chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# 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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import paddle
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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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from paddle.distributed.communication.stream.reduce_scatter import (
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_reduce_scatter_base as _reduce_scatter_base_stream,
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)
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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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from paddle.distributed.communication.reduce import _ReduceOp
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def reduce_scatter(
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tensor: Tensor,
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tensor_list: list[Tensor],
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op: _ReduceOp = ReduceOp.SUM,
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group: Group | None = None,
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sync_op: bool = True,
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) -> task:
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"""
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Reduces, then scatters a list of tensors to all processes in a group
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Args:
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tensor (Tensor): The output tensor on each rank. The result will overwrite this tenor after communication. Support
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float16, float32, float64, int32, int64, int8, uint8 or bool as the input data type.
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tensor_list (List[Tensor]]): List of tensors to reduce and scatter. Every element in the list must be a Tensor whose data type
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should be float16, float32, float64, int32, int64, int8, uint8, bool or bfloat16.
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op (ReduceOp.SUM|ReduceOp.MAX|ReduceOp.MIN|ReduceOp.PROD|ReduceOp.AVG, optional): The reduction used. If none is given, use ReduceOp.SUM as default.
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group (Group, 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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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.
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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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>>> if dist.get_rank() == 0:
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... data1 = paddle.to_tensor([0, 1])
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... data2 = paddle.to_tensor([2, 3])
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>>> else:
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... data1 = paddle.to_tensor([4, 5])
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... data2 = paddle.to_tensor([6, 7])
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>>> dist.reduce_scatter(data1, [data1, data2])
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>>> print(data1)
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>>> # [4, 6] (2 GPUs, out for rank 0)
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>>> # [8, 10] (2 GPUs, out for rank 1)
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"""
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if op not in [
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ReduceOp.AVG,
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ReduceOp.MAX,
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ReduceOp.MIN,
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ReduceOp.PROD,
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ReduceOp.SUM,
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]:
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raise RuntimeError(
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"Invalid ``op`` function. Expected ``op`` to be of type ``ReduceOp.SUM``, ``ReduceOp.Max``, ``ReduceOp.MIN``, ``ReduceOp.PROD`` or ``ReduceOp.AVG``."
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)
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# AVG is only supported when nccl >= 2.10
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if op == ReduceOp.AVG and paddle.base.core.nccl_version() < 21000:
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group = (
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paddle.distributed.collective._get_global_group()
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if group is None
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else group
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)
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tensor.scale_(1.0 / group.nranks)
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return stream.reduce_scatter(
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tensor,
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tensor_list,
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op=ReduceOp.SUM,
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group=group,
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sync_op=sync_op,
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use_calc_stream=False,
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)
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return stream.reduce_scatter(
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tensor,
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tensor_list,
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op=op,
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group=group,
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sync_op=sync_op,
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use_calc_stream=False,
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)
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def _reduce_scatter_base(
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output: Tensor,
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input: Tensor,
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op: _ReduceOp = ReduceOp.SUM,
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group: Group | None = None,
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sync_op: bool = True,
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) -> task | None:
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"""
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Reduces, then scatters a flattened tensor to all processes in a group.
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Args:
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output (Tensor): Output tensor. Its data type should be float16, float32, float64, int32, int64, int8, uint8, bool or bfloat16.
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input (Tensor): Input tensor that is of size output tensor size times world size. Its data type
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should be float16, float32, float64, int32, int64, int8, uint8, bool or bfloat16.
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op (ReduceOp.SUM|ReduceOp.MAX|ReduceOp.MIN|ReduceOp.PROD): Optional. The operation used. Default: ReduceOp.SUM.
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group (ProcessGroup, optional): The process group to work on. If None,
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the default process group will be used.
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sync_op (bool, optional): Whether this op is a sync op. The default value is True.
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Returns:
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Async task handle, if sync_op is set to False.
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None, if sync_op or if not part of the group.
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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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>>> rank = dist.get_rank()
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>>> data = paddle.arange(4) + rank
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>>> # [0, 1, 2, 3] (2 GPUs, for rank 0)
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>>> # [1, 2, 3, 4] (2 GPUs, for rank 1)
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>>> output = paddle.empty(shape=[2], dtype=data.dtype)
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>>> dist.collective._reduce_scatter_base(output, data)
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>>> print(output)
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>>> # [1, 3] (2 GPUs, out for rank 0)
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>>> # [5, 7] (2 GPUs, out for rank 1)
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"""
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if op not in [ReduceOp.MAX, ReduceOp.MIN, ReduceOp.PROD, ReduceOp.SUM]:
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raise RuntimeError(
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"Invalid ``op`` function. Expected ``op`` to be of type ``ReduceOp.SUM``, ``ReduceOp.Max``, ``ReduceOp.MIN`` or ``ReduceOp.PROD``."
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)
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return _reduce_scatter_base_stream(
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output,
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input,
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op=op,
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group=group,
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sync_op=sync_op,
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use_calc_stream=False,
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)
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