solve conflict (#54747)

python/paddle/distributed/fleet/meta_parallel/pipeline_parallel.py

@@ -10,11 +10,10 @@
 # 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
+import os
 import time
 import warnings
 
-import os
-
 import paddle
 from paddle import framework
 
@@ -176,6 +175,7 @@ class PipelineParallel(MetaParallelBase):
         self._enable_timer = self._strategy.hybrid_configs[
             "pp_configs"
         ].enable_timer
+
         self._profiling = self._strategy.hybrid_configs["pp_configs"].profiling
         self._records = []
         self._record_format = (
@@ -303,6 +303,7 @@ class PipelineParallel(MetaParallelBase):
         for model in models:
             # For virtual pipeline. Will separate parameters in different chunk into
             # different groups to get the best performance.
+
             parameter_list = [
                 p for p in model.parameters() if not p.stop_gradient
             ]

python/paddle/distributed/fleet/meta_parallel/pp_utils/p2p_communication.py

@@ -175,30 +175,63 @@ def _is_valid_send_recv_partial(tensor, mp_degree):
     if not _enable_partial_send_recv:
         return False
     tensor_numel = np.prod(tensor.shape)
-    assert tensor_numel != 0, "can't send/recv zero element"
+    assert tensor_numel > 0, "can't send/recv zero element"
     return mp_degree > 1 and tensor_numel % mp_degree == 0
 
 
-def _partial_send_op(tensor, group, dst, nranks, rank_id):
+def _send_on_calc_stream(tensor, group, dst, nranks=1, rank_id=0):
     assert (
         group is not None
-    ), "Group should be an instance for _partial_send_op."
+    ), "Group should be an instance for _send_on_calc_stream."
     dst_rank_in_group = group.get_group_rank(dst)
-    if framework.in_dynamic_mode():
-        return group.process_group.send_partial(
+    if _is_valid_send_recv_partial(tensor, nranks):
+        return group.process_group.send_partial_on_calc_stream(
             tensor, dst_rank_in_group, nranks, rank_id
         )
+    else:
+        return group.process_group.send_on_calc_stream(
+            tensor, dst_rank_in_group
+        )
 
 
-def _partial_recv_op(tensor, group, src, nranks, rank_id):
+def _recv_on_calc_stream(tensor, group, src, nranks=1, rank_id=0):
     assert (
         group is not None
-    ), "Group should be an instance for _partial_recv_op."
+    ), "Group should be an instance for _recv_on_calc_stream."
     src_rank_in_group = group.get_group_rank(src)
-    if framework.in_dynamic_mode():
-        return group.process_group.recv_partial(
+    if _is_valid_send_recv_partial(tensor, nranks):
+        return group.process_group.recv_partial_on_calc_stream(
             tensor, src_rank_in_group, nranks, rank_id
         )
+    else:
+        return group.process_group.recv_on_calc_stream(
+            tensor, src_rank_in_group
+        )
+
+
+class P2PonCalcStream:
+    def __init__(self, op, tensor, peer, group, nranks=1, rank_id=0):
+        """
+        Args:
+            op (function): The function to be executed on the calc stream.
+            tensor (Tensor): The tensor to be sent or received.
+            peer (int): The peer rank.
+            group (Group): The process group to p2p.
+            nranks (int): The number of ranks in model parallel group.
+            rank_id (int): The rank id in the model parallel group.
+        """
+        if op not in [_send_on_calc_stream, _recv_on_calc_stream]:
+            raise RuntimeError(
+                "Invalid ``op`` function. Expected ``op`` "
+                "to be of type ``_send_on_calc_stream`` or "
+                "``_recv_on_calc_stream``."
+            )
+        self.op = op
+        self.tensor = tensor
+        self.peer = peer
+        self.group = group
+        self.nranks = nranks
+        self.rank_id = rank_id
 
 
 def _partial_allgather_op(
@@ -231,46 +264,39 @@ def allgather_partial(
     )
 
