wehub-resource-sync
303 lines
12 KiB
Python
303 lines
12 KiB
Python
# Copyright (c) 2024 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 logging
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import paddle
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import paddle.distributed as dist
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from paddle.base.core import TensorDistAttr
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from paddle.base.executor import global_scope
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from paddle.base.framework import auto_complete_op_role
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from paddle.distributed.auto_parallel.static.process_group import (
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new_process_group,
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)
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from paddle.distributed.auto_parallel.static.utils import (
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get_pp_stage_by_process_mesh,
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)
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from paddle.distributed.fleet.meta_optimizers.common import OpRole
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from paddle.static.pir_io import get_pir_parameters
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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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logger = get_logger(logging.INFO)
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@register_pass("auto_parallel_sync_shared_params")
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class AutoParallelSyncSharedParamsPass(PassBase):
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def __init__(self):
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super().__init__()
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self.params_maybe_shared = []
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self.src_ranks = []
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self.dst_ranks = []
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self.comm_group = {}
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def _check_self(self):
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pipeline_strategy = self.get_attr('pipeline_strategy')
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if (not pipeline_strategy.enable) or pipeline_strategy.pp_degree <= 1:
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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 _find_fist_opt_user(self, main_program):
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for op in main_program.global_block().ops:
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if op.op_role == 2:
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return op
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def _get_comm_group(self, ranks=[]):
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ranks = sorted(ranks)
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if tuple(ranks) in self.comm_group:
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return self.comm_group[tuple(ranks)]
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# The communication group of this `all_reduce` op satisfies len (ranks)==2.
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# When `force_new_group=False` is set, the `send&recv` group will be returned,
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# At this point, `all_reduce` and `send&recv` share the same group, and
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# the process will hang up.
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group = new_process_group(ranks, force_new_group=True)
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self.comm_group[tuple(ranks)] = group.id
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return group.id
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def sync_shared_parameters(self, main_program, startup_program):
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if not self._check_self():
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logger.info(
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"AutoParallelSyncSharedParamsPass need support pipeline parallel, skip pass."
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)
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return []
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new_shared_params = []
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params, _ = get_pir_parameters(main_program)
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for param in params:
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users = param.all_used_ops()
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for user_op in users:
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if user_op.name() == "dist_op.reshard":
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reshard_op = user_op
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dist_attr = reshard_op.dist_attr
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src_dist_attr = dist_attr.operand(0).as_tensor_dist_attr()
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dst_dist_attr = dist_attr.result(0).as_tensor_dist_attr()
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src_mesh = src_dist_attr.process_mesh
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dst_mesh = dst_dist_attr.process_mesh
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# Shared parameter needs reshard on diff stage.
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pipeline_strategy = self.get_attr('pipeline_strategy')
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pp_degree = pipeline_strategy.pp_degree
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src_stage = get_pp_stage_by_process_mesh(
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src_mesh, pp_degree
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)
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dst_stage = get_pp_stage_by_process_mesh(
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dst_mesh, pp_degree
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)
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if (
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src_stage is None
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or dst_stage is None
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or src_stage == dst_stage
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):
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continue
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# Get shared parameter name
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param_name = param.get_defining_op().str_attr(
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'parameter_name'
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)
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# Add shared parameter builtin.parameter with "shared_" prefix.
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with (
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auto_complete_op_role(main_program, OpRole.Forward),
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paddle.static.program_guard(
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main_program, startup_program
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),
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):
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shared_param = paddle.pir.core.create_parameter(
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dtype=param.dtype,
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shape=param.shape,
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name="shared_" + param_name,
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process_mesh=dst_mesh,
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placements=src_dist_attr.placements,
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initializer=paddle.nn.initializer.Constant(value=0),
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)
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main_program.set_parameters_from(startup_program)
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# Record new shared parameter.
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new_shared_params.append("shared_" + param_name)
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# Set value for new shared parameter.
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concrete_program = self.get_attr("concrete_program")
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dy_params = concrete_program.parameters[0]
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dy_param = None
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for tmp_param in dy_params:
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if tmp_param.name == param_name:
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dy_param = tmp_param
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break
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assert dy_param is not None, (
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f"The parameter {param_name} was not found in the concrete_degram"
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)
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new_dist_attr = TensorDistAttr()
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new_dist_attr.process_mesh = dst_mesh
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new_dist_attr.dims_mapping = src_dist_attr.dims_mapping
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with paddle.no_grad():
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dy_shared_param = paddle.base.core.reshard(
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dy_param, new_dist_attr
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)
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paddle.device.synchronize()
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if dy_shared_param._is_initialized():
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pir_shared_param = (
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global_scope()
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.var("shared_" + param_name)
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.get_tensor()
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)
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pir_shared_param._share_data_with(
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dy_shared_param.get_tensor().get_tensor()
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)
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# record in params_maybe_shared
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self.params_maybe_shared.append(
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{
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'src_mesh': src_mesh,
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'dst_mesh': dst_mesh,
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'src_dist_attr': src_dist_attr,
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'dst_dist_attr': dst_dist_attr,
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'param_name': param_name,
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}
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)
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# New shared parameter must has same dist_attr with shared parameter
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new_src_dist_attr = (
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paddle.base.libpaddle.pir.create_tensor_dist_attribute(
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dst_dist_attr.process_mesh,
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src_dist_attr.dims_mapping,
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src_dist_attr.partial_status,
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)
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)
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if new_src_dist_attr == dst_dist_attr:
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# Remove useless reshared op.
