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[Typing][B-34] Add type annotations for `python/paddle/amp/auto_cast.py` by enkilee · Pull Request #66119 · PaddlePaddle/Paddle · GitHub
[Typing][B-34] Add type annotations for
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[Typing][B-34] Add type annotations for python/paddle/amp/auto_cast.py
#66119
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你的PR提交成功，感谢你对开源项目的贡献! |
megemini
reviewed
Jul 17, 2024
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这个文件有点麻烦，辛苦再确认一下 ～ 🤟🤟🤟
python/paddle/amp/auto_cast.py
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| from paddle.distributed.fleet.meta_optimizers.dygraph_optimizer.dygraph_sharding_optimizer import ( | ||
| DygraphShardingOptimizer, | ||
| DygraphShardingOptimizerV2, | ||
| ) |
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不要在这里导入了，按照原逻辑来吧 ～
python/paddle/amp/auto_cast.py
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| from typing import Generator | ||
|
|
||
| from paddle import Tensor, dtype | ||
| from python.paddle.nn.layer.layers import Layer |
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Suggested change
| from python.paddle.nn.layer.layers import Layer | |
| from paddle.nn import Layer |
python/paddle/amp/auto_cast.py
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| ): | ||
| custom_white_list: _CustomList, | ||
| custom_black_list: _CustomList, | ||
| level: AMP_LEVEL = 'O1', |
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Suggested change
| level: AMP_LEVEL = 'O1', | |
| level: str = 'O1', |
AMP_LEVEL 有点像枚举 ～
python/paddle/amp/auto_cast.py
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| custom_white_list: _CustomList, | ||
| custom_black_list: _CustomList, | ||
| level: AMP_LEVEL = 'O1', | ||
| dtype: dtype = 'float16', |
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Suggested change
| dtype: dtype = 'float16', | |
| dtype: str = 'float16', |
这里是作为 white_list() 的 key，应该只能是 str ～
python/paddle/amp/auto_cast.py
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| """ | ||
| Judge whether current custom device support bfloat16 amp. | ||
| """ | ||
| place = _current_expected_place() | ||
| return place.get_device_type() == 'npu' | ||
|
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|
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| def need_keep_fp32(layer, dtype): | ||
| def need_keep_fp32(layer: _LayerList, dtype: dtype) -> bool: |
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Suggested change
| def need_keep_fp32(layer: _LayerList, dtype: dtype) -> bool: | |
| def need_keep_fp32(layer: _LayerList, dtype: str) -> bool: |
后面
elif (layer._dtype == 'float16') or (
(dtype == 'float16')做字符串比对，应该只能是 str ～
python/paddle/amp/auto_cast.py
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| self._save_dtype = save_dtype | ||
|
|
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| def __call__(self, state_dict): | ||
| def __call__(self, state_dict: dict[Any, Any]) -> None: |
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Suggested change
| def __call__(self, state_dict: dict[Any, Any]) -> None: | |
| def __call__(self, state_dict: _StateDict) -> None: |
从 layer.register_state_dict_hook 中标注的内容
def register_state_dict_hook(
self, hook: _StateDictHook
) -> HookRemoveHelper:
...
