wehub-resource-sync
124 lines
3.3 KiB
Python
124 lines
3.3 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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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, framework
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from paddle.base import data_feeder
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paddle.enable_static()
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def concat_new(tensor, group=None):
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op_type = 'dist_concat'
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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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'float64',
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'int32',
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'int64',
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'int8',
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'uint8',
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'bool',
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'uint16',
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],
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op_type,
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)
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helper = framework.LayerHelper(op_type, **locals())
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ring_id = 0 if group is None else group.id
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nranks = 2
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out = helper.create_variable_for_type_inference(dtype=tensor.dtype)
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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': [out]},
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attrs={
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'ring_id': ring_id,
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'nranks': nranks,
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},
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)
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return out
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def concat_new_comm(tensor, group=None, rank=0):
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op_type = 'c_concat'
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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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'float64',
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'int32',
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'int64',
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],
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op_type,
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)
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helper = framework.LayerHelper(op_type, **locals())
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ring_id = 0 if group is None else group.id
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nranks = 2
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out = helper.create_variable_for_type_inference(dtype=tensor.dtype)
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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': [out]},
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attrs={
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'ring_id': ring_id,
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'nranks': nranks,
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'rank': rank,
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},
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)
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return out
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class TestCollectiveConcatAPI(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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tindata.desc.set_need_check_feed(False)
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toutdata = concat_new_comm(tindata, rank=rank)
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return [toutdata]
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def get_model_new(
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self, main_prog, startup_program, rank, dtype=None, reduce_type=None
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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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tindata.desc.set_need_check_feed(False)
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toutdata = concat_new(tindata)
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return [toutdata]
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if __name__ == "__main__":
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runtime_main(TestCollectiveConcatAPI, "concat")
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