 
-def partial_batch_isend_irecv(p2p_op_list):
+def batch_send_recv_on_calc_stream(p2p_op_list):
     group = p2p_op_list[0].group
     if _warn_cur_rank_not_in_group(group):
         return
-
-    if framework.in_dynamic_mode():
-        group = _get_global_group() if group is None else group
-        backend = group.backend
-        tasks = []
-        with _with_batch_p2p_guard(backend):
-            for p2p_op in p2p_op_list:
-                op = p2p_op.op
-                tensor = p2p_op.tensor
-                peer = p2p_op.peer
-                comm_group = p2p_op.group
-                nranks = p2p_op.nranks
-                rank_id = p2p_op.rank_id
-                task = op(tensor, comm_group, peer, nranks, rank_id)
-                if task is not None:
-                    tasks.append(task)
-        return tasks
-    else:
-        raise RuntimeError("Don't support static graph mode currently.")
-
-
-class PartialP2POp:
-    def __init__(self, op, nranks, rank_id, tensor, peer, group):
-        if op not in [_partial_recv_op, _partial_send_op]:
-            raise RuntimeError(
-                "Invalid ``op`` function. Expected ``op`` "
-                "to be of type ``_partial_send_op`` or "
-                "``_partial_recv_op``."
+    group = _get_global_group() if group is None else group
+    backend = group.backend
+    with _with_batch_p2p_guard(backend):
+        for p2p_op in p2p_op_list:
+            op = p2p_op.op
+            tensor = p2p_op.tensor
+            peer = p2p_op.peer
+            comm_group = p2p_op.group
+            nranks = p2p_op.nranks
+            rank_id = p2p_op.rank_id
+            op(tensor, comm_group, peer, nranks, rank_id)
+
+
+def _process_p2p_tuple_or_tensor(
+    tensors, p2p_func, pp_rank, pp_group, mp_degree=1, mp_rank=0
+):
+    ops = []
+    if isinstance(tensors, tuple):
+        for tensor in tensors:
+            op = P2PonCalcStream(
+                p2p_func, tensor, pp_rank, pp_group, mp_degree, mp_rank
             )
-
-        self.op = op
-        self.nranks = nranks
-        self.rank_id = rank_id
-        self.tensor = tensor
-        self.peer = peer
-        self.group = group
+            ops.append(op)
+    else:
+        op = P2PonCalcStream(
+            p2p_func, tensors, pp_rank, pp_group, mp_degree, mp_rank
+        )
+        ops.append(op)
+    return ops
 
 
 def _p2p_helper(
@@ -326,189 +352,60 @@ def _p2p_helper(
             )
 
