wehub-resource-sync
295 lines
11 KiB
Python
295 lines
11 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 import pir
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from paddle.base.framework import (
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in_dygraph_mode,
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in_pir_mode,
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)
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from paddle.distributed import fleet
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from paddle.nn import Layer
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from paddle.optimizer import Optimizer
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def is_tensor(tensor):
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if in_dygraph_mode():
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return isinstance(tensor, paddle.Tensor)
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elif in_pir_mode():
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return isinstance(tensor, pir.Value)
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else:
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raise RuntimeError(
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"PipelineParallel are only supported in dynamic or pir mode."
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)
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class ParallelOptimizer:
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def __init__(
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self,
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optimizer,
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level=None,
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sharding_mesh_dim=None,
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):
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self.level = None
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self.sharding_mesh_dim = None
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self.optimizer = None
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if isinstance(optimizer, ParallelOptimizer):
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self.optimizer = optimizer.optimizer
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if level is None:
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self.level = optimizer.level
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self.sharding_mesh_dim = optimizer.sharding_mesh_dim
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else:
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if isinstance(level, int):
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level = str(level)
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assert level in ("0", "1", "2", "3", None)
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if optimizer.level is not None:
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assert level == optimizer.level, (
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f"The level passed in is not identical with previous level. Current level is {level}, previous level is {optimizer.level}"
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)
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self.level = level
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self.sharding_mesh_dim = sharding_mesh_dim
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else:
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assert isinstance(optimizer, Optimizer)
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self.optimizer = optimizer
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if isinstance(level, int):
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level = str(level)
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assert level in ("0", "1", "2", "3", None)
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# level=0 and level=None are all mean pure dp
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self.level = level
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self.sharding_mesh_dim = sharding_mesh_dim
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self.is_initialized = False
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def parallelize(self):
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assert self.optimizer is not None
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if self.is_initialized:
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return self.optimizer
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mesh = fleet.auto.get_mesh()
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if self.level == "1":
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self.optimizer = dist.shard_optimizer(
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self.optimizer,
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dist.ShardingStage1(self.sharding_mesh_dim, mesh),
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)
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elif self.level == "2":
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self.optimizer = dist.shard_optimizer(
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self.optimizer,
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dist.ShardingStage2(self.sharding_mesh_dim, mesh),
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)
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elif self.level == "3":
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self.optimizer = dist.shard_optimizer(
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self.optimizer,
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dist.ShardingStage3(self.sharding_mesh_dim, mesh),
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)
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else:
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self.optimizer = dist.shard_optimizer(self.optimizer, None)
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self.is_initialized = True
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return self.optimizer
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def update_param_list(self, parallelized_parameters):
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self.optimizer._parameter_list = parallelized_parameters
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if isinstance(parallelized_parameters[0], dict):
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self.optimizer._param_groups = []
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for param_group in self.parallelized_parameters:
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self.optimizer._add_param_group(param_group.copy())
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else:
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self.optimizer._param_groups = self.optimizer._parameter_list
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class ParallelModel:
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def __init__(self, model):
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super().__init__()
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self.pp_parallelizer = None
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self.tp_parallelizer = None
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self.sharding_parallelizer = None
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self.model = None
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self.share_param_list = {}
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self.layer_param_placements = {}
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if isinstance(model, ParallelModel):
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self.pp_parallelizer = model.pp_parallelizer
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self.tp_parallelizer = model.tp_parallelizer
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self.sharding_parallelizer = model.sharding_parallelizer
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self.model = model.model
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else:
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assert isinstance(model, Layer)
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self.model = model
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self.is_parallelized = False
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def get_mesh(self, pp_idx=0):
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mesh = fleet.auto.get_mesh()
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if "pp" in mesh.dim_names:
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mesh = mesh.get_mesh_with_dim("pp", pp_idx)
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return mesh
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def parallelize_model(self):
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assert self.model is not None
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if self.is_parallelized:
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return self.model
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if self.pp_parallelizer is not None:
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assert callable(self.pp_parallelizer)
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self.model = self.pp_parallelizer(self.model)
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if self.tp_parallelizer is not None:
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assert callable(self.tp_parallelizer)
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self.model, self.layer_param_placements = self.tp_parallelizer(
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self.model
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)
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if self.sharding_parallelizer is not None:
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assert callable(self.sharding_parallelizer)
