riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-07-04 13:20:12 +06:00
Code Issues Actions 21 Packages Projects Releases Wiki Activity

Improve typing in TrainingArgument (#36944)

Browse Source 
* Better error message in TrainingArgument typing checks

* Better typing

* Small fixes

Signed-off-by: cyy <cyyever@outlook.com>

---------

Signed-off-by: cyy <cyyever@outlook.com>
This commit is contained in:
Yuanyuan Chen 2025-05-21 21:54:38 +08:00 committed by GitHub
parent 174684a9b6
commit ae3e4e2d97
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
2 changed files with 8 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
src/transformers/training_args.py
View File

@ -907,7 +907,7 @@ class TrainingArguments:
default="linear", default="linear",
metadata={"help": "The scheduler type to use."}, metadata={"help": "The scheduler type to use."},
) )
lr_scheduler_kwargs: Optional[Union[dict, str]] = field( lr_scheduler_kwargs: Optional[Union[dict[str, Any], str]] = field(
default_factory=dict, default_factory=dict,
metadata={ metadata={
"help": ( "help": (
@ -1230,7 +1230,7 @@ class TrainingArguments:
) )
}, },
) )
fsdp_config: Optional[Union[dict, str]] = field( fsdp_config: Optional[Union[dict[str, Any], str]] = field(
default=None, default=None,
metadata={ metadata={
"help": ( "help": (
@ -1366,7 +1366,7 @@ class TrainingArguments:
"help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass." "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
}, },
) )
gradient_checkpointing_kwargs: Optional[Union[dict, str]] = field( gradient_checkpointing_kwargs: Optional[Union[dict[str, Any], str]] = field(
default=None, default=None,
metadata={ metadata={
"help": "Gradient checkpointing key word arguments such as `use_reentrant`. Will be passed to `torch.utils.checkpoint.checkpoint` through `model.gradient_checkpointing_enable`." "help": "Gradient checkpointing key word arguments such as `use_reentrant`. Will be passed to `torch.utils.checkpoint.checkpoint` through `model.gradient_checkpointing_enable`."
@ -1451,7 +1451,7 @@ class TrainingArguments:
) )
}, },
) )
ddp_timeout: Optional[int] = field( ddp_timeout: int = field(
default=1800, default=1800,
metadata={ metadata={
"help": "Overrides the default timeout for distributed training (value should be given in seconds)." "help": "Overrides the default timeout for distributed training (value should be given in seconds)."
@ -1667,7 +1667,7 @@ class TrainingArguments:
) and self.metric_for_best_model is None: ) and self.metric_for_best_model is None:
self.metric_for_best_model = "loss" self.metric_for_best_model = "loss"
if self.greater_is_better is None and self.metric_for_best_model is not None: if self.greater_is_better is None and self.metric_for_best_model is not None:
self.greater_is_better = not (self.metric_for_best_model.endswith("loss")) self.greater_is_better = not self.metric_for_best_model.endswith("loss")
if self.run_name is None: if self.run_name is None:
self.run_name = self.output_dir self.run_name = self.output_dir
if self.framework == "pt" and is_torch_available(): if self.framework == "pt" and is_torch_available():
@ -2140,7 +2140,7 @@ class TrainingArguments:
f"Please run `pip install transformers[torch]` or `pip install 'accelerate>={ACCELERATE_MIN_VERSION}'`" f"Please run `pip install transformers[torch]` or `pip install 'accelerate>={ACCELERATE_MIN_VERSION}'`"
) )
# We delay the init of `PartialState` to the end for clarity # We delay the init of `PartialState` to the end for clarity
accelerator_state_kwargs = {"enabled": True, "use_configured_state": False} accelerator_state_kwargs: dict[str, Any] = {"enabled": True, "use_configured_state": False}
if isinstance(self.accelerator_config, AcceleratorConfig): if isinstance(self.accelerator_config, AcceleratorConfig):
accelerator_state_kwargs["use_configured_state"] = self.accelerator_config.pop( accelerator_state_kwargs["use_configured_state"] = self.accelerator_config.pop(
"use_configured_state", False "use_configured_state", False

2
tests/utils/test_hf_argparser.py
View File

@ -446,7 +446,7 @@ class HfArgumentParserTest(unittest.TestCase):
self.assertEqual( self.assertEqual(
len(raw_dict_fields), len(raw_dict_fields),
0, 0,
"Found invalid raw `dict` types in the `TrainingArgument` typings. " f"Found invalid raw `dict` types in the `TrainingArgument` typings, which are {raw_dict_fields}. "
"This leads to issues with the CLI. Please turn this into `typing.Optional[dict,str]`", "This leads to issues with the CLI. Please turn this into `typing.Optional[dict,str]`",
) )