mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
1dba329d20
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/62611 Enables optimizer overlap with backwards in DDP for Adam. Additional optimizers, especially Adagrad will be done in follow up diffs. 1. Implement `step_param` method based on `step` in _FunctionalAdam (perf permitting we can later dedupe `step` to call `step_param` 2. Modify tests to test all current functional optimizers. ghstack-source-id: 135207143 Test Plan: CI Reviewed By: SciPioneer Differential Revision: D29891783 fbshipit-source-id: 321915982afd5cb0a9c2e43d27550f433bff00d1
108 lines
4.0 KiB
Python
108 lines
4.0 KiB
Python
import unittest
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from torch.optim import SGD, Adam
|
|
from torch.testing._internal.common_utils import TestCase, run_tests, IS_WINDOWS
|
|
|
|
if not IS_WINDOWS:
|
|
from torch.distributed.optim.functional_sgd import _FunctionalSGD
|
|
from torch.distributed.optim.functional_adam import _FunctionalAdam
|
|
_SUPPORTED_OPTIM_MAPPING = {
|
|
SGD: _FunctionalSGD,
|
|
Adam: _FunctionalAdam
|
|
}
|
|
|
|
|
|
class MyModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
torch.manual_seed(0)
|
|
self.lin1 = nn.Linear(3, 3, bias=False)
|
|
self.lin2 = nn.Linear(3, 3, bias=False)
|
|
|
|
def forward(self, t1):
|
|
return self.lin2(F.relu(self.lin1(t1)))
|
|
|
|
|
|
class TestFunctionalOptimParity(TestCase):
|
|
def _validate_parameters(self, params_1, params_2):
|
|
for p1, p2 in zip(params_1, params_2):
|
|
self.assertEqual(p1, p2)
|
|
|
|
def _test_functional_optim_parity(self, optim_cls, *args, **kwargs):
|
|
module_optim = MyModule()
|
|
module_functional = MyModule()
|
|
optim_params = module_optim.parameters()
|
|
functional_params = module_functional.parameters()
|
|
optim = optim_cls(optim_params, *args, **kwargs)
|
|
functional_optim_cls = _SUPPORTED_OPTIM_MAPPING.get(optim_cls, None)
|
|
if not functional_optim_cls:
|
|
raise ValueError(f"Functional optimizer not implemented for {optim_cls}")
|
|
optim_functional = functional_optim_cls(
|
|
[], *args, **kwargs, allow_empty_param_list=True
|
|
)
|
|
if not hasattr(optim_functional, "step_param"):
|
|
raise ValueError(
|
|
f"Functional optimizer class {optim_functional} must implement step_param method."
|
|
)
|
|
|
|
# Initial weights should match
|
|
self._validate_parameters(
|
|
module_optim.parameters(), module_functional.parameters()
|
|
)
|
|
# Save old parameters to verify optimizer modifies them.
|
|
old_module_optim_params = [
|
|
param.clone().detach() for param in module_optim.parameters()
|
|
]
|
|
old_module_functional_params = [
|
|
param.clone().detach() for param in module_functional.parameters()
|
|
]
|
|
|
|
t1 = torch.randn(3, 3)
|
|
for _ in range(10):
|
|
module_optim.zero_grad()
|
|
module_functional.zero_grad()
|
|
# Forward + Backward
|
|
optim_out = module_optim(t1).sum()
|
|
functional_out = module_functional(t1).sum()
|
|
optim_out.backward()
|
|
functional_out.backward()
|
|
# Optimizer step
|
|
optim.step()
|
|
# Functional optimizer step_param
|
|
for param in module_functional.parameters():
|
|
grad = param.grad
|
|
optim_functional.step_param(param, grad)
|
|
|
|
# Validate parameters are equal
|
|
for optim_param, functional_param in zip(
|
|
module_optim.parameters(), module_functional.parameters()
|
|
):
|
|
self.assertEqual(optim_param, functional_param)
|
|
# Validate parameters are modified.
|
|
for i, (optim_param, functional_param) in enumerate(
|
|
zip(module_optim.parameters(), module_functional.parameters())
|
|
):
|
|
self.assertNotEqual(old_module_optim_params[i], optim_param)
|
|
self.assertNotEqual(old_module_functional_params[i], functional_param)
|
|
|
|
@unittest.skipIf(
|
|
IS_WINDOWS,
|
|
"Functional optimizer not support on windows, see https://github.com/pytorch/pytorch/issues/62137",
|
|
)
|
|
def test_functional_optim_parity_sgd(self):
|
|
self._test_functional_optim_parity(SGD, 1e-2, momentum=0.9, weight_decay=0.01)
|
|
|
|
@unittest.skipIf(
|
|
IS_WINDOWS,
|
|
"Functional optimizer not support on windows, see https://github.com/pytorch/pytorch/issues/62137",
|
|
)
|
|
def test_functional_optim_parity_adam(self):
|
|
self._test_functional_optim_parity(Adam, 1e-2, betas=(0.9, 0.999), eps=1e-6)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_tests()
|