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# Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
import time
from dataclasses import dataclass
from typing import Optional, Tuple
import ipdb
import torch
from modules.mb_conv_pre_glu import MBConvPreGLU
from modules.triton_mb_conv_pre_glu import TritonMBConvPreGLU
from modules.utils.compare_results import compare_results
from modules.utils.dtype import get_dtype_from_str
from modules.utils.export_onnx import export_onnx
from omegaconf import OmegaConf
from torch import nn
from torch.nn import functional as F
from torchprofile import profile_macs
@dataclass
class DevelopTritonFFNConfig:
batch_size: int = 16
input_size: int = 1024 // 32 // 1
num_channels: int = 1152
mlp_ratio: float = 2.5
ffn_type: str = "MBConvPreGLU"
act: Tuple[Optional[str]] = ("silu", "silu", None)
device: str = "cuda"
dtype: str = "fp16"
profile_macs: bool = False
test_correctness: bool = False
warmup_iterations: int = 50
iterations: int = 1000
random_weight: bool = True
backward: bool = False
autocast: bool = False
use_cuda_graph: bool = False
export_model: bool = False
opset: int = 17
export_path: str = ""
export_dtype: str = "fp32"
export_device: str = "cuda"
# def simulate_litemla(x: torch.Tensor, qkv_weight: torch.Tensor, proj_weight: torch.Tensor, proj_bias: torch.Tensor, num_heads: int, head_dim: int, eps: float, backward: bool):
# B, N, C = x.shape
# qkv = F.linear(x, qkv_weight).reshape(B, N, 3, C).permute(0, 2, 3, 1)
# q, k, v = qkv.unbind(1) # B, 3, C, N --> B, C, N
# q = q.reshape(B, C // head_dim, head_dim, N) # b, h, h_d, N
# k = k.reshape(B, C // head_dim, head_dim, N).transpose(-1, -2) # b, h, N, h_d
# v = v.reshape(B, C // head_dim, head_dim, N) # b, h, h_d, N
# q = F.relu(q) # B, h, h_d, N
# k = F.relu(k)
# q, k, v = q.float(), k.float(), v.float()
# if backward:
# k.retain_grad()
# v.retain_grad()
# q.retain_grad()
# v_pad = F.pad(v, (0, 0, 0, 1), mode="constant", value=1)
# vk = torch.matmul(v_pad, k)
# if backward:
# vk.retain_grad()
# vk_q = torch.matmul(vk, q)
# vk_q_numerator, vk_q_denominator = vk_q[:, :, :-1], vk_q[:, :, -1:]
# if backward:
# vk_q_numerator.retain_grad()
# vk_q_denominator.retain_grad()
# vk_q_divide = (vk_q_numerator / (vk_q_denominator + eps)).to(x.dtype)
# proj_input = vk_q_divide.view(B, C, N).permute(0, 2, 1) # B, N, C
# if backward:
# proj_input.retain_grad()
# y = F.linear(proj_input, proj_weight, proj_bias)
# output_dict = {
# "q": q,
# "k": k,
# "v": v,
# "vk": vk,
# "proj_input": proj_input,
# "vk_q_numerator": vk_q_numerator,
# "vk_q_denominator": vk_q_denominator,
# "vk_q_divide": vk_q_divide,
# "y": y,
# }
# return output_dict
def main():
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.cuda.manual_seed(0)
torch.manual_seed(0)
cfg = OmegaConf.structured(DevelopTritonFFNConfig)
cli_cfg = OmegaConf.from_cli()
cfg = OmegaConf.merge(cfg, OmegaConf.masked_copy(cli_cfg, cfg.keys()))
cfg: DevelopTritonFFNConfig = OmegaConf.to_object(cfg)
torch.set_grad_enabled(cfg.backward)
device = torch.device("cuda")
if cfg.autocast:
dtype = torch.float32
autocast_dtype = get_dtype_from_str(cfg.dtype)
else:
dtype = get_dtype_from_str(cfg.dtype)
autocast_dtype = None
print(cfg.ffn_type)
if cfg.ffn_type == "MBConvPreGLU":
block = MBConvPreGLU(
in_dim=cfg.num_channels,
out_dim=cfg.num_channels,
mid_dim=int(cfg.num_channels * cfg.mlp_ratio),
use_bias=(True, True, False),
norm=None,
act=cfg.act,
)
elif cfg.ffn_type == "TritonMBConvPreGLU":
block = TritonMBConvPreGLU(
in_dim=cfg.num_channels,
out_dim=cfg.num_channels,
mid_dim=int(cfg.num_channels * cfg.mlp_ratio),
use_bias=(True, True, False),
norm=None,
act=cfg.act,
)
else:
raise NotImplementedError
print(
f"bs: {cfg.batch_size}, ffn_type: {cfg.ffn_type}, mlp_ratio: {cfg.mlp_ratio}, latent_size: {cfg.input_size} X {cfg.input_size}"
)
print(f"MLP: {block.__class__.__name__}, MLP Parameters: {sum(p.numel() for p in block.parameters()) / 1e6:.2f}M")
if not cfg.backward:
block = block.eval()
block = block.to(device=device, dtype=dtype, memory_format=torch.channels_last)
if cfg.random_weight:
for param in block.parameters():
nn.init.trunc_normal_(param, std=0.001)
if cfg.profile_macs:
macs = profile_macs(block, x)
print(f"macs: {macs}")
