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https://github.com/zebrajr/pytorch.git
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c85323c5e8
This reverts commit b576a8c318.
Reverted https://github.com/pytorch/pytorch/pull/139184 on behalf of https://github.com/clee2000 due to Failing internally when trying to pickle distributed test files D67098795 ([comment](https://github.com/pytorch/pytorch/pull/139184#issuecomment-2539610187))
67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
# Owner(s): ["oncall: distributed"]
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import sys
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import torch
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from torch import distributed as dist
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from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
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from torch.nn import Linear, Module, Sequential
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from torch.optim import SGD
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from torch.testing._internal.common_distributed import skip_if_lt_x_gpu
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from torch.testing._internal.common_fsdp import FSDPTest
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from torch.testing._internal.common_utils import run_tests, TEST_WITH_DEV_DBG_ASAN
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if not dist.is_available():
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print("Distributed not available, skipping tests", file=sys.stderr)
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sys.exit(0)
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if TEST_WITH_DEV_DBG_ASAN:
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print(
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"Skip dev-asan as torch + multiprocessing spawn have known issues",
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file=sys.stderr,
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)
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sys.exit(0)
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class InnerModel(Module):
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def __init__(self) -> None:
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super().__init__()
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self.layers = Sequential(FSDP(Linear(5, 5)))
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def forward(self, x):
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return self.layers(x)
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class TestMultipleWrapping(FSDPTest):
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@skip_if_lt_x_gpu(2)
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def test_multiple_wrapping(self):
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"""
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This test simulates wrapping the module after training to run inference.
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This is required in cases where later in a session, the model is wrapped again in FSDP but
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contains nested FSDP wrappers within the module.
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"""
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inner_model = InnerModel()
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model = FSDP(inner_model).cuda()
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optim = SGD(model.parameters(), lr=0.1)
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for i in range(3):
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input = torch.rand((1, 5), dtype=torch.float).cuda()
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input.requires_grad = True
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output = model(input)
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output.sum().backward()
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optim.step()
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optim.zero_grad()
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input = torch.rand((1, 5), dtype=torch.float).cuda()
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output = model(input)
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# second time to rewrap the inner model
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rewrapped_model = FSDP(inner_model).cuda()
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rewrapped_output = rewrapped_model(input)
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self.assertEqual(output, rewrapped_output)
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if __name__ == "__main__":
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run_tests()
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