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Fix precommit check after moving to ruff (#927)

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Lint check in main branch is failing. This fixes the lint check after we
moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We
need to move to a `ruff.toml` file as well as fixing and ignoring some
additional checks.

Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
This commit is contained in:
Yuan Tang 2025-02-02 09:46:45 -05:00 • committed by GitHub
parent 4773092dd1
commit 34ab7a3b6c
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217 changed files with 981 additions and 2681 deletions
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34
llama_stack/providers/inline/post_training/torchtune/common/checkpointer.py
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@ -42,9 +42,7 @@ class TorchtuneCheckpointer:
self._model_type = ModelType[model_type]
self._output_dir = output_dir
# get ckpt paths
self._checkpoint_path = Path.joinpath(
self._checkpoint_dir, self._checkpoint_file
)
self._checkpoint_path = Path.joinpath(self._checkpoint_dir, self._checkpoint_file)
def load_checkpoint(self) -> Dict[str, Any]:
"""
@ -57,13 +55,9 @@ class TorchtuneCheckpointer:
llama3_vision_meta_to_tune,
)
state_dict[training.MODEL_KEY] = llama3_vision_meta_to_tune(
model_state_dict
)
state_dict[training.MODEL_KEY] = llama3_vision_meta_to_tune(model_state_dict)
else:
state_dict[training.MODEL_KEY] = convert_weights.meta_to_tune(
model_state_dict
)
state_dict[training.MODEL_KEY] = convert_weights.meta_to_tune(model_state_dict)
# llama3_2 has tied weights, so we need to remove the output.weight key
if self._model_type == ModelType.LLAMA3_2:
@ -82,10 +76,7 @@ class TorchtuneCheckpointer:
epoch: int,
adapter_only: bool = False,
) -> str:
model_file_path = (
Path(self._output_dir)
/ f"{self._model_id}-{self._training_algorithm}-{epoch}"
)
model_file_path = Path(self._output_dir) / f"{self._model_id}-{self._training_algorithm}-{epoch}"
model_file_path.mkdir(parents=True, exist_ok=True)
@ -116,22 +107,13 @@ class TorchtuneCheckpointer:
llama3_vision_tune_to_meta,
)
state_dict[training.MODEL_KEY] = llama3_vision_tune_to_meta(
model_state_dict
)
state_dict[training.MODEL_KEY] = llama3_vision_tune_to_meta(model_state_dict)
else:
# llama3_2 has tied weights, so we need to add the output.weight key
if (
self._model_type == ModelType.LLAMA3_2
and "output.weight" not in model_state_dict
):
model_state_dict["output.weight"] = model_state_dict[
"tok_embeddings.weight"
]
if self._model_type == ModelType.LLAMA3_2 and "output.weight" not in model_state_dict:
model_state_dict["output.weight"] = model_state_dict["tok_embeddings.weight"]
state_dict[training.MODEL_KEY] = convert_weights.tune_to_meta(
model_state_dict
)
state_dict[training.MODEL_KEY] = convert_weights.tune_to_meta(model_state_dict)
model_file_name = Path.joinpath(model_file_path, "consolidated.00.pth")