wehub-resource-sync
91 lines
2.8 KiB
Python
91 lines
2.8 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 test_collective_api_base as test_base
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import paddle
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import paddle.distributed as dist
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from paddle import base, framework
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from paddle.base import data_feeder
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paddle.enable_static()
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def all_reduce_new(tensor, reduce_type=str(dist.ReduceOp.SUM), group=None):
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op_type = 'all_reduce'
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data_feeder.check_variable_and_dtype(
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tensor,
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'tensor',
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[
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'float16',
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'float32',
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'int32',
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],
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op_type,
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)
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ring_id = 0 if group is None else group.id
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if not isinstance(ring_id, int):
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raise ValueError("The type of 'ring_id' for all_reduce should be int.")
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# TODO: Support task and use task.wait in static graph mode
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# Use use_calc_stream rather than sync_op
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helper = framework.LayerHelper(op_type, **locals())
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if not reduce_type.isdigit():
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raise ValueError(
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"The type of 'reduce_type' for all_reduce should be int."
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)
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helper.append_op(
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type=op_type,
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inputs={'x': [tensor]},
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outputs={'out': [tensor]},
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attrs={'ring_id': ring_id, 'reduce_type': int(reduce_type)},
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)
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class TestCollectiveAllreduceAPI(test_base.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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tindata = paddle.static.data(
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name="tindata", shape=[10, 1000], dtype=dtype
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)
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reduce_type = int(os.getenv("REDUCE_TYPE"))
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paddle.distributed.all_reduce(tindata, op=reduce_type)
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return [tindata]
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def get_model_new(
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self,
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main_prog,
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startup_program,
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rank,
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dtype='float32',
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reduce_type=str(dist.ReduceOp.SUM),
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):
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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", shape=[10, 1000], dtype=dtype
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)
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all_reduce_new(tindata, reduce_type)
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return [tindata]
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if __name__ == "__main__":
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test_base.runtime_main(TestCollectiveAllreduceAPI, "allreduce")
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