_StateDict = Union[Dict[str, Tensor], typing.OrderedDict[str, Tensor]]
_StateDictHook = Callable[[_StateDict], None]这里可以把 paddle/nn/layer/layers.py 中的 _StateDict 导进来直接用 ～～～
python/paddle/amp/auto_cast.py
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| _OptimizerBase: TypeAlias = Union[ | ||
| paddle.optimizer.Optimizer, | ||
| DygraphShardingOptimizer, | ||
| DygraphShardingOptimizerV2, | ||
| ] |
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Suggested change
| _OptimizerBase: TypeAlias = Union[ | |
| paddle.optimizer.Optimizer, | |
| DygraphShardingOptimizer, | |
| DygraphShardingOptimizerV2, | |
| ] | |
| class _OptimizerLike(Protocol): | |
| def minimize( | |
| self, | |
| loss: Tensor, | |
| startup_program: Program, | |
| parameters: list[Tensor], | |
| no_grad_set: set[Tensor], | |
| ) -> tuple[list[Operator], list[tuple[Tensor, Tensor]]]: ... | |
| def step(self) -> None: ... | |
| def set_state_dict(self, state_dict: dict[str, Tensor]) -> None: ... | |
| def clear_grad(self, set_to_zero: bool) -> None: ... |
这里定义一个 Protocol ，然后后面都用这个就行 ～
最好的办法是 Optimizer 和这里的 DygraphShardingOptimizer 都有一个公共父类，既然没有，我们定义一个 Protocol 暂时解决一下吧 ～
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@enkilee _OptimizerLike 改了一下，减少限制，可以试一下 ～
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收到
python/paddle/amp/auto_cast.py
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| def amp_decorate( | ||
| models, | ||
| optimizers=None, | ||
| level='O1', | ||
| dtype='float16', | ||
| master_weight=None, | ||
| save_dtype=None, | ||
| master_grad=False, | ||
| excluded_layers=None, | ||
| ): | ||
| models: Layer | list[Layer], | ||
| optimizers: _OptimizerList | None = None, | ||
| level: AMP_LEVEL = 'O1', | ||
| dtype: dtype = 'float16', | ||
| master_weight: bool | None = None, | ||
| save_dtype: dtype | None = None, | ||
| master_grad: bool = False, | ||
| excluded_layers: Layer | list[Layer] | None = None, | ||
| ) -> None: |
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这里可以用 overload
_ModelsT = TypeVar("_ModelsT", Layer, List[Layer])
_OptimizersT = TypeVar("_OptimizersT", _OptimizerBase, List[_OptimizerBase])
@overload
def amp_decorate(
models: _ModelsT,
optimizers: _OptimizersT = ...,
level: str = ...,
dtype: str = ...,
master_weight: bool | None = ...,
save_dtype: str | None = ...,
master_grad: bool = ...,
excluded_layers: Layer | list[Layer] | list[type[Layer]] | None= ...,
) -> tuple[_ModelsT, _OptimizersT]: ...
@overload
def amp_decorate(
models: _ModelsT,
optimizers: Literal[None] = ...,
level: str = ...,
dtype: str = ...,
master_weight: bool | None = ...,
save_dtype: str | None = ...,
master_grad: bool = ...,
excluded_layers: Layer | list[Layer] | list[type[Layer]] | None= ...,
) -> _ModelsT: ...
@dygraph_only
def amp_decorate(
models: _ModelsT,
optimizers: _OptimizersT | None = None,
level: str = 'O1',
dtype: str = 'float16',
master_weight: bool | None = None,
save_dtype: str | None = None,
master_grad: bool = False,
excluded_layers: Layer | list[Layer] | list[type[Layer]] | None= None,
) -> tuple[_ModelsT, _OptimizersT] | _ModelsT :
python/paddle/amp/auto_cast.py
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| enable: bool = True, | ||
| custom_white_list: _CustomList | None = None, | ||
| custom_black_list: _CustomList | None = None, | ||
| level: AMP_LEVEL = 'O1', | ||
| dtype: dtype = 'float16', | ||
| use_promote: bool = True, | ||
| ) -> None: |
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Suggested change
| enable: bool = True, | |
| custom_white_list: _CustomList | None = None, | |
| custom_black_list: _CustomList | None = None, | |
| level: AMP_LEVEL = 'O1', | |
| dtype: dtype = 'float16', | |
| use_promote: bool = True, | |
| ) -> None: | |
| enable: bool = True, | |
| custom_white_list: _CustomList | None = None, | |
| custom_black_list: _CustomList | None = None, | |
| level: str = 'O1', | |
| dtype: str = 'float16', | |
| use_promote: bool = True, | |
| ) -> ContextManager: |
python/paddle/amp/auto_cast.py
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| models: Layer | list[Layer], | ||
| optimizers: _OptimizerList | None = None, | ||
| level: AMP_LEVEL = 'O1', | ||
| dtype: dtype = 'float16', | ||
| master_weight: bool | None = None, | ||
| save_dtype: dtype | None = None, | ||
| master_grad: bool = False, | ||
| excluded_layers: Layer | list[Layer] | None = None, | ||
| ) -> None: |
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参考上面的 amp_decorate
SigureMo
approved these changes
Jul 21, 2024
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LGTMeow
lixcli
pushed a commit
to lixcli/Paddle
that referenced
this pull request
Jul 22, 2024
…py` (PaddlePaddle#66119) --------- Co-authored-by: SigureMo <sigure.qaq@gmail.com>
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B34