensions: {dims}')
def avg_pool_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D average pooling module.
"""
if dims == 1:
return nn.AvgPool1d(*args, **kwargs)
elif dims == 2:
return nn.AvgPool2d(*args, **kwargs)
elif dims == 3:
return nn.AvgPool3d(*args, **kwargs)
raise ValueError(f'unsupported dimensions: {dims}')
class DownsampleNew(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2):
super().__init__()
self.channels = channels
self.use_conv = use_conv
self.dims = dims
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(dims, channels, channels, 3, stride=stride,
padding=1)
else:
self.op = avg_pool_nd(stride)
def forward(self, input_0):
primals_2 = self.op.weight
primals_3 = self.op.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
francoismaze/improved-diffusion
|
Downsample
| false | 10,076 |
[
"MIT"
] | 0 |
bb403ba2437d6d834bb285b7259549fb3fa40f1b
|
https://github.com/francoismaze/improved-diffusion/tree/bb403ba2437d6d834bb285b7259549fb3fa40f1b
|
ConvBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_2/inductor_cache/tt/ctthexrcgmoykvsyasq7xirwxi6m3yxgjocmuvarikaawgqvdiws.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
# out => _unsafe_index, _unsafe_index_1
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = (xindex // 6) % 6
x2 = (xindex // 36)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_2/inductor_cache/ty/ctyzpua7fjfn7hztwdpigb3gi7yf6hmfsexovkmknohzjtrj2z3h.py
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
# out_1 => convolution
# out_2 => expm1, gt, mul, mul_2, where
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
# %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 1.0), kwargs = {})
# %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
# %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {})
triton_poi_fused_convolution_elu_1 = async_compile.triton('triton_poi_fused_convolution_elu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_elu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 1.0
tmp6 = tmp2 * tmp5
tmp7 = libdevice.expm1(tmp6)
tmp8 = tmp7 * tmp5
tmp9 = tl.where(tmp4, tmp6, tmp8)
tl.store(in_out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 576, grid=grid(576), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.elu]
triton_poi_fused_convolution_elu_1.run(buf2, primals_3, 256, grid=grid(256), stream=stream0)
del primals_3
return (buf2, primals_2, buf0, buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_elu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 1.0
tmp6 = tmp2 * tmp5
tmp7 = libdevice.expm1(tmp6)
tmp8 = tmp7 * tmp5
tmp9 = tl.where(tmp4, tmp6, tmp8)
tl.store(in_out_ptr0 + x3, tmp9, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(576)](primals_1, buf0, 576,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_elu_1[grid(256)](buf2, primals_3, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
return buf2, primals_2, buf0, buf2
class Conv3x3(nn.Module):
"""Layer to pad and convolve input
"""
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = nn.ZeroPad2d(1)
self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3)
def forward(self, x):
out = self.pad(x)
out = self.conv(out)
return out
class ConvBlockNew(nn.Module):
"""Layer to perform a convolution followed by ELU
"""
def __init__(self, in_channels, out_channels):
super(ConvBlockNew, self).__init__()
self.conv = Conv3x3(in_channels, out_channels)
self.nonlin = nn.ELU(inplace=True)
def forward(self, input_0):
primals_2 = self.conv.conv.weight
primals_3 = self.conv.conv.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Sid1057/sid1057.github.io
|
ConvBlock
| false | 17,936 |
[
"MIT"
] | 4 |
623d1731e308b42b6f86304dcfd671a061b414bf
|
https://github.com/Sid1057/sid1057.github.io/tree/623d1731e308b42b6f86304dcfd671a061b414bf
|
LayerScaleBlock
|
import torch
import torch.nn as nn
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
"""
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.
device)
random_tensor.floor_()
output = x.div(keep_prob) * random_tensor
return output
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class TalkingHeadAttn(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None,
attn_drop=0.0, proj_drop=0.0):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_l = nn.Linear(num_heads, num_heads)
self.proj_w = nn.Linear(num_heads, num_heads)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads
).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
attn = q @ k.transpose(-2, -1)
attn = self.proj_l(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
attn = attn.softmax(dim=-1)
attn = self.proj_w(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class LayerScaleBlock(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4.0, qkv_bias=False,
qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn
.GELU, norm_layer=nn.LayerNorm, attn_block=TalkingHeadAttn,
mlp_block=Mlp, init_values=0.0001):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = attn_block(dim, num_heads=num_heads, qkv_bias=qkv_bias,
qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path
) if drop_path > 0.0 else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = mlp_block(in_features=dim, hidden_features=
mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.gamma_1 = nn.Parameter(init_values * torch.ones(dim),
requires_grad=True)
self.gamma_2 = nn.Parameter(init_values * torch.ones(dim),
requires_grad=True)
def forward(self, x):
y = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
z = y + self.drop_path(self.gamma_2 * self.mlp(self.norm2(y)))
return y, z
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'dim': 4, 'num_heads': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_clone_mul_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK:
tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 12 * x2 + 48 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (4 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 64
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 16
y1 = yindex // 16
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused__softmax_5(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
tmp6 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
tmp9 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp3 + tmp1
tmp5 = triton_helpers.maximum(tmp2, tmp4)
tmp7 = tmp6 + tmp1
tmp8 = triton_helpers.maximum(tmp5, tmp7)
tmp10 = tmp9 + tmp1
tmp11 = triton_helpers.maximum(tmp8, tmp10)
tmp12 = tmp2 - tmp11
tmp13 = tl_math.exp(tmp12)
tmp14 = tmp4 - tmp11
tmp15 = tl_math.exp(tmp14)
tmp16 = tmp13 + tmp15
tmp17 = tmp7 - tmp11
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp20 = tmp10 - tmp11
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tl.store(out_ptr0 + x2, tmp11, xmask)
tl.store(out_ptr1 + x2, tmp22, xmask)
@triton.jit
def triton_poi_fused_clone_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 4
x2 = xindex // 16
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp6 = tl.load(in_ptr3 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 - tmp3
tmp5 = tl_math.exp(tmp4)
tmp7 = tmp5 / tmp6
tl.store(out_ptr0 + x3, tmp7, xmask)
@triton.jit
def triton_poi_fused_clone_7(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused_clone_8(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (8 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_clone_9(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_add_mul_10(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + x2, xmask)
tmp3 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp4 = tmp2 + tmp3
tmp5 = tmp1 * tmp4
tmp6 = tmp0 + tmp5
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_gelu_11(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = 0.7071067811865476
tmp4 = tmp0 * tmp3
tmp5 = libdevice.erf(tmp4)
tmp6 = 1.0
tmp7 = tmp5 + tmp6
tmp8 = tmp2 * tmp7
tl.store(out_ptr0 + x0, tmp8, xmask)
@triton.jit
def triton_poi_fused_add_mul_12(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + x2, xmask)
tmp3 = tmp1 * tmp2
tmp4 = tmp0 + tmp3
tl.store(out_ptr0 + x2, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17, primals_18
) = args
args.clear()
assert_size_stride(primals_1, (4,), (1,))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_5, (12, 4), (4, 1))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (4,), (1,))
assert_size_stride(primals_10, (4, 4), (4, 1))
assert_size_stride(primals_11, (4,), (1,))
assert_size_stride(primals_12, (4,), (1,))
assert_size_stride(primals_13, (4,), (1,))
assert_size_stride(primals_14, (4,), (1,))
assert_size_stride(primals_15, (16, 4), (4, 1))
assert_size_stride(primals_16, (16,), (1,))
assert_size_stride(primals_17, (4, 16), (16, 1))
assert_size_stride(primals_18, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(16)](primals_4, buf0,
buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(64)](primals_4, buf0,
buf1, primals_2, primals_3, buf2, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del primals_2
del primals_3
buf3 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_5, (4, 12), (1, 4), 0), out=buf3)
buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
triton_poi_fused_clone_mul_2[grid(16, 4)](buf3, buf4, 16, 4, XBLOCK
=4, YBLOCK=16, num_warps=1, num_stages=1)
buf5 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
triton_poi_fused_clone_3[grid(16, 4)](buf3, buf5, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf5, (16, 1, 4), (4, 0, 1), 0), out=buf6)
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_4[grid(64, 4)](buf6, buf7, 64, 4, XBLOCK=4,
YBLOCK=32, num_warps=4, num_stages=1)
buf8 = reinterpret_tensor(buf6, (64, 4), (4, 1), 0)
del buf6
extern_kernels.mm(reinterpret_tensor(buf7, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf8)
buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32)
buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32)
triton_poi_fused__softmax_5[grid(64)](buf8, primals_7, buf9, buf10,
64, XBLOCK=64, num_warps=1, num_stages=1)
buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_6[grid(256)](buf8, primals_7, buf9, buf10,
buf11, 256, XBLOCK=256, num_warps=4, num_stages=1)
buf12 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf11, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf12)
buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_7[grid(16, 16)](buf12, primals_9, buf13, 16,
16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
del primals_9
buf14 = reinterpret_tensor(buf9, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf9
triton_poi_fused_clone_8[grid(16, 4)](buf3, buf14, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
del buf3
buf15 = reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 1), 0)
del buf10
extern_kernels.bmm(reinterpret_tensor(buf13, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf14, (16, 4, 1), (4, 1, 0), 0), out=buf15)
buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_clone_9[grid(16, 4)](buf15, buf16, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf17 = reinterpret_tensor(buf15, (16, 4), (4, 1), 0)
del buf15
extern_kernels.mm(reinterpret_tensor(buf16, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf17)
buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_add_mul_10[grid(64)](primals_4, primals_1, buf17,
primals_11, buf18, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf19 = buf1
del buf1
buf20 = buf0
del buf0
triton_poi_fused_native_layer_norm_0[grid(16)](buf18, buf19, buf20,
16, XBLOCK=16, num_warps=1, num_stages=1)
buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(64)](buf18, buf19, buf20,
primals_13, primals_14, buf21, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del buf19
del buf20
del primals_14
buf22 = reinterpret_tensor(buf12, (16, 16), (16, 1), 0)
del buf12
extern_kernels.addmm(primals_16, reinterpret_tensor(buf21, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_15, (4, 16), (1, 4), 0),
alpha=1, beta=1, out=buf22)
del primals_16
buf23 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
triton_poi_fused_gelu_11[grid(256)](buf22, buf23, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf24 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_18, reinterpret_tensor(buf23, (16, 16),
(16, 1), 0), reinterpret_tensor(primals_17, (16, 4), (1, 16), 0
), alpha=1, beta=1, out=buf24)
del primals_18
buf25 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_add_mul_12[grid(64)](buf18, primals_12, buf24,
buf25, 64, XBLOCK=64, num_warps=1, num_stages=1)
return (buf18, buf25, primals_1, primals_4, primals_7, primals_11,
primals_12, primals_13, reinterpret_tensor(buf2, (16, 4), (4, 1), 0
), reinterpret_tensor(buf7, (64, 4), (4, 1), 0), buf8,
reinterpret_tensor(buf11, (64, 4), (4, 1), 0), reinterpret_tensor(
buf16, (16, 4), (4, 1), 0), buf17, buf18, reinterpret_tensor(buf21,
(16, 4), (4, 1), 0), buf22, reinterpret_tensor(buf23, (16, 16), (16,
1), 0), buf24, primals_17, primals_15, primals_10,
reinterpret_tensor(buf13, (16, 4, 4), (16, 1, 4), 0),
reinterpret_tensor(buf14, (16, 1, 4), (4, 1, 1), 0), primals_8,
primals_6, reinterpret_tensor(buf4, (16, 1, 4), (4, 1, 1), 0),
reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 4), 0), primals_5)
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
"""
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.
device)
random_tensor.floor_()
output = x.div(keep_prob) * random_tensor
return output
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
def __init__(self, in_features, hidden_features=None, out_features=None,
act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class TalkingHeadAttn(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None,
attn_drop=0.0, proj_drop=0.0):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_l = nn.Linear(num_heads, num_heads)
self.proj_w = nn.Linear(num_heads, num_heads)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads
).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
attn = q @ k.transpose(-2, -1)
attn = self.proj_l(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
attn = attn.softmax(dim=-1)
attn = self.proj_w(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class LayerScaleBlockNew(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4.0, qkv_bias=False,
qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn
.GELU, norm_layer=nn.LayerNorm, attn_block=TalkingHeadAttn,
mlp_block=Mlp, init_values=0.0001):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = attn_block(dim, num_heads=num_heads, qkv_bias=qkv_bias,
qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path
) if drop_path > 0.0 else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = mlp_block(in_features=dim, hidden_features=
mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.gamma_1 = nn.Parameter(init_values * torch.ones(dim),
requires_grad=True)
self.gamma_2 = nn.Parameter(init_values * torch.ones(dim),
requires_grad=True)
def forward(self, input_0):
primals_1 = self.gamma_1
primals_2 = self.gamma_2
primals_3 = self.norm1.weight
primals_7 = self.norm1.bias
primals_5 = self.attn.qkv.weight
primals_6 = self.attn.proj.weight
primals_9 = self.attn.proj.bias
primals_8 = self.attn.proj_l.weight
primals_11 = self.attn.proj_l.bias
primals_10 = self.attn.proj_w.weight
primals_12 = self.attn.proj_w.bias
primals_13 = self.norm2.weight
primals_14 = self.norm2.bias
primals_15 = self.mlp.fc1.weight
primals_16 = self.mlp.fc1.bias
primals_17 = self.mlp.fc2.weight
primals_18 = self.mlp.fc2.bias
primals_4 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17, primals_18])
return output[0], output[1]
|
yifanc96/yifanc-DL
|
LayerScaleBlock
| false | 11,101 |
[
"MIT"
] | 0 |
25d56cec776fb151c8f6bcbd997bca94f07f3597
|
https://github.com/yifanc96/yifanc-DL/tree/25d56cec776fb151c8f6bcbd997bca94f07f3597
|
JaccardLoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_0/inductor_cache/3u/c3u5ymm2y7v72mwkeem6ekyshydqrrvo7wiykmctvcwkoun4ir7s.py
# Topologically Sorted Source Nodes: [mul, intersection, add, add_1, sum_3, sub, add_2, truediv, losses, mean], Original ATen: [aten.mul, aten.sum, aten.add, aten.sub, aten.div, aten.rsub, aten.mean]
# Source node to ATen node mapping:
# add => add
# add_1 => add_1
# add_2 => add_2
# intersection => sum_2
# losses => sub_1
# mean => mean
# mul => mul
# sub => sub
# sum_3 => sum_3
# truediv => div
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %view), kwargs = {})
# %sum_2 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, 0.001), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %view), kwargs = {})
# %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add_1, [1]), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sum_3, %sum_2), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, 0.001), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %add_2), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_1,), kwargs = {})
triton_per_fused_add_div_mean_mul_rsub_sub_sum_0 = async_compile.triton('triton_per_fused_add_div_mean_mul_rsub_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_rsub_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_div_mean_mul_rsub_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp1 = tl.load(in_ptr1 + (r0), None)
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
tmp6 = tmp0 + tmp1
tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
tmp10 = 0.001
tmp11 = tmp5 + tmp10
tmp12 = tmp9 - tmp5
tmp13 = tmp12 + tmp10
tmp14 = tmp11 / tmp13
tmp15 = 1.0
tmp16 = tmp15 - tmp14
tmp17 = tmp16 / tmp15
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp17, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((1, ), (1, ), torch.float32)
buf2 = reinterpret_tensor(buf0, (), (), 0); del buf0 # reuse
# Topologically Sorted Source Nodes: [mul, intersection, add, add_1, sum_3, sub, add_2, truediv, losses, mean], Original ATen: [aten.mul, aten.sum, aten.add, aten.sub, aten.div, aten.rsub, aten.mean]
stream0 = get_raw_stream(0)
triton_per_fused_add_div_mean_mul_rsub_sub_sum_0.run(buf2, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
return (buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_add_div_mean_mul_rsub_sub_sum_0(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
tmp6 = tmp0 + tmp1
tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
tmp10 = 0.001
tmp11 = tmp5 + tmp10
tmp12 = tmp9 - tmp5
tmp13 = tmp12 + tmp10
tmp14 = tmp11 / tmp13
tmp15 = 1.0
tmp16 = tmp15 - tmp14
tmp17 = tmp16 / tmp15
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp17, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((1,), (1,), torch.float32)
buf2 = reinterpret_tensor(buf0, (), (), 0)
del buf0
get_raw_stream(0)
triton_per_fused_add_div_mean_mul_rsub_sub_sum_0[grid(1)](buf2,
arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf2,
def jaccard(outputs, targets, per_image=False, non_empty=False, min_pixels=5):
batch_size = outputs.size()[0]
eps = 0.001
if not per_image:
batch_size = 1
dice_target = targets.contiguous().view(batch_size, -1).float()
dice_output = outputs.contiguous().view(batch_size, -1)
target_sum = torch.sum(dice_target, dim=1)
intersection = torch.sum(dice_output * dice_target, dim=1)
losses = 1 - (intersection + eps) / (torch.sum(dice_output +
dice_target, dim=1) - intersection + eps)
if non_empty:
assert per_image is True
non_empty_images = 0
sum_loss = 0
for i in range(batch_size):
if target_sum[i] > min_pixels:
sum_loss += losses[i]
non_empty_images += 1
if non_empty_images == 0:
return 0
else:
return sum_loss / non_empty_images
return losses.mean()
class JaccardLossNew(nn.Module):
def __init__(self, weight=None, size_average=True, per_image=False,
non_empty=False, apply_sigmoid=False, min_pixels=5):
super().__init__()
self.size_average = size_average
self.register_buffer('weight', weight)
self.per_image = per_image
self.non_empty = non_empty
self.apply_sigmoid = apply_sigmoid
self.min_pixels = min_pixels
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
ktncktnc/SpaceNet_Off_Nadir_Solutions
|
JaccardLoss
| false | 15,860 |
[
"Apache-2.0"
] | 164 |
2a9ef1c3b72fb749c808ddb8593a85cb16b9f1ca
|
https://github.com/ktncktnc/SpaceNet_Off_Nadir_Solutions/tree/2a9ef1c3b72fb749c808ddb8593a85cb16b9f1ca
|
TwoMLPHead
|
import torch
from torch import nn
import torch.nn.functional as F
class TwoMLPHead(nn.Module):
"""
Standard heads for FPN-based models
Arguments:
in_channels (int): number of input channels
representation_size (int): size of the intermediate representation
"""
def __init__(self, in_channels, representation_size):
super(TwoMLPHead, self).__init__()
self.fc6 = nn.Linear(in_channels, representation_size)
self.fc7 = nn.Linear(representation_size, representation_size)
def forward(self, x):
x = x.flatten(start_dim=1)
x = F.relu(self.fc6(x))
x = F.relu(self.fc7(x))
return x
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'representation_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
(1, 4), 0), out=buf0)
del primals_2
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_relu_0[grid(16)](buf1, primals_3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4
), 0), out=buf2)
buf3 = buf2
del buf2
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_1[grid(16)](buf3,
primals_5, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_5
return buf3, primals_1, buf1, buf4, primals_4
class TwoMLPHeadNew(nn.Module):
"""
Standard heads for FPN-based models
Arguments:
in_channels (int): number of input channels
representation_size (int): size of the intermediate representation
"""
def __init__(self, in_channels, representation_size):
super(TwoMLPHeadNew, self).__init__()
self.fc6 = nn.Linear(in_channels, representation_size)
self.fc7 = nn.Linear(representation_size, representation_size)
def forward(self, input_0):
primals_1 = self.fc6.weight
primals_3 = self.fc6.bias
primals_2 = self.fc7.weight
primals_5 = self.fc7.bias
primals_4 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
GreenCUBIC/GasBotty
|
TwoMLPHead
| false | 9,528 |
[
"MIT"
] | 0 |
158f5991201c80bf4cbbbb9deabc9954ff19bbb1
|
https://github.com/GreenCUBIC/GasBotty/tree/158f5991201c80bf4cbbbb9deabc9954ff19bbb1
|
CosineBasisLinear
|
import torch
import numpy as np
from torch import nn
def cosine_basis_functions(x, n_basis_functions=64):
"""Cosine basis functions used to embed quantile thresholds.
Args:
x (torch.Tensor): Input.
n_basis_functions (int): Number of cosine basis functions.
Returns:
ndarray: Embedding with shape of (x.shape + (n_basis_functions,)).
"""
i_pi = torch.arange(1, n_basis_functions + 1, dtype=torch.float, device
=x.device) * np.pi
embedding = torch.cos(x[..., None] * i_pi)
assert embedding.shape == x.shape + (n_basis_functions,)
return embedding
class CosineBasisLinear(nn.Module):
"""Linear layer following cosine basis functions.
Args:
n_basis_functions (int): Number of cosine basis functions.
out_size (int): Output size.
"""
def __init__(self, n_basis_functions, out_size):
super().__init__()
self.linear = nn.Linear(n_basis_functions, out_size)
self.n_basis_functions = n_basis_functions
self.out_size = out_size
def forward(self, x):
"""Evaluate.
Args:
x (torch.Tensor): Input.
Returns:
torch.Tensor: Output with shape of (x.shape + (out_size,)).
"""
h = cosine_basis_functions(x, self.n_basis_functions)
h = h.reshape(-1, self.n_basis_functions)
out = self.linear(h)
out = out.reshape(*x.shape, self.out_size)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'n_basis_functions': 4, 'out_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_arange_cos_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4
x0 = xindex % 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp1 = 1 + x0
tmp2 = tmp1.to(tl.float32)
tmp3 = 3.141592653589793
tmp4 = tmp2 * tmp3
tmp5 = tmp0 * tmp4
tmp6 = tl_math.cos(tmp5)
tl.store(out_ptr0 + x2, tmp6, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_arange_cos_mul_0[grid(1024)](primals_1, buf0, 1024,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (256, 4),
(4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf1)
del primals_2
del primals_3
return reinterpret_tensor(buf1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0
), reinterpret_tensor(buf0, (256, 4), (4, 1), 0)
def cosine_basis_functions(x, n_basis_functions=64):
"""Cosine basis functions used to embed quantile thresholds.
Args:
x (torch.Tensor): Input.
n_basis_functions (int): Number of cosine basis functions.
Returns:
ndarray: Embedding with shape of (x.shape + (n_basis_functions,)).
"""
i_pi = torch.arange(1, n_basis_functions + 1, dtype=torch.float, device
=x.device) * np.pi
embedding = torch.cos(x[..., None] * i_pi)
assert embedding.shape == x.shape + (n_basis_functions,)
return embedding
class CosineBasisLinearNew(nn.Module):
"""Linear layer following cosine basis functions.
Args:
n_basis_functions (int): Number of cosine basis functions.
out_size (int): Output size.