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self.model = self.sharding_parallelizer(self.model)
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self._shard_all_param(self.model)
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self.is_parallelized = True
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return self.model
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def _process_share_weight_layer(
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self, layer, origin_weight, param_name, param_placements
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):
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ipp = (
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layer.pipeline_stage_index
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if hasattr(layer, "pipeline_stage_index")
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else 0
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)
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def create_pre_hook(origin_weight, param_name):
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def forward_pre_hook(layer, input):
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setattr(
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layer,
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param_name,
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None,
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)
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delattr(layer, param_name)
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mesh = self.get_mesh(ipp)
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share_weight = dist.reshard(
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origin_weight,
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mesh,
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param_placements,
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)
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setattr(
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layer,
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param_name,
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share_weight,
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)
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return forward_pre_hook
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def create_post_hook(origin_weight, param_name):
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def forward_post_hook(layer, input, output):
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setattr(
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layer,
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param_name,
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origin_weight,
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)
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return forward_post_hook
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layer.register_forward_pre_hook(
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create_pre_hook(origin_weight, param_name)
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)
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layer.register_forward_post_hook(
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create_post_hook(origin_weight, param_name)
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)
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def _shard_all_param(self, model):
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param_name_to_shard_param = {}
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param_name_to_pp_stage = {}
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def shard_layer_param(layer):
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if self.pp_parallelizer is not None:
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assert hasattr(layer, "pipeline_stage_index")
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for param_name in list(layer._parameters.keys()):
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param = getattr(layer, param_name)
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if param is not None:
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param_full_name = param.name
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ipp = (
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layer.pipeline_stage_index
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if hasattr(layer, "pipeline_stage_index")
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else 0
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)
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mesh = self.get_mesh(ipp)
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param_placements = [
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dist.Replicate() for _ in range(len(mesh._shape))
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]
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if layer in self.layer_param_placements:
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if param_name in self.layer_param_placements[layer]:
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param_placements = (
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self.layer_param_placements[layer][param_name]
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if self.layer_param_placements[layer][
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param_name
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]
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is not None
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else param_placements
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)
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if not param.is_dist():
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if param_full_name in param_name_to_shard_param:
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setattr(
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layer,
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param_name,
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param_name_to_shard_param[param_full_name],
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)
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if ipp != param_name_to_pp_stage[param_full_name]:
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self._process_share_weight_layer(
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layer,
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param_name_to_shard_param[param_full_name],
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param_name,
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param_placements,
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)
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else:
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param = dist.shard_tensor(
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param, mesh, param_placements
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)
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param_name_to_shard_param[param_full_name] = param
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param_name_to_pp_stage[param_full_name] = ipp
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setattr(layer, param_name, param)
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else:
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if (
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param_full_name in param_name_to_shard_param
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and ipp != param_name_to_pp_stage[param_full_name]
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):
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self._process_share_weight_layer(
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layer,
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param_name_to_shard_param[param_full_name],
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param_name,
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param_placements,
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)
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elif param_full_name not in param_name_to_shard_param:
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param_name_to_shard_param[param_full_name] = param
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param_name_to_pp_stage[param_full_name] = ipp
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for name, layer in model.named_sublayers():
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shard_layer_param(layer)
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def parallelize_model_and_optimizer(model, optimizer=None):
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if not isinstance(model, ParallelModel):
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assert not isinstance(optimizer, ParallelOptimizer)
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logging.warning(
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"The method `parallelize_model_and_optimizer` won't do anything since the model is not parallelized."
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)
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return model, optimizer
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parallelized_model = model.parallelize_model()
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parallelized_optimizer = None
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if optimizer is not None:
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assert isinstance(optimizer, ParallelOptimizer)
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optimizer.update_param_list(parallelized_model.parameters())
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parallelized_optimizer = optimizer.parallelize()
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return parallelized_model, parallelized_optimizer
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