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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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import os | ||
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import numpy as np | ||
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import paddle | ||
import paddle.distributed as dist | ||
from paddle.base import core | ||
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class TestReshardRToSCrossMesh: | ||
def __init__(self): | ||
self._shape = eval(os.getenv("shape")) | ||
self._dtype = os.getenv("dtype") | ||
self._seeds = eval(os.getenv("seeds")) | ||
self._shard = eval(os.getenv("shard")) | ||
self._backend = os.getenv("backend") | ||
self._in_mesh = dist.ProcessMesh([0, 1], dim_names=["x"]) | ||
self._out_mesh = dist.ProcessMesh([1, 0], dim_names=["x"]) | ||
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def run_test_case(self): | ||
# cpu does not support send/recv | ||
if self._backend == "cpu": | ||
return | ||
elif self._backend == "gpu": | ||
place = paddle.CUDAPlace(dist.get_rank()) | ||
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dev_ctx = core.DeviceContext.create(place) | ||
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paddle.seed(self._seeds) | ||
value = paddle.uniform(self._shape, self._dtype) | ||
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in_shard_specs = [None for i in range(len(self._shape))] | ||
out_shard_specs = [None for i in range(len(self._shape))] | ||
out_shard_specs[self._shard] = "x" | ||
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dist_attr = dist.DistAttr( | ||
mesh=self._in_mesh, sharding_specs=in_shard_specs | ||
) | ||
out_dist_attr = dist.DistAttr( | ||
mesh=self._out_mesh, sharding_specs=out_shard_specs | ||
) | ||
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input_tensor = dist.shard_tensor(value, dist_attr=dist_attr) | ||
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reshard_func = core.RToSReshardFunctionCrossMesh() | ||
assert reshard_func.is_suitable(input_tensor, out_dist_attr) | ||
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out = reshard_func.eval(dev_ctx, input_tensor, out_dist_attr) | ||
out_shape = list(self._shape) | ||
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if out_shape[self._shard] % 2 == 0: | ||
out_shape[self._shard] = out_shape[self._shard] // 2 | ||
split_shape = self._in_mesh.shape[0] | ||
else: | ||
split_shape = [ | ||
out_shape[self._shard] // 2 + 1, | ||
out_shape[self._shard] // 2, | ||
] | ||
out_shape[self._shard] = ( | ||
split_shape[0] if dist.get_rank() == 1 else split_shape[1] | ||
) | ||
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out_expected_local_tensor_list = paddle.split( | ||
value, num_or_sections=split_shape, axis=self._shard | ||
) | ||
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np.testing.assert_equal( | ||
out._local_value().numpy(), | ||
out_expected_local_tensor_list[0].numpy() | ||
if dist.get_rank() == 1 | ||
else out_expected_local_tensor_list[1].numpy(), | ||
) | ||
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assert np.equal(out.shape, input_tensor.shape).all() | ||
assert np.equal(out._local_shape, out_shape).all() | ||
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if __name__ == '__main__': | ||
TestReshardRToSCrossMesh().run_test_case() |
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