wehub-resource-sync
72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
# Copyright (c) 2020 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 legacy_test.test_collective_api_base import (
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TestCollectiveAPIRunnerBase,
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runtime_main,
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)
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import paddle
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from paddle import base
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from paddle.distributed import fleet
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paddle.enable_static()
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class TestParallelEmbeddingAPI(TestCollectiveAPIRunnerBase):
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def __init__(self):
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self.global_ring_id = 0
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def get_model(self, main_prog, startup_program, rank, dtype="float32"):
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with base.program_guard(main_prog, startup_program):
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fleet.init(is_collective=True)
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np.random.seed(2020)
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# (num_embeddings, embedding_dim) = (12, 8)
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size = (12, 8)
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np_array = np.random.rand(size[0], size[1])
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paddle.seed(2020)
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data_in = paddle.randint(0, size[0], shape=(10, 4))
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data = paddle.static.data(
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name='tindata', shape=[10, 1000], dtype=dtype
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)
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per_part_size = size[0] // 2
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if rank == 0:
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param_attr = paddle.base.ParamAttr(
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initializer=paddle.nn.initializer.Assign(
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np_array[0:per_part_size, :]
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),
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)
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else:
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param_attr = paddle.base.ParamAttr(
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initializer=paddle.nn.initializer.Assign(
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np_array[per_part_size : size[0], :]
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),
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)
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emb_out = paddle.distributed.split(
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data_in,
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size,
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operation="embedding",
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num_partitions=2,
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weight_attr=param_attr,
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)
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return [data_in, emb_out]
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if __name__ == "__main__":
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runtime_main(TestParallelEmbeddingAPI, "parallel_embedding")
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