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bdcdf94bdd
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/71620 Remove from_functional_optim and make it the default constructor since that is the only way _OptimizerHookState is now being built. Also, no longer need to expose create_functional_optim helper function ghstack-source-id: 147577174 Test Plan: CI Reviewed By: cbalioglu Differential Revision: D33700593 fbshipit-source-id: ba089ce3bf66ccf8f71cffdd0f4d4bddc03e8b14 (cherry picked from commit a50b2caf0e19f9793fbf18b371d30e3dd8c5c0cf)
95 lines
3.6 KiB
Python
95 lines
3.6 KiB
Python
# Owner(s): ["oncall: distributed"]
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.optim import SGD, Adam, AdamW
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from torch.testing._internal.common_utils import TestCase, run_tests
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from torch.distributed.optim.utils import functional_optim_map
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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torch.manual_seed(0)
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self.lin1 = nn.Linear(3, 3, bias=False)
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self.lin2 = nn.Linear(3, 3, bias=False)
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def forward(self, t1):
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return self.lin2(F.relu(self.lin1(t1)))
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class TestFunctionalOptimParity(TestCase):
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def _validate_parameters(self, params_1, params_2):
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for p1, p2 in zip(params_1, params_2):
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self.assertEqual(p1, p2)
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def _test_functional_optim_parity(self, optim_cls, *args, **kwargs):
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module_optim = MyModule()
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module_functional = MyModule()
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optim_params = module_optim.parameters()
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functional_params = module_functional.parameters()
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optim = optim_cls(optim_params, *args, **kwargs)
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functional_optim_cls = functional_optim_map.get(optim_cls, None)
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if not functional_optim_cls:
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raise ValueError(f"Functional optimizer not implemented for {optim_cls}")
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optim_functional = functional_optim_cls(
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[], *args, **kwargs, _allow_empty_param_list=True
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)
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if not hasattr(optim_functional, "step_param"):
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raise ValueError(
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f"Functional optimizer class {optim_functional} must implement step_param method."
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)
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# Initial weights should match
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self._validate_parameters(
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module_optim.parameters(), module_functional.parameters()
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)
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# Save old parameters to verify optimizer modifies them.
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old_module_optim_params = [
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param.clone().detach() for param in module_optim.parameters()
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]
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old_module_functional_params = [
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param.clone().detach() for param in module_functional.parameters()
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]
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t1 = torch.randn(3, 3)
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for _ in range(10):
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module_optim.zero_grad()
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module_functional.zero_grad()
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# Forward + Backward
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optim_out = module_optim(t1).sum()
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functional_out = module_functional(t1).sum()
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optim_out.backward()
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functional_out.backward()
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# Optimizer step
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optim.step()
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# Functional optimizer step_param
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for param in module_functional.parameters():
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grad = param.grad
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optim_functional.step_param(param, grad)
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# Validate parameters are equal
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for optim_param, functional_param in zip(
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module_optim.parameters(), module_functional.parameters()
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):
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self.assertEqual(optim_param, functional_param)
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# Validate parameters are modified.
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for i, (optim_param, functional_param) in enumerate(
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zip(module_optim.parameters(), module_functional.parameters())
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):
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self.assertNotEqual(old_module_optim_params[i], optim_param)
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self.assertNotEqual(old_module_functional_params[i], functional_param)
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def test_functional_optim_parity_sgd(self):
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self._test_functional_optim_parity(SGD, 1e-2, momentum=0.9, weight_decay=0.01)
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def test_functional_optim_parity_adam(self):
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self._test_functional_optim_parity(Adam, 1e-2, betas=(0.9, 0.999), eps=1e-6)
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def test_functional_optim_parity_adam_w(self):
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self._test_functional_optim_parity(AdamW, 1e-2, betas=(0.9, 0.999), eps=1e-6)
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if __name__ == "__main__":
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run_tests()
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