wehub-resource-sync
77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
# Copyright (c) 2018 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_base import (
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TestCollectiveRunnerBase,
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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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paddle.enable_static()
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class TestCollectiveSendRecv(TestCollectiveRunnerBase):
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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):
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ring_id = self.global_ring_id
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with base.program_guard(main_prog, startup_program):
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tindata = paddle.static.data(
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name="tindata",
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shape=[10, 1000],
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dtype='float64',
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)
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tindata.desc.set_need_check_feed(False)
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if self.rank == 0:
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data1 = paddle.assign(np.array([[0, 1, 2]], dtype='float32'))
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data2 = paddle.assign(np.array([[3, 4, 5]], dtype='float32'))
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elif self.rank == 1:
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data1 = paddle.assign(np.array([[3, 4, 5]], dtype='float32'))
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data2 = paddle.assign(np.array([[0, 1, 2]], dtype='float32'))
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tensor_array = paddle.tensor.create_array(dtype='float32')
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i = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=0)
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paddle.tensor.array_write(data1, i, tensor_array)
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paddle.tensor.array_write(data2, i + 1, tensor_array)
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if self.rank == 0:
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main_prog.global_block().append_op(
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type="send_v2",
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inputs={'X': tensor_array},
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attrs={
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'ring_id': ring_id,
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'peer': 1,
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'use_calc_stream': True,
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},
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)
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else:
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main_prog.global_block().append_op(
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type="recv_v2",
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outputs={'Out': tensor_array},
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attrs={
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'peer': 0,
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'ring_id': ring_id,
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'dtype': data1.dtype,
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'out_shape': [1, 3],
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'use_calc_stream': True,
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},
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)
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return tensor_array
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if __name__ == "__main__":
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runtime_main(TestCollectiveSendRecv, "sendrecv_array", 0)
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