chore: import upstream snapshot with attribution

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

@@ -0,0 +1,135 @@
+# 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 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,
+)
+
+if TYPE_CHECKING:
+    from paddle import Tensor
+    from paddle.base.core import task
+    from paddle.distributed.communication.group import Group
+
+
+def _send_in_dygraph(
+    tensor, dst_rank_in_group, group, sync_op, use_calc_stream
+):
+    if use_calc_stream:
+        return group.process_group.send_on_calc_stream(
+            tensor, dst_rank_in_group
+        )
+
+    task = group.process_group.send(tensor, dst_rank_in_group, sync_op)
+    if sync_op:
+        task.wait()
+
+    return task
+
+
+def _send_in_static_mode(
+    tensor, dst_rank_in_group, group, sync_op, use_calc_stream
+):
+    op_type = 'send_v2'
+    data_feeder.check_variable_and_dtype(
+        tensor,
+        'tensor',
+        ['float16', 'float32', 'float64', 'int32', 'int64', 'uint16'],
+        'send',
+    )
+
+    ring_id = 0 if group is None else group.id
+    helper = framework.LayerHelper(op_type, **locals())
+    helper.append_op(
+        type=op_type,
+        inputs={'X': [tensor]},
+        attrs={
+            'ring_id': ring_id,
+            'peer': dst_rank_in_group,
+            'use_calc_stream': sync_op,
+        },
+    )
+
+
+def send(
+    tensor: Tensor,
+    dst: int = 0,
+    group: Group | None = None,
+    sync_op: bool = True,
+    use_calc_stream: bool = False,
+) -> task | None:
+    """
+
+    Send a tensor to the destination device.
+
+    Args:
+        tensor (Tensor): The tensor to send. Support float16, float32, float64, int32, int64, int8, uint8 or bool as its data type.
+        dst (int, optional): Rank of the destination device. If none is given, use `0` as default.
+        group (Group, 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.
+
+    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]])
+            ...     task = dist.stream.send(data, dst=1, sync_op=False)
+            >>> else:
+            ...     data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
+            ...     task = dist.stream.recv(data, src=0, sync_op=False)
+            >>> task.wait()  # type: ignore[union-attr]
+            >>> out = data.numpy()
+            >>> print(out)
+            [[4, 5, 6], [4, 5, 6]]
+    """
+    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
+        dst_rank_in_group = _get_or_throw_group_rank(dst, group)
+
+        return _send_in_dygraph(
+            tensor, dst_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 _send_in_static_mode(
+            tensor, dst, group, sync_op, use_calc_stream
+        )