wehub-resource-sync
280 lines
9.0 KiB
Python
280 lines
9.0 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 __future__ import annotations
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import copy
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from typing import TYPE_CHECKING
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from . import constants
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if TYPE_CHECKING:
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from paddle._typing.dtype_like import _DTypeLiteral
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class BaseConfig:
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def __init__(self, category, config_dict=None):
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self._category = category
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self._config_dict = None
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if config_dict is not None:
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if isinstance(config_dict, dict):
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self._config_dict = config_dict
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else:
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raise ValueError(
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f"Expected a dictionary. But received: {config_dict}"
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)
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# Initialize attributes by the default config
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config = constants.get_category_default_config(self._category)
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for field, default_value in config.items():
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setattr(self, field, default_value)
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# Override attributes by the config_dict
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if self._config_dict:
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self.from_dict(self._config_dict)
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def from_dict(self, config_dict):
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config = constants.get_category_default_config(self._category)
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for field in config.keys():
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value = config_dict.get(field, constants.NOT_FOUND)
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# Use the default value if we cannot found the value
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if value != constants.NOT_FOUND:
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setattr(self, field, value)
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def to_dict(self):
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result_dict = {}
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config = constants.get_category_default_config(self._category)
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for field in config.keys():
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value = getattr(self, field)
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result_dict[field] = value
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for field, value in self.__dict__.items():
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if isinstance(value, BaseConfig):
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result_dict[field] = value.to_dict()
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return result_dict
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def __repr__(self):
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result_dict = self.to_dict()
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string = "{"
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for k, v in result_dict.items():
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string += f'"{k}":"{v}",'
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return string + "}"
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def __deepcopy__(self, memo):
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cls = self.__class__
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result = cls.__new__(cls)
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memo[id(self)] = result
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for k, v in self.__dict__.items():
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setattr(result, k, copy.deepcopy(v, memo))
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return result
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def get(self, k, d=None):
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result_dict = self.to_dict()
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return result_dict.get(k, d)
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class RecomputeConfig(BaseConfig):
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def __init__(self, config_dict=None):
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category = constants.RECOMPUTE
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super().__init__(category, config_dict)
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class FusedLinearPromotionConfig(BaseConfig):
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def __init__(self, config_dict=None):
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category = constants.FUSEDLINEARPROMOTION
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super().__init__(category, config_dict)
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class AMPConfig(BaseConfig):
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enable: bool
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dtype: _DTypeLiteral
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level: str
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init_loss_scaling: float
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incr_every_n_steps: int
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decr_every_n_nan_or_inf: int
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incr_ratio: float
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decr_ratio: float
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use_dynamic_loss_scaling: bool
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custom_white_list: list[str]
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custom_black_list: list[str]
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custom_black_varnames: list[str]
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use_fp16_guard: bool
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use_bf16_guard: bool
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use_master_grad: bool
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def __init__(self, config_dict=None):
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category = constants.AMP
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super().__init__(category, config_dict)
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class ShardingConfig(BaseConfig):
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enable: bool
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stage: int
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degree: int
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def __init__(self, config_dict=None):
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category = constants.SHARDING
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super().__init__(category, config_dict)
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class GradientMergeConfig(BaseConfig):
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enable: bool
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k_steps: int
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avg: bool
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def __init__(self, config_dict=None):
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category = constants.GRADIENT_MERGE
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super().__init__(category, config_dict)
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class PipelineConfig(BaseConfig):
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enable: bool
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schedule_mode: str
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micro_batch_size: int
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accumulate_steps: int
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def __init__(self, config_dict=None):
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category = constants.PIPELINE
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super().__init__(category, config_dict)
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class QATConfig(BaseConfig):
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def __init__(self, config_dict=None):
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category = constants.QAT
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super().__init__(category, config_dict)
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class TuningConfig(BaseConfig):
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def __init__(self, config_dict=None):
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category = constants.TUNING
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super().__init__(category, config_dict)
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class DatasetConfig(BaseConfig):
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def __init__(self, config_dict=None):
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category = constants.DATASET
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super().__init__(category, config_dict)
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class FusedPassesConfig(BaseConfig):
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def __init__(self, config_dict=None):
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category = constants.FUSED_PASSES
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super().__init__(category, config_dict)
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class DPOptimizationConfig(BaseConfig):
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def __init__(self, config_dict=None):
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category = constants.DP_OPTIMIZATION
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super().__init__(category, config_dict)
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class MPOptimizationConfig(BaseConfig):
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def __init__(self, config_dict=None):
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category = constants.MP_OPTIMIZATION
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super().__init__(category, config_dict)
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class SPOptimizationConfig(BaseConfig):
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def __init__(self, config_dict=None):
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category = constants.SP_OPTIMIZATION
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super().__init__(category, config_dict)
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class Strategy(BaseConfig):
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"""
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The `Strategy` object is used to configure the parallelization and optimization for static graph.
