mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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d339df2e90
* Support DP+TP+EP hybrid parallel deployment strategy * Support DP+TP+EP hybrid parallel deployment strategy * fix conflict * add moe_tp_ep function split_allgather_out * del tp_group in moe_cutlass_backend * for ci * fix parallel_config for ci * del log
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
"""
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# Copyright (c) 2025 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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"""
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from contextlib import contextmanager, nullcontext
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import paddle
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import paddle.distributed as dist
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from paddle.distributed import fleet
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_TP_AR = None
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@contextmanager
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def capture_custom_allreduce():
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global _TP_AR
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ar_context = nullcontext()
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if _TP_AR is not None:
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ar_context = _TP_AR.capture()
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with ar_context:
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yield
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def use_custom_allreduce(custom_all_reduce_max_bytes: int = 8192 * 1024):
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hcg = fleet.get_hybrid_communicate_group()
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model_parallel_group = hcg.get_model_parallel_group()
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global _TP_AR
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from fastdeploy.distributed.custom_all_reduce import CustomAllreduce
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_TP_AR = CustomAllreduce(model_parallel_group, custom_all_reduce_max_bytes)
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try:
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@paddle.jit.marker.unified
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def tensor_model_parallel_all_reduce(
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input_: paddle.Tensor,
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group_: paddle.distributed.communication.group.Group = None,
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) -> paddle.Tensor:
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"""All-reduce the input tensor across model parallel group."""
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global _TP_AR
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if _TP_AR is not None and _TP_AR.should_custom_ar(input_):
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# TODO: supports different_group custom allreduce
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_TP_AR.custom_all_reduce(input_)
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elif paddle.in_dynamic_mode():
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if group_ is not None:
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dist.all_reduce(input_, group=group_)
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else:
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hcg = fleet.get_hybrid_communicate_group()
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mp_group = hcg.get_model_parallel_group()
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dist.all_reduce(input_, group=mp_group)
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else:
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dist.all_reduce(input_)
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except:
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tensor_model_parallel_all_reduce = None
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