wehub-resource-sync
105 lines
3.5 KiB
Python
105 lines
3.5 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 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.framework import core
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class TestReshardRToX:
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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._in_mesh = dist.ProcessMesh([0], dim_names=["x"])
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self._out_mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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def _set_place(self):
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if self._backend == "cpu":
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paddle.set_device("cpu")
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place = paddle.CPUPlace()
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elif 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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def test_r_to_s(self):
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self._set_place()
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a = paddle.ones(self._shape)
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input_tensor = dist.shard_tensor(a, self._in_mesh, [dist.Replicate()])
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out = dist.reshard(
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input_tensor, self._out_mesh, [dist.Shard(self._shard)]
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)
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out_shape = list(self._shape)
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if out_shape[self._shard] % 2 == 0:
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out_shape[self._shard] = out_shape[self._shard] // 2
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np.testing.assert_equal(out.numpy(), a.numpy())
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else:
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out_shape[self._shard] = (
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out_shape[self._shard] // 2
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if dist.get_rank() == 1
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else out_shape[self._shard] // 2 + 1
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)
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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 test_r_to_r(self):
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self._set_place()
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a = paddle.ones(self._shape)
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input_tensor = dist.shard_tensor(a, self._in_mesh, [dist.Replicate()])
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out = dist.reshard(input_tensor, self._out_mesh, [dist.Replicate()])
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if dist.get_rank() == 0:
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assert np.equal(out.shape, input_tensor.shape).all()
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np.testing.assert_equal(out._local_value().numpy(), a.numpy())
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def test_r_to_p(self):
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self._set_place()
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a = paddle.ones(self._shape)
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input_tensor = dist.shard_tensor(a, self._in_mesh, [dist.Replicate()])
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out = dist.reshard(
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input_tensor,
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self._out_mesh,
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[dist.Partial(dist.ReduceType.kRedSum)],
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)
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if dist.get_rank() == 0:
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np.testing.assert_equal(
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out._local_value().numpy(), input_tensor.numpy()
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)
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else:
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zeros = paddle.zeros(self._shape)
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np.testing.assert_equal(out._local_value().numpy(), zeros.numpy())
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assert np.equal(out.shape, input_tensor.shape).all()
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assert np.equal(out._local_shape, input_tensor._local_shape).all()
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def run_test_case(self):
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self.test_r_to_s()
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self.test_r_to_r()
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self.test_r_to_p()
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if __name__ == '__main__':
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TestReshardRToX().run_test_case()
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