mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
lzy
134 lines
4.2 KiB
Python
134 lines
4.2 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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from fastdeploy.utils import register_custom_python_op
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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(
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tp_group: paddle.distributed.communication.group.Group = None, custom_all_reduce_max_bytes: int = 8192 * 1024
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):
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if tp_group is None:
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hcg = fleet.get_hybrid_communicate_group()
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tp_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(tp_group, custom_all_reduce_max_bytes)
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def custom_ar_clear_ipc_handles():
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global _TP_AR
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if _TP_AR is not None:
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_TP_AR.clear_ipc_handles()
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try:
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def tensor_model_parallel_all_reduce_infer_meta(x: "paddle.static.MetaTensor", group_) -> paddle.static.MetaTensor:
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return paddle.static.MetaTensor(shape=x.shape, dtype=x.dtype)
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@register_custom_python_op(
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name="tensor_model_parallel_all_reduce",
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infer_meta=tensor_model_parallel_all_reduce_infer_meta,
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input_names=[
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"input_",
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],
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output_names=["out"],
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inplace_map={},
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)
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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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if input_.shape[0] == 0:
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return input_
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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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input_ = _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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return input_
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@paddle.jit.marker.unified
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def decode_alltoall_transpose(
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input_: paddle.Tensor,
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out: paddle.Tensor = None,
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) -> paddle.Tensor:
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"""alltoall and transpose in decode."""
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if input_.shape[0] == 0:
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return input_
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global _TP_AR
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input_ = _TP_AR.decode_alltoall_transpose(input_, out)
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return input_
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except:
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tensor_model_parallel_all_reduce = None
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from paddle.distributed.communication import stream
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from paddle.distributed.communication.reduce import ReduceOp
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try:
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def all_reduce(
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tensor,
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op,
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group,
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sync_op: bool = True,
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):
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return stream.all_reduce(tensor, op=op, group=group, sync_op=sync_op, use_calc_stream=True)
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@paddle.jit.marker.unified
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def tensor_model_parallel_all_reduce_custom(input_: paddle.Tensor) -> paddle.Tensor:
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"""All-reduce the input tensor across model parallel group on calc stream."""
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if input_.shape[0] == 0:
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return input_
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if paddle.in_dynamic_mode():
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hcg = dist.fleet.get_hybrid_communicate_group()
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mp_group = hcg.get_model_parallel_group()
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all_reduce(input_, op=ReduceOp.SUM, 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_custom = None
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