wehub-resource-sync
157 lines
4.8 KiB
Python
157 lines
4.8 KiB
Python
# Copyright (c) 2022 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 __future__ import annotations
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from typing import TYPE_CHECKING
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from paddle import _C_ops, framework
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from paddle.base import data_feeder
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from paddle.distributed.communication.group import (
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_get_global_group,
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_get_or_throw_group_rank,
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_warn_cur_rank_not_in_group,
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)
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from paddle.distributed.communication.reduce import _to_inplace_op
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from paddle.framework import in_pir_mode
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if TYPE_CHECKING:
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from paddle import Tensor
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from paddle.base.core import task
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from paddle.distributed.communication.group import Group
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def _broadcast_in_dygraph(
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tensor, src_rank_in_group, group, sync_op, use_calc_stream
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):
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if use_calc_stream:
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return group.process_group.broadcast_on_calc_stream(
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tensor, src_rank_in_group
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)
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task = group.process_group.broadcast(tensor, src_rank_in_group, sync_op)
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if sync_op:
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task.wait()
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return task
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def _broadcast_in_static_mode(
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tensor, src_rank_in_group, group, sync_op, use_calc_stream
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):
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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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],
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'broadcast',
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)
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op_type = 'broadcast'
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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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if in_pir_mode():
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op_type = _to_inplace_op(op_type)
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getattr(_C_ops, op_type)(tensor, ring_id, src_rank_in_group, sync_op)
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return
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op = 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={
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'root': src_rank_in_group,
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'ring_id': ring_id,
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},
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)
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if sync_op:
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op.dist_attr.execution_stream = "default"
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def broadcast(
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tensor: Tensor,
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src: int,
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group: Group | None = None,
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sync_op: bool = True,
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use_calc_stream: bool = False,
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) -> task | None:
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"""
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Broadcast a tensor to all devices.
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Args:
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tensor (Tensor): The tensor to broadcast. Support float16, float32, float64, int32, int64, int8, uint8 or bool as its data type.
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src (int, optional): Rank of the source device.
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group (Group|None, optional): Communicate in which group. If none is given, use the global group as default.
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sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
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use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
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option is designed for high performance demand, be careful to turn it on except you are clearly know its meaning.
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Returns:
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Return a task object.
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Warning:
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This API only supports the dygraph mode now.
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Examples:
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.. code-block:: pycon
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>>> # doctest: +REQUIRES(env: DISTRIBUTED)
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>>> import paddle
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>>> import paddle.distributed as dist
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>>> dist.init_parallel_env()
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>>> local_rank = dist.get_rank()
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>>> if local_rank == 0:
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... data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
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>>> else:
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... data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
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>>> task = dist.stream.broadcast(data, src=1, sync_op=False)
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>>> task.wait() # type: ignore[union-attr]
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>>> out = data.numpy()
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>>> print(out)
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>>> # [[1, 2, 3], [1, 2, 3]] (2 GPUs)
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"""
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if _warn_cur_rank_not_in_group(group):
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return
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if not sync_op and use_calc_stream:
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raise RuntimeError(
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"use_calc_stream can only be True in sync op behavior."
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)
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if framework.in_dynamic_mode():
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group = _get_global_group() if group is None else group
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src_rank_in_group = _get_or_throw_group_rank(src, group)
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return _broadcast_in_dygraph(
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tensor, src_rank_in_group, group, sync_op, use_calc_stream
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)
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else:
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assert group is None, (
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"Group can not be used in static graph mode for now."
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)
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return _broadcast_in_static_mode(
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tensor, src, group, sync_op, use_calc_stream
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)
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