wehub-resource-sync
161 lines
6.4 KiB
Python
161 lines
6.4 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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import logging
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from ..auto_parallel.static.utils import (
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get_logger,
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)
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from .pass_base import PassBase, register_pass
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from .pass_utils import AutoParallelStreamType, split_matmul_grad_to_matmul
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logger = get_logger(logging.INFO)
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# For allreduce pattern in the backward phase of column parallel linear:
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# dX, dY = matmul_grad(X, Y, dOut)
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# dX = all_reduce_sum(dX)
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# Split matmul_grad to 2 matmul:
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# dX = matmul(dOut, Y^T)
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# dX = all_reduce_sum(dX)
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# dY = matmul(X^T, dOut)
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#
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# Then the all_reduce sum can overlap with the compute of dY.
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@register_pass("allreduce_matmul_grad_overlapping")
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class AllreduceMatmulGradOverlappingPass(PassBase):
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def __init__(self):
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super().__init__()
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self.op_namescope = "/auto_parallel/allreduce_matmul_grad_overlapping"
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self.set_attr("dist_context", None)
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def _check_self(self):
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if self.get_attr("dist_context") is None:
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return False
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return True
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def _check_conflict(self, other_pass):
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return True
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def _apply_single_impl(self, main_program, startup_program, context):
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self.dist_context = self.get_attr("dist_context")
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block = main_program.global_block()
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matmul_grad_id_to_allreduce_id = (
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self._get_all_matmul_grad_and_allreduce_pairs(block)
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)
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logger.info(
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f"overlap matmul_grad and allreduce: {matmul_grad_id_to_allreduce_id}"
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)
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self._split_matmul_grad_and_multi_streaming_allreduce(
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block, matmul_grad_id_to_allreduce_id
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)
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def _get_all_matmul_grad_and_allreduce_pairs(self, block):
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ops = block.ops
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op_num = len(ops)
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matmul_grad_id_to_allreduce_id = collections.OrderedDict()
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for i, op_i in enumerate(ops):
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if (
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op_i.type == 'matmul_v2_grad'
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and op_i.attr("trans_x") is False
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and op_i.attr("trans_y") is False
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):
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x_grad = op_i.output("X@GRAD")
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for j in range(i + 1, op_num):
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op_j = ops[j]
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if (
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op_j.type == 'c_allreduce_sum'
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and op_j.input("X") == x_grad
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):
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matmul_grad_id_to_allreduce_id[i] = j
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return matmul_grad_id_to_allreduce_id
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def _split_matmul_grad_and_multi_streaming_allreduce(
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self, block, matmul_grad_id_to_allreduce_id
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):
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ops = block.ops
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for matmul_grad_id, allreduce_id in reversed(
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matmul_grad_id_to_allreduce_id.items()
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):
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matmul_grad_op = ops[matmul_grad_id]
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allreduce_op = ops[allreduce_id]
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# NOTE(Sonder): When there are ops between matmul_grad and allreduce, we should check whether
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# these ops rely on the output of the intermediate ops. If so, we should not split the matmul_grad.
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# Otherwise, the output of the intermediate ops will get wrong results.
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skip_overlapping = False
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moved_ops_output = []
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matmul_grad_output = matmul_grad_op.output('Y@GRAD')[0]
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for idx in range(matmul_grad_id + 1, allreduce_id):
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if matmul_grad_output in ops[idx].desc.input_arg_names():
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moved_ops_output.extend(ops[idx].desc.output_arg_names())
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else:
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for input_name in ops[idx].desc.input_arg_names():
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if input_name in moved_ops_output:
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skip_overlapping = True
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if skip_overlapping:
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continue
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# matmul_grad_op => matmul_v2 + reshape + reshape + matmul_v2 + reshape
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split_matmul_grad_to_matmul(
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block, matmul_grad_id, self.dist_context, self.op_namescope
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)
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# NOTE(Ruibiao): Required OP scheduling order: matmul(dOut, Y^T) -> all_reduce_sum(dX) -> matmul(X^T, dOut).
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# all_reduce_sum(dX) and matmul(X^T, dOut) cannot be swapped. Otherwise, after buffer_shared_inplace_pass
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# adding share_buffer OP before all_reduce_sum, all_reduce_sum will synchronous with comp-stream, and then
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# the matmul op before it cannot be overlapped.
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allreduce_op_dist_attr = (
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self.dist_context.get_op_dist_attr_for_program(allreduce_op)
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)
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allreduce_op_dist_attr.execution_stream = (
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AutoParallelStreamType.MP_STREAM.value
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)
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allreduce_op_inputs = allreduce_op.desc.input_names()
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allreduce_op_outputs = allreduce_op.desc.output_names()
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allreduce_op_inputs = {
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name: allreduce_op.input(name) for name in allreduce_op_inputs
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}
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allreduce_op_outputs = {
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name: allreduce_op.output(name) for name in allreduce_op_outputs
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}
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# matmul_v2 + reshape + reshape + matmul_v2 + reshape + ... + original all_reduce_sum
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# =>
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# matmul_v2 + new all_reduce_sum + reshape + reshape + matmul_v2 + reshape + ... + original all_reduce_sum
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#
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# NOTE(liym27): new all_reduce_sum must be inserted to "the next of the first matmul_v2", otherwise another
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# pass fused_linear_param_grad_add will not work.
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allreduce_op = block._insert_op_without_sync(
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index=matmul_grad_id + 1,
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type=allreduce_op.type,
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inputs=allreduce_op_inputs,
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outputs=allreduce_op_outputs,
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attrs=allreduce_op.all_attrs(),
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)
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self.dist_context.set_op_dist_attr_for_program(
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allreduce_op, allreduce_op_dist_attr
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)
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# Remove the original allreduce op
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block._remove_op(allreduce_id + 5, sync=False)
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block._sync_with_cpp()
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