     ops = []
-    partial_ops = []
     pipe_group = _hcg.get_pipe_parallel_group()
+
     # start to p2p communicate
     if tensor_send_prev is not None:
         src_rank = _hcg._get_p2p_prev_rank()
-        if isinstance(tensor_send_prev, tuple):
-            for d in tensor_send_prev:
-                if _is_valid_send_recv_partial(d, mp_degree):
-                    op = PartialP2POp(
-                        _partial_send_op,
-                        mp_degree,
-                        mp_rank,
-                        d,
-                        src_rank,
-                        pipe_group,
-                    )
-                    partial_ops.append(op)
-                else:
-                    op = paddle.distributed.P2POp(
-                        paddle.distributed.isend,
-                        d,
-                        src_rank,
-                        pipe_group,
-                    )
-                    ops.append(op)
-        else:
-            if _is_valid_send_recv_partial(tensor_send_prev, mp_degree):
-                op = PartialP2POp(
-                    _partial_send_op,
-                    mp_degree,
-                    mp_rank,
-                    tensor_send_prev,
-                    src_rank,
-                    pipe_group,
-                )
-                partial_ops.append(op)
-            else:
-                op = paddle.distributed.P2POp(
-                    paddle.distributed.isend,
-                    tensor_send_prev,
-                    src_rank,
-                    pipe_group,
-                )
-                ops.append(op)
-
+        ops.extend(
+            _process_p2p_tuple_or_tensor(
+                tensor_send_prev,
+                _send_on_calc_stream,
+                src_rank,
+                pipe_group,
+                mp_degree,
+                mp_rank,
+            )
+        )
     if tensor_recv_prev is not None:
         dst_rank = _hcg._get_p2p_prev_rank()
-        if isinstance(tensor_recv_prev, tuple):
-            for d in tensor_recv_prev:
-                if _is_valid_send_recv_partial(d, mp_degree):
-                    op = PartialP2POp(
-                        _partial_recv_op,
-                        mp_degree,
-                        mp_rank,
-                        d,
-                        dst_rank,
-                        pipe_group,
-                    )
-                    partial_ops.append(op)
-                else:
-                    op = paddle.distributed.P2POp(
-                        paddle.distributed.irecv,
-                        d,
-                        dst_rank,
-                        pipe_group,
-                    )
-                    ops.append(op)
-        else:
-            if _is_valid_send_recv_partial(tensor_recv_prev, mp_degree):
-                op = PartialP2POp(
-                    _partial_recv_op,
-                    mp_degree,
-                    mp_rank,
-                    tensor_recv_prev,
-                    dst_rank,
-                    pipe_group,
-                )
-                partial_ops.append(op)
-            else:
-                op = paddle.distributed.P2POp(
-                    paddle.distributed.irecv,
-                    tensor_recv_prev,
-                    dst_rank,
-                    pipe_group,
-                )
-                ops.append(op)
-
+        ops.extend(
+            _process_p2p_tuple_or_tensor(
+                tensor_recv_prev,
+                _recv_on_calc_stream,
+                dst_rank,
+                pipe_group,
+                mp_degree,
+                mp_rank,
+            )
+        )
     if tensor_send_next is not None:
         src_rank = _hcg._get_p2p_next_rank()
-        if isinstance(tensor_send_next, tuple):
-            for d in tensor_send_next:
-                if _is_valid_send_recv_partial(d, mp_degree):
-                    op = PartialP2POp(
-                        _partial_send_op,
-                        mp_degree,
-                        mp_rank,
-                        d,
-                        src_rank,
-                        pipe_group,
-                    )
-                    partial_ops.append(op)
-                else:
-                    op = paddle.distributed.P2POp(
-                        paddle.distributed.isend,
-                        d,
-                        src_rank,
-                        pipe_group,
-                    )
-                    ops.append(op)
-        else:
-            if _is_valid_send_recv_partial(tensor_send_next, mp_degree):
-                op = PartialP2POp(
-                    _partial_send_op,
-                    mp_degree,
-                    mp_rank,
-                    tensor_send_next,
-                    src_rank,
-                    pipe_group,
-                )
-                partial_ops.append(op)
-            else:
-                op = paddle.distributed.P2POp(
-                    paddle.distributed.isend,
-                    tensor_send_next,
-                    src_rank,
-                    pipe_group,
-                )
-                ops.append(op)
+        ops.extend(
+            _process_p2p_tuple_or_tensor(
+                tensor_send_next,
+                _send_on_calc_stream,
+                src_rank,
+                pipe_group,
+                mp_degree,
+                mp_rank,
+            )
+        )
 
     if tensor_recv_next is not None:
         dst_rank = _hcg._get_p2p_next_rank()
-        if isinstance(tensor_recv_next, tuple):
-            for d in tensor_recv_next:
-                if _is_valid_send_recv_partial(d, mp_degree):
-                    op = PartialP2POp(
-                        _partial_recv_op,
-                        mp_degree,
-                        mp_rank,
-                        d,
-                        dst_rank,
-                        pipe_group,
-                    )
-                    partial_ops.append(op)
-                else:
-                    op = paddle.distributed.P2POp(
-                        paddle.distributed.irecv,
-                        d,
-                        dst_rank,
-                        pipe_group,
-                    )
-                    ops.append(op)
-        else:
-            if _is_valid_send_recv_partial(tensor_recv_next, mp_degree):
-                op = PartialP2POp(
-                    _partial_recv_op,
-                    mp_degree,
-                    mp_rank,
-                    tensor_recv_next,
-                    dst_rank,
-                    pipe_group,
-                )
-                partial_ops.append(op)
-            else:
-                op = paddle.distributed.P2POp(
-                    paddle.distributed.irecv,
-                    tensor_recv_next,
-                    dst_rank,
-                    pipe_group,
-                )
-                ops.append(op)
-
+        ops.extend(
+            _process_p2p_tuple_or_tensor(
+                tensor_recv_next,
+                _recv_on_calc_stream,
+                dst_rank,
+                pipe_group,
+                mp_degree,
+                mp_rank,
+            )
+        )
     if len(ops) > 0:
-        reqs = paddle.distributed.batch_isend_irecv(ops)
-        for req in reqs:
-            req.wait()
-
-    if len(partial_ops) > 0:
-        reqs = partial_batch_isend_irecv(partial_ops)
-        for req in reqs:
-            req.wait()
-
-    # block cpu to wait the result
-    paddle.device.synchronize()
+        batch_send_recv_on_calc_stream(ops)
 
     tensors_for_all_gather = []
     if tensor_recv_prev is not None: