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import copy
import math
import random
import unittest
import torch
import torch.nn.functional as F
from torch import nn
from torch.testing._internal.common_device_type import largeTensorTest
try:
import apex
except ImportError as e:
HAS_APEX = False
else:
HAS_APEX = True
class Model(torch.nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.relu2 = nn.ReLU()
self.pool2 = nn.MaxPool2d(2)
self.fc1 = nn.Linear(256, 120)
self.relu3 = nn.ReLU()
self.fc2 = nn.Linear(120, 84)
self.relu4 = nn.ReLU()
self.fc3 = nn.Linear(84, 10)
self.relu5 = nn.ReLU()
def forward(self, x):
y = self.conv1(x)
y = self.relu1(y)
y = self.pool1(y)
y = self.conv2(y)
y = self.relu2(y)
y = self.pool2(y)
y = y.reshape(y.shape[0], -1)
y = self.fc1(y)
y = self.relu3(y)
y = self.fc2(y)
y = self.relu4(y)
y = self.fc3(y)
y = self.relu5(y)
return y
@unittest.skipIf(not HAS_APEX, "`apex` is not found.")
class AdamTest(unittest.TestCase):
def setUp(self, seed=0):
super().setUp()
torch.manual_seed(seed)
self.model = Model().cuda()
self.model_ = Model().cuda()
self.model_.load_state_dict(copy.deepcopy(self.model.state_dict()))
self.lr = 0.00001
params = [p for p in self.model.parameters() if p.requires_grad]
self.optimizer = torch.optim.Adam(params, lr=self.lr)
def testGradScaler(self):
params_ = [p for p in self.model_.parameters() if p.requires_grad]
optimizer_ = apex.optimizers.FusedAdam(params_, lr=self.lr, capturable=False)
scaler = torch.cuda.amp.GradScaler(enabled=True)
scaler_ = torch.cuda.amp.GradScaler(enabled=True)
for i in range(100):
x = torch.rand([32, 1, 28, 28]).cuda().to(memory_format=torch.channels_last)
x_ = x.clone()
gt = torch.rand([32, 10]).cuda()
gt_ = gt.clone()
# Reference
with torch.cuda.amp.autocast(enabled=True):
y = self.model(x)
loss = ((gt - y) ** 2).mean()
scaler.scale(loss).backward()
scaler.step(self.optimizer)
scaler.update()
# DUT
with torch.cuda.amp.autocast(enabled=True):
y = self.model_(x)
loss_ = ((gt_ - y) ** 2).mean()
scaler_.scale(loss_).backward()
scaler_.step(optimizer_)
scaler_.update()
for module in zip(self.model.modules(), self.model_.modules()):
m = module[0]
m_ = module[1]
if isinstance(m, nn.Conv2d) or isinstance(m_, nn.Linear):
torch.testing.assert_close(m.weight, m_.weight, atol=1e-3, rtol=1e-3, equal_nan=True)
torch.testing.assert_close(m.weight.grad, m_.weight.grad, atol=1e-3, rtol=1e-3, equal_nan=True)
# Init for next iteration
self.optimizer.zero_grad()
optimizer_.zero_grad()
self.model_.load_state_dict(copy.deepcopy(self.model.state_dict()))
def testGradScalerCapturable(self):
params_ = [p for p in self.model_.parameters() if p.requires_grad]
optimizer_ = apex.optimizers.FusedAdam(params_, lr=self.lr, capturable=True)
scaler = torch.cuda.amp.GradScaler(enabled=True)
scaler_ = torch.cuda.amp.GradScaler(enabled=True)
for i in range(100):
x = torch.rand([32, 1, 28, 28]).cuda().to(memory_format=torch.channels_last)
x_ = x.clone()
gt = torch.rand([32, 10]).cuda()
gt_ = gt.clone()
# Reference
with torch.cuda.amp.autocast(enabled=True):
y = self.model(x)
loss = ((gt - y) ** 2).mean()
scaler.scale(loss).backward()
scaler.step(self.optimizer)
scaler.update()
# DUT
with torch.cuda.amp.autocast(enabled=True):
y = self.model_(x)
loss_ = ((gt_ - y) ** 2).mean()
scaler_.scale(loss_).backward()
scaler_.step(optimizer_)
scaler_.update()
for module in zip(self.model.modules(), self.model_.modules()):
