wehub-resource-sync
160 lines
6.4 KiB
Python
160 lines
6.4 KiB
Python
# Copyright (c) 2022 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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from paddle.distributed.auto_parallel.static.operators.common import (
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is_amp_flag_sync_op,
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is_data_parallel_reduce_op,
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is_global_norm_sync_op,
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)
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from paddle.distributed.auto_parallel.static.utils import (
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OpRole,
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insert_dependencies_for_vars,
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is_comm_op,
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)
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from .auto_parallel_sharding import ShardingPass, _supported_optimizer_type
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from .pass_base import PassBase, register_pass
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def _sharding_pass_applied(pass_ctx):
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for applied_pass in pass_ctx.passes:
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if isinstance(applied_pass, ShardingPass):
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return True
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return False
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# NOTE we add the "auto_parallel" prefix to the pass in order to
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# indicate that this pass should obey some constrains by auto_parallel
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# for example all ops and vars should has dist attr before and after pass
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# should use dist op instead of custom comm op
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@register_pass("auto_parallel_supplement_explicit_dependencies")
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class AutoParalSupplementDepPass(PassBase):
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"""
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Functional Concern.
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for strategies like amp & global norm, there is a collective communication to sync gradient information in every rank.
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after partition the gradients to each rank, the order of that collective communication is different in each rank
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and might cause hang problem in graph based random order executor. here supplement explicit dependencies for those cases.
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TODO Performance Concern.
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global collective will introduce global synchronization which forces the fast workers to wait for slow ones.
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therefore we should conduct this collective when all the ranks reach a same stage.
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BUT the depend API offered by executor could only ensure "conduct-not-before" but not "conduct-right-after".
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Some ranks might call the collectives first than other ranks while they still some local could be performed to wait for slow peers.
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IR Pass currently could not have the fully control of time the to perform these global collectives.
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"""
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def __init__(self):
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super().__init__()
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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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# TODO general this pass for all case.
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if not _sharding_pass_applied(context):
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return
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self._dist_context = self.get_attr("dist_context", None)
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self.flags_sync_stream = "flags_sync_stream"
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main_block = main_program.global_block()
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startup_block = startup_program.global_block()
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# last dp grad communication
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last_dp_reduce_op_idx = -1
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last_dp_reduce_varname = None
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for idx, op in reversed(list(enumerate(main_block.ops))):
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if is_data_parallel_reduce_op(op):
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last_dp_reduce_op_idx = idx
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last_dp_reduce_varname = op.output_arg_names[0]
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break
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assert last_dp_reduce_op_idx > 0
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assert last_dp_reduce_varname is not None
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# analyze deps for amp & global norm
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deps_map = {}
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prior_varname = last_dp_reduce_varname
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for idx, op in enumerate(main_block.ops):
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if is_amp_flag_sync_op(op) or is_global_norm_sync_op(op):
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op_namescope = None
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if is_amp_flag_sync_op(op):
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op_namescope = "amp_flag_sync_dep"
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op.dist_attr.execution_stream = self.flags_sync_stream
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elif is_global_norm_sync_op(op):
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op_namescope = "global_norm_sync_dep"
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deps_map[idx] = (prior_varname, op.input("X")[0], op_namescope)
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prior_varname = op.output("Out")[0]
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# analyze deps for check_finite_and_unscale
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# ensure it is performed after last backward computation, therefore reduce the
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# straggling of the amp-flag-sync
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first_check_op = True
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for idx, op in enumerate(main_block.ops):
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if op.type == "check_finite_and_unscale":
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if first_check_op:
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last_backward_op = None
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for last_idx in range(idx - 1, 0, -1):
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if not is_comm_op(main_block.ops[last_idx]):
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last_backward_op = main_block.ops[last_idx]
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break
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prior_varname = last_backward_op.output_arg_names[0]
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first_check_op = False
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deps_map[idx] = (
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prior_varname,
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op.input("Scale")[0],
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"check_finite_dep",
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)
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# analyze deps for optimizer
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# optimizers order should be fixed to allow broadcast to overlap with optimizer
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first_optimizer_op = True
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for idx, op in enumerate(main_block.ops):
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if op.type in _supported_optimizer_type:
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if first_optimizer_op:
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first_optimizer_op = False
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else:
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deps_map[idx] = (
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prior_varname,
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op.input("Param")[0],
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"optimizer_order_dep",
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)
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prior_varname = op.output("ParamOut")[0]
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# insert deps
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indice = sorted(deps_map.keys(), reverse=True)
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for idx in indice:
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prior_var = main_block.var(deps_map[idx][0])
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post_var = main_block.var(deps_map[idx][1])
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op_namescope = deps_map[idx][2]
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depend_op = insert_dependencies_for_vars(
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main_block,
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idx,
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prior_var,
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post_var,
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self._dist_context,
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OpRole.Optimize,
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is_recompute=False,
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sync=False,
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op_namescope=op_namescope,
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)
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main_block._sync_with_cpp()
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