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# Owner(s): ["oncall: distributed"]
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from copy import deepcopy
import torch
import torch.nn as nn
from torch.distributed.optim import (
_apply_optimizer_in_backward,
_get_in_backward_optimizers,
)
# TODO (rohan-varma): Add FSDP & DDP tests once supported
def _validate_params(params_list, fn):
ref_params = params_list[0]
for param_list in params_list[1:]:
for p1, p2 in zip(ref_params, param_list):
fn(p1, p2)
class ApplyOverlappedOptimizerTest(unittest.TestCase):
def _run_training_loop_and_validate(self, inp, models, optimizers):
for i in range(6):
for model in models:
model(inp).sum().backward()
for opt in optimizers:
opt.step()
with self.subTest(i):
_validate_params(
[model.parameters() for model in models],
torch.testing.assert_allclose,
)
for opt in optimizers:
opt.zero_grad(set_to_none=True)
def _test_apply_optimizer_in_backward(self, share_params) -> None:
weight_optimizer_kwargs = {"lr": 1.0}
bias_optimizer_kwargs = {"lr": 0.5}
model = nn.Sequential(nn.Linear(10, 10), nn.Linear(10, 10))
if share_params:
model[0].weight = model[1].weight
# Use different optimizers for weights & biases.
weights = [m.weight for m in model]
biases = [m.bias for m in model]
optim_weight = torch.optim.SGD(weights, **weight_optimizer_kwargs)
optim_bias = torch.optim.SGD(biases, **bias_optimizer_kwargs)
model_with_opt_in_bwd = deepcopy(model)
# Apply different optimizer in backwards for weights and biases.
_apply_optimizer_in_backward(
torch.optim.SGD,
[m.weight for m in model_with_opt_in_bwd],
optimizer_kwargs=weight_optimizer_kwargs,
)
_apply_optimizer_in_backward(
torch.optim.SGD,
[m.bias for m in model_with_opt_in_bwd],
optimizer_kwargs=bias_optimizer_kwargs,
)
_validate_params(
[
model.parameters(),
model_with_opt_in_bwd.parameters(),
],
torch.testing.assert_allclose,
)
self._run_training_loop_and_validate(
torch.randn(4, 10),
[model, model_with_opt_in_bwd],
[optim_weight, optim_bias],
)
def test_apply_optimizer_in_backward(self) -> None:
self._test_apply_optimizer_in_backward(share_params=False)
def test_apply_optimizer_in_backward_shared_params(self) -> None:
self._test_apply_optimizer_in_backward(share_params=True)
def test_no_register_hook(self):
model_with_hook = nn.Sequential(nn.Linear(10, 10), nn.Linear(10, 10))
initial_model = deepcopy(model_with_hook)
model_no_hook = deepcopy(model_with_hook)
_apply_optimizer_in_backward(
torch.optim.SGD,
model_with_hook.parameters(),
optimizer_kwargs={"lr": 0.03},
)
_apply_optimizer_in_backward(
torch.optim.SGD,
model_no_hook.parameters(),
optimizer_kwargs={"lr": 0.03},
register_hook=False,
)
inp = torch.randn(4, 10)
model_with_hook(inp).sum().backward()
model_no_hook(inp).sum().backward()
for p1, p2 in zip(model_with_hook.parameters(), initial_model.parameters()):
with self.assertRaises(AssertionError):
torch.testing.assert_allclose(p1, p2)
for p1, p2 in zip(model_no_hook.parameters(), initial_model.parameters()):
torch.testing.assert_allclose(p1, p2)
def test_multiple_optim_for_params(self) -> None:
model = nn.Sequential(nn.Linear(10, 10), nn.Linear(10, 10))
opt_0_kwargs = {"lr": 0.03}
opt_1_kwargs = {"lr": 0.01}
opt_0 = torch.optim.SGD(model.parameters(), **opt_0_kwargs)
opt_1 = torch.optim.SGD(model.parameters(), **opt_1_kwargs)
model_with_opt_in_bwd = deepcopy(model)
_apply_optimizer_in_backward(
torch.optim.SGD,
model_with_opt_in_bwd.parameters(),
optimizer_kwargs=opt_0_kwargs,
)
_apply_optimizer_in_backward(
torch.optim.SGD,
model_with_opt_in_bwd.parameters(),
optimizer_kwargs=opt_1_kwargs,
)
self._run_training_loop_and_validate(
torch.randn(4, 10),
[model, model_with_opt_in_bwd],
[opt_0, opt_1],
)
def test_get_optimizers_in_backward(self):
# Create a simple test model
class TestModel(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear1 = torch.nn.Linear(10, 5)
self.linear2 = torch.nn.Linear(5, 2)
model = TestModel()
# Apply optimizers in backward
_apply_optimizer_in_backward(torch.optim.SGD, model.parameters(), {"lr": 0.01})
in_backward_optims = _get_in_backward_optimizers(model)
self.assertEqual(len(list(model.parameters())), len(in_backward_optims))
result = set(in_backward_optims)
expected = {
optim for p in model.parameters() for optim in p._in_backward_optimizers
}
self.assertEqual(result, expected)
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