wehub-resource-sync
94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
# Copyright (c) 2023 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 math
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import os
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import numpy as np
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import paddle
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import paddle.distributed as dist
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from paddle.base import core
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class TestReshardPToS:
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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._shard = eval(os.getenv("shard"))
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self._backend = os.getenv("backend")
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self._mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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self._out_mesh = dist.ProcessMesh([1, 0], dim_names=["x"])
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def reshard_same_mesh(self):
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if self._backend == "gpu":
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place = paddle.CUDAPlace(dist.get_rank())
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dev_ctx = core.DeviceContext.create(place)
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paddle.seed(self._seeds)
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value = paddle.uniform(self._shape, self._dtype)
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input_tensor = dist.shard_tensor(value, self._mesh, [dist.Partial()])
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out_shape = list(self._shape)
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split_value_of_front = math.ceil(
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out_shape[self._shard] / self._mesh.shape[0]
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)
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split_value_of_last = (
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split_value_of_front
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- split_value_of_front * self._mesh.shape[0]
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+ out_shape[self._shard]
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)
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split_sections = [split_value_of_front] * self._mesh.shape[0]
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split_sections[len(split_sections) - 1] = split_value_of_last
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if dist.get_rank() == self._mesh.process_ids[self._mesh.shape[0] - 1]:
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out_shape[self._shard] = split_value_of_last
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else:
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out_shape[self._shard] = split_value_of_front
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out_expected_local_tensor_list = paddle.split(
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value, num_or_sections=split_sections, axis=self._shard
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)
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out = dist.reshard(input_tensor, self._mesh, [dist.Shard(self._shard)])
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np.testing.assert_equal(
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out._local_value().numpy(),
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out_expected_local_tensor_list[dist.get_rank()].numpy(),
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)
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np.testing.assert_equal(out.numpy(), value.numpy())
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assert np.equal(out.shape, input_tensor.shape).all()
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assert np.equal(out._local_shape, out_shape).all()
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def reshard_cross_mesh(self):
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if self._backend != "gpu":
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return
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a = paddle.ones([10, 10])
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input_tensor = dist.shard_tensor(a, self._mesh, [dist.Partial()])
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dist.reshard(input_tensor, self._out_mesh, [dist.Shard(self._shard)])
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def run_test_case(self):
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self.reshard_same_mesh()
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self.reshard_cross_mesh()
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if __name__ == '__main__':
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TestReshardPToS().run_test_case()
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