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Args:
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config (dict|string, optional): If this is None, the default configurations will used.
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If this is a dictionary, the recognized key-value of it will be used to override the default
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configurations while other default configurations are left unchanged. If this is a string,
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it is interpreted as the path to a YAML configuration and will be loaded to override the
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corresponding default configurations.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.distributed.fleet import auto
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>>> strategy = auto.Strategy()
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>>> sharding = strategy.sharding
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>>> assert sharding.enable == False
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>>> assert sharding.stage == 1
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>>> assert sharding.degree == 8
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>>> sharding.enable = True
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>>> sharding.stage = 2
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>>> sharding.degree = 2
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>>> assert sharding.enable == True
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>>> assert sharding.stage == 2
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>>> assert sharding.degree == 2
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"""
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def __init__(self, config=None):
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if config is not None:
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if isinstance(config, dict):
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self._config_dict = copy.deepcopy(config)
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# elif os.path.exists(config):
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# with open(config, "rb") as yaml_file:
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# self._config_dict = yaml.load(yaml_file, Loader=yaml.Loader)
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else:
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raise ValueError(
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f"Expected a dictionary. But received: {config}"
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)
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else:
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self._config_dict = {}
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category = constants.BASE
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super().__init__(category, self._config_dict)
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config_dict = self._config_dict.get(constants.RECOMPUTE, None)
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self.recompute = RecomputeConfig(config_dict)
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config_dict = self._config_dict.get(constants.AMP, None)
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self.amp = AMPConfig(config_dict)
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config_dict = self._config_dict.get(constants.SHARDING, None)
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self.sharding = ShardingConfig(config_dict)
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config_dict = self._config_dict.get(constants.GRADIENT_MERGE, None)
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self.gradient_merge = GradientMergeConfig(config_dict)
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config_dict = self._config_dict.get(constants.PIPELINE, None)
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self.pipeline = PipelineConfig(config_dict)
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config_dict = self._config_dict.get(constants.QAT, None)
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self.qat = QATConfig(config_dict)
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config_dict = self._config_dict.get(constants.TUNING, None)
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self.tuning = TuningConfig(config_dict)
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config_dict = self._config_dict.get(constants.DATASET, None)
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self.dataset = DatasetConfig(config_dict)
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config_dict = self._config_dict.get(constants.FUSED_PASSES, None)
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self.fused_passes = FusedPassesConfig(config_dict)
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config_dict = self._config_dict.get(
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constants.FUSEDLINEARPROMOTION, None
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)
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self.fused_linear_promotion = FusedLinearPromotionConfig(config_dict)
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config_dict = self._config_dict.get(constants.DP_OPTIMIZATION, None)
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self.dp_optimization = DPOptimizationConfig(config_dict)
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config_dict = self._config_dict.get(constants.MP_OPTIMIZATION, None)
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self.mp_optimization = MPOptimizationConfig(config_dict)
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config_dict = self._config_dict.get(constants.SP_OPTIMIZATION, None)
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self.sp_optimization = SPOptimizationConfig(config_dict)
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