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import pytest
import torch
from torch_ema import ExponentialMovingAverage
@pytest.mark.parametrize("decay", [0.995, 0.9])
@pytest.mark.parametrize("use_num_updates", [True, False])
@pytest.mark.parametrize("explicit_params", [True, False])
def test_val_error(decay, use_num_updates, explicit_params):
"""Confirm that EMA validation error is lower than raw validation error."""
torch.manual_seed(0)
x_train = torch.rand((100, 10))
y_train = torch.rand(100).round().long()
x_val = torch.rand((100, 10))
y_val = torch.rand(100).round().long()
model = torch.nn.Linear(10, 2)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
ema = ExponentialMovingAverage(
model.parameters(),
decay=decay,
use_num_updates=use_num_updates
)
# Train for a few epochs
model.train()
for _ in range(20):
logits = model(x_train)
loss = torch.nn.functional.cross_entropy(logits, y_train)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if explicit_params:
ema.update(model.parameters())
else:
ema.update()
# Validation: original
model.eval()
logits = model(x_val)
loss_orig = torch.nn.functional.cross_entropy(logits, y_val)
print(f"Original loss: {loss_orig}")
# Validation: with EMA
# First save original parameters before replacing with EMA version
if explicit_params:
ema.store(model.parameters())
else:
ema.store()
# Copy EMA parameters to model
if explicit_params:
ema.copy_to(model.parameters())
else:
ema.copy_to()
logits = model(x_val)
loss_ema = torch.nn.functional.cross_entropy(logits, y_val)
print(f"EMA loss: {loss_ema}")
assert loss_ema < loss_orig, "EMA loss wasn't lower"
# Test restore
if explicit_params:
ema.restore(model.parameters())
else:
ema.restore()
model.eval()
logits = model(x_val)
loss_orig2 = torch.nn.functional.cross_entropy(logits, y_val)
assert torch.allclose(loss_orig, loss_orig2), \
"Restored model wasn't the same as stored model"
@pytest.mark.parametrize("explicit_params", [True, False])
def test_contextmanager(explicit_params):
"""Confirm that EMA validation error is lower than raw validation error."""
torch.manual_seed(0)
x_train = torch.rand((100, 10))
y_train = torch.rand(100).round().long()
x_val = torch.rand((100, 10))
y_val = torch.rand(100).round().long()
model = torch.nn.Linear(10, 2)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
ema = ExponentialMovingAverage(
model.parameters(),
decay=0.99,
)
# Train for a few epochs
model.train()
for _ in range(20):
logits = model(x_train)
loss = torch.nn.functional.cross_entropy(logits, y_train)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if explicit_params:
ema.update(model.parameters())
else:
ema.update()
final_weight = model.weight.clone().detach()
# Validation: original
model.eval()
logits = model(x_val)
loss_orig = torch.nn.functional.cross_entropy(logits, y_val)
print(f"Original loss: {loss_orig}")
# Validation: with EMA
if explicit_params:
cm = ema.average_parameters(model.parameters())
else:
cm = ema.average_parameters()
with cm:
logits = model(x_val)
loss_ema = torch.nn.functional.cross_entropy(logits, y_val)
print(f"EMA loss: {loss_ema}")
assert loss_ema < loss_orig, "EMA loss wasn't lower"
assert torch.all(model.weight == final_weight), "Restore failed"
@pytest.mark.parametrize("decay", [0.995, 0.9, 0.0, 1.0])
@pytest.mark.parametrize("use_num_updates", [True, False])
@pytest.mark.parametrize("explicit_params", [True, False])
def test_store_restore(decay, use_num_updates, explicit_params):
model = torch.nn.Linear(10, 2)
ema = ExponentialMovingAverage(
model.parameters(),
decay=decay,
use_num_updates=use_num_updates
)
orig_weight = model.weight.clone().detach()
if explicit_params:
ema.store(model.parameters())
else:
ema.store()
with torch.no_grad():
model.weight.uniform_(0.0, 1.0)
if explicit_params:
ema.restore(model.parameters())
else:
ema.restore()
assert torch.all(model.weight == orig_weight)
@pytest.mark.parametrize("decay", [0.995, 0.9, 0.0, 1.0])
@pytest.mark.parametrize("explicit_params", [True, False])
def test_update(decay, explicit_params):
model = torch.nn.Linear(10, 2, bias=False)
with torch.no_grad():
model.weight.fill_(0.0)
ema = ExponentialMovingAverage(
model.parameters(),
decay=decay,
use_num_updates=False
)
with torch.no_grad():
model.weight.fill_(1.0)
if explicit_params:
ema.update(model.parameters())
else:
ema.update()
assert torch.all(model.weight == 1.0), "ema.update changed model weights"
if explicit_params:
ema.copy_to(model.parameters())
else:
ema.copy_to()
assert torch.allclose(
model.weight,
torch.full(size=(1,), fill_value=(1.0 - decay))
), "average was wrong"
def test_explicit_params():
model = torch.nn.Linear(10, 2)
with torch.no_grad():
model.weight.fill_(0.0)
ema = ExponentialMovingAverage(model.parameters(), decay=0.9)
model2 = torch.nn.Linear(10, 2)
with torch.no_grad():
model2.weight.fill_(1.0)
ema.update(model2.parameters())
ema.copy_to()
assert not torch.all(model.weight == 0.0)
def test_some_untrainable():
class Mod(torch.nn.Module):
def __init__(self):
super().__init__()
self.x = torch.nn.Parameter(torch.randn(3))
self.y = torch.nn.Parameter(torch.randn(3))
self.y.requires_grad_(False)
def forward(self, x):
return self.x * x + self.y
model = Mod()
ema = ExponentialMovingAverage(model.parameters(), decay=0.9)
ema.update()
with torch.no_grad():
model.x *= 1.1
ema.update()
ema.store()
ema.copy_to()
def test_to():
m = torch.nn.Linear(11, 3)
ema = ExponentialMovingAverage(m.parameters(), decay=0.9)
assert ema.shadow_params[0].dtype == torch.get_default_dtype()
ema.to(dtype=torch.float16)
assert ema.shadow_params[0].dtype == torch.float16
ema.store()
# we store whatever we get
assert ema.collected_params[0].dtype == torch.get_default_dtype()
m = m.to(torch.float16)
ema.store(m.parameters())
assert ema.collected_params[0].dtype == torch.float16
ema.to(dtype=torch.float64)
assert ema.collected_params[0].dtype == torch.float64
assert ema.shadow_params[0].dtype == torch.float64
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