if cfg.export_model:
export_dtype = get_dtype_from_str(cfg.export_dtype)
export_device = torch.device(cfg.export_device)
assert cfg.export_path != ""
export_onnx(
block.to(device=export_device, dtype=export_dtype),
(1, cfg.input_size**2, cfg.num_channels),
cfg.export_path,
cfg.opset,
export_dtype,
export_device,
)
elif cfg.test_correctness:
if cfg.ffn_type in ["MBConvPreGLU", "TritonMBConvPreGLU"]:
ref_block = (
MBConvPreGLU(
in_dim=cfg.num_channels,
out_dim=cfg.num_channels,
mid_dim=int(cfg.num_channels * cfg.mlp_ratio),
use_bias=(True, True, False),
norm=None,
act=cfg.act,
)
.eval()
.to(device=device, memory_format=torch.channels_last)
)
else:
raise NotImplementedError(f"ffn_type {cfg.ffn_type} is not supported")
block.load_state_dict(ref_block.state_dict())
correct = True
for i in range(10):
ref_x = torch.randn(
cfg.batch_size, cfg.input_size**2, cfg.num_channels, device=device, requires_grad=cfg.backward
)
x = ref_x.clone().detach().to(dtype=dtype).requires_grad_(cfg.backward)
with torch.autocast(device_type="cuda", dtype=autocast_dtype, enabled=cfg.autocast):
output = block(x)
ref_output = ref_block(ref_x)
if cfg.backward:
dy = 0.1 * torch.randn_like(output)
output.backward(dy)
ref_output.backward(dy.float())
output_float = output.float()
if not torch.allclose(output_float, ref_output):
correct = False
max_error_pos = (output_float - ref_output).abs().view(-1).argmax()
print(f"comparing forward results")
print(
f"max error: {(output_float - ref_output).abs().max()}, mean error: {(output_float - ref_output).abs().mean()}"
)
print(f"max error pos: {ref_output.view(-1)[max_error_pos]} {output_float.view(-1)[max_error_pos]}")
if cfg.backward:
for (name, param), (ref_name, ref_param) in zip(block.named_parameters(), ref_block.named_parameters()):
assert name == ref_name
compare_results(f"{name} grad", param.grad, ref_param.grad)
compare_results(f"x grad", x.grad, ref_x.grad)
if correct:
print("correct!")
elif cfg.use_cuda_graph:
x = torch.randn(
cfg.batch_size,
cfg.input_size**2,
cfg.num_channels,
device=device,
dtype=dtype,
requires_grad=cfg.backward,
)
grad_y = 0.1 * torch.randn_like(x)
s = torch.cuda.Stream()
s.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(s):
for i in range(cfg.warmup_iterations):
with torch.autocast(device_type="cuda", dtype=autocast_dtype, enabled=cfg.autocast):
y = block(x)
if cfg.backward:
y.backward(grad_y)
torch.cuda.current_stream().wait_stream(s)
g = torch.cuda.CUDAGraph()
# Sets grads to None before capture, so backward() will create
# .grad attributes with allocations from the graph's private pool
with torch.cuda.graph(g):
with torch.autocast(device_type="cuda", dtype=autocast_dtype, enabled=cfg.autocast):
y = block(x)
if cfg.backward:
y.backward(grad_y)
torch.cuda.synchronize()
start_time = time.time()
for i in range(cfg.iterations):
g.replay()
torch.cuda.synchronize()
end_time = time.time()
print(f"using cuda graph:")
print(f"each step takes {(end_time - start_time) * 1000 / cfg.iterations:.2f} ms")
print(f"max memory allocated: {torch.cuda.max_memory_allocated() / 1024 ** 3:.4f} GB\n{'-' * 80}")
else:
x = torch.randn(
cfg.batch_size,
cfg.input_size**2,
cfg.num_channels,
device=device,
dtype=dtype,
requires_grad=cfg.backward,
)
grad_y = 0.1 * torch.randn_like(x)
for i in range(cfg.warmup_iterations):
# ipdb.set_trace()
with torch.autocast(device_type="cuda", dtype=autocast_dtype, enabled=cfg.autocast):
y = block(x)
if cfg.backward:
y.backward(grad_y)
torch.cuda.synchronize()
start_time = time.time()
for i in range(cfg.iterations):
with torch.autocast(device_type="cuda", dtype=autocast_dtype, enabled=cfg.autocast):
y = block(x)
if cfg.backward:
y.backward(grad_y)
torch.cuda.synchronize()
end_time = time.time()
print(f"each step takes {(end_time - start_time) * 1000 / cfg.iterations:.2f} ms")
# ipdb.set_trace()
print(f"max memory allocated: {torch.cuda.max_memory_allocated() / 1024 ** 3:.4f} GB\n{'-' * 80}")
if __name__ == "__main__":
main()
"""
# 64x64 fp16
python -m develop_triton_ffn ffn_type=MBConvPreGLU test_correctness=True
each step takes 12.45 ms
max memory allocated: 1.8467 GB
python -m develop_triton_ffn ffn_type=TritonMBConvPreGLU test_correctness=True
"""
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