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import unittest
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from apex import amp
from utils import common_init, FLOAT
class MyModel(torch.nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 3, 1, 1)
self.bn1 = nn.BatchNorm2d(6)
self.param = nn.Parameter(torch.randn(1))
def forward(self, x):
x = x * self.param
x = F.relu(self.conv1(x))
x = self.bn1(x)
return x
class TestCheckpointing(unittest.TestCase):
def setUp(self):
self.initial_lr = 1e-3
self.test_opt_levels = ("O0", "O1", "O2", "O3")
def seed(self):
torch.manual_seed(2809)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
def check_state_dict_fp32(self, state_dict):
for key in state_dict:
if 'num_batches_tracked' in key:
continue
param = state_dict[key]
self.assertEqual(param.type(), FLOAT,
'Parameter in state_dict not FLOAT')
def train_step(self, model, optimizer, data, loss_ids):
optimizer.zero_grad()
output = model(data)
# Call backward for num_losses-1
for idx in loss_ids:
loss = output.mean()
with amp.scale_loss(loss, optimizer, loss_id=idx) as scaled_loss:
scaled_loss.backward(retain_graph=True)
optimizer.step()
return output
def compare_models(self, modelA, modelB, test_setup=''):
state_dictA = modelA.state_dict()
state_dictB = modelB.state_dict()
self.assertEqual(len(state_dictA), len(state_dictB),
'state_dicts have different lengths' + test_setup)
for key in state_dictA:
paramA = state_dictA[key]
paramB = state_dictB[key]
self.assertTrue((paramA==paramB).all(),
msg='Parameters in state_dices not equal.' +
'key: {}\nparam: {}\nrestored: {}\ndiff: {} for {}'.format(
key, paramA, paramB, paramA - paramB, test_setup))
def test_restoring(self):
nb_epochs = 10
nb_epochs_restore = nb_epochs // 2
for opt_level in self.test_opt_levels:
for res_opt_level in self.test_opt_levels:
for amp_before_load in [True, False]:
for num_losses in range(1, 3):
test_setup = ('#' * 75 + '\n' + \
f'opt_level {opt_level}\n' + \
f'restore_opt_level {res_opt_level}\n' + \
f'amp_before_load {amp_before_load}\n' + \
f'num_losses {num_losses}\n')
self.seed()
# Create reference model
model = MyModel().to('cuda')
optimizer = optim.SGD(model.parameters(),
lr=self.initial_lr)
# Initialize with num_losses*2 for the original model and the restored one
model, optimizer = amp.initialize(
model, optimizer, opt_level=opt_level,
num_losses=num_losses*2, verbosity=0)
# Compare training behavior for same restore option
# We cannot really generalize it, since a saved model in O0
# would introduce a skipped step in O1, which will raise an error
if opt_level == res_opt_level:
# train for nb_epochs and restore after nb_epochs_restore
for epoch in range(nb_epochs):
x = torch.randn(16, 3, 24, 24, device='cuda')
output = self.train_step(
model, optimizer, x, range(num_losses))
