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"""Tests for c++ MLP"""
from itertools import product
from time import time
import torch
from torch import nn
from torch.testing._internal import common_utils
from torch.testing._internal.common_device_type import instantiate_device_type_tests
from torch.testing._internal.common_device_type import onlyCUDA
from apex.mlp import MLP
batch_size = 1024
mlp_sizes = [480, 1024, 1024, 512, 256, 1]
num_iters = 10
# note(crcrpar): On Ampere, this test should be run without TF32 enabled.
class TestMLP(common_utils.TestCase):
def test_creation(self):
MLP(mlp_sizes)
def test_numeric(self):
mlp = MLP(mlp_sizes).cuda()
mlp_layers = []
for i in range(mlp.num_layers):
linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1])
with torch.no_grad():
mlp.weights[i].copy_(linear.weight)
mlp.biases[i].copy_(linear.bias)
mlp_layers.append(linear)
mlp_layers.append(nn.ReLU())
ref_mlp = nn.Sequential(*mlp_layers).cuda()
test_input = (
torch.empty(batch_size, mlp_sizes[0], device="cuda")
.uniform_(-1.0, 1.0)
.requires_grad_()
)
ref_input = test_input.clone().detach().requires_grad_()
mlp_out = mlp(test_input)
ref_out = ref_mlp(ref_input)
self.assertEqual(mlp_out, ref_out)
# Use mean value as scalar loss. Multiply 10 to make it big enough not zero out
mlp_out.mean().mul(10.0).backward()
ref_out.mean().mul(10.0).backward()
self.assertEqual(test_input.grad, ref_input.grad)
self.assertEqual(mlp.biases[0].grad, ref_mlp[0].bias.grad)
def _test_mlp_impl(self, use_activation: str, bias: bool, enable_autocast: bool):
mlp = MLP(mlp_sizes, bias=bias, activation=use_activation).cuda()
mlp_layers = []
for i in range(mlp.num_layers):
linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1], bias=bias)
with torch.no_grad():
mlp.weights[i].copy_(linear.weight)
if bias:
mlp.biases[i].copy_(linear.bias)
mlp_layers.append(linear)
if use_activation == "relu":
mlp_layers.append(nn.ReLU())
if use_activation == "sigmoid":
mlp_layers.append(nn.Sigmoid())
ref_mlp = nn.Sequential(*mlp_layers).cuda()
test_input = (
torch.empty(batch_size, mlp_sizes[0], device="cuda")
.uniform_(-1.0, 1.0)
.requires_grad_()
)
ref_input = test_input.clone().detach().requires_grad_()
with torch.cuda.amp.autocast_mode.autocast(enabled=enable_autocast):
mlp_out = mlp(test_input)
mlp_loss = mlp_out.mean().mul(10.0)
# Use mean value as scalar loss. Multiply 10 to make it big enough not zero out
ref_out = ref_mlp(ref_input)
ref_loss = ref_out.mean().mul(10.0)
mlp_loss.backward()
ref_loss.backward()
if enable_autocast:
self.assertEqual(mlp_out.dtype, torch.float16)
self.assertEqual(ref_out.dtype, torch.float16)
else:
self.assertEqual(mlp_out, ref_out)
self.assertEqual(test_input.grad, ref_input.grad)
self.assertEqual(mlp.weights[0].grad, ref_mlp[0].weight.grad)
@common_utils.parametrize(
"use_activation,bias",
list(product(("none", "relu", "sigmoid"), (True, False))),
)
def test_mlp(self, use_activation: str, bias: bool):
self._test_mlp_impl(use_activation, bias, enable_autocast=False)
@common_utils.parametrize(
"use_activation,bias",
list(product(("none", "relu", "sigmoid"), (True, False))),
)
def test_mlp_autocast_fp16(self, use_activation: str, bias: bool):
self._test_mlp_impl(use_activation, bias, enable_autocast=True)
def test_no_grad(self):
mlp = MLP(mlp_sizes).cuda()
mlp_layers = []
for i in range(mlp.num_layers):
linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1])
with torch.no_grad():
mlp.weights[i].copy_(linear.weight)
mlp.biases[i].copy_(linear.bias)
mlp_layers.append(linear)
mlp_layers.append(nn.ReLU(inplace=True))
ref_mlp = nn.Sequential(*mlp_layers).cuda()
test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1.0, 1.0)
ref_input = test_input.clone().detach()
mlp_out = mlp(test_input)
ref_out = ref_mlp(ref_input)
self.assertEqual(mlp_out, ref_out)
# Use mean value as scalar loss. Multiply 10 to make it big enough not zero out
mlp_out.mean().mul(10.0).backward()
ref_out.mean().mul(10.0).backward()
self.assertEqual(mlp.weights[0].grad, ref_mlp[0].weight.grad)
def test_performance_half(self):
mlp = MLP(mlp_sizes).cuda().half()
mlp_layers = []
for i in range(mlp.num_layers):
linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1])
mlp.weights[i].data.copy_(linear.weight)
mlp.biases[i].data.copy_(linear.bias)
mlp_layers.append(linear)
mlp_layers.append(nn.ReLU(inplace=True))
ref_mlp = nn.Sequential(*mlp_layers).cuda().half()
test_input = (
torch.empty(batch_size, mlp_sizes[0], device="cuda", dtype=torch.half)
.fill_(10.0)
.requires_grad_()
)
ref_input = (
torch.empty(batch_size, mlp_sizes[0], device="cuda", dtype=torch.half)
.fill_(10.0)
.requires_grad_()
)
# Warm up GPU
for _ in range(100):
ref_out = ref_mlp(ref_input)
ref_loss = ref_out.mean()
ref_mlp.zero_grad()
ref_loss.backward()
mlp_out = mlp(test_input)
test_loss = mlp_out.mean()
mlp.zero_grad()
test_loss.backward()
torch.cuda.profiler.start()
torch.cuda.synchronize()
start_time = time()
for _ in range(num_iters):
ref_out = ref_mlp(ref_input)
ref_loss = ref_out.mean()
ref_mlp.zero_grad()
ref_loss.backward()
torch.cuda.synchronize()
stop_time = time()
ref_time = (stop_time - start_time) * 1000.0 / num_iters
print(f"\nPytorch MLP time {ref_time:.4f} ms")
torch.cuda.synchronize()
start_time = time()
for _ in range(num_iters):
mlp_out = mlp(test_input)
test_loss = mlp_out.mean()
mlp.zero_grad()
test_loss.backward()
torch.cuda.synchronize()
stop_time = time()
actual_time = (stop_time - start_time) * 1000.0 / num_iters
print(f"C++ MLP time {actual_time:.4f} ms")
torch.cuda.profiler.stop()
self.assertLessEqual(
actual_time,
ref_time,
msg=f"Custom extension took {actual_time:.4f} while PyTorch took {ref_time:.4f}",
)
instantiate_device_type_tests(TestMLP, globals(), only_for=("cuda",))
if __name__ == "__main__":
common_utils.run_tests()
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