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reshard_op.result(0).replace_all_uses_with(shared_param)
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reshard_op.erase()
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else:
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# Update reshard op dist_attr.
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reshard_op.dist_attr = (
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paddle.base.libpaddle.pir.create_op_dist_attribute(
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dst_mesh,
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[new_src_dist_attr],
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[dst_dist_attr],
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-1,
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)
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)
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reshard_op.operand(0).set_source(shared_param)
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self.src_ranks.extend(src_mesh.process_ids)
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self.dst_ranks.extend(dst_mesh.process_ids)
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if len(self.params_maybe_shared) == 0:
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logger.info("No parameter need to share, skip pass.")
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return []
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# Must initialize the redundant communication group for the allreduce op here.
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# Otherwise, it will hang during gradient synchronization.
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for idx in range(len(self.src_ranks)):
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rank_1 = self.src_ranks[idx]
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rank_2 = self.dst_ranks[idx]
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new_process_group(sorted([rank_1, rank_2]))
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self._get_comm_group([rank_1, rank_2])
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return new_shared_params
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def sync_shared_parameter_gradient(
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self, main_program, startup_program, params_grads
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):
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if not self._check_self():
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logger.info(
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"AutoParallelSyncSharedParamsPass need support pipeline parallel, skip pass."
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)
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return params_grads
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if len(self.params_maybe_shared) == 0:
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logger.info("No parameter need to share, skip pass.")
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return params_grads
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# Only support one shared parameter.
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# TODO: support more shared parameters
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assert len(self.params_maybe_shared) == 1, (
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"Currently, only one shared parameter is supported, and it cannot support more at the moment."
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)
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cur_rank = paddle.distributed.get_rank()
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if cur_rank not in self.src_ranks and cur_rank not in self.dst_ranks:
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return params_grads
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pre_name = ""
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if cur_rank in self.dst_ranks:
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pre_name = "shared_"
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for param_mess in self.params_maybe_shared:
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param_name = pre_name + param_mess['param_name']
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src_mesh_ids = param_mess['src_mesh'].process_ids
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dst_mesh_ids = param_mess['dst_mesh'].process_ids
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# Get (param, grad) value
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param_value = main_program.get_parameter_value_by_name(param_name)
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grad_idx = None
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for p_idx, (p_param, _) in enumerate(params_grads):
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if p_param.is_same(param_value):
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grad_idx = p_idx
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break
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assert grad_idx is not None, (
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f"Parameter {param_name} not found in params_grades, unable to find corresponding gradient value."
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)
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grad_value = params_grads[p_idx][1]
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# Create allreduce op comm group.
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cur_rank = paddle.distributed.get_rank()
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if cur_rank in self.src_ranks:
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idx = src_mesh_ids.index(cur_rank)
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peer_rank = dst_mesh_ids[idx]
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if cur_rank in self.dst_ranks:
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idx = dst_mesh_ids.index(cur_rank)
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peer_rank = src_mesh_ids[idx]
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ar_group_id = self._get_comm_group([cur_rank, peer_rank])
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# Insert allreduce op in the end of backward.
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insert_pos = self._find_fist_opt_user(main_program)
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paddle.pir.set_insertion_point(insert_pos)
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# Build allreduce op to sync gradient.
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with auto_complete_op_role(main_program, OpRole.Backward):
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allreduce_val = paddle._C_ops.all_reduce(
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grad_value,
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ar_group_id,
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dist.ReduceOp.SUM,
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)
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allreduce_val.update_dist_attr(grad_value.dist_attr())
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allreduce_op = allreduce_val.get_defining_op()
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# Update all_used_ops
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for user in grad_value.all_used_ops():
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if user.name() == "pd_op.all_reduce":
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continue
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for idx, operand in enumerate(user.operands()):
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if user.operand_source(idx).is_same(grad_value):
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user.operand(idx).set_source(allreduce_val)
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# Update (param, grad) value
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params_grads[p_idx] = (param_value, allreduce_val)
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return params_grads
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def _apply_single_impl(self, main_program, startup_program, context):
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return
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