chore: import upstream snapshot with attribution

python/paddle/distributed/communication/stream/broadcast.py

@@ -0,0 +1,156 @@
+# 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
+
+from paddle import _C_ops, framework
+from paddle.base import data_feeder
+from paddle.distributed.communication.group import (
+    _get_global_group,
+    _get_or_throw_group_rank,
+    _warn_cur_rank_not_in_group,
+)
+from paddle.distributed.communication.reduce import _to_inplace_op
+from paddle.framework import in_pir_mode
+
+if TYPE_CHECKING:
+    from paddle import Tensor
+    from paddle.base.core import task
+    from paddle.distributed.communication.group import Group
+
+
+def _broadcast_in_dygraph(
+    tensor, src_rank_in_group, group, sync_op, use_calc_stream
+):
+    if use_calc_stream:
+        return group.process_group.broadcast_on_calc_stream(
+            tensor, src_rank_in_group
+        )
+
+    task = group.process_group.broadcast(tensor, src_rank_in_group, sync_op)
+    if sync_op:
+        task.wait()
+
+    return task
+
+
+def _broadcast_in_static_mode(
+    tensor, src_rank_in_group, group, sync_op, use_calc_stream
+):
+    data_feeder.check_variable_and_dtype(
+        tensor,
+        'tensor',
+        [
+            'float16',
+            'float32',
+            'float64',
+            'int32',
+            'int64',
+            'int8',
+            'uint8',
+            'bool',
+        ],
+        'broadcast',
+    )
+
+    op_type = 'broadcast'
+    helper = framework.LayerHelper(op_type, **locals())
+    ring_id = 0 if group is None else group.id
+
+    if in_pir_mode():
+        op_type = _to_inplace_op(op_type)
+        getattr(_C_ops, op_type)(tensor, ring_id, src_rank_in_group, sync_op)
+        return
+
+    op = helper.append_op(
+        type=op_type,
+        inputs={'x': [tensor]},
+        outputs={'out': [tensor]},
+        attrs={
+            'root': src_rank_in_group,
+            'ring_id': ring_id,
+        },
+    )
+    if sync_op:
+        op.dist_attr.execution_stream = "default"
+
+
+def broadcast(
+    tensor: Tensor,
+    src: int,
+    group: Group | None = None,
+    sync_op: bool = True,
+    use_calc_stream: bool = False,
+) -> task | None:
+    """
+
+    Broadcast a tensor to all devices.
+
+    Args:
+        tensor (Tensor): The tensor to broadcast. Support float16, float32, float64, int32, int64, int8, uint8 or bool as its data type.
+        src (int, optional): Rank of the source device.
+        group (Group|None, optional): Communicate in which group. If none is given, use the global group as default.
+        sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
+        use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
+            option is designed for high performance demand, be careful to turn it on except you are clearly know its meaning.
+
+    Returns:
+        Return a task object.
+
+    Warning:
+        This API only supports the dygraph mode now.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
+            >>> import paddle
+            >>> import paddle.distributed as dist
+
+            >>> dist.init_parallel_env()
+            >>> local_rank = dist.get_rank()
+            >>> if local_rank == 0:
+            ...     data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
+            >>> else:
+            ...     data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
+            >>> task = dist.stream.broadcast(data, src=1, sync_op=False)
+            >>> task.wait()  # type: ignore[union-attr]
+            >>> out = data.numpy()
+            >>> print(out)
+            >>> # [[1, 2, 3], [1, 2, 3]] (2 GPUs)
+    """
+    if _warn_cur_rank_not_in_group(group):
+        return
+
+    if not sync_op and use_calc_stream:
+        raise RuntimeError(
+            "use_calc_stream can only be True in sync op behavior."
+        )
+
+    if framework.in_dynamic_mode():
+        group = _get_global_group() if group is None else group
+        src_rank_in_group = _get_or_throw_group_rank(src, group)
+
+        return _broadcast_in_dygraph(
+            tensor, src_rank_in_group, group, sync_op, use_calc_stream
+        )
+    else:
+        assert group is None, (
+            "Group can not be used in static graph mode for now."
+        )
+        return _broadcast_in_static_mode(
+            tensor, src, group, sync_op, use_calc_stream
+        )