"""
def __init__(self, n_basis_functions, out_size):
super().__init__()
self.linear = nn.Linear(n_basis_functions, out_size)
self.n_basis_functions = n_basis_functions
self.out_size = out_size
def forward(self, input_0):
primals_2 = self.linear.weight
primals_3 = self.linear.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
g-votte/pfrl
|
CosineBasisLinear
| false | 15,381 |
[
"MIT"
] | 824 |
4c30c1d73f0941a2b649b62937eec346bb55a95e
|
https://github.com/g-votte/pfrl/tree/4c30c1d73f0941a2b649b62937eec346bb55a95e
|
TwoLayerCNN
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class TwoLayerCNN(nn.Module):
def __init__(self, C, M, embedding, channel, mtc_input, *args, **kwargs):
super(TwoLayerCNN, self).__init__()
self.C = C
self.M = M
self.embedding = embedding
self.mtc_input = C if mtc_input else 1
self.conv1 = nn.Conv1d(self.mtc_input, channel, 3, 1, padding=1,
bias=False)
self.flat_size = M // 2 * C // self.mtc_input * channel
self.fc1 = nn.Linear(self.flat_size, embedding)
def forward(self, x):
N = len(x)
x = x.view(-1, self.mtc_input, self.M)
x = F.relu(self.conv1(x))
x = F.max_pool1d(x, 2)
x = x.view(N, self.flat_size)
x = self.fc1(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'C': 4, 'M': 4, 'embedding': 4, 'channel': 4, 'mtc_input': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(in_out_ptr0 + x0, tmp2, xmask)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 2 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 2 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp1 > tmp0
tmp3 = tl.full([1], 1, tl.int8)
tmp4 = tl.full([1], 0, tl.int8)
tmp5 = tl.where(tmp2, tmp3, tmp4)
tmp6 = triton_helpers.maximum(tmp1, tmp0)
tl.store(out_ptr0 + x0, tmp5, xmask)
tl.store(out_ptr1 + x0, tmp6, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3), (12, 3, 1))
assert_size_stride(primals_3, (4, 8), (8, 1))
assert_size_stride(primals_4, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,),
padding=(1,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4), (16, 4, 1))
buf1 = buf0
del buf0
buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(64)](buf1, buf5, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 1, 2), (8, 2, 2, 1), torch.int8)
buf3 = empty_strided_cuda((4, 4, 1, 2), (8, 2, 2, 1), torch.float32)
triton_poi_fused_max_pool2d_with_indices_1[grid(32)](buf1, buf2,
buf3, 32, XBLOCK=32, num_warps=1, num_stages=1)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_4, reinterpret_tensor(buf3, (4, 8), (8,
1), 0), reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), alpha
=1, beta=1, out=buf4)
del primals_4
return buf4, primals_2, primals_1, reinterpret_tensor(buf1, (4, 4, 1, 4
), (16, 4, 4, 1), 0), buf2, reinterpret_tensor(buf3, (4, 8), (8, 1), 0
), primals_3, buf5
class TwoLayerCNNNew(nn.Module):
def __init__(self, C, M, embedding, channel, mtc_input, *args, **kwargs):
super(TwoLayerCNNNew, self).__init__()
self.C = C
self.M = M
self.embedding = embedding
self.mtc_input = C if mtc_input else 1
self.conv1 = nn.Conv1d(self.mtc_input, channel, 3, 1, padding=1,
bias=False)
self.flat_size = M // 2 * C // self.mtc_input * channel
self.fc1 = nn.Linear(self.flat_size, embedding)
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = self.fc1.weight
primals_4 = self.fc1.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
imvladikon/string-embed
|
TwoLayerCNN
| false | 3,669 |
[
"MIT"
] | 0 |
49e5ab0ada37b497dac51974aff16eeac65627a0
|
https://github.com/imvladikon/string-embed/tree/49e5ab0ada37b497dac51974aff16eeac65627a0
|
BasicNN
|
# AOT ID: ['1_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_7/inductor_cache/wm/cwmlpspwkzmiv4efvkrjowkoawwpe42tnpzkiacrjumkzfjsy3qc.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.div]
# Source node to ATen node mapping:
# x_1 => div
# Graph fragment:
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, 255.0), kwargs = {})
triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 784
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = 0.00392156862745098
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_7/inductor_cache/sp/cspbvdtjzgrogiqrdvxgtnc5djfqn22gfjy4t6u5773zfamx7rey.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# softmax => amax, div_1, exp, sub, sum_1
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%addmm, [1], True), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%addmm, %amax), kwargs = {})
# %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused__softmax_1 = async_compile.triton('triton_per_fused__softmax_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 2],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 2
RBLOCK: tl.constexpr = 2
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = triton_helpers.max2(tmp1, 1)[:, None]
tmp4 = tmp0 - tmp3
tmp5 = tl_math.exp(tmp4)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
tmp8 = tl.sum(tmp6, 1)[:, None]
tmp9 = tmp5 / tmp8
tl.store(out_ptr2 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp9, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (1, 1, 28, 28), (784, 784, 28, 1))
assert_size_stride(primals_2, (2, 784), (784, 1))
assert_size_stride(primals_3, (2, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((1, 1, 28, 28), (784, 1, 28, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.div]
stream0 = get_raw_stream(0)
triton_poi_fused_div_0.run(primals_1, buf0, 784, grid=grid(784), stream=stream0)
del primals_1
buf1 = empty_strided_cuda((1, 2), (2, 1), torch.float32)
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (1, 784), (0, 1), 0), reinterpret_tensor(primals_2, (784, 2), (1, 784), 0), alpha=1, beta=1, out=buf1)
del primals_2
del primals_3
buf4 = empty_strided_cuda((1, 2), (2, 1), torch.float32)
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
triton_per_fused__softmax_1.run(buf1, buf4, 1, 2, grid=grid(1), stream=stream0)
del buf1
return (buf4, reinterpret_tensor(buf0, (1, 784), (784, 1), 0), buf4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((1, 1, 28, 28), (784, 784, 28, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((2, 784), (784, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 784
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.00392156862745098
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
tl.constexpr):
RBLOCK: tl.constexpr = 2
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = triton_helpers.max2(tmp1, 1)[:, None]
tmp4 = tmp0 - tmp3
tmp5 = tl_math.exp(tmp4)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
tmp8 = tl.sum(tmp6, 1)[:, None]
tmp9 = tmp5 / tmp8
tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp9, None)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (1, 1, 28, 28), (784, 784, 28, 1))
assert_size_stride(primals_2, (2, 784), (784, 1))
assert_size_stride(primals_3, (2,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((1, 1, 28, 28), (784, 1, 28, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused_div_0[grid(784)](primals_1, buf0, 784, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((1, 2), (2, 1), torch.float32)
extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (1, 784),
(0, 1), 0), reinterpret_tensor(primals_2, (784, 2), (1, 784), 0
), alpha=1, beta=1, out=buf1)
del primals_2
del primals_3
buf4 = empty_strided_cuda((1, 2), (2, 1), torch.float32)
triton_per_fused__softmax_1[grid(1)](buf1, buf4, 1, 2, XBLOCK=1,
num_warps=2, num_stages=1)
del buf1
return buf4, reinterpret_tensor(buf0, (1, 784), (784, 1), 0), buf4
class BasicNNNew(nn.Module):
def __init__(self):
super(BasicNNNew, self).__init__()
self.net = nn.Linear(28 * 28, 2)
def forward(self, input_0):
primals_2 = self.net.weight
primals_3 = self.net.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
dbczumar/clipper
|
BasicNN
| false | 3,401 |
[
"Apache-2.0"
] | 0 |
80c97d27a38d60caaebb2a1ae6a995dd7ff1c82d
|
https://github.com/dbczumar/clipper/tree/80c97d27a38d60caaebb2a1ae6a995dd7ff1c82d
|
JointBoneLoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_0/inductor_cache/fm/cfm6a6dnefwe7wmwtjo6xtq4yv4kexu4kskkj242y6hsuzo4qect.py
# Topologically Sorted Source Nodes: [getitem, getitem_1, sub, J, getitem_2, getitem_3, sub_1, Y, sub_2, loss, sum_1, truediv, loss_1], Original ATen: [aten.index, aten.sub, aten.linalg_vector_norm, aten.abs, aten.sum, aten.div]
# Source node to ATen node mapping:
# J => pow_1, pow_2, sum_1
# Y => pow_3, pow_4, sum_2
# getitem => index
# getitem_1 => index_1
# getitem_2 => index_2
# getitem_3 => index_3
# loss => abs_1
# loss_1 => div_1
# sub => sub
# sub_1 => sub_1
# sub_2 => sub_2
# sum_1 => sum_3
# truediv => div
# Graph fragment:
# %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg0_1, [None, %lift_fresh_copy]), kwargs = {})
# %index_1 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg0_1, [None, %lift_fresh_copy_1]), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%index, %index_1), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1]), kwargs = {})
# %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
# %index_2 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg1_1, [None, %lift_fresh_copy_2]), kwargs = {})
# %index_3 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg1_1, [None, %lift_fresh_copy_3]), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%index_2, %index_3), kwargs = {})
# %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
# %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, [-1]), kwargs = {})
# %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%pow_2, %pow_4), kwargs = {})
# %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_2,), kwargs = {})
# %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%abs_1,), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, 4), kwargs = {})
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%div, 6), kwargs = {})
triton_red_fused_abs_div_index_linalg_vector_norm_sub_sum_0 = async_compile.triton('triton_red_fused_abs_div_index_linalg_vector_norm_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.reduction(
size_hints=[1, 128],
reduction_hint=ReductionHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_abs_div_index_linalg_vector_norm_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused_abs_div_index_linalg_vector_norm_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
xnumel = 1
rnumel = 96
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rbase = tl.arange(0, RBLOCK)[None, :]
_tmp65 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r1 = (rindex // 4) % 6
r0 = rindex % 4
r2 = (rindex // 24)
r3 = rindex
tmp0 = r1
tmp1 = tl.full([1, 1], 3, tl.int64)
tmp2 = tmp0 < tmp1
tmp3 = tl.full([1, 1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.full([1, 1], 2, tl.int64)
tmp6 = tmp0 < tmp5
tmp7 = tl.full([1, 1], 0, tl.int64)
tmp8 = tl.where(tmp6, tmp7, tmp7)
tmp9 = tl.where(tmp4, tmp7, tmp8)
tmp10 = tl.full([1, 1], 4, tl.int64)
tmp11 = tmp0 < tmp10
tmp12 = tl.full([1, 1], 5, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tl.where(tmp13, tmp3, tmp5)
tmp15 = tl.where(tmp11, tmp3, tmp14)
tmp16 = tl.where(tmp2, tmp9, tmp15)
tmp17 = tl.load(in_ptr0 + ((4*r0) + (16*tmp16) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp18 = tl.where(tmp6, tmp5, tmp1)
tmp19 = tl.where(tmp4, tmp3, tmp18)
tmp20 = tl.where(tmp13, tmp1, tmp1)
tmp21 = tl.where(tmp11, tmp5, tmp20)
tmp22 = tl.where(tmp2, tmp19, tmp21)
tmp23 = tl.load(in_ptr0 + ((4*r0) + (16*tmp22) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp24 = tmp17 - tmp23
tmp25 = tmp24 * tmp24
tmp26 = tl.load(in_ptr0 + (1 + (4*r0) + (16*tmp16) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp27 = tl.load(in_ptr0 + (1 + (4*r0) + (16*tmp22) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp28 = tmp26 - tmp27
tmp29 = tmp28 * tmp28
tmp30 = tmp25 + tmp29
tmp31 = tl.load(in_ptr0 + (2 + (4*r0) + (16*tmp16) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp32 = tl.load(in_ptr0 + (2 + (4*r0) + (16*tmp22) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp33 = tmp31 - tmp32
tmp34 = tmp33 * tmp33
tmp35 = tmp30 + tmp34
tmp36 = tl.load(in_ptr0 + (3 + (4*r0) + (16*tmp16) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp37 = tl.load(in_ptr0 + (3 + (4*r0) + (16*tmp22) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp38 = tmp36 - tmp37
tmp39 = tmp38 * tmp38
tmp40 = tmp35 + tmp39
tmp41 = tl.load(in_ptr1 + ((4*r0) + (16*tmp16) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp42 = tl.load(in_ptr1 + ((4*r0) + (16*tmp22) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp43 = tmp41 - tmp42
tmp44 = tmp43 * tmp43
tmp45 = tl.load(in_ptr1 + (1 + (4*r0) + (16*tmp16) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp46 = tl.load(in_ptr1 + (1 + (4*r0) + (16*tmp22) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp47 = tmp45 - tmp46
tmp48 = tmp47 * tmp47
tmp49 = tmp44 + tmp48
tmp50 = tl.load(in_ptr1 + (2 + (4*r0) + (16*tmp16) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp51 = tl.load(in_ptr1 + (2 + (4*r0) + (16*tmp22) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp52 = tmp50 - tmp51
tmp53 = tmp52 * tmp52
tmp54 = tmp49 + tmp53
tmp55 = tl.load(in_ptr1 + (3 + (4*r0) + (16*tmp16) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp56 = tl.load(in_ptr1 + (3 + (4*r0) + (16*tmp22) + (64*r2)), rmask, eviction_policy='evict_last', other=0.0)
tmp57 = tmp55 - tmp56
tmp58 = tmp57 * tmp57
tmp59 = tmp54 + tmp58
tmp60 = libdevice.sqrt(tmp40)
tmp61 = libdevice.sqrt(tmp59)
tmp62 = tmp60 - tmp61
tmp63 = tl_math.abs(tmp62)
tmp64 = tl.broadcast_to(tmp63, [XBLOCK, RBLOCK])
tmp66 = _tmp65 + tmp64
_tmp65 = tl.where(rmask, tmp66, _tmp65)
tmp65 = tl.sum(_tmp65, 1)[:, None]
tmp67 = 0.25
tmp68 = tmp65 * tmp67
tmp69 = 0.16666666666666666
tmp70 = tmp68 * tmp69
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp70, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf2 = empty_strided_cuda((), (), torch.float32)
buf3 = buf2; del buf2 # reuse
# Topologically Sorted Source Nodes: [getitem, getitem_1, sub, J, getitem_2, getitem_3, sub_1, Y, sub_2, loss, sum_1, truediv, loss_1], Original ATen: [aten.index, aten.sub, aten.linalg_vector_norm, aten.abs, aten.sum, aten.div]
stream0 = get_raw_stream(0)
triton_red_fused_abs_div_index_linalg_vector_norm_sub_sum_0.run(buf3, arg0_1, arg1_1, 1, 96, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
return (buf3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_red_fused_abs_div_index_linalg_vector_norm_sub_sum_0(in_out_ptr0,
in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.
constexpr):
rnumel = 96
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rbase = tl.arange(0, RBLOCK)[None, :]
_tmp65 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r1 = rindex // 4 % 6
r0 = rindex % 4
r2 = rindex // 24
tmp0 = r1
tmp1 = tl.full([1, 1], 3, tl.int64)
tmp2 = tmp0 < tmp1
tmp3 = tl.full([1, 1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.full([1, 1], 2, tl.int64)
tmp6 = tmp0 < tmp5
tmp7 = tl.full([1, 1], 0, tl.int64)
tmp8 = tl.where(tmp6, tmp7, tmp7)
tmp9 = tl.where(tmp4, tmp7, tmp8)
tmp10 = tl.full([1, 1], 4, tl.int64)
tmp11 = tmp0 < tmp10
tmp12 = tl.full([1, 1], 5, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tl.where(tmp13, tmp3, tmp5)
tmp15 = tl.where(tmp11, tmp3, tmp14)
tmp16 = tl.where(tmp2, tmp9, tmp15)
tmp17 = tl.load(in_ptr0 + (4 * r0 + 16 * tmp16 + 64 * r2), rmask,
eviction_policy='evict_last', other=0.0)
tmp18 = tl.where(tmp6, tmp5, tmp1)
tmp19 = tl.where(tmp4, tmp3, tmp18)
tmp20 = tl.where(tmp13, tmp1, tmp1)
tmp21 = tl.where(tmp11, tmp5, tmp20)
tmp22 = tl.where(tmp2, tmp19, tmp21)
tmp23 = tl.load(in_ptr0 + (4 * r0 + 16 * tmp22 + 64 * r2), rmask,
eviction_policy='evict_last', other=0.0)
tmp24 = tmp17 - tmp23
tmp25 = tmp24 * tmp24
tmp26 = tl.load(in_ptr0 + (1 + 4 * r0 + 16 * tmp16 + 64 * r2),
rmask, eviction_policy='evict_last', other=0.0)
tmp27 = tl.load(in_ptr0 + (1 + 4 * r0 + 16 * tmp22 + 64 * r2),
rmask, eviction_policy='evict_last', other=0.0)
tmp28 = tmp26 - tmp27
tmp29 = tmp28 * tmp28
tmp30 = tmp25 + tmp29
tmp31 = tl.load(in_ptr0 + (2 + 4 * r0 + 16 * tmp16 + 64 * r2),
rmask, eviction_policy='evict_last', other=0.0)
tmp32 = tl.load(in_ptr0 + (2 + 4 * r0 + 16 * tmp22 + 64 * r2),
rmask, eviction_policy='evict_last', other=0.0)
tmp33 = tmp31 - tmp32
tmp34 = tmp33 * tmp33
tmp35 = tmp30 + tmp34
tmp36 = tl.load(in_ptr0 + (3 + 4 * r0 + 16 * tmp16 + 64 * r2),
rmask, eviction_policy='evict_last', other=0.0)
tmp37 = tl.load(in_ptr0 + (3 + 4 * r0 + 16 * tmp22 + 64 * r2),
rmask, eviction_policy='evict_last', other=0.0)
tmp38 = tmp36 - tmp37
tmp39 = tmp38 * tmp38
tmp40 = tmp35 + tmp39
tmp41 = tl.load(in_ptr1 + (4 * r0 + 16 * tmp16 + 64 * r2), rmask,
eviction_policy='evict_last', other=0.0)
tmp42 = tl.load(in_ptr1 + (4 * r0 + 16 * tmp22 + 64 * r2), rmask,
eviction_policy='evict_last', other=0.0)
tmp43 = tmp41 - tmp42
tmp44 = tmp43 * tmp43
tmp45 = tl.load(in_ptr1 + (1 + 4 * r0 + 16 * tmp16 + 64 * r2),
rmask, eviction_policy='evict_last', other=0.0)
tmp46 = tl.load(in_ptr1 + (1 + 4 * r0 + 16 * tmp22 + 64 * r2),
rmask, eviction_policy='evict_last', other=0.0)
tmp47 = tmp45 - tmp46
tmp48 = tmp47 * tmp47
tmp49 = tmp44 + tmp48
tmp50 = tl.load(in_ptr1 + (2 + 4 * r0 + 16 * tmp16 + 64 * r2),
rmask, eviction_policy='evict_last', other=0.0)
tmp51 = tl.load(in_ptr1 + (2 + 4 * r0 + 16 * tmp22 + 64 * r2),
rmask, eviction_policy='evict_last', other=0.0)
tmp52 = tmp50 - tmp51
tmp53 = tmp52 * tmp52
tmp54 = tmp49 + tmp53
tmp55 = tl.load(in_ptr1 + (3 + 4 * r0 + 16 * tmp16 + 64 * r2),
rmask, eviction_policy='evict_last', other=0.0)
tmp56 = tl.load(in_ptr1 + (3 + 4 * r0 + 16 * tmp22 + 64 * r2),
rmask, eviction_policy='evict_last', other=0.0)
tmp57 = tmp55 - tmp56
tmp58 = tmp57 * tmp57
tmp59 = tmp54 + tmp58
tmp60 = libdevice.sqrt(tmp40)
tmp61 = libdevice.sqrt(tmp59)
tmp62 = tmp60 - tmp61
tmp63 = tl_math.abs(tmp62)
tmp64 = tl.broadcast_to(tmp63, [XBLOCK, RBLOCK])
tmp66 = _tmp65 + tmp64
_tmp65 = tl.where(rmask, tmp66, _tmp65)
tmp65 = tl.sum(_tmp65, 1)[:, None]
tmp67 = 0.25
tmp68 = tmp65 * tmp67
tmp69 = 0.16666666666666666
tmp70 = tmp68 * tmp69
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp70, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf2 = empty_strided_cuda((), (), torch.float32)
buf3 = buf2
del buf2
get_raw_stream(0)
triton_red_fused_abs_div_index_linalg_vector_norm_sub_sum_0[grid(1)](
buf3, arg0_1, arg1_1, 1, 96, XBLOCK=1, RBLOCK=64, num_warps=2,
num_stages=1)
del arg0_1
del arg1_1
return buf3,
class JointBoneLossNew(torch.nn.Module):
def __init__(self, joint_num):
super(JointBoneLossNew, self).__init__()
id_i, id_j = [], []
for i in range(joint_num):
for j in range(i + 1, joint_num):
id_i.append(i)
id_j.append(j)
self.id_i = id_i
self.id_j = id_j
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
SevenMoGod/movenet.pytorch
|
JointBoneLoss
| false | 14,395 |
[
"MIT"
] | 87 |
95ec8535245228aa4335243e68722810e50bcaf8
|
https://github.com/SevenMoGod/movenet.pytorch/tree/95ec8535245228aa4335243e68722810e50bcaf8
|
Conv2d
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_8/inductor_cache/vw/cvwklowagvx5sksvhxwnsr3o6vulpztvjpqkljpmfcmgvcblg6xo.py
# Topologically Sorted Source Nodes: [y], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# y => convolution
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 25) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [y], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 5, 5), (100, 25, 5, 1))
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [y], Original ATen: [aten.convolution]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_0.run(buf1, primals_2, 400, grid=grid(400), stream=stream0)
del primals_2
return (reinterpret_tensor(buf1, (4, 4, 4, 5), (100, 25, 5, 1), 0), primals_1, primals_3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 25 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(2, 2), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 5, 5), (100, 25, 5, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(400)](buf1, primals_2, 400,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
return reinterpret_tensor(buf1, (4, 4, 4, 5), (100, 25, 5, 1), 0
), primals_1, primals_3
class Conv2dNew(nn.Conv2d):
"""
:param in_channels: Scalar
:param out_channels: Scalar
:param kernel_size: Scalar
:param activation_fn: activation function
:param drop_rate: Scalar. dropout rate
:param stride: Scalar
:param padding: padding type
:param dilation: Scalar
:param groups: Scalar.
:param bias: Boolean.
