wehub-resource-sync
62 lines
2.2 KiB
Python
62 lines
2.2 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 numpy as np
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from semi_auto_parallel_util import SemiAutoParallelTestBase
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import paddle
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import paddle.distributed as dist
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class TestItemApiForSemiAutoParallel(SemiAutoParallelTestBase):
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def __init__(self):
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super().__init__()
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paddle.seed(self._seed)
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np.random.seed(self._seed)
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def test_item_api(self):
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mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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a = paddle.rand(shape=[6, 8])
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b = dist.shard_tensor(a, mesh, [dist.Shard(0)])
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np.testing.assert_equal(b.item(0, 0), a[0][0].item())
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np.testing.assert_equal(b.item(3, 5), a[3][5].item())
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def test_item_api_with_pp(self):
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mesh0 = dist.ProcessMesh([0], dim_names=["x"])
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mesh1 = dist.ProcessMesh([1], dim_names=["y"])
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a = paddle.rand(shape=[6, 8])
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b = dist.shard_tensor(a, mesh0, [dist.Replicate()])
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c = dist.reshard(b, mesh1, [dist.Replicate()])
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if c.item(0, 0): # in device 1
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np.testing.assert_equal(c.item(0, 0), a[0][0].item())
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np.testing.assert_equal(c.item(3, 5), a[3][5].item())
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else: # in device 0
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np.testing.assert_equal(c.item(3, 5), None)
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def run_test_case(self):
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if self._backend == "cpu":
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paddle.set_device("cpu")
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elif self._backend == "gpu":
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paddle.set_device("gpu:" + str(dist.get_rank()))
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# only gpu can run pipeline
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self.test_item_api_with_pp()
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else:
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raise ValueError("Only support cpu or gpu backend.")
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self.test_item_api()
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if __name__ == '__main__':
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TestItemApiForSemiAutoParallel().run_test_case()
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