m = module[0]
m_ = module[1]
if isinstance(m, nn.Conv2d) or isinstance(m_, nn.Linear):
torch.testing.assert_close(m.weight, m_.weight, atol=1e-3, rtol=1e-3, equal_nan=True)
torch.testing.assert_close(m.weight.grad, m_.weight.grad, atol=1e-3, rtol=1e-3, equal_nan=True)
# Init for next iteration
self.optimizer.zero_grad()
optimizer_.zero_grad()
self.model_.load_state_dict(copy.deepcopy(self.model.state_dict()))
def testGradScalerCapturableMaster(self):
# Cast conv layers to FP16
for m in self.model_.modules():
if m.__class__ in [torch.nn.Conv2d]:
m.half()
params_ = [p for p in self.model_.parameters() if p.requires_grad]
optimizer_ = apex.optimizers.FusedAdam(params_, lr=self.lr, capturable=True, master_weights=True)
scaler = torch.cuda.amp.GradScaler(enabled=True)
scaler_ = torch.cuda.amp.GradScaler(enabled=True)
for i in range(100):
x = torch.rand([32, 1, 28, 28]).cuda().to(memory_format=torch.channels_last)
x_ = x.clone()
gt = torch.rand([32, 10]).cuda()
gt_ = gt.clone()
# Reference
with torch.cuda.amp.autocast(enabled=True):
y = self.model(x)
loss = ((gt - y) ** 2).mean()
scaler.scale(loss).backward()
scaler.step(self.optimizer)
scaler.update()
# DUT
with torch.cuda.amp.autocast(enabled=True):
y = self.model_(x)
loss_ = ((gt_ - y) ** 2).mean()
scaler_.scale(loss_).backward()
scaler_.step(optimizer_)
scaler_.update()
for module in zip(self.model.modules(), self.model_.modules()):
m = module[0]
m_ = module[1]
if isinstance(m, nn.Conv2d) or isinstance(m_, nn.Linear):
torch.testing.assert_close(m.weight, m_.weight.float(), atol=1e-3, rtol=1e-3, equal_nan=True)
torch.testing.assert_close(m.weight.grad, m_.weight.grad.float(), atol=1e-3, rtol=1e-3, equal_nan=True)
# Init for next iteration
self.optimizer.zero_grad()
optimizer_.zero_grad()
self.model_.load_state_dict(copy.deepcopy(self.model.state_dict()))
def testNative(self):
params_ = [p for p in self.model_.parameters() if p.requires_grad]
optimizer_ = apex.optimizers.FusedAdam(params_, lr=self.lr, capturable=False)
for i in range(100):
x = torch.rand([32, 1, 28, 28]).cuda().to(memory_format=torch.channels_last)
x_ = x.clone()
gt = torch.rand([32, 10]).cuda()
gt_ = gt.clone()
# Reference
y = self.model(x)
loss = ((gt - y) ** 2).mean()
loss.backward()
self.optimizer.step()
# DUT
y = self.model_(x)
loss_ = ((gt_ - y) ** 2).mean()
loss_.backward()
optimizer_.step()
for module in zip(self.model.modules(), self.model_.modules()):
m = module[0]
m_ = module[1]
if isinstance(m, nn.Conv2d) or isinstance(m_, nn.Linear):
torch.testing.assert_close(m.weight, m_.weight, atol=1e-3, rtol=1e-3, equal_nan=True)
torch.testing.assert_close(m.weight.grad, m_.weight.grad, atol=1e-3, rtol=1e-3, equal_nan=True)
# Init for next iteration
self.optimizer.zero_grad()
optimizer_.zero_grad()
self.model_.load_state_dict(copy.deepcopy(self.model.state_dict()))
@largeTensorTest('60GB', 'cuda')
def testLargeTensor(self):
t = torch.zeros(2359332864, dtype=torch.half, device='cuda')
t2 = torch.zeros(2359332864, dtype=torch.half, device='cuda')
grad = torch.randn_like(t)
t.grad = grad
t2.grad = grad
params = [t]
params2 = [t2]
optimizer = apex.optimizers.FusedAdam(params, lr=self.lr)
optimizer.step()
optimizer2 = torch.optim.Adam(params2, lr=self.lr)
torch.testing.assert_close(t, t2)
torch.cuda.synchronize()
if __name__ == '__main__':
unittest.main()
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