wehub-resource-sync
80 lines
2.7 KiB
Python
80 lines
2.7 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 numpy as np
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import paddle
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import paddle.distributed as dist
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class TestElementWiseCoShard:
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def run_unary_case_0(self):
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mesh = dist.ProcessMesh([[0, 1], [2, 3]], dim_names=['x', 'y'])
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placements = [
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dist.Shard(0, shard_order=0),
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dist.Shard(0, shard_order=1),
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]
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x = paddle.to_tensor(
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[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]], dtype="float32"
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)
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x = dist.shard_tensor(x, mesh, placements)
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# paddle.round
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out = paddle.round(x)
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np.testing.assert_equal(out.shape, [4, 2])
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assert out.placements, "The output should be a DistTensor"
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np.testing.assert_equal(
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out.placements[0], dist.Shard(dim=0, shard_order=0)
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)
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np.testing.assert_equal(
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out.placements[1], dist.Shard(dim=0, shard_order=1)
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)
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def run_unary_case_with_partial(self):
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mesh = dist.ProcessMesh([[0, 1], [2, 3]], dim_names=['x', 'y'])
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# TODO(ooooo): Test co_shard when matmul is supported.
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x_placements = [
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dist.Shard(0),
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dist.Shard(1),
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]
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x = paddle.to_tensor(
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[[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]], dtype="float32"
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)
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y = paddle.to_tensor(
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[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]], dtype="float32"
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)
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x = dist.shard_tensor(x, mesh, x_placements)
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y = dist.shard_tensor(
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y, mesh, [dist.Replicate() for _ in range(mesh.ndim)]
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)
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# Generate partial placement
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matmul_out = paddle.matmul(x, y)
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# paddle.cast
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out = paddle.cast(matmul_out, 'float64')
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np.testing.assert_equal(out.shape, [2, 2])
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assert out.placements, "The output should be a DistTensor"
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np.testing.assert_equal(out.placements[0], dist.Shard(0))
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np.testing.assert_equal(out.placements[1], dist.Partial())
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def run_test_case_main(self):
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self.run_unary_case_0()
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self.run_unary_case_with_partial()
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if __name__ == '__main__':
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TestElementWiseCoShard().run_test_case_main()
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