:param bn: Boolean. whether it uses batch normalization
"""
def __init__(self, in_channels, out_channels, kernel_size,
activation_fn=None, drop_rate=0.0, stride=1, padding='same',
dilation=1, groups=1, bias=True, bn=False):
self.activation_fn = activation_fn
self.drop_rate = drop_rate
if padding == 'same':
padding = kernel_size // 2 * dilation
self.even_kernel = not bool(kernel_size % 2)
super(Conv2dNew, self).__init__(in_channels, out_channels,
kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
self.drop_out = nn.Dropout(drop_rate) if drop_rate > 0 else None
self.batch_norm = nn.BatchNorm2d(out_channels, eps=0.001, momentum=
0.001) if bn else None
def forward(self, input_0):
primals_1 = self.weight
primals_2 = self.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
CookiePPP/mellotron
|
Conv2d
| false | 9,058 |
[
"BSD-3-Clause"
] | 0 |
488425981c19cd0eddddea13d1348da4bfef8d26
|
https://github.com/CookiePPP/mellotron/tree/488425981c19cd0eddddea13d1348da4bfef8d26
|
Mish
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_6/inductor_cache/nf/cnfomfgkrddid5xfz2abcevz3avk7wtz7mughpoinpbdg3kjz27l.py
# Topologically Sorted Source Nodes: [softplus, tanh, mul_], Original ATen: [aten.softplus, aten.tanh, aten.mul]
# Source node to ATen node mapping:
# mul_ => mul
# softplus => exp, gt, log1p, where
# tanh => tanh
# Graph fragment:
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%arg0_1, 20), kwargs = {})
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%arg0_1,), kwargs = {})
# %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {})
# %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %arg0_1, %log1p), kwargs = {})
# %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%where,), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %tanh), kwargs = {})
# %copy_ : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%arg0_1, %mul), kwargs = {})
triton_poi_fused_mul_softplus_tanh_0 = async_compile.triton('triton_poi_fused_mul_softplus_tanh_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_softplus_tanh_0', 'mutated_arg_names': ['in_ptr0', 'out_ptr1'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_softplus_tanh_0(in_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = 20.0
tmp2 = tmp0 > tmp1
tmp3 = tl_math.exp(tmp0)
tmp4 = libdevice.log1p(tmp3)
tmp5 = tl.where(tmp2, tmp0, tmp4)
tmp6 = libdevice.tanh(tmp5)
tmp7 = tmp0 * tmp6
tl.store(out_ptr1 + (x0), tmp7, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [softplus, tanh, mul_], Original ATen: [aten.softplus, aten.tanh, aten.mul]
stream0 = get_raw_stream(0)
triton_poi_fused_mul_softplus_tanh_0.run(arg0_1, arg0_1, 256, grid=grid(256), stream=stream0)
return (arg0_1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_mul_softplus_tanh_0(in_ptr0, out_ptr1, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 20.0
tmp2 = tmp0 > tmp1
tmp3 = tl_math.exp(tmp0)
tmp4 = libdevice.log1p(tmp3)
tmp5 = tl.where(tmp2, tmp0, tmp4)
tmp6 = libdevice.tanh(tmp5)
tmp7 = tmp0 * tmp6
tl.store(out_ptr1 + x0, tmp7, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
get_raw_stream(0)
triton_poi_fused_mul_softplus_tanh_0[grid(256)](arg0_1, arg0_1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
return arg0_1,
class MishNew(nn.Module):
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
csharpshooter/DeepLearning
|
Mish
| false | 1,747 |
[
"MIT"
] | 0 |
c1d20660c32076468970f7376931e1fcd0d2644e
|
https://github.com/csharpshooter/DeepLearning/tree/c1d20660c32076468970f7376931e1fcd0d2644e
|
SphereLoss
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_2/inductor_cache/54/c54anezm5tp7djzvtp7bcl42ylzihy5446xuvla3ymvra5m7c4ma.py
# Topologically Sorted Source Nodes: [x_norm_1], Original ATen: [aten.div]
# Source node to ATen node mapping:
# x_norm_1 => div
# Graph fragment:
# %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, %expand), kwargs = {})
triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-12
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_2/inductor_cache/52/c52poxxuyzi26vb6oxpqnooguyjqzr62bsuca6tf73qb4cnpqt7y.py
# Topologically Sorted Source Nodes: [w_norm_1], Original ATen: [aten.div]
# Source node to ATen node mapping:
# w_norm_1 => div_1
# Graph fragment:
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_2, %expand_1), kwargs = {})
triton_poi_fused_div_1 = async_compile.triton('triton_poi_fused_div_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_div_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (12 + x0), xmask, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-12
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_2/inductor_cache/rh/crhl52hzepf4uwf2gepe3xefe3htjq2pvyzmbzyufavdxzhczb56.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
# %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 1), kwargs = {})
# %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [1], True), kwargs = {})
# %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
# %mul_tensor_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_tensor, 14), kwargs = {})
triton_poi_fused_2 = async_compile.triton('triton_poi_fused_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp3 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tmp4 = tmp3 * tmp1
tmp6 = tmp5 * tmp1
tmp7 = triton_helpers.maximum(tmp4, tmp6)
tmp9 = tmp8 * tmp1
tmp10 = triton_helpers.maximum(tmp7, tmp9)
tmp12 = tmp11 * tmp1
tmp13 = triton_helpers.maximum(tmp10, tmp12)
tmp14 = tmp2 - tmp13
tmp15 = 14.0
tmp16 = tmp14 * tmp15
tl.store(out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_2/inductor_cache/ia/ciaslwmz6eghlz44hmcrzjkybxoxqhu46c4ud6lpguuy7zetvqal.py
# Topologically Sorted Source Nodes: [loss], Original ATen: [aten._log_softmax, aten.mul, aten.sum, aten.neg, aten.div]
# Source node to ATen node mapping:
# loss => div_2, exp, log, mul_1, neg, sub_1, sum_3, sum_4
# Graph fragment:
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul_tensor_1,), kwargs = {})
# %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
# %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_3,), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor_1, %log), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %primals_3), kwargs = {})
# %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_1,), kwargs = {})
# %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_4,), kwargs = {})
# %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Scalar](args = (%neg, 4), kwargs = {})
triton_per_fused__log_softmax_div_mul_neg_sum_3 = async_compile.triton('triton_per_fused__log_softmax_div_mul_neg_sum_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 16],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__log_softmax_div_mul_neg_sum_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__log_softmax_div_mul_neg_sum_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
r1 = (rindex // 4)
tmp0 = tl.load(in_ptr0 + (r2), None)
tmp1 = tl.load(in_ptr0 + (4*r1), None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*r1)), None, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + (4*r1)), None, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + (4*r1)), None, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr1 + (r2), None)
tmp2 = tl_math.exp(tmp1)
tmp4 = tl_math.exp(tmp3)
tmp5 = tmp2 + tmp4
tmp7 = tl_math.exp(tmp6)
tmp8 = tmp5 + tmp7
tmp10 = tl_math.exp(tmp9)
tmp11 = tmp8 + tmp10
tmp12 = tl_math.log(tmp11)
tmp13 = tmp0 - tmp12
tmp15 = tmp13 * tmp14
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp18 = tl.sum(tmp16, 1)[:, None]
tmp19 = -tmp18
tmp20 = 0.25
tmp21 = tmp19 * tmp20
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp21, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_norm_1], Original ATen: [aten.div]
stream0 = get_raw_stream(0)
triton_poi_fused_div_0.run(primals_1, buf0, 16, grid=grid(16), stream=stream0)
del primals_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [w_norm_1], Original ATen: [aten.div]
triton_poi_fused_div_1.run(primals_2, buf1, 16, grid=grid(16), stream=stream0)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [w_norm_1, cos_th], Original ATen: [aten.div, aten.mm]
extern_kernels.mm(buf0, buf1, out=buf2)
buf3 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [], Original ATen: []
triton_poi_fused_2.run(buf2, buf3, 16, grid=grid(16), stream=stream0)
buf4 = empty_strided_cuda((), (), torch.float32)
buf5 = buf4; del buf4 # reuse
# Topologically Sorted Source Nodes: [loss], Original ATen: [aten._log_softmax, aten.mul, aten.sum, aten.neg, aten.div]
triton_per_fused__log_softmax_div_mul_neg_sum_3.run(buf5, buf3, primals_3, 1, 16, grid=grid(1), stream=stream0)
del buf3
return (buf5, primals_2, primals_3, buf2, reinterpret_tensor(buf0, (4, 4), (1, 4), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.utils.data
import torch.nn as nn
from torchvision.transforms import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-12
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + x2, tmp15, xmask)
@triton.jit
def triton_poi_fused_div_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (12 + x0), xmask, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-12
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + x2, tmp15, xmask)
@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tmp4 = tmp3 * tmp1
tmp6 = tmp5 * tmp1
tmp7 = triton_helpers.maximum(tmp4, tmp6)
tmp9 = tmp8 * tmp1
tmp10 = triton_helpers.maximum(tmp7, tmp9)
tmp12 = tmp11 * tmp1
tmp13 = triton_helpers.maximum(tmp10, tmp12)
tmp14 = tmp2 - tmp13
tmp15 = 14.0
tmp16 = tmp14 * tmp15
tl.store(out_ptr0 + x2, tmp16, xmask)
@triton.jit
def triton_per_fused__log_softmax_div_mul_neg_sum_3(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
r1 = rindex // 4
tmp0 = tl.load(in_ptr0 + r2, None)
tmp1 = tl.load(in_ptr0 + 4 * r1, None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * r1), None, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + 4 * r1), None, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + 4 * r1), None, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr1 + r2, None)
tmp2 = tl_math.exp(tmp1)
tmp4 = tl_math.exp(tmp3)
tmp5 = tmp2 + tmp4
tmp7 = tl_math.exp(tmp6)
tmp8 = tmp5 + tmp7
tmp10 = tl_math.exp(tmp9)
tmp11 = tmp8 + tmp10
tmp12 = tl_math.log(tmp11)
tmp13 = tmp0 - tmp12
tmp15 = tmp13 * tmp14
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp18 = tl.sum(tmp16, 1)[:, None]
tmp19 = -tmp18
tmp20 = 0.25
tmp21 = tmp19 * tmp20
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp21, None)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_div_0[grid(16)](primals_1, buf0, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_div_1[grid(16)](primals_2, buf1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf0, buf1, out=buf2)
buf3 = buf1
del buf1
triton_poi_fused_2[grid(16)](buf2, buf3, 16, XBLOCK=16, num_warps=1,
num_stages=1)
buf4 = empty_strided_cuda((), (), torch.float32)
buf5 = buf4
del buf4
triton_per_fused__log_softmax_div_mul_neg_sum_3[grid(1)](buf5, buf3,
primals_3, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
del buf3
return buf5, primals_2, primals_3, buf2, reinterpret_tensor(buf0, (4, 4
), (1, 4), 0)
class SphereLossNew(nn.Module):
def __init__(self, in_feats, n_classes, scale=14, *args, **kwargs):
super(SphereLossNew, self).__init__(*args, **kwargs)
self.scale = scale
self.cross_entropy = nn.CrossEntropyLoss()
self.W = torch.nn.Parameter(torch.randn(in_feats, n_classes),
requires_grad=True)
nn.init.xavier_normal_(self.W, gain=1)
def forward(self, input_0, input_1):
primals_1 = self.W
primals_2 = input_0
primals_3 = input_1
output = call([primals_1, primals_2, primals_3])
return output[0]
|
ace19-dev/image-retrieval-pytorch
|
SphereLoss
| false | 18,236 |
[
"MIT"
] | 9 |
19bd4ae5efea5b6184c345f693646bcd9a0fc8cf
|
https://github.com/ace19-dev/image-retrieval-pytorch/tree/19bd4ae5efea5b6184c345f693646bcd9a0fc8cf
|
GCN
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_2/inductor_cache/d2/cd2onkfnnqqav3af2iyomd6l7n3rqdwwvxsyw3ac7zfntydugnbi.py
# Topologically Sorted Source Nodes: [add, x], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# add => add
# x => relu
# Graph fragment:
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_4, %primals_4), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
# %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_add_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_add_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_2/inductor_cache/nd/cndmfvh7lrojuqo776y2t4r3iwqonobmuxwcqkx2pam6myjioeyl.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.add]
# Source node to ATen node mapping:
# x_3 => add_2
# Graph fragment:
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_14, %primals_8), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, ), (1, ))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (4, ), (1, ))
assert_size_stride(primals_7, (4, 4), (4, 1))
assert_size_stride(primals_8, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [support], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), primals_2, out=buf0)
del primals_2
buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0), out=buf1)
buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf1 # reuse
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [add, x], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_add_relu_threshold_backward_0.run(buf2, primals_4, buf10, 256, grid=grid(256), stream=stream0)
del primals_4
buf3 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [support_1], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0), primals_5, out=buf3)
buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), out=buf4)
buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf4 # reuse
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [add_1, x_2], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
triton_poi_fused_add_relu_threshold_backward_0.run(buf5, primals_6, buf9, 256, grid=grid(256), stream=stream0)
del primals_6
buf6 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [support_2], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0), primals_7, out=buf6)
buf7 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [output_2], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0), out=buf7)
del buf6
buf8 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf7 # reuse
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.add]
triton_poi_fused_add_1.run(buf8, primals_8, 256, grid=grid(256), stream=stream0)
del primals_8
return (reinterpret_tensor(buf8, (4, 64), (64, 1), 0), reinterpret_tensor(primals_1, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf5, (4, 64), (1, 4), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), buf9, reinterpret_tensor(buf2, (4, 64), (1, 4), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), buf10, reinterpret_tensor(primals_3, (4, 64), (1, 4), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
import math
from torch.nn.modules import Module
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x2, tmp2, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (4, 4), (4, 1))
assert_size_stride(primals_8, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
primals_2, out=buf0)
del primals_2
buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4,
1), 0), reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1), 0),
out=buf1)
buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf1
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_add_relu_threshold_backward_0[grid(256)](buf2,
primals_4, buf10, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_4
buf3 = buf0
del buf0
extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0),
primals_5, out=buf3)
buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4,
1), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0),
out=buf4)
buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf4
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_add_relu_threshold_backward_0[grid(256)](buf5,
primals_6, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_6
buf6 = buf3
del buf3
extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0),
primals_7, out=buf6)
buf7 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4,
1), 0), reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0),
out=buf7)
del buf6
buf8 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf7
triton_poi_fused_add_1[grid(256)](buf8, primals_8, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_8
return reinterpret_tensor(buf8, (4, 64), (64, 1), 0), reinterpret_tensor(
primals_1, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf5, (4,
64), (1, 4), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0
), buf9, reinterpret_tensor(buf2, (4, 64), (1, 4), 0
), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0
), buf10, reinterpret_tensor(primals_3, (4, 64), (1, 4), 0)
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
Z = f(X, A) = softmax(A` * ReLU(A` * X * W0)* W1)
A` = D'^(-0.5) * A * D'^(-0.5)
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.FloatTensor(in_features, out_features))
if bias:
self.bias = Parameter(torch.FloatTensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
stdv = 1.0 / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward(self, input, adj):
support = torch.matmul(input, self.weight)
output = torch.matmul(adj, support)
if self.bias is not None:
return output + self.bias
else:
return output
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
class GCNNew(Module):
"""
simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, nfeat, dropout):
super(GCNNew, self).__init__()
self.gc1 = GraphConvolution(nfeat, nfeat)
self.gc2 = GraphConvolution(nfeat, nfeat)
self.gc3 = GraphConvolution(nfeat, nfeat)
self.dropout = dropout
def forward(self, input_0, input_1):
primals_2 = self.gc1.weight
primals_4 = self.gc1.bias
primals_5 = self.gc2.weight
primals_6 = self.gc2.bias
primals_7 = self.gc3.weight
primals_8 = self.gc3.bias
primals_1 = input_0
primals_3 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
Brain03Yao/M2TGCN
|
GCN
| false | 17,008 |
[
"MIT"
] | 6 |
72c65687fa52c618740cd6d1db7366116f68398c
|
https://github.com/Brain03Yao/M2TGCN/tree/72c65687fa52c618740cd6d1db7366116f68398c
|
Fusion
|
import torch
import torch.nn as nn
import torch.utils.checkpoint
class Fusion(nn.Module):
"""
The subnetwork that is used in TFN for video and audio in the pre-fusion stage
"""
def __init__(self, in_size, hidden_size, n_class, dropout, modal_name=
'text'):
"""
Args:
in_size: input dimension
hidden_size: hidden layer dimension
dropout: dropout probability
Output:
(return value in forward) a tensor of shape (batch_size, hidden_size)
"""
super(Fusion, self).__init__()
self.drop = nn.Dropout(p=dropout)
self.linear_1 = nn.Linear(in_size, hidden_size)
self.linear_2 = nn.Linear(hidden_size, hidden_size)
self.linear_3 = nn.Linear(hidden_size, n_class)
def forward(self, x):
"""
Args:
x: tensor of shape (batch_size, in_size)
"""
dropped = self.drop(x)
y_1 = torch.tanh(self.linear_1(dropped))
self.linear_2(y_1)
y_2 = torch.tanh(self.linear_2(y_1))
y_3 = self.linear_3(y_2)
return y_2, y_3
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_size': 4, 'hidden_size': 4, 'n_class': 4, 'dropout': 0.5}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = libdevice.tanh(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
del primals_2
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_tanh_0[grid(256)](buf1, primals_3, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf2
triton_poi_fused_tanh_0[grid(256)](buf3, primals_5, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf4)
del primals_7
return buf3, reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
), buf1, buf3, primals_6, primals_4
class FusionNew(nn.Module):
"""
The subnetwork that is used in TFN for video and audio in the pre-fusion stage
"""
def __init__(self, in_size, hidden_size, n_class, dropout, modal_name=
'text'):
"""
Args:
in_size: input dimension
hidden_size: hidden layer dimension
dropout: dropout probability
Output:
(return value in forward) a tensor of shape (batch_size, hidden_size)
"""
super(FusionNew, self).__init__()
self.drop = nn.Dropout(p=dropout)
self.linear_1 = nn.Linear(in_size, hidden_size)
self.linear_2 = nn.Linear(hidden_size, hidden_size)
self.linear_3 = nn.Linear(hidden_size, n_class)
def forward(self, input_0):
primals_2 = self.linear_1.weight
primals_3 = self.linear_1.bias
primals_4 = self.linear_2.weight
primals_5 = self.linear_2.bias
primals_6 = self.linear_3.weight
primals_7 = self.linear_3.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0], output[1]
|
dumpmemory/MMSA
|
Fusion
| false | 6,613 |
[
"MIT"
] | 1 |
08b3a7f4529c380356eeb1cf6bf9a89e7c9701e7
|
https://github.com/dumpmemory/MMSA/tree/08b3a7f4529c380356eeb1cf6bf9a89e7c9701e7
|
Net
|
import torch
import torch.nn as nn
import torch.nn.functional
class Net(nn.Module):
def __init__(self, num_inputs=784, num_outputs=10, num_hiddens=256,
is_training=True):
super(Net, self).__init__()
self.num_inputs = num_inputs
self.num_outputs = num_outputs
self.num_hiddens = num_hiddens
self.linear_1 = nn.Linear(num_inputs, num_hiddens)
self.linear_2 = nn.Linear(num_hiddens, num_outputs)
self.relu = nn.ReLU()
def forward(self, X):
X = X.reshape((-1, self.num_inputs))
H1 = self.relu(self.linear_1(X))
out = self.linear_2(H1)
return out
def get_inputs():
return [torch.rand([4, 784])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 256
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 784), (784, 1))
assert_size_stride(primals_2, (256, 784), (784, 1))
assert_size_stride(primals_3, (256,), (1,))
assert_size_stride(primals_4, (10, 256), (256, 1))
assert_size_stride(primals_5, (10,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (784,
256), (1, 784), 0), out=buf0)
del primals_2
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_relu_0[grid(1024)](buf1, primals_3, 1024, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
(256, 10), (1, 256), 0), alpha=1, beta=1, out=buf2)
del primals_5
return buf2, primals_1, buf1, primals_4
class NetNew(nn.Module):
def __init__(self, num_inputs=784, num_outputs=10, num_hiddens=256,
is_training=True):
super(NetNew, self).__init__()
self.num_inputs = num_inputs
self.num_outputs = num_outputs
self.num_hiddens = num_hiddens
self.linear_1 = nn.Linear(num_inputs, num_hiddens)
self.linear_2 = nn.Linear(num_hiddens, num_outputs)
self.relu = nn.ReLU()
def forward(self, input_0):
primals_2 = self.linear_1.weight
primals_3 = self.linear_1.bias
primals_4 = self.linear_2.weight
primals_5 = self.linear_2.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
samjz06/d2l-pytorch
|
Net
| false | 7,593 |
[
"Apache-2.0"
] | 1 |
80eca3f7d217eefb4f6ae08aae24c6a3c2714898
|
https://github.com/samjz06/d2l-pytorch/tree/80eca3f7d217eefb4f6ae08aae24c6a3c2714898
|
BinaryLogisticRegressionLoss
|
import torch
import torch.nn as nn
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > threshold).float()
num_positive = max(torch.sum(pmask), 1)
num_entries = len(label)
ratio = num_entries / num_positive
ratio = min(max(ratio, ratio_range[0]), ratio_range[1])
coef_0 = 0.5 * ratio / (ratio - 1)
coef_1 = 0.5 * ratio
loss = coef_1 * pmask * torch.log(reg_score + eps) + coef_0 * (1.0 - pmask
) * torch.log(1.0 - reg_score + eps)
loss = -torch.mean(loss)
return loss
class BinaryLogisticRegressionLoss(nn.Module):
"""Binary Logistic Regression Loss.
It will calculate binary logistic regression loss given reg_score and
label.
"""
def forward(self, reg_score, label, threshold=0.5, ratio_range=(1.05,
21), eps=1e-05):
"""Calculate Binary Logistic Regression Loss.
Args:
reg_score (torch.Tensor): Predicted score by model.
label (torch.Tensor): Groundtruth labels.
threshold (float): Threshold for positive instances.
Default: 0.5.
ratio_range (tuple): Lower bound and upper bound for ratio.
Default: (1.05, 21)
eps (float): Epsilon for small value. Default: 1e-5.
Returns:
torch.Tensor: Returned binary logistic loss.
