wehub-resource-sync
69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import paddle
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import paddle.distributed as dist
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from paddle.distributed import Partial
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from paddle.distributed.auto_parallel.api import dtensor_to_local
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class TestDtensorToLocalAPI:
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def __init__(self):
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self._shape = eval(os.getenv("shape"))
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self._dtype = os.getenv("dtype")
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self._seeds = eval(os.getenv("seeds"))
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self._backend = os.getenv("backend")
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self._shard = eval(os.getenv("shard"))
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self._mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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def run_test_cases(self):
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self.test_case_forward_backward()
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def test_case_forward_backward(self):
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a = paddle.ones(self._shape)
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a.stop_gradient = False
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input_tensor = dist.shard_tensor(a, self._mesh, [Partial()])
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input_tensor.register_hook(
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self.check_grad_mesh(
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input_tensor.process_mesh, input_tensor.placements
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)
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)
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tensor1 = dtensor_to_local(
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input_tensor, input_tensor.process_mesh, input_tensor.placements
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)
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assert not tensor1.is_dist()
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tensor2 = tensor1 + 2
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tensor3 = tensor2 * 3
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tensor3.register_hook(self.check_grad_mesh(None, None))
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tensor3.backward()
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def check_grad_mesh(self, org_mesh, org_placements):
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def _check_mesh(grad):
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if hasattr(grad, "process_mesh") and hasattr(grad, "placements"):
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assert grad.process_mesh == org_mesh
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assert grad.placements == org_placements
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else:
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assert org_mesh is None and org_placements is None
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return _check_mesh
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if __name__ == '__main__':
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TestDtensorToLocalAPI().run_test_cases()
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