wehub-resource-sync
150 lines
5.0 KiB
Python
150 lines
5.0 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, Any
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import paddle
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import paddle.distributed as dist
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from paddle import framework
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from paddle.distributed.communication import stream
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from .serialization_utils import (
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convert_object_to_tensor,
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convert_tensor_to_object,
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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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def broadcast(
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tensor: Tensor, src: int, group: Group | None = None, sync_op: bool = True
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) -> task:
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"""
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Broadcast a tensor from the source to all others.
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As shown below, one process is started with a GPU and GPU0 owns data 0. Through broadcast operator,
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data 0 will be sent to all GPUs from GPU0.
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.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/broadcast.png
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:width: 800
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:alt: broadcast
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:align: center
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Args:
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tensor (Tensor): The tensor to send if current rank is the source, or the tensor to receive otherwise. Its data type
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should be float16, float32, float64, int32, int64, int8, uint8, bool, bfloat16, complex64 or complex128.
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src (int): The source rank in global view.
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group (Group, optional): The group instance return by new_group or None for global default group.
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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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Return a task object.
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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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... 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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>>> dist.broadcast(data, src=1)
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>>> print(data)
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>>> # [[1, 2, 3], [1, 2, 3]] (2 GPUs)
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"""
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return stream.broadcast(
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tensor,
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src,
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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 broadcast_object_list(
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object_list: list[Any], src: int, group: Group | None = None
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) -> None:
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"""
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Broadcast picklable objects from the source to all others. Similar to broadcast(), but python object can be passed in.
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Args:
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object_list (list): The list of objects to send if current rank is the source, or the list of objects to receive otherwise.
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src (int): The source rank in global view.
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group (Group): The group instance return by new_group or None for global default group.
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Returns:
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None.
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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.distributed as dist
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>>> dist.init_parallel_env()
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>>> if dist.get_rank() == 0:
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... object_list = [{"foo": [1, 2, 3]}]
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>>> else:
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... object_list = [{"bar": [4, 5, 6]}]
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>>> dist.broadcast_object_list(object_list, src=1)
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>>> print(object_list)
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>>> # [{"bar": [4, 5, 6]}] (2 GPUs)
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"""
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assert framework.in_dynamic_mode(), (
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"broadcast_object_list doesn't support static graph mode."
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)
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rank = dist.get_rank()
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obj_tensors = []
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obj_nums = len(object_list)
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if rank == src:
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obj_sizes = []
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for obj in object_list:
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obj_tensor, obj_size = convert_object_to_tensor(obj)
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obj_tensors.append(obj_tensor)
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obj_sizes.append(obj_size)
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obj_size_tensor = paddle.stack(obj_sizes)
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else:
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obj_size_tensor = paddle.empty([obj_nums], dtype="int64")
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broadcast(obj_size_tensor, src, group)
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if rank == src:
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# cast to uint8 to keep the same dtype
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obj_data_tensor = paddle.concat(obj_tensors).cast("uint8")
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else:
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data_len = paddle.sum(obj_size_tensor).item()
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obj_data_tensor = paddle.empty([data_len], dtype="uint8")
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broadcast(obj_data_tensor, src, group)
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offset = 0
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for i in range(obj_nums):
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data_len = obj_size_tensor[i]
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object_list[i] = convert_tensor_to_object(
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obj_data_tensor[offset : offset + data_len], data_len
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)
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offset += data_len
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