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77 lines
2.5 KiB
77 lines
2.5 KiB
4 years ago
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# 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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from __future__ import print_function
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import numpy as np
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import argparse
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import os
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import sys
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import signal
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import time
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import socket
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from contextlib import closing
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from six import string_types
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import math
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.profiler as profiler
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import paddle.fluid.unique_name as nameGen
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from paddle.fluid import core
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import paddle.distributed.fleet as fleet
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from paddle.fluid.incubate.fleet.base import role_maker
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import unittest
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from multiprocessing import Process
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import paddle.fluid.layers as layers
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from functools import reduce
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from test_collective_api_base import TestCollectiveAPIRunnerBase, runtime_main
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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):
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with fluid.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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np_array = np.random.rand(10, 8)
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paddle.seed(2020)
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data_in = paddle.randint(0, 8, shape=(10, 4))
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data = paddle.static.data(
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name='tindata', shape=[10, 1000], dtype="float32")
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if rank == 0:
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param_attr = paddle.fluid.ParamAttr(
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initializer=paddle.fluid.initializer.NumpyArrayInitializer(
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np_array[0:5, :]), )
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else:
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param_attr = paddle.fluid.ParamAttr(
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initializer=paddle.fluid.initializer.NumpyArrayInitializer(
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np_array[5:10, :]), )
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emb_out = paddle.distributed.split(
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data_in, (8, 8),
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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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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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