chore: import upstream snapshot with attribution

python/paddle/distributed/communication/broadcast.py

@@ -0,0 +1,149 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+import paddle
+import paddle.distributed as dist
+from paddle import framework
+from paddle.distributed.communication import stream
+
+from .serialization_utils import (
+    convert_object_to_tensor,
+    convert_tensor_to_object,
+)
+
+if TYPE_CHECKING:
+    from paddle import Tensor
+    from paddle.base.core import task
+    from paddle.distributed.communication.group import Group
+
+
+def broadcast(
+    tensor: Tensor, src: int, group: Group | None = None, sync_op: bool = True
+) -> task:
+    """
+
+    Broadcast a tensor from the source to all others.
+    As shown below, one process is started with a GPU and GPU0 owns data 0. Through broadcast operator,
+    data 0 will be sent to all GPUs from GPU0.
+
+    .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/broadcast.png
+        :width: 800
+        :alt: broadcast
+        :align: center
+
+    Args:
+        tensor (Tensor): The tensor to send if current rank is the source, or the tensor to receive otherwise. Its data type
+            should be float16, float32, float64, int32, int64, int8, uint8, bool, bfloat16, complex64 or complex128.
+        src (int): The source rank in global view.
+        group (Group, optional): The group instance return by new_group or None for global default group.
+        sync_op (bool, optional): Whether this op is a sync op. The default value is True.
+
+    Returns:
+        Return a task object.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
+            >>> import paddle
+            >>> import paddle.distributed as dist
+
+            >>> dist.init_parallel_env()
+            >>> if dist.get_rank() == 0:
+            ...     data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
+            >>> else:
+            ...     data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
+            >>> dist.broadcast(data, src=1)
+            >>> print(data)
+            >>> # [[1, 2, 3], [1, 2, 3]] (2 GPUs)
+    """
+    return stream.broadcast(
+        tensor,
+        src,
+        group=group,
+        sync_op=sync_op,
+        use_calc_stream=False,
+    )
+
+
+def broadcast_object_list(
+    object_list: list[Any], src: int, group: Group | None = None
+) -> None:
+    """
+
+    Broadcast picklable objects from the source to all others. Similar to broadcast(), but python object can be passed in.
+
+    Args:
+        object_list (list): The list of objects to send if current rank is the source, or the list of objects to receive otherwise.
+        src (int): The source rank in global view.
+        group (Group): The group instance return by new_group or None for global default group.
+
+    Returns:
+        None.
+
+    Warning:
+        This API only supports the dygraph mode.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
+            >>> import paddle.distributed as dist
+
+            >>> dist.init_parallel_env()
+            >>> if dist.get_rank() == 0:
+            ...     object_list = [{"foo": [1, 2, 3]}]
+            >>> else:
+            ...     object_list = [{"bar": [4, 5, 6]}]
+            >>> dist.broadcast_object_list(object_list, src=1)
+            >>> print(object_list)
+            >>> # [{"bar": [4, 5, 6]}] (2 GPUs)
+    """
+    assert framework.in_dynamic_mode(), (
+        "broadcast_object_list doesn't support static graph mode."
+    )
+
+    rank = dist.get_rank()
+    obj_tensors = []
+    obj_nums = len(object_list)
+
+    if rank == src:
+        obj_sizes = []
+        for obj in object_list:
+            obj_tensor, obj_size = convert_object_to_tensor(obj)
+            obj_tensors.append(obj_tensor)
+            obj_sizes.append(obj_size)
+        obj_size_tensor = paddle.stack(obj_sizes)
+    else:
+        obj_size_tensor = paddle.empty([obj_nums], dtype="int64")
+    broadcast(obj_size_tensor, src, group)
+
+    if rank == src:
+        # cast to uint8 to keep the same dtype
+        obj_data_tensor = paddle.concat(obj_tensors).cast("uint8")
+    else:
+        data_len = paddle.sum(obj_size_tensor).item()
+        obj_data_tensor = paddle.empty([data_len], dtype="uint8")
+    broadcast(obj_data_tensor, src, group)
+
+    offset = 0
+    for i in range(obj_nums):
+        data_len = obj_size_tensor[i]
+        object_list[i] = convert_tensor_to_object(
+            obj_data_tensor[offset : offset + data_len], data_len
+        )
+        offset += data_len