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a0d026688c
This PR is greatly simplified now that it stacked on top of a PR that builds with distributed always. We only need to stub functions that may not be defined due to a backend not being enabled. Signed-off-by: Edward Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/159889 Approved by: https://github.com/wconstab ghstack dependencies: #160449
42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates
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# Owner(s): ["oncall: distributed"]
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import torch
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from torch._subclasses.fake_tensor import FakeTensorMode
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from torch.distributed.tensor import DTensor
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from torch.distributed.tensor.placement_types import Shard
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from torch.testing._internal.common_utils import run_tests, TestCase
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from torch.testing._internal.distributed.fake_pg import FakeStore
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class TestFakeDTensor(TestCase):
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def test_fake_dtensor_operations(self):
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# Use FakeTensorMode to handle CUDA tensors without actual CUDA
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fake_mode = FakeTensorMode()
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world_size = 4
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fake_store = FakeStore()
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torch.distributed.init_process_group(
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"fake", store=fake_store, rank=0, world_size=world_size
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)
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device_mesh = torch.distributed.device_mesh.init_device_mesh(
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"cuda",
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(2, world_size // 2),
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)
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# Create fake CUDA tensor using FakeTensorMode
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with fake_mode:
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x = torch.randn(1, 1, device="cuda")
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x = DTensor.from_local(x, device_mesh, [Shard(0), Shard(1)])
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# Test basic DTensor operations
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self.assertIsInstance(x, DTensor)
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# Test sum operation
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r = x.sum(1)
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self.assertIsInstance(r, DTensor)
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if __name__ == "__main__":
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run_tests()
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