# Initialize model one step before comparing.
# Otherwise the batchnorm layers will be updated
# additionally in restore_model
if epoch == (nb_epochs_restore - 1):
# Load model and optimizer
checkpoint = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'amp': amp.state_dict()
}
# Check state_dict for FP32 tensors
self.check_state_dict_fp32(checkpoint['model'])
# Restore model
restore_model = MyModel().to('cuda')
restore_optimizer = optim.SGD(
restore_model.parameters(),
lr=self.initial_lr)
if amp_before_load:
restore_model, restore_optimizer = amp.initialize(
restore_model,
restore_optimizer,
opt_level=res_opt_level,
num_losses=num_losses*2,
verbosity=0)
restore_model.load_state_dict(checkpoint['model'])
restore_optimizer.load_state_dict(checkpoint['optimizer'])
# FIXME: We cannot test the amp.state_dict in the same script
# amp.load_state_dict(checkpoint['amp'])
if not amp_before_load:
restore_model, restore_optimizer = amp.initialize(
restore_model,
restore_optimizer,
opt_level=res_opt_level,
num_losses=num_losses*2,
verbosity=0)
elif epoch >= nb_epochs_restore:
restore_output = self.train_step(
restore_model,
restore_optimizer,
x,
range(num_losses, num_losses*2))
self.assertTrue(
torch.allclose(output.float(), restore_output.float()),
'Output of reference and restored models differ for ' + test_setup)
self.compare_models(model, restore_model, test_setup)
# if opt_level != res_opt_level
else:
# skip tests for different opt_levels
continue
def test_loss_scale_decrease(self):
num_losses = 3
nb_decrease_loss_scales = [0, 1, 2]
for opt_level in self.test_opt_levels:
#print('#' * 75 + f'\n opt_level {opt_level}\n')
# Create new tmp copy for this run
nb_decrease_loss_scales_tmp = list(nb_decrease_loss_scales)
model = MyModel().to('cuda')
optimizer = optim.SGD(model.parameters(),
lr=self.initial_lr)
model, optimizer = amp.initialize(
model, optimizer, opt_level=opt_level, num_losses=num_losses,
verbosity=0)
if amp._amp_state.opt_properties.loss_scale != 'dynamic':
#print('Static loss scale set. Skipping opt_level.')
continue
# force to skip some updates to decrease the loss_scale
initial_loss_scales = []
for idx in range(num_losses):
initial_loss_scales.append(
amp._amp_state.loss_scalers[idx].loss_scale())
for _ in range(len(nb_decrease_loss_scales)):
x = torch.randn(16, 3, 24, 24, device='cuda')
for idx in range(num_losses):
while nb_decrease_loss_scales_tmp[idx] > 0:
optimizer.zero_grad()
output = model(x * 2**17)
loss = output.mean()
with amp.scale_loss(loss, optimizer, loss_id=idx) as scaled_loss:
scaled_loss.backward(retain_graph=True)
optimizer.step()
nb_decrease_loss_scales_tmp[idx] -= 1
# Check loss scales afterwards
updated_loss_scales = []
for idx in range(num_losses):
updated_loss_scales.append(
amp._amp_state.loss_scalers[idx].loss_scale())
for factor, update_ls, init_ls in zip(nb_decrease_loss_scales,
updated_loss_scales,
initial_loss_scales):
self.assertEqual(update_ls, init_ls / 2**factor)
# Check state dict
amp_state_dict = amp.state_dict()
for scaler_idx, factor, init_ls in zip(amp_state_dict,
nb_decrease_loss_scales,
initial_loss_scales):
scaler = amp_state_dict[scaler_idx]
self.assertEqual(scaler['loss_scale'], init_ls / 2**factor)
unskipped_target = 0
self.assertEqual(scaler['unskipped'], unskipped_target)
def test_state_dict(self):
for opt_level in self.test_opt_levels:
# Skip O3
if opt_level == 'O3':
continue
model = MyModel().to('cuda')
optimizer = optim.Adam(model.parameters(), lr=1e-3)
model, optimizer = amp.initialize(
model, optimizer, opt_level=opt_level, verbosity=0)
# Export state_dict and check for Half
state_dict = model.state_dict()
for key in state_dict:
self.assertFalse('Half' in state_dict[key].type())
# Check, if model is still trainable
# Create dummy data
data = torch.randn(10, 3, 4, 4, device='cuda')
target = torch.randn(10, 6, 4, 4, device='cuda')
# Get initnial loss
optimizer.zero_grad()
output = model(data)
loss = F.mse_loss(output, target)
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
optimizer.step()
last_loss = loss.item()
# train for some epochs
for epoch in range(10):
optimizer.zero_grad()
output = model(data)
loss = F.mse_loss(output, target)
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
optimizer.step()
self.assertTrue(loss.item() < last_loss)
last_loss = loss.item()
if __name__=='__main__':
unittest.main()
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