"""
return binary_logistic_regression_loss(reg_score, label, threshold,
ratio_range, eps)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused__to_copy_add_clamp_div_gt_log_mean_mul_neg_reciprocal_rsub_sub_sum_0(
in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp19 = tl.load(in_ptr1 + r0, None)
tmp1 = 0.5
tmp2 = tmp0 > tmp1
tmp3 = tmp2.to(tl.float32)
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = 1.0
tmp8 = triton_helpers.maximum(tmp6, tmp7)
tmp9 = tl.full([1], 1, tl.int32)
tmp10 = tmp9 / tmp8
tmp11 = 256.0
tmp12 = tmp10 * tmp11
tmp13 = 1.05
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = 21.0
tmp16 = triton_helpers.minimum(tmp14, tmp15)
tmp17 = tmp16 * tmp1
tmp18 = tmp17 * tmp3
tmp20 = 1e-05
tmp21 = tmp19 + tmp20
tmp22 = tl_math.log(tmp21)
tmp23 = tmp18 * tmp22
tmp24 = tmp16 - tmp7
tmp25 = tmp17 / tmp24
tmp26 = tmp7 - tmp3
tmp27 = tmp25 * tmp26
tmp28 = tmp7 - tmp19
tmp29 = tmp28 + tmp20
tmp30 = tl_math.log(tmp29)
tmp31 = tmp27 * tmp30
tmp32 = tmp23 + tmp31
tmp33 = tl.broadcast_to(tmp32, [RBLOCK])
tmp35 = triton_helpers.promote_to_tensor(tl.sum(tmp33, 0))
tmp36 = tmp35 / tmp11
tmp37 = -tmp36
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp37, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf2 = empty_strided_cuda((), (), torch.float32)
buf3 = buf2
del buf2
get_raw_stream(0)
triton_per_fused__to_copy_add_clamp_div_gt_log_mean_mul_neg_reciprocal_rsub_sub_sum_0[
grid(1)](buf3, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf3,
def binary_logistic_regression_loss(reg_score, label, threshold=0.5,
ratio_range=(1.05, 21), eps=1e-05):
"""Binary Logistic Regression Loss."""
label = label.view(-1)
reg_score = reg_score.contiguous().view(-1)
pmask = (label > threshold).float()
num_positive = max(torch.sum(pmask), 1)
num_entries = len(label)
ratio = num_entries / num_positive
ratio = min(max(ratio, ratio_range[0]), ratio_range[1])
coef_0 = 0.5 * ratio / (ratio - 1)
coef_1 = 0.5 * ratio
loss = coef_1 * pmask * torch.log(reg_score + eps) + coef_0 * (1.0 - pmask
) * torch.log(1.0 - reg_score + eps)
loss = -torch.mean(loss)
return loss
class BinaryLogisticRegressionLossNew(nn.Module):
"""Binary Logistic Regression Loss.
It will calculate binary logistic regression loss given reg_score and
label.
"""
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
scenarios/dev
|
BinaryLogisticRegressionLoss
| false | 4,455 |
[
"Apache-2.0"
] | 0 |
9f91ebc142cea1c31231d233571ad59460ab6fba
|
https://github.com/scenarios/dev/tree/9f91ebc142cea1c31231d233571ad59460ab6fba
|
UpConv
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_0/inductor_cache/rc/crcgvn2t5cupsa3uzyzlz4vj5mtkxy2mopv5gmyjpcsh5voeodtr.py
# Topologically Sorted Source Nodes: [out, inputs], Original ATen: [aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
# inputs => _unsafe_index_1, _unsafe_index_2
# out => _unsafe_index
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {})
# %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_2 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_1, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused__unsafe_index_reflection_pad2d_0 = async_compile.triton('triton_poi_fused__unsafe_index_reflection_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[2048],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 10) % 10
x0 = xindex % 10
x2 = (xindex // 100)
x5 = xindex
tmp0 = 7 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x1)))))
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tmp5 = 7 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x0)))))
tmp6 = tmp5.to(tl.float32)
tmp7 = tmp6 * tmp2
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.load(in_ptr0 + (tmp8 + (4*tmp4) + (16*x2)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x5), tmp9, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_0/inductor_cache/cr/ccrfebtywtkn5ct3gnhnmnfdgmakzatlpfhdiithbltpuxxs3veo.py
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.elu]
# Source node to ATen node mapping:
# out_1 => convolution
# out_2 => expm1, gt, mul_4, mul_6, where
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_2, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
# %mul_4 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 1.0), kwargs = {})
# %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_4,), kwargs = {})
# %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
# %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul_4, %mul_6), kwargs = {})
triton_poi_fused_convolution_elu_1 = async_compile.triton('triton_poi_fused_convolution_elu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_elu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 64) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 1.0
tmp6 = tmp2 * tmp5
tmp7 = libdevice.expm1(tmp6)
tmp8 = tmp7 * tmp5
tmp9 = tl.where(tmp4, tmp6, tmp8)
tl.store(in_out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 10, 10), (400, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out, inputs], Original ATen: [aten._unsafe_index, aten.reflection_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused__unsafe_index_reflection_pad2d_0.run(primals_1, buf0, 1600, grid=grid(1600), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1))
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.elu]
triton_poi_fused_convolution_elu_1.run(buf2, primals_3, 1024, grid=grid(1024), stream=stream0)
del primals_3
return (buf2, primals_2, buf0, buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused__unsafe_index_reflection_pad2d_0(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 10 % 10
x0 = xindex % 10
x2 = xindex // 100
x5 = xindex
tmp0 = 7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x1))
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tmp5 = 7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0))
tmp6 = tmp5.to(tl.float32)
tmp7 = tmp6 * tmp2
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * tmp4 + 16 * x2), xmask,
eviction_policy='evict_last')
tl.store(out_ptr0 + x5, tmp9, xmask)
@triton.jit
def triton_poi_fused_convolution_elu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 64 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 1.0
tmp6 = tmp2 * tmp5
tmp7 = libdevice.expm1(tmp6)
tmp8 = tmp7 * tmp5
tmp9 = tl.where(tmp4, tmp6, tmp8)
tl.store(in_out_ptr0 + x3, tmp9, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 10, 10), (400, 100, 10, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused__unsafe_index_reflection_pad2d_0[grid(1600)](primals_1
, buf0, 1600, XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_elu_1[grid(1024)](buf2, primals_3,
1024, XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
return buf2, primals_2, buf0, buf2
class Conv3x3(nn.Module):
def __init__(self, in_channels, out_channels, use_refl=True):
super(Conv3x3, self).__init__()
if use_refl:
self.pad = nn.ReflectionPad2d(1)
else:
self.pad = nn.ZeroPad2d(1)
self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3)
def forward(self, inputs):
inputs = self.pad(inputs)
out = self.conv(inputs)
return out
class UpConvNew(nn.Module):
def __init__(self, in_channels, out_channels):
super(UpConvNew, self).__init__()
self.upsamp = nn.UpsamplingNearest2d(scale_factor=2)
self.conv = Conv3x3(in_channels=in_channels, out_channels=out_channels)
self.elu = nn.ELU(inplace=True)
def forward(self, input_0):
primals_2 = self.conv.conv.weight
primals_3 = self.conv.conv.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
prstrive/EPCDepth
|
UpConv
| false | 16,287 |
[
"MIT"
] | 76 |
84119c806741334b652749ee953e3eab60a3718c
|
https://github.com/prstrive/EPCDepth/tree/84119c806741334b652749ee953e3eab60a3718c
|
SSIM
|
import torch
import torch.nn as nn
class SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
self.sig_x_pool = nn.AvgPool2d(3, 1)
self.sig_y_pool = nn.AvgPool2d(3, 1)
self.sig_xy_pool = nn.AvgPool2d(3, 1)
self.refl = nn.ReflectionPad2d(1)
self.C1 = 0.01 ** 2
self.C2 = 0.03 ** 2
def forward(self, x, y):
x = self.refl(x)
y = self.refl(y)
mu_x = self.mu_x_pool(x)
mu_y = self.mu_y_pool(y)
sigma_x = self.sig_x_pool(x ** 2) - mu_x ** 2
sigma_y = self.sig_y_pool(y ** 2) - mu_y ** 2
sigma_xy = self.sig_xy_pool(x * y) - mu_x * mu_y
SSIM_n = (2 * mu_x * mu_y + self.C1) * (2 * sigma_xy + self.C2)
SSIM_d = (mu_x ** 2 + mu_y ** 2 + self.C1) * (sigma_x + sigma_y +
self.C2)
return torch.clamp((1 - SSIM_n / SSIM_d) / 2, 0, 1)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_mul_reflection_pad2d_0(in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_add_avg_pool2d_clamp_div_mul_pow_reflection_pad2d_rsub_sub_1(
in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 6 * x1 + 36 * x2), xmask)
tmp1 = tl.load(in_ptr0 + (1 + x0 + 6 * x1 + 36 * x2), xmask)
tmp3 = tl.load(in_ptr0 + (2 + x0 + 6 * x1 + 36 * x2), xmask)
tmp5 = tl.load(in_ptr0 + (6 + x0 + 6 * x1 + 36 * x2), xmask)
tmp7 = tl.load(in_ptr0 + (7 + x0 + 6 * x1 + 36 * x2), xmask)
tmp9 = tl.load(in_ptr0 + (8 + x0 + 6 * x1 + 36 * x2), xmask)
tmp11 = tl.load(in_ptr0 + (12 + x0 + 6 * x1 + 36 * x2), xmask)
tmp13 = tl.load(in_ptr0 + (13 + x0 + 6 * x1 + 36 * x2), xmask)
tmp15 = tl.load(in_ptr0 + (14 + x0 + 6 * x1 + 36 * x2), xmask)
tmp19 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tmp20 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-3 + x0) + -4 *
tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), xmask)
tmp22 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-2 + x0) + -4 *
tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), xmask)
tmp24 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + x1) + 16 * x2), xmask, eviction_policy
='evict_last')
tmp26 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-3 + x0) + -4 *
tl_math.abs(-3 + x1) + 16 * x2), xmask)
tmp28 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-2 + x0) + -4 *
tl_math.abs(-3 + x1) + 16 * x2), xmask)
tmp30 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-2 + x1) + 16 * x2), xmask, eviction_policy
='evict_last')
tmp32 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-3 + x0) + -4 *
tl_math.abs(-2 + x1) + 16 * x2), xmask)
tmp34 = tl.load(in_ptr1 + (15 + -1 * tl_math.abs(-2 + x0) + -4 *
tl_math.abs(-2 + x1) + 16 * x2), xmask)
tmp55 = tl.load(in_ptr2 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tmp56 = tl.load(in_ptr2 + (15 + -1 * tl_math.abs(-3 + x0) + -4 *
tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), xmask)
tmp58 = tl.load(in_ptr2 + (15 + -1 * tl_math.abs(-2 + x0) + -4 *
tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), xmask)
tmp60 = tl.load(in_ptr2 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + x1) + 16 * x2), xmask, eviction_policy
='evict_last')
tmp62 = tl.load(in_ptr2 + (15 + -1 * tl_math.abs(-3 + x0) + -4 *
tl_math.abs(-3 + x1) + 16 * x2), xmask)
tmp64 = tl.load(in_ptr2 + (15 + -1 * tl_math.abs(-2 + x0) + -4 *
tl_math.abs(-3 + x1) + 16 * x2), xmask)
tmp66 = tl.load(in_ptr2 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-2 + x1) + 16 * x2), xmask, eviction_policy
='evict_last')
tmp68 = tl.load(in_ptr2 + (15 + -1 * tl_math.abs(-3 + x0) + -4 *
tl_math.abs(-2 + x1) + 16 * x2), xmask)
tmp70 = tl.load(in_ptr2 + (15 + -1 * tl_math.abs(-2 + x0) + -4 *
tl_math.abs(-2 + x1) + 16 * x2), xmask)
tmp2 = tmp1 + tmp0
tmp4 = tmp3 + tmp2
tmp6 = tmp5 + tmp4
tmp8 = tmp7 + tmp6
tmp10 = tmp9 + tmp8
tmp12 = tmp11 + tmp10
tmp14 = tmp13 + tmp12
tmp16 = tmp15 + tmp14
tmp17 = 0.1111111111111111
tmp18 = tmp16 * tmp17
tmp21 = tmp20 + tmp19
tmp23 = tmp22 + tmp21
tmp25 = tmp24 + tmp23
tmp27 = tmp26 + tmp25
tmp29 = tmp28 + tmp27
tmp31 = tmp30 + tmp29
tmp33 = tmp32 + tmp31
tmp35 = tmp34 + tmp33
tmp36 = tmp35 * tmp17
tmp37 = tmp19 * tmp19
tmp38 = tmp20 * tmp20
tmp39 = tmp38 + tmp37
tmp40 = tmp22 * tmp22
tmp41 = tmp40 + tmp39
tmp42 = tmp24 * tmp24
tmp43 = tmp42 + tmp41
tmp44 = tmp26 * tmp26
tmp45 = tmp44 + tmp43
tmp46 = tmp28 * tmp28
tmp47 = tmp46 + tmp45
tmp48 = tmp30 * tmp30
tmp49 = tmp48 + tmp47
tmp50 = tmp32 * tmp32
tmp51 = tmp50 + tmp49
tmp52 = tmp34 * tmp34
tmp53 = tmp52 + tmp51
tmp54 = tmp53 * tmp17
tmp57 = tmp56 + tmp55
tmp59 = tmp58 + tmp57
tmp61 = tmp60 + tmp59
tmp63 = tmp62 + tmp61
tmp65 = tmp64 + tmp63
tmp67 = tmp66 + tmp65
tmp69 = tmp68 + tmp67
tmp71 = tmp70 + tmp69
tmp72 = tmp71 * tmp17
tmp73 = tmp55 * tmp55
tmp74 = tmp56 * tmp56
tmp75 = tmp74 + tmp73
tmp76 = tmp58 * tmp58
tmp77 = tmp76 + tmp75
tmp78 = tmp60 * tmp60
tmp79 = tmp78 + tmp77
tmp80 = tmp62 * tmp62
tmp81 = tmp80 + tmp79
tmp82 = tmp64 * tmp64
tmp83 = tmp82 + tmp81
tmp84 = tmp66 * tmp66
tmp85 = tmp84 + tmp83
tmp86 = tmp68 * tmp68
tmp87 = tmp86 + tmp85
tmp88 = tmp70 * tmp70
tmp89 = tmp88 + tmp87
tmp90 = tmp89 * tmp17
tmp91 = 2.0
tmp92 = tmp36 * tmp91
tmp93 = tmp92 * tmp72
tmp94 = 0.0001
tmp95 = tmp93 + tmp94
tmp96 = tmp36 * tmp72
tmp97 = tmp18 - tmp96
tmp98 = tmp97 * tmp91
tmp99 = 0.0009
tmp100 = tmp98 + tmp99
tmp101 = tmp95 * tmp100
tmp102 = tmp36 * tmp36
tmp103 = tmp72 * tmp72
tmp104 = tmp102 + tmp103
tmp105 = tmp104 + tmp94
tmp106 = tmp54 - tmp102
tmp107 = tmp90 - tmp103
tmp108 = tmp106 + tmp107
tmp109 = tmp108 + tmp99
tmp110 = tmp105 * tmp109
tmp111 = tmp101 / tmp110
tmp112 = 1.0
tmp113 = tmp112 - tmp111
tmp114 = 0.5
tmp115 = tmp113 * tmp114
tmp116 = 0.0
tmp117 = triton_helpers.maximum(tmp115, tmp116)
tmp118 = triton_helpers.minimum(tmp117, tmp112)
tl.store(in_out_ptr0 + x3, tmp118, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf2 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_reflection_pad2d_0[grid(576)](arg0_1, arg1_1,
buf2, 576, XBLOCK=256, num_warps=4, num_stages=1)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf6 = buf0
del buf0
buf7 = buf6
del buf6
triton_poi_fused_add_avg_pool2d_clamp_div_mul_pow_reflection_pad2d_rsub_sub_1[
grid(256)](buf7, buf2, arg0_1, arg1_1, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del arg0_1
del arg1_1
del buf2
return buf7,
class SSIMNew(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self):
super(SSIMNew, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
self.sig_x_pool = nn.AvgPool2d(3, 1)
self.sig_y_pool = nn.AvgPool2d(3, 1)
self.sig_xy_pool = nn.AvgPool2d(3, 1)
self.refl = nn.ReflectionPad2d(1)
self.C1 = 0.01 ** 2
self.C2 = 0.03 ** 2
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
Morbotu/drone-PWS
|
SSIM
| false | 11,724 |
[
"MIT"
] | 0 |
face9cbf30a55783592cce8af59c1c70da982b6a
|
https://github.com/Morbotu/drone-PWS/tree/face9cbf30a55783592cce8af59c1c70da982b6a
|
NeuralNet
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_8/inductor_cache/r3/cr3febcwm3t44fuoitsx3ou2p6xg4sk4f7unagmmrvffasxf47te.py
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_1 => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
# %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.relu, aten.threshold_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf6, 256, grid=grid(256), stream=stream0)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2 # reuse
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.relu, aten.threshold_backward]
triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf5, 256, grid=grid(256), stream=stream0)
del primals_5
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4)
del primals_7
return (reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(buf3, (64, 4), (4, 1), 0), primals_6, buf5, primals_4, buf6, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
primals_2, buf6, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf2
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf3,
primals_5, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf4)
del primals_7
return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(
buf3, (64, 4), (4, 1), 0), primals_6, buf5, primals_4, buf6
class NeuralNetNew(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNetNew, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = nn.Linear(hidden_size, hidden_size)
self.l3 = nn.Linear(hidden_size, num_classes)
self.relu = nn.ReLU()
def forward(self, input_0):
primals_1 = self.l1.weight
primals_2 = self.l1.bias
primals_4 = self.l2.weight
primals_5 = self.l2.bias
primals_6 = self.l3.weight
primals_7 = self.l3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
AlejandroE25/pytorch-chatbot
|
NeuralNet
| false | 8,836 |
[
"MIT"
] | 0 |
b9d7926f2f897f3a8513e8796b38f928715738af
|
https://github.com/AlejandroE25/pytorch-chatbot/tree/b9d7926f2f897f3a8513e8796b38f928715738af
|
VarianceC
|
import torch
import torch.utils.data
import torch
import torch.nn as nn
class VarianceC(nn.Module):
def __init__(self):
super(VarianceC, self).__init__()
def forward(self, x):
mean_x = torch.mean(x, dim=1, keepdim=True)
sub_x = x.sub(mean_x)
x = torch.mean(torch.mul(sub_x, sub_x), dim=1, keepdim=True)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_mean_mul_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tl.store(out_ptr0 + x2, tmp20, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mean_mul_sub_0[grid(64)](arg0_1, buf0, 64, XBLOCK=
64, num_warps=1, num_stages=1)
del arg0_1
return buf0,
class VarianceCNew(nn.Module):
def __init__(self):
super(VarianceCNew, self).__init__()
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
caixin1998/pl-template
|
VarianceC
| false | 10,112 |
[
"BSD-3-Clause"
] | 0 |
6918f0289ab2b32d107e5722617d25c9a683399c
|
https://github.com/caixin1998/pl-template/tree/6918f0289ab2b32d107e5722617d25c9a683399c
|
Conv1dCompression
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_4/inductor_cache/bs/cbstxeghddltznr7shuzsnth6ngv6mnftr2w7pqzzm5flm72plbl.py
# Topologically Sorted Source Nodes: [c_mem], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# c_mem => convolution
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [4], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 4], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (16*x1)), xmask & ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x1 + (4*y0)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_4/inductor_cache/tc/ctcagp37ljugm52zu6ckorigrppqo67voefe2f2odg5r6hyllhyu.py
# Topologically Sorted Source Nodes: [c_mem], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# c_mem => convolution
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [4], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [c_mem], Original ATen: [aten.convolution]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_0.run(primals_1, buf0, 16, 4, grid=grid(16, 4), stream=stream0)
# Topologically Sorted Source Nodes: [c_mem], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(4,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 1), (4, 1, 1))
del buf0
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [c_mem], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf2, primals_3, 16, grid=grid(16), stream=stream0)
del primals_3
return (reinterpret_tensor(buf2, (1, 4, 4), (1, 4, 1), 0), primals_2, reinterpret_tensor(primals_1, (4, 4, 4), (4, 1, 16), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x1), xmask & ymask, eviction_policy
='evict_last')
tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x2, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(4,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 1), (4, 1, 1))
del buf0
buf2 = buf1
del buf1
triton_poi_fused_convolution_1[grid(16)](buf2, primals_3, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del primals_3
return reinterpret_tensor(buf2, (1, 4, 4), (1, 4, 1), 0
), primals_2, reinterpret_tensor(primals_1, (4, 4, 4), (4, 1, 16), 0)
class Conv1dCompressionNew(Module):
"""
## 1D Convolution Compression $f_c$
This is a simple wrapper around
[`nn.Conv1d`](https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html)
with some tensor dimension permutations.
