chore: import upstream snapshot with attribution

python/paddle/distributed/passes/allreduce_matmul_grad_overlapping.py

@@ -0,0 +1,160 @@
+# Copyright (c) 2023 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.
+
+import collections
+import logging
+
+from ..auto_parallel.static.utils import (
+    get_logger,
+)
+from .pass_base import PassBase, register_pass
+from .pass_utils import AutoParallelStreamType, split_matmul_grad_to_matmul
+
+logger = get_logger(logging.INFO)
+
+
+# For allreduce pattern in the backward phase of column parallel linear:
+#   dX, dY = matmul_grad(X, Y, dOut)
+#   dX = all_reduce_sum(dX)
+# Split matmul_grad to 2 matmul:
+#   dX = matmul(dOut, Y^T)
+#   dX = all_reduce_sum(dX)
+#   dY = matmul(X^T, dOut)
+#
+# Then the all_reduce sum can overlap with the compute of dY.
+@register_pass("allreduce_matmul_grad_overlapping")
+class AllreduceMatmulGradOverlappingPass(PassBase):
+    def __init__(self):
+        super().__init__()
+        self.op_namescope = "/auto_parallel/allreduce_matmul_grad_overlapping"
+        self.set_attr("dist_context", None)
+
+    def _check_self(self):
+        if self.get_attr("dist_context") is None:
+            return False
+        return True
+
+    def _check_conflict(self, other_pass):
+        return True
+
+    def _apply_single_impl(self, main_program, startup_program, context):
+        self.dist_context = self.get_attr("dist_context")
+        block = main_program.global_block()
+
+        matmul_grad_id_to_allreduce_id = (
+            self._get_all_matmul_grad_and_allreduce_pairs(block)
+        )
+        logger.info(
+            f"overlap matmul_grad and allreduce: {matmul_grad_id_to_allreduce_id}"
+        )
+
+        self._split_matmul_grad_and_multi_streaming_allreduce(
+            block, matmul_grad_id_to_allreduce_id
+        )
+
+    def _get_all_matmul_grad_and_allreduce_pairs(self, block):
+        ops = block.ops
+        op_num = len(ops)
+        matmul_grad_id_to_allreduce_id = collections.OrderedDict()
+        for i, op_i in enumerate(ops):
+            if (
+                op_i.type == 'matmul_v2_grad'
+                and op_i.attr("trans_x") is False
+                and op_i.attr("trans_y") is False
+            ):
+                x_grad = op_i.output("X@GRAD")
+                for j in range(i + 1, op_num):
+                    op_j = ops[j]
+                    if (
+                        op_j.type == 'c_allreduce_sum'
+                        and op_j.input("X") == x_grad
+                    ):
+                        matmul_grad_id_to_allreduce_id[i] = j
+        return matmul_grad_id_to_allreduce_id
+
+    def _split_matmul_grad_and_multi_streaming_allreduce(
+        self, block, matmul_grad_id_to_allreduce_id
+    ):
+        ops = block.ops
+
+        for matmul_grad_id, allreduce_id in reversed(
+            matmul_grad_id_to_allreduce_id.items()
+        ):
+            matmul_grad_op = ops[matmul_grad_id]
+            allreduce_op = ops[allreduce_id]
+
+            # NOTE(Sonder): When there are ops between matmul_grad and allreduce, we should check whether
+            # these ops rely on the output of the intermediate ops. If so, we should not split the matmul_grad.
+            # Otherwise, the output of the intermediate ops will get wrong results.
+            skip_overlapping = False
+            moved_ops_output = []
+            matmul_grad_output = matmul_grad_op.output('Y@GRAD')[0]
+
+            for idx in range(matmul_grad_id + 1, allreduce_id):
+                if matmul_grad_output in ops[idx].desc.input_arg_names():
+                    moved_ops_output.extend(ops[idx].desc.output_arg_names())
+                else:
+                    for input_name in ops[idx].desc.input_arg_names():
+                        if input_name in moved_ops_output:
+                            skip_overlapping = True
+
+            if skip_overlapping:
+                continue
+
+            # matmul_grad_op => matmul_v2 + reshape + reshape + matmul_v2 + reshape
+            split_matmul_grad_to_matmul(
+                block, matmul_grad_id, self.dist_context, self.op_namescope
+            )
+
+            # NOTE(Ruibiao): Required OP scheduling order: matmul(dOut, Y^T) -> all_reduce_sum(dX) -> matmul(X^T, dOut).
+            # all_reduce_sum(dX) and matmul(X^T, dOut) cannot be swapped. Otherwise, after buffer_shared_inplace_pass
+            # adding share_buffer OP before all_reduce_sum, all_reduce_sum will synchronous with comp-stream, and then
+            # the matmul op before it cannot be overlapped.
+            allreduce_op_dist_attr = (
+                self.dist_context.get_op_dist_attr_for_program(allreduce_op)
+            )
+            allreduce_op_dist_attr.execution_stream = (
+                AutoParallelStreamType.MP_STREAM.value
+            )
+
+            allreduce_op_inputs = allreduce_op.desc.input_names()
+            allreduce_op_outputs = allreduce_op.desc.output_names()
+
+            allreduce_op_inputs = {
+                name: allreduce_op.input(name) for name in allreduce_op_inputs
+            }
+            allreduce_op_outputs = {
+                name: allreduce_op.output(name) for name in allreduce_op_outputs
+            }
+
+            # matmul_v2 + reshape + reshape + matmul_v2 + reshape + ... + original all_reduce_sum
+            # =>
+            # matmul_v2 + new all_reduce_sum + reshape + reshape + matmul_v2 + reshape + ... + original all_reduce_sum
+            #
+            # NOTE(liym27): new all_reduce_sum must be inserted to "the next of the first matmul_v2", otherwise another
+            # pass fused_linear_param_grad_add will not work.
+            allreduce_op = block._insert_op_without_sync(
+                index=matmul_grad_id + 1,
+                type=allreduce_op.type,
+                inputs=allreduce_op_inputs,
+                outputs=allreduce_op_outputs,
+                attrs=allreduce_op.all_attrs(),
+            )
+            self.dist_context.set_op_dist_attr_for_program(
+                allreduce_op, allreduce_op_dist_attr
+            )
+            # Remove the original allreduce op
+            block._remove_op(allreduce_id + 5, sync=False)
+
+        block._sync_with_cpp()