"""
def __init__(self, compression_rate: 'int', d_model: 'int'):
"""
* `compression_rate` $c$
* `d_model` is the embedding size
"""
super().__init__()
self.conv = nn.Conv1d(d_model, d_model, kernel_size=
compression_rate, stride=compression_rate)
def forward(self, input_0):
primals_1 = self.conv.weight
primals_3 = self.conv.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Aarsh2001/annotated_deep_learning_paper_implementations
|
Conv1dCompression
| false | 4,786 |
[
"MIT"
] | 1 |
ff0d5c065da1a46769f5f66fddc252c178f8fa37
|
https://github.com/Aarsh2001/annotated_deep_learning_paper_implementations/tree/ff0d5c065da1a46769f5f66fddc252c178f8fa37
|
NormalSampler
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_7/inductor_cache/gi/cgif32a2wfs45bh7axmyrqfirotrk647j5jhcoati6xtmmnm2id6.py
# Topologically Sorted Source Nodes: [mul, std, z], Original ATen: [aten.mul, aten.exp, aten.addcmul]
# Source node to ATen node mapping:
# mul => mul
# std => exp
# z => add, mul_1, mul_2
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, 0.5), kwargs = {})
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul,), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, 1), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %randn), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, %mul_2), kwargs = {})
triton_poi_fused_addcmul_exp_mul_0 = async_compile.triton('triton_poi_fused_addcmul_exp_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_addcmul_exp_mul_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_addcmul_exp_mul_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = tl.load(in_ptr1 + (x0), xmask)
tmp7 = tl.load(in_out_ptr0 + (x0), xmask)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tl_math.exp(tmp3)
tmp5 = 1.0
tmp6 = tmp4 * tmp5
tmp8 = tmp6 * tmp7
tmp9 = tmp0 + tmp8
tl.store(in_out_ptr0 + (x0), tmp9, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [epsilon], Original ATen: [aten.randn]
buf0 = torch.ops.aten.randn.default([4, 4, 4, 4], device=device(type='cuda', index=0), pin_memory=False)
buf1 = buf0
del buf0
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [mul, std, z], Original ATen: [aten.mul, aten.exp, aten.addcmul]
stream0 = get_raw_stream(0)
triton_poi_fused_addcmul_exp_mul_0.run(buf2, arg0_1, arg1_1, 256, grid=grid(256), stream=stream0)
del arg0_1
del arg1_1
return (buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import device
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_addcmul_exp_mul_0(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask)
tmp7 = tl.load(in_out_ptr0 + x0, xmask)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tl_math.exp(tmp3)
tmp5 = 1.0
tmp6 = tmp4 * tmp5
tmp8 = tmp6 * tmp7
tmp9 = tmp0 + tmp8
tl.store(in_out_ptr0 + x0, tmp9, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = torch.ops.aten.randn.default([4, 4, 4, 4], device=device(
type='cuda', index=0), pin_memory=False)
buf1 = buf0
del buf0
buf2 = buf1
del buf1
get_raw_stream(0)
triton_poi_fused_addcmul_exp_mul_0[grid(256)](buf2, arg0_1, arg1_1,
256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
return buf2,
class NormalSamplerNew(nn.Module):
"""p(z)"""
def __init__(self):
super(NormalSamplerNew, self).__init__()
self.register_buffer('eps', torch.tensor(1e-10))
def kl_divergence(self, mean, log_var, z):
"""
L elbo(x) = Eq(z|x)[log p(x|z)] - KL(q(z|x)||p(z))
D_{KL}(q(z|x)||p(z))
"""
return -0.5 * torch.sum(1 + log_var - mean.pow(2) - log_var.exp(),
dim=1)
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
ChengF-Lab/scGGN
|
NormalSampler
| false | 2,099 |
[
"MIT"
] | 0 |
eab585219e6d3eb06c94057f0e3b276d1846e8b6
|
https://github.com/ChengF-Lab/scGGN/tree/eab585219e6d3eb06c94057f0e3b276d1846e8b6
|
Discriminator
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_9/inductor_cache/iu/ciuohqr5pqwxxdqpgaszyr2gmkyh4webkx47a5whlcxapcaqrmpd.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128, 32], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 96
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 6
y1 = (yindex // 6)
tmp0 = tl.load(in_ptr0 + (x2 + (25*y3)), xmask & ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (y0 + (6*x2) + (150*y1)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/mo/cmo7kprbhn5foykjllxlvsdu5fash7avpwgsvrlgsnxi6osh7vsj.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32, 4096], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 24
xnumel = 4096
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
x2 = xindex
y3 = yindex
y0 = yindex % 6
y1 = (yindex // 6)
tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (y0 + (6*x2) + (24576*y1)), tmp0, ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/zd/czdmxdjo6vbpeyu4gsmsvm5mqdlzdrlububsx2rsj2j5l2h4dsrz.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_2 = async_compile.triton('triton_poi_fused_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512, 32], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 512
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 16
y1 = (yindex // 16)
tmp0 = tl.load(in_ptr0 + (x2 + (25*y3)), xmask & ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (y0 + (16*x2) + (400*y1)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/5h/c5hkuqsyg6b4mbsdxblt4xagz75onxk3mtokac7gyjwh7ljruai5.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_3 = async_compile.triton('triton_poi_fused_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[2048, 32], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 2048
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 32
y1 = (yindex // 32)
tmp0 = tl.load(in_ptr0 + (x2 + (25*y3)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (y0 + (32*x2) + (800*y1)), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/gi/cgimqxxs6qckr3romidticzvnpearx2sdhoesal5xonmke5jjfzs.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_4 = async_compile.triton('triton_poi_fused_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8192, 32], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 8192
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 64
y1 = (yindex // 64)
tmp0 = tl.load(in_ptr0 + (x2 + (25*y3)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (y0 + (64*x2) + (1600*y1)), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/7p/c7pmnbahcm3yi2qyuxtlqhxjvvjyzmylknsgacn2efuf4vcdsb3o.py
# Topologically Sorted Source Nodes: [_weight_norm], Original ATen: [aten._weight_norm_interface]
# Source node to ATen node mapping:
# _weight_norm => pow_1, pow_2, sum_1
# Graph fragment:
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_2, 2), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1, 2, 3], True), kwargs = {})
# %pow_2 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
triton_per_fused__weight_norm_interface_5 = async_compile.triton('triton_per_fused__weight_norm_interface_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__weight_norm_interface_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__weight_norm_interface_5(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 150
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + (150*x0)), rmask & xmask, other=0.0)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = tl.where(rmask & xmask, tmp2, 0)
tmp5 = tl.sum(tmp4, 1)[:, None]
tmp6 = libdevice.sqrt(tmp5)
tl.debug_barrier()
tl.store(in_out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/ah/cahfaxtna75ni4ziltb65ap3fcjvdtspp55d36wuo54p7phmd4d7.py
# Topologically Sorted Source Nodes: [_weight_norm, x], Original ATen: [aten._weight_norm_interface, aten.convolution]
# Source node to ATen node mapping:
# _weight_norm => div, mul
# x => convolution
# Graph fragment:
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, %pow_2), kwargs = {})
# %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %div), kwargs = {})
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_4, %mul, %primals_3, [2, 2], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused__weight_norm_interface_convolution_6 = async_compile.triton('triton_poi_fused__weight_norm_interface_convolution_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128, 32], tile_hint=TileHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__weight_norm_interface_convolution_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__weight_norm_interface_convolution_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 96
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 6
y1 = (yindex // 6)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (6*x2) + (150*y1)), xmask & ymask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (y1), ymask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + (y1), ymask, eviction_policy='evict_last')
tmp3 = tmp1 / tmp2
tmp4 = tmp0 * tmp3
tl.store(out_ptr0 + (x2 + (25*y3)), tmp4, xmask & ymask)
tl.store(out_ptr1 + (y0 + (6*x2) + (150*y1)), tmp4, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/j4/cj4nml7l3vz2a35zvcdcxuvsdtqjohvfzpywagxxromilqxzhbpz.py
# Topologically Sorted Source Nodes: [x, sub, relu, x_1], Original ATen: [aten.convolution, aten.sub, aten.relu, aten.add, aten.threshold_backward]
# Source node to ATen node mapping:
# relu => relu
# sub => sub
# x => convolution
# x_1 => add
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_4, %mul, %primals_3, [2, 2], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution, %primals_5), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%sub,), kwargs = {})
# %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu, %primals_5), kwargs = {})
# %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_add_convolution_relu_sub_threshold_backward_7 = async_compile.triton('triton_poi_fused_add_convolution_relu_sub_threshold_backward_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i1', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_relu_sub_threshold_backward_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_relu_sub_threshold_backward_7(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 65536
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 16
tmp0 = tl.load(in_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (0))
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 - tmp4
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp8 = tmp7 + tmp4
tmp9 = 0.0
tmp10 = tmp7 <= tmp9
tl.store(out_ptr0 + (x2), tmp8, None)
tl.store(out_ptr1 + (x2), tmp10, None)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/le/cle6rmavp35boyijii2h4xuy6bdwo7zsyw5rt263pugye2575zk7.py
# Topologically Sorted Source Nodes: [_weight_norm_1], Original ATen: [aten._weight_norm_interface]
# Source node to ATen node mapping:
# _weight_norm_1 => pow_3, pow_4, sum_2
# Graph fragment:
# %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_7, 2), kwargs = {})
# %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, [1, 2, 3], True), kwargs = {})
# %pow_4 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
triton_per_fused__weight_norm_interface_8 = async_compile.triton('triton_per_fused__weight_norm_interface_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[32, 512],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__weight_norm_interface_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__weight_norm_interface_8(in_out_ptr0, in_ptr0, xnumel, rnumel):
xnumel = 32
XBLOCK: tl.constexpr = 1
rnumel = 400
RBLOCK: tl.constexpr = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + (400*x0)), rmask, other=0.0)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
tmp4 = tl.where(rmask, tmp2, 0)
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp6 = libdevice.sqrt(tmp5)
tl.debug_barrier()
tl.store(in_out_ptr0 + (x0), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/jy/cjyd2bfnjahrfv64m5cnh3lmmfssee6lcgjle7w3my7ijmbkorga.py
# Topologically Sorted Source Nodes: [_weight_norm_1, x_2], Original ATen: [aten._weight_norm_interface, aten.convolution]
# Source node to ATen node mapping:
# _weight_norm_1 => div_1, mul_1
# x_2 => convolution_1
# Graph fragment:
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_6, %pow_4), kwargs = {})
# %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_7, %div_1), kwargs = {})
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add, %mul_1, %primals_8, [2, 2], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused__weight_norm_interface_convolution_9 = async_compile.triton('triton_poi_fused__weight_norm_interface_convolution_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512, 32], tile_hint=TileHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__weight_norm_interface_convolution_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__weight_norm_interface_convolution_9(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 512
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 16
y1 = (yindex // 16)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (16*x2) + (400*y1)), xmask & ymask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (y1), ymask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + (y1), ymask, eviction_policy='evict_last')
tmp3 = tmp1 / tmp2
tmp4 = tmp0 * tmp3
tl.store(out_ptr0 + (x2 + (25*y3)), tmp4, xmask & ymask)
tl.store(out_ptr1 + (y0 + (16*x2) + (400*y1)), tmp4, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/hf/chf2oujwjqo77d2oewwcjflyfxfwpqvtvewb45opuxhypalaj4hi.py
# Topologically Sorted Source Nodes: [x_2, sub_1, relu_1, x_3], Original ATen: [aten.convolution, aten.sub, aten.relu, aten.add, aten.threshold_backward]
# Source node to ATen node mapping:
# relu_1 => relu_1
# sub_1 => sub_1
# x_2 => convolution_1
# x_3 => add_1
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add, %mul_1, %primals_8, [2, 2], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_1, %primals_9), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%sub_1,), kwargs = {})
# %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu_1, %primals_9), kwargs = {})
# %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_add_convolution_relu_sub_threshold_backward_10 = async_compile.triton('triton_poi_fused_add_convolution_relu_sub_threshold_backward_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32768],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i1', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_relu_sub_threshold_backward_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_relu_sub_threshold_backward_10(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 32768
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 32
tmp0 = tl.load(in_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (0))
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 - tmp4
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp8 = tmp7 + tmp4
tmp9 = 0.0
tmp10 = tmp7 <= tmp9
tl.store(out_ptr0 + (x2), tmp8, None)
tl.store(out_ptr1 + (x2), tmp10, None)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/pi/cpiwvvp45ekwuhrxsyobb3n5ljdhjaagu4euvuzvumipx34ehfq4.py
# Topologically Sorted Source Nodes: [_weight_norm_2], Original ATen: [aten._weight_norm_interface]
# Source node to ATen node mapping:
# _weight_norm_2 => pow_5, pow_6, sum_3
# Graph fragment:
# %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_11, 2), kwargs = {})
# %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_5, [1, 2, 3], True), kwargs = {})
# %pow_6 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_3, 0.5), kwargs = {})
triton_per_fused__weight_norm_interface_11 = async_compile.triton('triton_per_fused__weight_norm_interface_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[64, 1024],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__weight_norm_interface_11', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__weight_norm_interface_11(in_out_ptr0, in_ptr0, xnumel, rnumel):
xnumel = 64
XBLOCK: tl.constexpr = 1
rnumel = 800
RBLOCK: tl.constexpr = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + (800*x0)), rmask, other=0.0)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
tmp4 = tl.where(rmask, tmp2, 0)
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp6 = libdevice.sqrt(tmp5)
tl.debug_barrier()
tl.store(in_out_ptr0 + (x0), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/gs/cgsknv5coj4ejltnr3jcylmxgk6jlqfs4afhc7njvwvkix2alhpl.py
# Topologically Sorted Source Nodes: [_weight_norm_2, x_4], Original ATen: [aten._weight_norm_interface, aten.convolution]
# Source node to ATen node mapping:
# _weight_norm_2 => div_2, mul_2
# x_4 => convolution_2
# Graph fragment:
# %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_10, %pow_6), kwargs = {})
# %mul_2 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_11, %div_2), kwargs = {})
# %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add_1, %mul_2, %primals_12, [2, 2], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused__weight_norm_interface_convolution_12 = async_compile.triton('triton_poi_fused__weight_norm_interface_convolution_12', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[2048, 32], tile_hint=TileHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__weight_norm_interface_convolution_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__weight_norm_interface_convolution_12(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 2048
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 32
y1 = (yindex // 32)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (32*x2) + (800*y1)), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (y1), None, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + (y1), None, eviction_policy='evict_last')
tmp3 = tmp1 / tmp2
tmp4 = tmp0 * tmp3
tl.store(out_ptr0 + (x2 + (25*y3)), tmp4, xmask)
tl.store(out_ptr1 + (y0 + (32*x2) + (800*y1)), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/6z/c6zwf7iqh7p7uhqktvfopwmrrh7valfg7nvxllhwstvwc5uzjf6w.py
# Topologically Sorted Source Nodes: [x_4, sub_2, relu_2, x_5], Original ATen: [aten.convolution, aten.sub, aten.relu, aten.add, aten.threshold_backward]
# Source node to ATen node mapping:
# relu_2 => relu_2
# sub_2 => sub_2
# x_4 => convolution_2
# x_5 => add_2
# Graph fragment:
# %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add_1, %mul_2, %primals_12, [2, 2], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {})
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_2, %primals_13), kwargs = {})
# %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%sub_2,), kwargs = {})
# %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu_2, %primals_13), kwargs = {})
# %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
triton_poi_fused_add_convolution_relu_sub_threshold_backward_13 = async_compile.triton('triton_poi_fused_add_convolution_relu_sub_threshold_backward_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16384],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i1', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_relu_sub_threshold_backward_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_relu_sub_threshold_backward_13(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 16384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (0))
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 - tmp4
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp8 = tmp7 + tmp4
tmp9 = 0.0
tmp10 = tmp7 <= tmp9
tl.store(out_ptr0 + (x2), tmp8, None)
tl.store(out_ptr1 + (x2), tmp10, None)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/3k/c3kgdgezw74wchseqbghu26t2cbtgdk7bhf7d5o6twleljcuh3si.py
# Topologically Sorted Source Nodes: [_weight_norm_3], Original ATen: [aten._weight_norm_interface]
# Source node to ATen node mapping:
# _weight_norm_3 => pow_7, pow_8, sum_4
# Graph fragment:
# %pow_7 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_15, 2), kwargs = {})
# %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_7, [1, 2, 3], True), kwargs = {})
# %pow_8 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_4, 0.5), kwargs = {})
triton_red_fused__weight_norm_interface_14 = async_compile.triton('triton_red_fused__weight_norm_interface_14', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.reduction(
size_hints=[128, 2048],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__weight_norm_interface_14', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused__weight_norm_interface_14(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
xnumel = 128
rnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rbase = tl.arange(0, RBLOCK)[None, :]
x0 = xindex
_tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r1 = rindex
tmp0 = tl.load(in_ptr0 + (r1 + (1600*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = _tmp3 + tmp2
_tmp3 = tl.where(rmask & xmask, tmp4, _tmp3)
tmp3 = tl.sum(_tmp3, 1)[:, None]
tmp5 = libdevice.sqrt(tmp3)
tl.debug_barrier()
tl.store(in_out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/32/c32t4jsszdt33uczvurnkh5wbiplpdpqacvw7mhtf6n5kdxl54zb.py
# Topologically Sorted Source Nodes: [_weight_norm_3, x_6], Original ATen: [aten._weight_norm_interface, aten.convolution]
# Source node to ATen node mapping:
# _weight_norm_3 => div_3, mul_3
# x_6 => convolution_3
# Graph fragment:
# %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_14, %pow_8), kwargs = {})
# %mul_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_15, %div_3), kwargs = {})
# %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add_2, %mul_3, %primals_16, [2, 2], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused__weight_norm_interface_convolution_15 = async_compile.triton('triton_poi_fused__weight_norm_interface_convolution_15', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8192, 32], tile_hint=TileHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__weight_norm_interface_convolution_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__weight_norm_interface_convolution_15(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 8192
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 64
y1 = (yindex // 64)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (64*x2) + (1600*y1)), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (y1), None, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + (y1), None, eviction_policy='evict_last')
tmp3 = tmp1 / tmp2
tmp4 = tmp0 * tmp3
tl.store(out_ptr0 + (x2 + (25*y3)), tmp4, xmask)
tl.store(out_ptr1 + (y0 + (64*x2) + (1600*y1)), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/7r/c7rdxixjosbh7ymn3x5j2sl25x6yqhleclggjp63z2jy7jyjuqvn.py
# Topologically Sorted Source Nodes: [x_6, sub_3, relu_3, x_7], Original ATen: [aten.convolution, aten.sub, aten.relu, aten.add, aten.threshold_backward]
# Source node to ATen node mapping:
# relu_3 => relu_3
# sub_3 => sub_3
# x_6 => convolution_3
# x_7 => add_3
# Graph fragment:
# %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add_2, %mul_3, %primals_16, [2, 2], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {})
# %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_3, %primals_17), kwargs = {})
# %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%sub_3,), kwargs = {})
# %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu_3, %primals_17), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_poi_fused_add_convolution_relu_sub_threshold_backward_16 = async_compile.triton('triton_poi_fused_add_convolution_relu_sub_threshold_backward_16', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8192],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i1', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_relu_sub_threshold_backward_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_relu_sub_threshold_backward_16(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 8192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 128
tmp0 = tl.load(in_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (0))
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 - tmp4
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp8 = tmp7 + tmp4
tmp9 = 0.0
tmp10 = tmp7 <= tmp9
tl.store(out_ptr0 + (x2), tmp8, None)
tl.store(out_ptr1 + (x2), tmp10, None)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/h2/ch2muopo5hd67wmawis4b2yeteunbgvli4oplje4f6sgnddstzxr.py
# Topologically Sorted Source Nodes: [_weight_norm_4], Original ATen: [aten._weight_norm_interface]
# Source node to ATen node mapping:
# _weight_norm_4 => div_4, mul_4, pow_10, pow_9, sum_5
# Graph fragment:
# %pow_9 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_19, 2), kwargs = {})
# %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_9, [1, 2, 3], True), kwargs = {})
# %pow_10 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_5, 0.5), kwargs = {})
# %div_4 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_18, %pow_10), kwargs = {})
# %mul_4 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_19, %div_4), kwargs = {})
triton_per_fused__weight_norm_interface_17 = async_compile.triton('triton_per_fused__weight_norm_interface_17', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 128],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__weight_norm_interface_17', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__weight_norm_interface_17(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 128
RBLOCK: tl.constexpr = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp6 = tl.load(in_ptr1 + (0))
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = tl.sum(tmp2, 1)[:, None]
tmp5 = libdevice.sqrt(tmp4)
tmp8 = tmp7 / tmp5
tmp9 = tmp0 * tmp8
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp5, None)
tl.store(out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp9, None)
''', device_str='cuda')
# kernel path: runs/run_shard_9/inductor_cache/xg/cxgirr7ywaqvjxpseg4bhe4k4fyif3g5cipvyiq4q2imyyntrosj.py
# Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# x_8 => convolution_4
# Graph fragment:
# %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add_3, %mul_4, %primals_20, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_18 = async_compile.triton('triton_poi_fused_convolution_18', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_18', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_18(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = tl.load(in_ptr0 + (0))
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tl.store(in_out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20 = args
args.clear()
assert_size_stride(primals_1, (16, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_2, (16, 6, 5, 5), (150, 25, 5, 1))
assert_size_stride(primals_3, (16, ), (1, ))
assert_size_stride(primals_4, (4, 6, 64, 64), (24576, 4096, 64, 1))
assert_size_stride(primals_5, (1, ), (1, ))
assert_size_stride(primals_6, (32, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_7, (32, 16, 5, 5), (400, 25, 5, 1))
assert_size_stride(primals_8, (32, ), (1, ))
assert_size_stride(primals_9, (1, ), (1, ))
assert_size_stride(primals_10, (64, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_11, (64, 32, 5, 5), (800, 25, 5, 1))
assert_size_stride(primals_12, (64, ), (1, ))
assert_size_stride(primals_13, (1, ), (1, ))
assert_size_stride(primals_14, (128, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_15, (128, 64, 5, 5), (1600, 25, 5, 1))
assert_size_stride(primals_16, (128, ), (1, ))
assert_size_stride(primals_17, (1, ), (1, ))
assert_size_stride(primals_18, (1, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_19, (1, 128, 1, 1), (128, 1, 1, 1))
assert_size_stride(primals_20, (1, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 6, 5, 5), (150, 1, 30, 6), torch.float32)
# Unsorted Source Nodes: [], Original ATen: []
stream0 = get_raw_stream(0)
triton_poi_fused_0.run(primals_2, buf0, 96, 25, grid=grid(96, 25), stream=stream0)
del primals_2
buf1 = empty_strided_cuda((4, 6, 64, 64), (24576, 1, 384, 6), torch.float32)
# Unsorted Source Nodes: [], Original ATen: []
triton_poi_fused_1.run(primals_4, buf1, 24, 4096, grid=grid(24, 4096), stream=stream0)
del primals_4
buf2 = empty_strided_cuda((32, 16, 5, 5), (400, 1, 80, 16), torch.float32)
# Unsorted Source Nodes: [], Original ATen: []
triton_poi_fused_2.run(primals_7, buf2, 512, 25, grid=grid(512, 25), stream=stream0)
del primals_7
buf3 = empty_strided_cuda((64, 32, 5, 5), (800, 1, 160, 32), torch.float32)
# Unsorted Source Nodes: [], Original ATen: []
triton_poi_fused_3.run(primals_11, buf3, 2048, 25, grid=grid(2048, 25), stream=stream0)
del primals_11
buf4 = empty_strided_cuda((128, 64, 5, 5), (1600, 1, 320, 64), torch.float32)
# Unsorted Source Nodes: [], Original ATen: []
triton_poi_fused_4.run(primals_15, buf4, 8192, 25, grid=grid(8192, 25), stream=stream0)
del primals_15
buf5 = empty_strided_cuda((16, 1, 1, 1), (1, 16, 16, 16), torch.float32)
buf6 = reinterpret_tensor(buf5, (16, 1, 1, 1), (1, 1, 1, 1), 0); del buf5 # reuse
# Topologically Sorted Source Nodes: [_weight_norm], Original ATen: [aten._weight_norm_interface]
triton_per_fused__weight_norm_interface_5.run(buf6, buf0, 16, 150, grid=grid(16), stream=stream0)
buf7 = empty_strided_cuda((16, 6, 5, 5), (150, 25, 5, 1), torch.float32)
buf8 = empty_strided_cuda((16, 6, 5, 5), (150, 1, 30, 6), torch.float32)
# Topologically Sorted Source Nodes: [_weight_norm, x], Original ATen: [aten._weight_norm_interface, aten.convolution]
triton_poi_fused__weight_norm_interface_convolution_6.run(buf0, primals_1, buf6, buf7, buf8, 96, 25, grid=grid(96, 25), stream=stream0)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
buf9 = extern_kernels.convolution(buf1, buf8, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 16, 32, 32), (16384, 1, 512, 16))
del buf8
buf10 = empty_strided_cuda((4, 16, 32, 32), (16384, 1, 512, 16), torch.float32)
buf37 = empty_strided_cuda((4, 16, 32, 32), (16384, 1, 512, 16), torch.bool)
# Topologically Sorted Source Nodes: [x, sub, relu, x_1], Original ATen: [aten.convolution, aten.sub, aten.relu, aten.add, aten.threshold_backward]
triton_poi_fused_add_convolution_relu_sub_threshold_backward_7.run(buf9, primals_3, primals_5, buf10, buf37, 65536, grid=grid(65536), stream=stream0)
del buf9
del primals_3
del primals_5
buf11 = empty_strided_cuda((32, 1, 1, 1), (1, 32, 32, 32), torch.float32)
buf12 = reinterpret_tensor(buf11, (32, 1, 1, 1), (1, 1, 1, 1), 0); del buf11 # reuse
# Topologically Sorted Source Nodes: [_weight_norm_1], Original ATen: [aten._weight_norm_interface]
triton_per_fused__weight_norm_interface_8.run(buf12, buf2, 32, 400, grid=grid(32), stream=stream0)
buf13 = empty_strided_cuda((32, 16, 5, 5), (400, 25, 5, 1), torch.float32)
buf14 = empty_strided_cuda((32, 16, 5, 5), (400, 1, 80, 16), torch.float32)
# Topologically Sorted Source Nodes: [_weight_norm_1, x_2], Original ATen: [aten._weight_norm_interface, aten.convolution]
triton_poi_fused__weight_norm_interface_convolution_9.run(buf2, primals_6, buf12, buf13, buf14, 512, 25, grid=grid(512, 25), stream=stream0)
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
buf15 = extern_kernels.convolution(buf10, buf14, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf15, (4, 32, 16, 16), (8192, 1, 512, 32))
del buf14
buf16 = empty_strided_cuda((4, 32, 16, 16), (8192, 1, 512, 32), torch.float32)
buf36 = empty_strided_cuda((4, 32, 16, 16), (8192, 1, 512, 32), torch.bool)
# Topologically Sorted Source Nodes: [x_2, sub_1, relu_1, x_3], Original ATen: [aten.convolution, aten.sub, aten.relu, aten.add, aten.threshold_backward]
triton_poi_fused_add_convolution_relu_sub_threshold_backward_10.run(buf15, primals_8, primals_9, buf16, buf36, 32768, grid=grid(32768), stream=stream0)
del buf15
del primals_8
del primals_9
buf17 = empty_strided_cuda((64, 1, 1, 1), (1, 64, 64, 64), torch.float32)
buf18 = reinterpret_tensor(buf17, (64, 1, 1, 1), (1, 1, 1, 1), 0); del buf17 # reuse
# Topologically Sorted Source Nodes: [_weight_norm_2], Original ATen: [aten._weight_norm_interface]
triton_per_fused__weight_norm_interface_11.run(buf18, buf3, 64, 800, grid=grid(64), stream=stream0)
buf19 = empty_strided_cuda((64, 32, 5, 5), (800, 25, 5, 1), torch.float32)
buf20 = empty_strided_cuda((64, 32, 5, 5), (800, 1, 160, 32), torch.float32)
# Topologically Sorted Source Nodes: [_weight_norm_2, x_4], Original ATen: [aten._weight_norm_interface, aten.convolution]
triton_poi_fused__weight_norm_interface_convolution_12.run(buf3, primals_10, buf18, buf19, buf20, 2048, 25, grid=grid(2048, 25), stream=stream0)
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
buf21 = extern_kernels.convolution(buf16, buf20, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf21, (4, 64, 8, 8), (4096, 1, 512, 64))
del buf20
buf22 = empty_strided_cuda((4, 64, 8, 8), (4096, 1, 512, 64), torch.float32)
buf35 = empty_strided_cuda((4, 64, 8, 8), (4096, 1, 512, 64), torch.bool)
# Topologically Sorted Source Nodes: [x_4, sub_2, relu_2, x_5], Original ATen: [aten.convolution, aten.sub, aten.relu, aten.add, aten.threshold_backward]
triton_poi_fused_add_convolution_relu_sub_threshold_backward_13.run(buf21, primals_12, primals_13, buf22, buf35, 16384, grid=grid(16384), stream=stream0)
del buf21
del primals_12
del primals_13
buf23 = empty_strided_cuda((128, 1, 1, 1), (1, 128, 128, 128), torch.float32)
buf24 = reinterpret_tensor(buf23, (128, 1, 1, 1), (1, 1, 1, 1), 0); del buf23 # reuse
# Topologically Sorted Source Nodes: [_weight_norm_3], Original ATen: [aten._weight_norm_interface]
triton_red_fused__weight_norm_interface_14.run(buf24, buf4, 128, 1600, grid=grid(128), stream=stream0)
buf25 = empty_strided_cuda((128, 64, 5, 5), (1600, 25, 5, 1), torch.float32)
buf26 = empty_strided_cuda((128, 64, 5, 5), (1600, 1, 320, 64), torch.float32)
# Topologically Sorted Source Nodes: [_weight_norm_3, x_6], Original ATen: [aten._weight_norm_interface, aten.convolution]
triton_poi_fused__weight_norm_interface_convolution_15.run(buf4, primals_14, buf24, buf25, buf26, 8192, 25, grid=grid(8192, 25), stream=stream0)
# Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.convolution]
buf27 = extern_kernels.convolution(buf22, buf26, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf27, (4, 128, 4, 4), (2048, 1, 512, 128))
del buf26
buf28 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128), torch.float32)
buf34 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128), torch.bool)
# Topologically Sorted Source Nodes: [x_6, sub_3, relu_3, x_7], Original ATen: [aten.convolution, aten.sub, aten.relu, aten.add, aten.threshold_backward]
triton_poi_fused_add_convolution_relu_sub_threshold_backward_16.run(buf27, primals_16, primals_17, buf28, buf34, 8192, grid=grid(8192), stream=stream0)
del buf27
del primals_16
del primals_17
buf29 = empty_strided_cuda((1, 1, 1, 1), (1, 1, 1, 1), torch.float32)
buf30 = buf29; del buf29 # reuse
buf31 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 1, 1), torch.float32)
# Topologically Sorted Source Nodes: [_weight_norm_4], Original ATen: [aten._weight_norm_interface]
triton_per_fused__weight_norm_interface_17.run(buf30, primals_19, primals_18, buf31, 1, 128, grid=grid(1), stream=stream0)
# Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
buf32 = extern_kernels.convolution(buf28, buf31, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf32, (4, 1, 4, 4), (16, 1, 4, 1))
buf33 = buf32; del buf32 # reuse
# Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
triton_poi_fused_convolution_18.run(buf33, primals_20, 64, grid=grid(64), stream=stream0)
del primals_20
return (reinterpret_tensor(buf33, (1, 64), (64, 1), 0), buf7, buf13, buf19, buf25, buf31, primals_1, buf0, buf1, primals_6, buf2, primals_10, buf3, primals_14, buf4, primals_18, primals_19, buf6, buf7, buf10, buf12, buf13, buf16, buf18, buf19, buf22, buf24, buf25, buf28, buf30, buf31, buf34, buf35, buf36, buf37, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((16, 1, 1, 1), (1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((16, 6, 5, 5), (150, 25, 5, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 6, 64, 64), (24576, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((32, 1, 1, 1), (1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((32, 16, 5, 5), (400, 25, 5, 1), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((64, 1, 1, 1), (1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((64, 32, 5, 5), (800, 25, 5, 1), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_14 = rand_strided((128, 1, 1, 1), (1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_15 = rand_strided((128, 64, 5, 5), (1600, 25, 5, 1), device='cuda:0', dtype=torch.float32)
primals_16 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_17 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_18 = rand_strided((1, 1, 1, 1), (1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_19 = rand_strided((1, 128, 1, 1), (128, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_20 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.weight_norm as weightNorm
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 96
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 6
y1 = yindex // 6
tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask & ymask, eviction_policy
='evict_last')
tl.store(out_ptr0 + (y0 + 6 * x2 + 150 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 24
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
x2 = xindex
y3 = yindex
y0 = yindex % 6
y1 = yindex // 6
tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 6 * x2 + 24576 * y1), tmp0, ymask)
@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 512
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 16
y1 = yindex // 16
tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask & ymask, eviction_policy
='evict_last')
tl.store(out_ptr0 + (y0 + 16 * x2 + 400 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 32
y1 = yindex // 32
tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 32 * x2 + 800 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 64
y1 = yindex // 64
tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 64 * x2 + 1600 * y1), tmp0, xmask)
@triton.jit
def triton_per_fused__weight_norm_interface_5(in_out_ptr0, in_ptr0, xnumel,
rnumel, XBLOCK: tl.constexpr):
xnumel = 16
rnumel = 150
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 150 * x0), rmask & xmask, other=0.0)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = tl.where(rmask & xmask, tmp2, 0)
tmp5 = tl.sum(tmp4, 1)[:, None]
tmp6 = libdevice.sqrt(tmp5)
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp6, xmask)
@triton.jit
def triton_poi_fused__weight_norm_interface_convolution_6(in_ptr0, in_ptr1,
in_ptr2, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr,
XBLOCK: tl.constexpr):
ynumel = 96
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 6
y1 = yindex // 6
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 6 * x2 + 150 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y1, ymask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + y1, ymask, eviction_policy='evict_last')
tmp3 = tmp1 / tmp2
tmp4 = tmp0 * tmp3
tl.store(out_ptr0 + (x2 + 25 * y3), tmp4, xmask & ymask)
tl.store(out_ptr1 + (y0 + 6 * x2 + 150 * y1), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_add_convolution_relu_sub_threshold_backward_7(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 16
tmp0 = tl.load(in_ptr0 + x2, None)
tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + 0)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 - tmp4
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp8 = tmp7 + tmp4
tmp9 = 0.0
tmp10 = tmp7 <= tmp9
tl.store(out_ptr0 + x2, tmp8, None)
tl.store(out_ptr1 + x2, tmp10, None)
@triton.jit
def triton_per_fused__weight_norm_interface_8(in_out_ptr0, in_ptr0, xnumel,
rnumel):
XBLOCK: tl.constexpr = 1
rnumel = 400
RBLOCK: tl.constexpr = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 400 * x0), rmask, other=0.0)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
tmp4 = tl.where(rmask, tmp2, 0)
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp6 = libdevice.sqrt(tmp5)
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp6, None)
@triton.jit
def triton_poi_fused__weight_norm_interface_convolution_9(in_ptr0, in_ptr1,
in_ptr2, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr,
XBLOCK: tl.constexpr):
ynumel = 512
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 16
y1 = yindex // 16
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 400 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y1, ymask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + y1, ymask, eviction_policy='evict_last')
tmp3 = tmp1 / tmp2
tmp4 = tmp0 * tmp3
tl.store(out_ptr0 + (x2 + 25 * y3), tmp4, xmask & ymask)
tl.store(out_ptr1 + (y0 + 16 * x2 + 400 * y1), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_add_convolution_relu_sub_threshold_backward_10(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 32
tmp0 = tl.load(in_ptr0 + x2, None)
tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + 0)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 - tmp4
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp8 = tmp7 + tmp4
tmp9 = 0.0
tmp10 = tmp7 <= tmp9
tl.store(out_ptr0 + x2, tmp8, None)
tl.store(out_ptr1 + x2, tmp10, None)
@triton.jit
def triton_per_fused__weight_norm_interface_11(in_out_ptr0, in_ptr0, xnumel,
rnumel):
XBLOCK: tl.constexpr = 1
rnumel = 800
RBLOCK: tl.constexpr = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 800 * x0), rmask, other=0.0)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
tmp4 = tl.where(rmask, tmp2, 0)
tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp6 = libdevice.sqrt(tmp5)
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp6, None)
@triton.jit
def triton_poi_fused__weight_norm_interface_convolution_12(in_ptr0, in_ptr1,
in_ptr2, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr,
XBLOCK: tl.constexpr):
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 32
y1 = yindex // 32
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 32 * x2 + 800 * y1), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y1, None, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + y1, None, eviction_policy='evict_last')
tmp3 = tmp1 / tmp2
tmp4 = tmp0 * tmp3
tl.store(out_ptr0 + (x2 + 25 * y3), tmp4, xmask)
tl.store(out_ptr1 + (y0 + 32 * x2 + 800 * y1), tmp4, xmask)
@triton.jit
def triton_poi_fused_add_convolution_relu_sub_threshold_backward_13(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_ptr0 + x2, None)
tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + 0)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 - tmp4
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp8 = tmp7 + tmp4
tmp9 = 0.0
tmp10 = tmp7 <= tmp9
tl.store(out_ptr0 + x2, tmp8, None)
tl.store(out_ptr1 + x2, tmp10, None)
@triton.jit
def triton_red_fused__weight_norm_interface_14(in_out_ptr0, in_ptr0, xnumel,
rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
xnumel = 128
rnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rbase = tl.arange(0, RBLOCK)[None, :]
x0 = xindex
_tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r1 = rindex
tmp0 = tl.load(in_ptr0 + (r1 + 1600 * x0), rmask & xmask,
eviction_policy='evict_first', other=0.0)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = _tmp3 + tmp2
_tmp3 = tl.where(rmask & xmask, tmp4, _tmp3)
tmp3 = tl.sum(_tmp3, 1)[:, None]
tmp5 = libdevice.sqrt(tmp3)
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp5, xmask)
@triton.jit
def triton_poi_fused__weight_norm_interface_convolution_15(in_ptr0, in_ptr1,
in_ptr2, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr,
XBLOCK: tl.constexpr):
xnumel = 25
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 64
y1 = yindex // 64
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 64 * x2 + 1600 * y1), xmask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y1, None, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + y1, None, eviction_policy='evict_last')
tmp3 = tmp1 / tmp2
tmp4 = tmp0 * tmp3
tl.store(out_ptr0 + (x2 + 25 * y3), tmp4, xmask)
tl.store(out_ptr1 + (y0 + 64 * x2 + 1600 * y1), tmp4, xmask)
@triton.jit
def triton_poi_fused_add_convolution_relu_sub_threshold_backward_16(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 128
tmp0 = tl.load(in_ptr0 + x2, None)
tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + 0)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 - tmp4
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp8 = tmp7 + tmp4
tmp9 = 0.0
tmp10 = tmp7 <= tmp9
tl.store(out_ptr0 + x2, tmp8, None)
tl.store(out_ptr1 + x2, tmp10, None)
@triton.jit
def triton_per_fused__weight_norm_interface_17(in_out_ptr0, in_ptr0,
in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 128
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp6 = tl.load(in_ptr1 + 0)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = tl.sum(tmp2, 1)[:, None]
tmp5 = libdevice.sqrt(tmp4)
tmp8 = tmp7 / tmp5
tmp9 = tmp0 * tmp8
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp5, None)
tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp9, None)
@triton.jit
def triton_poi_fused_convolution_18(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tl.store(in_out_ptr0 + x0, tmp3, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17,
primals_18, primals_19, primals_20) = args
args.clear()
assert_size_stride(primals_1, (16, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_2, (16, 6, 5, 5), (150, 25, 5, 1))
assert_size_stride(primals_3, (16,), (1,))
assert_size_stride(primals_4, (4, 6, 64, 64), (24576, 4096, 64, 1))
assert_size_stride(primals_5, (1,), (1,))
assert_size_stride(primals_6, (32, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_7, (32, 16, 5, 5), (400, 25, 5, 1))
assert_size_stride(primals_8, (32,), (1,))
assert_size_stride(primals_9, (1,), (1,))
assert_size_stride(primals_10, (64, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_11, (64, 32, 5, 5), (800, 25, 5, 1))
assert_size_stride(primals_12, (64,), (1,))
assert_size_stride(primals_13, (1,), (1,))
assert_size_stride(primals_14, (128, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_15, (128, 64, 5, 5), (1600, 25, 5, 1))
assert_size_stride(primals_16, (128,), (1,))
assert_size_stride(primals_17, (1,), (1,))
assert_size_stride(primals_18, (1, 1, 1, 1), (1, 1, 1, 1))
assert_size_stride(primals_19, (1, 128, 1, 1), (128, 1, 1, 1))
assert_size_stride(primals_20, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 6, 5, 5), (150, 1, 30, 6), torch.float32
)
get_raw_stream(0)
triton_poi_fused_0[grid(96, 25)](primals_2, buf0, 96, 25, XBLOCK=32,
YBLOCK=32, num_warps=4, num_stages=1)
del primals_2
buf1 = empty_strided_cuda((4, 6, 64, 64), (24576, 1, 384, 6), torch
.float32)
triton_poi_fused_1[grid(24, 4096)](primals_4, buf1, 24, 4096,
XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
del primals_4
buf2 = empty_strided_cuda((32, 16, 5, 5), (400, 1, 80, 16), torch.
float32)
triton_poi_fused_2[grid(512, 25)](primals_7, buf2, 512, 25, XBLOCK=
32, YBLOCK=32, num_warps=4, num_stages=1)
del primals_7
buf3 = empty_strided_cuda((64, 32, 5, 5), (800, 1, 160, 32), torch.
float32)
triton_poi_fused_3[grid(2048, 25)](primals_11, buf3, 2048, 25,
XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
del primals_11
buf4 = empty_strided_cuda((128, 64, 5, 5), (1600, 1, 320, 64),
torch.float32)
triton_poi_fused_4[grid(8192, 25)](primals_15, buf4, 8192, 25,
XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
del primals_15
buf5 = empty_strided_cuda((16, 1, 1, 1), (1, 16, 16, 16), torch.float32
)
buf6 = reinterpret_tensor(buf5, (16, 1, 1, 1), (1, 1, 1, 1), 0)
del buf5
triton_per_fused__weight_norm_interface_5[grid(16)](buf6, buf0, 16,
150, XBLOCK=1, num_warps=2, num_stages=1)
buf7 = empty_strided_cuda((16, 6, 5, 5), (150, 25, 5, 1), torch.float32
)
buf8 = empty_strided_cuda((16, 6, 5, 5), (150, 1, 30, 6), torch.float32
)
triton_poi_fused__weight_norm_interface_convolution_6[grid(96, 25)](
buf0, primals_1, buf6, buf7, buf8, 96, 25, XBLOCK=32, YBLOCK=8,
num_warps=4, num_stages=1)
buf9 = extern_kernels.convolution(buf1, buf8, stride=(2, 2),
padding=(2, 2), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 16, 32, 32), (16384, 1, 512, 16))
del buf8
buf10 = empty_strided_cuda((4, 16, 32, 32), (16384, 1, 512, 16),
torch.float32)
buf37 = empty_strided_cuda((4, 16, 32, 32), (16384, 1, 512, 16),
torch.bool)
triton_poi_fused_add_convolution_relu_sub_threshold_backward_7[grid
(65536)](buf9, primals_3, primals_5, buf10, buf37, 65536,
XBLOCK=512, num_warps=4, num_stages=1)
del buf9
del primals_3
del primals_5
buf11 = empty_strided_cuda((32, 1, 1, 1), (1, 32, 32, 32), torch.
float32)
buf12 = reinterpret_tensor(buf11, (32, 1, 1, 1), (1, 1, 1, 1), 0)
del buf11
triton_per_fused__weight_norm_interface_8[grid(32)](buf12, buf2, 32,
400, num_warps=4, num_stages=1)
buf13 = empty_strided_cuda((32, 16, 5, 5), (400, 25, 5, 1), torch.
float32)
buf14 = empty_strided_cuda((32, 16, 5, 5), (400, 1, 80, 16), torch.
float32)
triton_poi_fused__weight_norm_interface_convolution_9[grid(512, 25)](
buf2, primals_6, buf12, buf13, buf14, 512, 25, XBLOCK=1, YBLOCK
=256, num_warps=4, num_stages=1)
buf15 = extern_kernels.convolution(buf10, buf14, stride=(2, 2),
padding=(2, 2), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf15, (4, 32, 16, 16), (8192, 1, 512, 32))
del buf14
buf16 = empty_strided_cuda((4, 32, 16, 16), (8192, 1, 512, 32),
torch.float32)
buf36 = empty_strided_cuda((4, 32, 16, 16), (8192, 1, 512, 32),
torch.bool)
triton_poi_fused_add_convolution_relu_sub_threshold_backward_10[grid
(32768)](buf15, primals_8, primals_9, buf16, buf36, 32768,
XBLOCK=256, num_warps=4, num_stages=1)
del buf15
del primals_8
del primals_9
buf17 = empty_strided_cuda((64, 1, 1, 1), (1, 64, 64, 64), torch.
float32)
buf18 = reinterpret_tensor(buf17, (64, 1, 1, 1), (1, 1, 1, 1), 0)
del buf17
triton_per_fused__weight_norm_interface_11[grid(64)](buf18, buf3,
64, 800, num_warps=8, num_stages=1)
buf19 = empty_strided_cuda((64, 32, 5, 5), (800, 25, 5, 1), torch.
float32)
buf20 = empty_strided_cuda((64, 32, 5, 5), (800, 1, 160, 32), torch
.float32)
triton_poi_fused__weight_norm_interface_convolution_12[grid(2048, 25)](
buf3, primals_10, buf18, buf19, buf20, 2048, 25, XBLOCK=32,
YBLOCK=32, num_warps=4, num_stages=1)
buf21 = extern_kernels.convolution(buf16, buf20, stride=(2, 2),
padding=(2, 2), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf21, (4, 64, 8, 8), (4096, 1, 512, 64))
del buf20
buf22 = empty_strided_cuda((4, 64, 8, 8), (4096, 1, 512, 64), torch
.float32)
buf35 = empty_strided_cuda((4, 64, 8, 8), (4096, 1, 512, 64), torch
.bool)
triton_poi_fused_add_convolution_relu_sub_threshold_backward_13[grid
(16384)](buf21, primals_12, primals_13, buf22, buf35, 16384,
XBLOCK=128, num_warps=4, num_stages=1)
del buf21
del primals_12
del primals_13
buf23 = empty_strided_cuda((128, 1, 1, 1), (1, 128, 128, 128),
torch.float32)
buf24 = reinterpret_tensor(buf23, (128, 1, 1, 1), (1, 1, 1, 1), 0)
del buf23
triton_red_fused__weight_norm_interface_14[grid(128)](buf24, buf4,
128, 1600, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
buf25 = empty_strided_cuda((128, 64, 5, 5), (1600, 25, 5, 1), torch
.float32)
buf26 = empty_strided_cuda((128, 64, 5, 5), (1600, 1, 320, 64),
torch.float32)
triton_poi_fused__weight_norm_interface_convolution_15[grid(8192, 25)](
buf4, primals_14, buf24, buf25, buf26, 8192, 25, XBLOCK=32,
YBLOCK=128, num_warps=8, num_stages=1)
buf27 = extern_kernels.convolution(buf22, buf26, stride=(2, 2),
padding=(2, 2), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf27, (4, 128, 4, 4), (2048, 1, 512, 128))
del buf26
buf28 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128),
torch.float32)
buf34 = empty_strided_cuda((4, 128, 4, 4), (2048, 1, 512, 128),
torch.bool)
triton_poi_fused_add_convolution_relu_sub_threshold_backward_16[grid
(8192)](buf27, primals_16, primals_17, buf28, buf34, 8192,
XBLOCK=256, num_warps=4, num_stages=1)
del buf27
del primals_16
del primals_17
buf29 = empty_strided_cuda((1, 1, 1, 1), (1, 1, 1, 1), torch.float32)
buf30 = buf29
del buf29
buf31 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 1, 1), torch.
float32)
triton_per_fused__weight_norm_interface_17[grid(1)](buf30,
primals_19, primals_18, buf31, 1, 128, XBLOCK=1, num_warps=2,
num_stages=1)
buf32 = extern_kernels.convolution(buf28, buf31, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf32, (4, 1, 4, 4), (16, 1, 4, 1))
buf33 = buf32
del buf32
triton_poi_fused_convolution_18[grid(64)](buf33, primals_20, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_20
return (reinterpret_tensor(buf33, (1, 64), (64, 1), 0), buf7, buf13,
buf19, buf25, buf31, primals_1, buf0, buf1, primals_6, buf2,
primals_10, buf3, primals_14, buf4, primals_18, primals_19, buf6,
buf7, buf10, buf12, buf13, buf16, buf18, buf19, buf22, buf24, buf25,
buf28, buf30, buf31, buf34, buf35, buf36, buf37)
class TReLU(nn.Module):
def __init__(self):
super(TReLU, self).__init__()
self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
self.alpha.data.fill_(0)
def forward(self, x):
x = F.relu(x - self.alpha) + self.alpha
return x
class DiscriminatorNew(nn.Module):
def __init__(self):
super(DiscriminatorNew, self).__init__()
self.conv0 = weightNorm(nn.Conv2d(6, 16, 5, 2, 2))
self.conv1 = weightNorm(nn.Conv2d(16, 32, 5, 2, 2))
self.conv2 = weightNorm(nn.Conv2d(32, 64, 5, 2, 2))
self.conv3 = weightNorm(nn.Conv2d(64, 128, 5, 2, 2))
self.conv4 = weightNorm(nn.Conv2d(128, 1, 1, 1, 0))
self.relu0 = TReLU()
self.relu1 = TReLU()
self.relu2 = TReLU()
self.relu3 = TReLU()
def forward(self, input_0):
primals_3 = self.conv0.bias
primals_1 = self.conv0.weight_g
primals_2 = self.conv0.weight_v
primals_8 = self.conv1.bias
primals_6 = self.conv1.weight_g
primals_7 = self.conv1.weight_v
primals_12 = self.conv2.bias
primals_10 = self.conv2.weight_g
primals_11 = self.conv2.weight_v
primals_16 = self.conv3.bias
primals_14 = self.conv3.weight_g
primals_15 = self.conv3.weight_v
primals_5 = self.conv4.bias
primals_18 = self.conv4.weight_g
primals_19 = self.conv4.weight_v
primals_9 = self.relu0.alpha
primals_13 = self.relu1.alpha
primals_17 = self.relu2.alpha
primals_20 = self.relu3.alpha
primals_4 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17, primals_18, primals_19,
primals_20])
return output[0]
|
archiroid003/ICCV2019-LearningToPaint
|
Discriminator
| false | 12,134 |
[
"MIT"
] | 0 |
4b5fc263e4843c159a61e5956956b3f7812693f8
|
https://github.com/archiroid003/ICCV2019-LearningToPaint/tree/4b5fc263e4843c159a61e5956956b3f7812693f8
|
LanguageModelCriterion
|
import torch
import torch.nn as nn
from torch.autograd import *
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
def forward(self, input, target, mask):
target = target[:, :input.size(1)]
mask = mask[:, :input.size(1)]
output = -input.gather(2, target.unsqueeze(2)).squeeze(2) * mask
output = torch.sum(output) / torch.sum(mask)
return output
def get_inputs():
return [torch.ones([4, 4, 4], dtype=torch.int64), torch.ones([4, 4],
dtype=torch.int64), torch.rand([4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_div_mul_neg_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp9 = tl.load(in_ptr2 + r0, None)
tmp1 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tl.device_assert((0 <= tmp4) & (tmp4 < 4),
'index out of bounds: 0 <= tmp4 < 4')
tmp6 = tl.load(in_ptr1 + (tmp4 + 4 * r0), None, eviction_policy=
'evict_last')
tmp7 = -tmp6
tmp8 = tmp7.to(tl.float32)
tmp10 = tmp8 * tmp9
tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
tmp13 = tl.sum(tmp11, 1)[:, None]
tmp14 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
tmp16 = tl.sum(tmp14, 1)[:, None]
tmp17 = tmp13 / tmp16
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp17, None)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(arg1_1, (4, 4), (4, 1))
assert_size_stride(arg2_1, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf2 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_div_mul_neg_sum_0[grid(1)](buf2, arg1_1, arg0_1,
arg2_1, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
del arg2_1
return buf2,
class LanguageModelCriterionNew(nn.Module):
def __init__(self):
super(LanguageModelCriterionNew, self).__init__()
def forward(self, input_0, input_1, input_2):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
output = call([arg0_1, arg1_1, arg2_1])
return output[0]
|
Zhendong-Wang/arsm_image_captioning
|
LanguageModelCriterion
| false | 9,661 |
[
"MIT"
] | 0 |
2282b76ab03b53952269d94d6c4b19ab98636ca5
|
https://github.com/Zhendong-Wang/arsm_image_captioning/tree/2282b76ab03b53952269d94d6c4b19ab98636ca5
|
MyEntropy
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_7/inductor_cache/td/ctdj5kazgiki6gdaadhqtp2x7tq2ee5ey5hqqdcoqmp54jyhf74f.py
# Topologically Sorted Source Nodes: [logsoftmax], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
# logsoftmax => amax, sub
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [1], True), kwargs = {})
# %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax), kwargs = {})
triton_poi_fused__log_softmax_0 = async_compile.triton('triton_poi_fused__log_softmax_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = (xindex // 64)
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_7/inductor_cache/56/c56zvtoyao27obzv5t3wc5ghkyyf6yov7jqfbnej5tgl6xhbdwlv.py
# Topologically Sorted Source Nodes: [logsoftmax, getitem, loss, mean], Original ATen: [aten._log_softmax, aten.index, aten.neg, aten.mean]
# Source node to ATen node mapping:
# getitem => index
# logsoftmax => exp, log, sub_1, sum_1
# loss => neg
# mean => mean
# Graph fragment:
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
# %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
# %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%sub_1, [%iota_default, %arg1_1]), kwargs = {})
# %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%index,), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%neg,), kwargs = {})
triton_per_fused__log_softmax_index_mean_neg_1 = async_compile.triton('triton_per_fused__log_softmax_index_mean_neg_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 64],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__log_softmax_index_mean_neg_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__log_softmax_index_mean_neg_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 64
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = (rindex // 16)
r0 = rindex % 16
tmp0 = tl.load(in_ptr0 + (r1), None, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
tmp9 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
tmp12 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
tmp15 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
tmp1 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tl.device_assert((0 <= tmp4) & (tmp4 < 4), "index out of bounds: 0 <= tmp4 < 4")
tmp6 = tl.load(in_ptr1 + (r0 + (16*tmp4) + (64*r1)), None)
tmp8 = tl_math.exp(tmp7)
tmp10 = tl_math.exp(tmp9)
tmp11 = tmp8 + tmp10
tmp13 = tl_math.exp(tmp12)
tmp14 = tmp11 + tmp13
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp14 + tmp16
tmp18 = tl_math.log(tmp17)
tmp19 = tmp6 - tmp18
tmp20 = -tmp19
tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
tmp23 = tl.sum(tmp21, 1)[:, None]
tmp24 = 64.0
tmp25 = tmp23 / tmp24
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp25, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [logsoftmax], Original ATen: [aten._log_softmax]
stream0 = get_raw_stream(0)
triton_poi_fused__log_softmax_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [logsoftmax, getitem, loss, mean], Original ATen: [aten._log_softmax, aten.index, aten.neg, aten.mean]
triton_per_fused__log_softmax_index_mean_neg_1.run(buf2, arg1_1, buf0, 1, 64, grid=grid(1), stream=stream0)
del arg1_1
del buf0
return (buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + x3, tmp8, xmask)
@triton.jit
def triton_per_fused__log_softmax_index_mean_neg_1(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex // 16
r0 = rindex % 16
tmp0 = tl.load(in_ptr0 + r1, None, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
tmp9 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
tmp12 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
tmp15 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
tmp1 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tl.device_assert((0 <= tmp4) & (tmp4 < 4),
'index out of bounds: 0 <= tmp4 < 4')
tmp6 = tl.load(in_ptr1 + (r0 + 16 * tmp4 + 64 * r1), None)
tmp8 = tl_math.exp(tmp7)
tmp10 = tl_math.exp(tmp9)
tmp11 = tmp8 + tmp10
tmp13 = tl_math.exp(tmp12)
tmp14 = tmp11 + tmp13
tmp16 = tl_math.exp(tmp15)
tmp17 = tmp14 + tmp16
tmp18 = tl_math.log(tmp17)
tmp19 = tmp6 - tmp18
tmp20 = -tmp19
tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
tmp23 = tl.sum(tmp21, 1)[:, None]
tmp24 = 64.0
tmp25 = tmp23 / tmp24
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp25, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__log_softmax_0[grid(256)](arg0_1, buf0, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = buf1
del buf1
triton_per_fused__log_softmax_index_mean_neg_1[grid(1)](buf2,
arg1_1, buf0, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
del arg1_1
del buf0
return buf2,
class MyEntropyNew(nn.Module):
def __init__(self):
super(MyEntropyNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
atimashov/object_detection
|
MyEntropy
| false | 3,135 |
[
"MIT"
] | 0 |
922cd88f429156fa4668c7d718b2665e4ab875fd
|
https://github.com/atimashov/object_detection/tree/922cd88f429156fa4668c7d718b2665e4ab875fd
|
BasicCNN1
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_8/inductor_cache/3r/c3rslv2exiiurkywxt4oxifqli4zlmoxh26mfj7b2xmxbacdj7n6.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128, 16], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 96
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 3
y1 = (yindex // 3)
tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask & ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (y0 + (3*x2) + (27*y1)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/5b/c5brnjme4e4oybuabwsko4vuljormwjqoawce7jgxo5fbkhzx55r.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 4096], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 12
xnumel = 4096
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
x2 = xindex
y3 = yindex
y0 = yindex % 3
y1 = (yindex // 3)
tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (y0 + (3*x2) + (12288*y1)), tmp0, ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/wv/cwvtp6qflpb42kxrujmda5zselv7wvkz3fgp2tryo2ftsisaildr.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_2 = async_compile.triton('triton_poi_fused_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[2048, 16], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 2048
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 32
y1 = (yindex // 32)
tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (y0 + (32*x2) + (288*y1)), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/nw/cnwm6ljuusoqjcwr2jdx6p2ue7ldghxjdr3oe62stiuqhsboiczy.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_3 = async_compile.triton('triton_poi_fused_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8192, 16], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 8192
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 64
y1 = (yindex // 64)
tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (y0 + (64*x2) + (576*y1)), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/cb/ccbdveqsgunhla4cfhtfjdkif3utkipupej6kcqiiji3bjat2x63.py
# Topologically Sorted Source Nodes: [conv2d, relu], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# conv2d => convolution
# relu => relu
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_4 = async_compile.triton('triton_poi_fused_convolution_relu_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[524288],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 524288
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 32
tmp0 = tl.load(in_out_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/nl/cnlxxnmf5ygykjzvjljgvug3jvcu7du2i7h7ssfgcz5bdrzqhq5k.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
# x => getitem, getitem_1
# Graph fragment:
# %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
# %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_5 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 131072
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 32
x1 = (xindex // 32) % 32
x2 = (xindex // 1024)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (64*x1) + (4096*x2)), None)
tmp1 = tl.load(in_ptr0 + (32 + x0 + (64*x1) + (4096*x2)), None)
tmp3 = tl.load(in_ptr0 + (2048 + x0 + (64*x1) + (4096*x2)), None)
tmp5 = tl.load(in_ptr0 + (2080 + x0 + (64*x1) + (4096*x2)), None)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + (x3), tmp6, None)
tl.store(out_ptr1 + (x3), tmp16, None)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/na/cnaiponhu6kavyqptxmbfaxdb2osqwlqk74kcqgtnst5s3wwic5o.py
# Topologically Sorted Source Nodes: [conv2d_1, relu_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# conv2d_1 => convolution_1
# relu_1 => relu_1
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_6 = async_compile.triton('triton_poi_fused_convolution_relu_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[262144],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 262144
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/4x/c4xqpnjemnncshp3uobexba3gggcnvtfyqm77kedjl5hqdw3frxf.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
# x_1 => getitem_2, getitem_3
# Graph fragment:
# %getitem_2 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 0), kwargs = {})
# %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_7 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_7(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 65536
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 64
x1 = (xindex // 64) % 16
x2 = (xindex // 1024)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (128*x1) + (4096*x2)), None)
tmp1 = tl.load(in_ptr0 + (64 + x0 + (128*x1) + (4096*x2)), None)
tmp3 = tl.load(in_ptr0 + (2048 + x0 + (128*x1) + (4096*x2)), None)
tmp5 = tl.load(in_ptr0 + (2112 + x0 + (128*x1) + (4096*x2)), None)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + (x3), tmp6, None)
tl.store(out_ptr1 + (x3), tmp16, None)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/sl/csldasgiybuk75pltndkezbj76bkzandkkdq65rbg7u7qjxpoaag.py
# Topologically Sorted Source Nodes: [conv2d_2, relu_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# conv2d_2 => convolution_2
# relu_2 => relu_2
# Graph fragment:
# %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem_2, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
triton_poi_fused_convolution_relu_8 = async_compile.triton('triton_poi_fused_convolution_relu_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 131072
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 128
tmp0 = tl.load(in_out_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/uf/cufauwupgjdrp6mauwivuddmfomag5gobvtyuvy22h7zpdtrudh5.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
# x_2 => _low_memory_max_pool2d_with_offsets_2, getitem_5
# Graph fragment:
# %_low_memory_max_pool2d_with_offsets_2 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_2, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
# %getitem_5 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_2, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_9 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256, 128], tile_hint=TileHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i8', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_9(in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 256
xnumel = 128
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 8
y1 = (yindex // 8)
y5 = yindex
y4 = (yindex // 64)
y6 = yindex % 64
tmp0 = tl.load(in_ptr0 + (x2 + (256*y0) + (4096*y1)), xmask & ymask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (128 + x2 + (256*y0) + (4096*y1)), xmask & ymask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2048 + x2 + (256*y0) + (4096*y1)), xmask & ymask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (2176 + x2 + (256*y0) + (4096*y1)), xmask & ymask, eviction_policy='evict_last')
tmp2 = tmp1 > tmp0
tmp3 = tl.full([1, 1], 1, tl.int8)
tmp4 = tl.full([1, 1], 0, tl.int8)
tmp5 = tl.where(tmp2, tmp3, tmp4)
tmp6 = triton_helpers.maximum(tmp1, tmp0)
tmp8 = tmp7 > tmp6
tmp9 = tl.full([1, 1], 2, tl.int8)
tmp10 = tl.where(tmp8, tmp9, tmp5)
tmp11 = triton_helpers.maximum(tmp7, tmp6)
tmp13 = tmp12 > tmp11
tmp14 = tl.full([1, 1], 3, tl.int8)
tmp15 = tl.where(tmp13, tmp14, tmp10)
tmp16 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x2 + (128*y5)), tmp15, xmask & ymask)
tl.store(out_ptr1 + (y6 + (64*x2) + (8192*y4)), tmp16, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/4z/c4zkjrymdmkix2ew2ul2mfnu6lkblf6zlcrmopfi6cbx5ypowtyn.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# x_5 => relu_3
# Graph fragment:
# %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_9), kwargs = {})
# %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_relu_10 = async_compile.triton('triton_poi_fused_relu_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 3456
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 216
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args
args.clear()
assert_size_stride(primals_1, (32, 3, 3, 3), (27, 9, 3, 1))
assert_size_stride(primals_2, (32, ), (1, ))
assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_4, (64, 32, 3, 3), (288, 9, 3, 1))
assert_size_stride(primals_5, (64, ), (1, ))
assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_7, (128, ), (1, ))
assert_size_stride(primals_8, (216, 2048), (2048, 1))
assert_size_stride(primals_9, (216, ), (1, ))
assert_size_stride(primals_10, (10, 216), (216, 1))
assert_size_stride(primals_11, (10, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((32, 3, 3, 3), (27, 1, 9, 3), torch.float32)
# Unsorted Source Nodes: [], Original ATen: []
stream0 = get_raw_stream(0)
triton_poi_fused_0.run(primals_1, buf0, 96, 9, grid=grid(96, 9), stream=stream0)
del primals_1
buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch.float32)
# Unsorted Source Nodes: [], Original ATen: []
triton_poi_fused_1.run(primals_3, buf1, 12, 4096, grid=grid(12, 4096), stream=stream0)
del primals_3
buf2 = empty_strided_cuda((64, 32, 3, 3), (288, 1, 96, 32), torch.float32)
# Unsorted Source Nodes: [], Original ATen: []
triton_poi_fused_2.run(primals_4, buf2, 2048, 9, grid=grid(2048, 9), stream=stream0)
del primals_4
buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch.float32)
# Unsorted Source Nodes: [], Original ATen: []
triton_poi_fused_3.run(primals_6, buf3, 8192, 9, grid=grid(8192, 9), stream=stream0)
del primals_6
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf4 = extern_kernels.convolution(buf1, buf0, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 32, 64, 64), (131072, 1, 2048, 32))
buf5 = buf4; del buf4 # reuse
# Topologically Sorted Source Nodes: [conv2d, relu], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_4.run(buf5, primals_2, 524288, grid=grid(524288), stream=stream0)
del primals_2
buf6 = empty_strided_cuda((4, 32, 32, 32), (32768, 1, 1024, 32), torch.float32)
buf7 = empty_strided_cuda((4, 32, 32, 32), (32768, 1, 1024, 32), torch.int8)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.max_pool2d_with_indices]
triton_poi_fused_max_pool2d_with_indices_5.run(buf5, buf6, buf7, 131072, grid=grid(131072), stream=stream0)
# Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
buf8 = extern_kernels.convolution(buf6, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 64, 32, 32), (65536, 1, 2048, 64))
buf9 = buf8; del buf8 # reuse
# Topologically Sorted Source Nodes: [conv2d_1, relu_1], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_6.run(buf9, primals_5, 262144, grid=grid(262144), stream=stream0)
del primals_5
buf10 = empty_strided_cuda((4, 64, 16, 16), (16384, 1, 1024, 64), torch.float32)
buf11 = empty_strided_cuda((4, 64, 16, 16), (16384, 1, 1024, 64), torch.int8)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.max_pool2d_with_indices]
triton_poi_fused_max_pool2d_with_indices_7.run(buf9, buf10, buf11, 65536, grid=grid(65536), stream=stream0)
# Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
buf12 = extern_kernels.convolution(buf10, buf3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf12, (4, 128, 16, 16), (32768, 1, 2048, 128))
buf13 = buf12; del buf12 # reuse
# Topologically Sorted Source Nodes: [conv2d_2, relu_2], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_8.run(buf13, primals_7, 131072, grid=grid(131072), stream=stream0)
del primals_7
buf14 = empty_strided_cuda((4, 128, 8, 8), (8192, 1, 1024, 128), torch.int8)
buf15 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
triton_poi_fused_max_pool2d_with_indices_9.run(buf13, buf14, buf15, 256, 128, grid=grid(256, 128), stream=stream0)
buf16 = empty_strided_cuda((16, 216), (216, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf15, (16, 2048), (2048, 1), 0), reinterpret_tensor(primals_8, (2048, 216), (1, 2048), 0), out=buf16)
buf17 = buf16; del buf16 # reuse
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.relu]
triton_poi_fused_relu_10.run(buf17, primals_9, 3456, grid=grid(3456), stream=stream0)
del primals_9
buf18 = empty_strided_cuda((16, 10), (10, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_11, buf17, reinterpret_tensor(primals_10, (216, 10), (1, 216), 0), alpha=1, beta=1, out=buf18)
del primals_11
return (buf18, buf0, buf1, buf2, buf3, buf5, buf6, buf7, buf9, buf10, buf11, buf13, buf14, reinterpret_tensor(buf15, (16, 2048), (2048, 1), 0), buf17, primals_10, primals_8, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((32, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((64, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((216, 2048), (2048, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((216, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((10, 216), (216, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((10, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 96
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 3
y1 = yindex // 3
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 3 * x2 + 27 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 12
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
x2 = xindex
y3 = yindex
y0 = yindex % 3
y1 = yindex // 3
tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
'evict_last')
tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp0, ymask)
@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 32
y1 = yindex // 32
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 32 * x2 + 288 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
xnumel = 9
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
tl.full([XBLOCK, YBLOCK], True, tl.int1)
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 64
y1 = yindex // 64
tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
)
tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 32
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_5(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 32
x1 = xindex // 32 % 32
x2 = xindex // 1024
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1 + 4096 * x2), None)
tmp1 = tl.load(in_ptr0 + (32 + x0 + 64 * x1 + 4096 * x2), None)
tmp3 = tl.load(in_ptr0 + (2048 + x0 + 64 * x1 + 4096 * x2), None)
tmp5 = tl.load(in_ptr0 + (2080 + x0 + 64 * x1 + 4096 * x2), None)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x3, tmp6, None)
tl.store(out_ptr1 + x3, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_7(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 64
x1 = xindex // 64 % 16
x2 = xindex // 1024
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1 + 4096 * x2), None)
tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 4096 * x2), None)
tmp3 = tl.load(in_ptr0 + (2048 + x0 + 128 * x1 + 4096 * x2), None)
tmp5 = tl.load(in_ptr0 + (2112 + x0 + 128 * x1 + 4096 * x2), None)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = tmp1 > tmp0
tmp8 = tl.full([1], 1, tl.int8)
tmp9 = tl.full([1], 0, tl.int8)
tmp10 = tl.where(tmp7, tmp8, tmp9)
tmp11 = tmp3 > tmp2
tmp12 = tl.full([1], 2, tl.int8)
tmp13 = tl.where(tmp11, tmp12, tmp10)
tmp14 = tmp5 > tmp4
tmp15 = tl.full([1], 3, tl.int8)
tmp16 = tl.where(tmp14, tmp15, tmp13)
tl.store(out_ptr0 + x3, tmp6, None)
tl.store(out_ptr1 + x3, tmp16, None)
@triton.jit
def triton_poi_fused_convolution_relu_8(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 128
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_9(in_ptr0, out_ptr0, out_ptr1,
ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 256
xnumel = 128
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 8
y1 = yindex // 8
y5 = yindex
y4 = yindex // 64
y6 = yindex % 64
tmp0 = tl.load(in_ptr0 + (x2 + 256 * y0 + 4096 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (128 + x2 + 256 * y0 + 4096 * y1), xmask &
ymask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2048 + x2 + 256 * y0 + 4096 * y1), xmask &
ymask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (2176 + x2 + 256 * y0 + 4096 * y1), xmask &
ymask, eviction_policy='evict_last')
tmp2 = tmp1 > tmp0
tmp3 = tl.full([1, 1], 1, tl.int8)
tmp4 = tl.full([1, 1], 0, tl.int8)
tmp5 = tl.where(tmp2, tmp3, tmp4)
tmp6 = triton_helpers.maximum(tmp1, tmp0)
tmp8 = tmp7 > tmp6
tmp9 = tl.full([1, 1], 2, tl.int8)
tmp10 = tl.where(tmp8, tmp9, tmp5)
tmp11 = triton_helpers.maximum(tmp7, tmp6)
tmp13 = tmp12 > tmp11
tmp14 = tl.full([1, 1], 3, tl.int8)
tmp15 = tl.where(tmp13, tmp14, tmp10)
tmp16 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x2 + 128 * y5), tmp15, xmask & ymask)
tl.store(out_ptr1 + (y6 + 64 * x2 + 8192 * y4), tmp16, xmask & ymask)
@triton.jit
def triton_poi_fused_relu_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 3456
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 216
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (32, 3, 3, 3), (27, 9, 3, 1))
assert_size_stride(primals_2, (32,), (1,))
assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
assert_size_stride(primals_4, (64, 32, 3, 3), (288, 9, 3, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_7, (128,), (1,))
assert_size_stride(primals_8, (216, 2048), (2048, 1))
assert_size_stride(primals_9, (216,), (1,))
assert_size_stride(primals_10, (10, 216), (216, 1))
assert_size_stride(primals_11, (10,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((32, 3, 3, 3), (27, 1, 9, 3), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(96, 9)](primals_1, buf0, 96, 9, XBLOCK=16,
YBLOCK=64, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
.float32)
triton_poi_fused_1[grid(12, 4096)](primals_3, buf1, 12, 4096,
XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((64, 32, 3, 3), (288, 1, 96, 32), torch.
float32)
triton_poi_fused_2[grid(2048, 9)](primals_4, buf2, 2048, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch
.float32)
triton_poi_fused_3[grid(8192, 9)](primals_6, buf3, 8192, 9, XBLOCK=
16, YBLOCK=64, num_warps=4, num_stages=1)
del primals_6
buf4 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 32, 64, 64), (131072, 1, 2048, 32))
buf5 = buf4
del buf4
triton_poi_fused_convolution_relu_4[grid(524288)](buf5, primals_2,
524288, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_2
buf6 = empty_strided_cuda((4, 32, 32, 32), (32768, 1, 1024, 32),
torch.float32)
buf7 = empty_strided_cuda((4, 32, 32, 32), (32768, 1, 1024, 32),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_5[grid(131072)](buf5, buf6,
buf7, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
buf8 = extern_kernels.convolution(buf6, buf2, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 64, 32, 32), (65536, 1, 2048, 64))
buf9 = buf8
del buf8
triton_poi_fused_convolution_relu_6[grid(262144)](buf9, primals_5,
262144, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_5
buf10 = empty_strided_cuda((4, 64, 16, 16), (16384, 1, 1024, 64),
torch.float32)
buf11 = empty_strided_cuda((4, 64, 16, 16), (16384, 1, 1024, 64),
torch.int8)
triton_poi_fused_max_pool2d_with_indices_7[grid(65536)](buf9, buf10,
buf11, 65536, XBLOCK=256, num_warps=4, num_stages=1)
buf12 = extern_kernels.convolution(buf10, buf3, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf12, (4, 128, 16, 16), (32768, 1, 2048, 128))
buf13 = buf12
del buf12
triton_poi_fused_convolution_relu_8[grid(131072)](buf13, primals_7,
131072, XBLOCK=512, num_warps=8, num_stages=1)
del primals_7
buf14 = empty_strided_cuda((4, 128, 8, 8), (8192, 1, 1024, 128),
torch.int8)
buf15 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.
float32)
triton_poi_fused_max_pool2d_with_indices_9[grid(256, 128)](buf13,
buf14, buf15, 256, 128, XBLOCK=128, YBLOCK=2, num_warps=4,
num_stages=1)
buf16 = empty_strided_cuda((16, 216), (216, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf15, (16, 2048), (2048, 1),
0), reinterpret_tensor(primals_8, (2048, 216), (1, 2048), 0),
out=buf16)
buf17 = buf16
del buf16
triton_poi_fused_relu_10[grid(3456)](buf17, primals_9, 3456, XBLOCK
=256, num_warps=4, num_stages=1)
del primals_9
buf18 = empty_strided_cuda((16, 10), (10, 1), torch.float32)
extern_kernels.addmm(primals_11, buf17, reinterpret_tensor(
primals_10, (216, 10), (1, 216), 0), alpha=1, beta=1, out=buf18)
del primals_11
return (buf18, buf0, buf1, buf2, buf3, buf5, buf6, buf7, buf9, buf10,
buf11, buf13, buf14, reinterpret_tensor(buf15, (16, 2048), (2048, 1
), 0), buf17, primals_10, primals_8)
class BasicCNN1New(nn.Module):
def __init__(self):
super().__init__()
self.layer_names = ['conv1', 'conv2', 'conv3', 'fc1', 'output_layer']
self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(4 * 4 * 128, 216)
self.output_layer = nn.Linear(216, 10)
self.dropout = nn.Dropout(0.2)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_6 = self.conv3.weight
primals_7 = self.conv3.bias
primals_8 = self.fc1.weight
primals_9 = self.fc1.bias
primals_10 = self.output_layer.weight
primals_11 = self.output_layer.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0]
|
fnc11/CosDefence
|
BasicCNN1
| false | 10,119 |
[
"MIT"
] | 0 |
94f451b7d4b36cb3b9fcc85098dae242f311532b
|
https://github.com/fnc11/CosDefence/tree/94f451b7d4b36cb3b9fcc85098dae242f311532b
|
PreNet
|
import torch
import torch.nn as nn
class PreNet(nn.Module):
def __init__(self):
super(PreNet, self).__init__()
self.conv1 = nn.Conv2d(4, 8, 3, padding=1)
self.act1 = nn.LeakyReLU(0.2, inplace=False)
self.conv2 = nn.Conv2d(8, 16, 3, padding=1)
self.act2 = nn.LeakyReLU(0.2, inplace=False)
def forward(self, labels):
conv1 = self.act1(self.conv1(labels))
conv2 = self.act2(self.conv2(conv1))
return conv2
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 8
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.2
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, xmask)
tl.store(out_ptr1 + x3, tmp7, xmask)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 16
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.2
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, xmask)
tl.store(out_ptr1 + x3, tmp7, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (8, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (8,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (16, 8, 3, 3), (72, 9, 3, 1))
assert_size_stride(primals_5, (16,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1))
buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.bool)
buf2 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0[grid(512)](buf0,
primals_2, buf1, buf2, 512, XBLOCK=256, num_warps=4, num_stages=1)
del buf0
del primals_2
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 16, 4, 4), (256, 16, 4, 1))
buf4 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.bool)
buf5 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32
)
triton_poi_fused_convolution_leaky_relu_1[grid(1024)](buf3,
primals_5, buf4, buf5, 1024, XBLOCK=256, num_warps=4, num_stages=1)
del buf3
del primals_5
return buf5, primals_1, primals_3, primals_4, buf1, buf2, buf4
class PreNetNew(nn.Module):
def __init__(self):
super(PreNetNew, self).__init__()
self.conv1 = nn.Conv2d(4, 8, 3, padding=1)
self.act1 = nn.LeakyReLU(0.2, inplace=False)
self.conv2 = nn.Conv2d(8, 16, 3, padding=1)
self.act2 = nn.LeakyReLU(0.2, inplace=False)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
karavik18/Federated_Learning_for_Missing_MRI_Sequence
|
PreNet
| false | 7,012 |
[
"Apache-2.0"
] | 1 |
42924f8475f354e6b429d05867f99530aa485b96
|
https://github.com/karavik18/Federated_Learning_for_Missing_MRI_Sequence/tree/42924f8475f354e6b429d05867f99530aa485b96
|
LigthSpeechLoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_4/inductor_cache/ox/coxh4zxlwehvc7dn52ckdepn62ln2jmlbsk5mwd7tp7xur2tywk6.py
# Topologically Sorted Source Nodes: [mel_loss], Original ATen: [aten.mse_loss]
# Source node to ATen node mapping:
# mel_loss => mean, pow_1, sub
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %arg0_1), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
triton_per_fused_mse_loss_0 = async_compile.triton('triton_per_fused_mse_loss_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mse_loss_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_mse_loss_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp1 = tl.load(in_ptr1 + (r0), None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = 256.0
tmp8 = tmp6 / tmp7
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp8, None)
''', device_str='cuda')
# kernel path: runs/run_shard_4/inductor_cache/bq/cbqlvx3fqrosdwrna6ssfaprt3lceueeyt3u5o4low2mfdmh2ur6.py
# Topologically Sorted Source Nodes: [similarity_loss], Original ATen: [aten.sub, aten.abs, aten.mean]
# Source node to ATen node mapping:
# similarity_loss => abs_1, mean_1, sub_1
# Graph fragment:
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg3_1, %arg2_1), kwargs = {})
# %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_1,), kwargs = {})
# %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_1,), kwargs = {})
triton_per_fused_abs_mean_sub_1 = async_compile.triton('triton_per_fused_abs_mean_sub_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_mean_sub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_abs_mean_sub_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp1 = tl.load(in_ptr1 + (r0), None)
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = 256.0
tmp8 = tmp6 / tmp7
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp8, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg4_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg5_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf3 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [mel_loss], Original ATen: [aten.mse_loss]
stream0 = get_raw_stream(0)
triton_per_fused_mse_loss_0.run(buf3, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf4 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [similarity_loss], Original ATen: [aten.sub, aten.abs, aten.mean]
triton_per_fused_abs_mean_sub_1.run(buf4, arg3_1, arg2_1, 1, 256, grid=grid(1), stream=stream0)
del arg2_1
del arg3_1
buf2 = empty_strided_cuda((), (), torch.float32)
buf5 = buf2; del buf2 # reuse
# Topologically Sorted Source Nodes: [duration_loss], Original ATen: [aten.sub, aten.abs, aten.mean]
triton_per_fused_abs_mean_sub_1.run(buf5, arg5_1, arg4_1, 1, 256, grid=grid(1), stream=stream0)
del arg4_1
del arg5_1
return (buf3, buf4, buf5, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg3_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg4_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg5_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_mse_loss_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = 256.0
tmp8 = tmp6 / tmp7
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp8, None)
@triton.jit
def triton_per_fused_abs_mean_sub_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = 256.0
tmp8 = tmp6 / tmp7
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp8, None)
def call(args):
arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg4_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg5_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf3 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_mse_loss_0[grid(1)](buf3, arg1_1, arg0_1, 1, 256,
num_warps=2, num_stages=1)
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf4 = buf1
del buf1
triton_per_fused_abs_mean_sub_1[grid(1)](buf4, arg3_1, arg2_1, 1,
256, num_warps=2, num_stages=1)
del arg2_1
del arg3_1
buf2 = empty_strided_cuda((), (), torch.float32)
buf5 = buf2
del buf2
triton_per_fused_abs_mean_sub_1[grid(1)](buf5, arg5_1, arg4_1, 1,
256, num_warps=2, num_stages=1)
del arg4_1
del arg5_1
return buf3, buf4, buf5
class LigthSpeechLossNew(nn.Module):
""" LigthSpeech Loss """
def __init__(self):
super(LigthSpeechLossNew, self).__init__()
def forward(self, input_0, input_1, input_2, input_3, input_4, input_5):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
arg3_1 = input_3
arg4_1 = input_4
arg5_1 = input_5
output = call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1])
return output[0], output[1], output[2]
|
entn-at/LightSpeech
|
LigthSpeechLoss
| false | 6,654 |
[
"MIT"
] | 1 |
48250fbcede4b258ba13ab17e3e83afc5fe85a01
|
https://github.com/entn-at/LightSpeech/tree/48250fbcede4b258ba13ab17e3e83afc5fe85a01
|
myEncoder
|
import torch
import torch.nn.functional as F
class myEncoder(torch.nn.Module):
def __init__(self, fomSize, romSize):
super(myEncoder, self).__init__()
self.fc1 = torch.nn.Linear(fomSize, 200)
self.fc2 = torch.nn.Linear(200, 64)
self.fc3 = torch.nn.Linear(64, romSize)
def forward(self, x):
x = self.fc1(x)
x = F.elu(x)
x = self.fc2(x)
x = F.elu(x)
x = self.fc3(x)
x = F.elu(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'fomSize': 4, 'romSize': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_elu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 12800
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tl.store(out_ptr0 + x0, tmp7, xmask)
@triton.jit
def triton_poi_fused_elu_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, None)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tl.store(out_ptr0 + x0, tmp7, None)
@triton.jit
def triton_poi_fused_elu_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 1.0
tmp4 = tmp0 * tmp3
tmp5 = libdevice.expm1(tmp4)
tmp6 = tmp5 * tmp3
tmp7 = tl.where(tmp2, tmp4, tmp6)
tl.store(out_ptr0 + x0, tmp7, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (200, 4), (4, 1))
assert_size_stride(primals_2, (200,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (64, 200), (200, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (4, 64), (64, 1))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 200), (200, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 200), (1, 4),
0), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1),
torch.float32)
get_raw_stream(0)
triton_poi_fused_elu_0[grid(12800)](buf0, buf1, 12800, XBLOCK=128,
num_warps=4, num_stages=1)
buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 200),
(200, 1), 0), reinterpret_tensor(primals_4, (200, 64), (1, 200),
0), alpha=1, beta=1, out=buf2)
del primals_5
buf3 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.
float32)
triton_poi_fused_elu_1[grid(4096)](buf2, buf3, 4096, XBLOCK=256,
num_warps=4, num_stages=1)
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 64),
(64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0),
alpha=1, beta=1, out=buf4)
del primals_7
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_elu_2[grid(256)](buf4, buf5, 256, XBLOCK=256,
num_warps=4, num_stages=1)
return buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf0, reinterpret_tensor(buf1, (64, 200), (200, 1), 0
), buf2, reinterpret_tensor(buf3, (64, 64), (64, 1), 0
), buf4, primals_6, primals_4
class myEncoderNew(torch.nn.Module):
def __init__(self, fomSize, romSize):
super(myEncoderNew, self).__init__()
self.fc1 = torch.nn.Linear(fomSize, 200)
self.fc2 = torch.nn.Linear(200, 64)
self.fc3 = torch.nn.Linear(64, romSize)
def forward(self, input_0):
primals_1 = self.fc1.weight
primals_2 = self.fc1.bias
primals_4 = self.fc2.weight
primals_5 = self.fc2.bias
primals_6 = self.fc3.weight
primals_7 = self.fc3.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
Pressio/pressio4py
|
myEncoder
| false | 17,834 |
[
"Unlicense",
"BSD-3-Clause"
] | 4 |
36676dbd112a7c7960ccbf302ff14d4376c819ec
|
https://github.com/Pressio/pressio4py/tree/36676dbd112a7c7960ccbf302ff14d4376c819ec
|
ContrastiveLoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_8/inductor_cache/3v/c3vyztzgw3iiqfspecgmyuijbvk6r675ogzi4x5mnjsc4kdyk3gp.py
# Topologically Sorted Source Nodes: [distances], Original ATen: [aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul]
# Source node to ATen node mapping:
# distances => clamp_min, clamp_min_1, div, div_1, mul, pow_1, pow_2, pow_3, pow_4, sum_1, sum_2
# Graph fragment:
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg1_1, 2), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1], True), kwargs = {})
# %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
# %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_2, 1e-08), kwargs = {})
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg1_1, %clamp_min), kwargs = {})
# %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 2), kwargs = {})
# %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, [1], True), kwargs = {})
# %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
# %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_4, 1e-08), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %clamp_min_1), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_1, %div), kwargs = {})
triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0 = async_compile.triton('triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = (xindex // 64)
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr1 + (x3), xmask)
tmp17 = tl.load(in_ptr1 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp19 = tl.load(in_ptr1 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr1 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr1 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-08
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tmp18 = tmp17 * tmp17
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = libdevice.sqrt(tmp27)
tmp29 = triton_helpers.maximum(tmp28, tmp13)
tmp30 = tmp16 / tmp29
tmp31 = tmp15 * tmp30
tl.store(out_ptr0 + (x3), tmp31, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/26/c26z3t2s2o7fbb3srtzysccunz5ychms4ztfx6xensu3es5gjxgt.py
# Topologically Sorted Source Nodes: [distances], Original ATen: [aten.sum]
# Source node to ATen node mapping:
# distances => sum_3
# Graph fragment:
# %sum_3 : [num_users=3] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
triton_poi_fused_sum_1 = async_compile.triton('triton_poi_fused_sum_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_sum_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x1 = (xindex // 16)
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
tmp1 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
tmp3 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
tmp5 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_8/inductor_cache/ur/curaeceqtciv33kgsrtwhpmo4qktwur2c3s37rzlprbqh72g36v2.py
# Topologically Sorted Source Nodes: [sub, sub_1, relu, pow_1, mul, sub_2, pow_2, mul_1, truediv, losses, mean], Original ATen: [aten.rsub, aten.relu, aten.pow, aten.mul, aten.div, aten.add, aten.mean]
# Source node to ATen node mapping:
# losses => add
# mean => mean
# mul => mul_1
# mul_1 => mul_2
# pow_1 => pow_5
# pow_2 => pow_6
# relu => relu
# sub => sub
# sub_1 => sub_1
# sub_2 => sub_2
# truediv => div_2
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg2_1), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (0.5, %sum_3), kwargs = {})
# %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub_1,), kwargs = {})
# %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%relu, 2), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %pow_5), kwargs = {})
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sum_3), kwargs = {})
# %pow_6 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 2), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg2_1, %pow_6), kwargs = {})
# %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_2, 4), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %div_2), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%add,), kwargs = {})
triton_per_fused_add_div_mean_mul_pow_relu_rsub_2 = async_compile.triton('triton_per_fused_add_div_mean_mul_pow_relu_rsub_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_pow_relu_rsub_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_div_mean_mul_pow_relu_rsub_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r2 = rindex
r0 = rindex % 64
tmp0 = tl.load(in_ptr0 + (r2), None)
tmp3 = tl.load(in_ptr1 + (r0), None, eviction_policy='evict_last')
tmp1 = 1.0
tmp2 = tmp1 - tmp0
tmp4 = 0.5
tmp5 = tmp4 - tmp3
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp8 = tmp7 * tmp7
tmp9 = tmp2 * tmp8
tmp10 = tmp1 - tmp3
tmp11 = tmp10 * tmp10
tmp12 = tmp0 * tmp11
tmp13 = 0.25
tmp14 = tmp12 * tmp13
tmp15 = tmp9 + tmp14
tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
tmp19 = 256.0
tmp20 = tmp18 / tmp19
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp20, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [distances], Original ATen: [aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul]
stream0 = get_raw_stream(0)
triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0.run(arg1_1, arg0_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [distances], Original ATen: [aten.sum]
triton_poi_fused_sum_1.run(buf0, buf1, 64, grid=grid(64), stream=stream0)
del buf0
buf2 = empty_strided_cuda((), (), torch.float32)
buf3 = buf2; del buf2 # reuse
# Topologically Sorted Source Nodes: [sub, sub_1, relu, pow_1, mul, sub_2, pow_2, mul_1, truediv, losses, mean], Original ATen: [aten.rsub, aten.relu, aten.pow, aten.mul, aten.div, aten.add, aten.mean]
triton_per_fused_add_div_mean_mul_pow_relu_rsub_2.run(buf3, arg2_1, buf1, 1, 256, grid=grid(1), stream=stream0)
del arg2_1
return (buf3, buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1, arg2_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
from torch.nn import CosineSimilarity
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp16 = tl.load(in_ptr1 + x3, xmask)
tmp17 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp19 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp22 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp25 = tl.load(in_ptr1 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-08
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tmp18 = tmp17 * tmp17
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = libdevice.sqrt(tmp27)
tmp29 = triton_helpers.maximum(tmp28, tmp13)
tmp30 = tmp16 / tmp29
tmp31 = tmp15 * tmp30
tl.store(out_ptr0 + x3, tmp31, xmask)
@triton.jit
def triton_poi_fused_sum_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x1 = xindex // 16
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_per_fused_add_div_mean_mul_pow_relu_rsub_2(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r2 = rindex
r0 = rindex % 64
tmp0 = tl.load(in_ptr0 + r2, None)
tmp3 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
tmp1 = 1.0
tmp2 = tmp1 - tmp0
tmp4 = 0.5
tmp5 = tmp4 - tmp3
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp8 = tmp7 * tmp7
tmp9 = tmp2 * tmp8
tmp10 = tmp1 - tmp3
tmp11 = tmp10 * tmp10
tmp12 = tmp0 * tmp11
tmp13 = 0.25
tmp14 = tmp12 * tmp13
tmp15 = tmp9 + tmp14
tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
tmp19 = 256.0
tmp20 = tmp18 / tmp19
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp20, None)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0[grid(256)](
arg1_1, arg0_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_sum_1[grid(64)](buf0, buf1, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del buf0
buf2 = empty_strided_cuda((), (), torch.float32)
buf3 = buf2
del buf2
triton_per_fused_add_div_mean_mul_pow_relu_rsub_2[grid(1)](buf3,
arg2_1, buf1, 1, 256, num_warps=2, num_stages=1)
del arg2_1
return buf3, buf1
class ContrastiveLossNew(nn.Module):
"""
Contrastive loss
Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 otherwise
"""
def __init__(self, margin=0.5):
super(ContrastiveLossNew, self).__init__()
self.margin = margin
self.distance = CosineSimilarity()
self.eps = 1e-06
self.mse = torch.nn.MSELoss()
def forward(self, input_0, input_1, input_2):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
output = call([arg0_1, arg1_1, arg2_1])
return output[0], output[1]
|
elloworl/FRMiner2.0
|
ContrastiveLoss
| false | 10,020 |
[
"MIT"
] | 0 |
f596530d18512a1b1b8b8d56772f006f9f53f429
|
https://github.com/elloworl/FRMiner2.0/tree/f596530d18512a1b1b8b8d56772f006f9f53f429
|
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