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feat: include lr in eval paths #2

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24 changes: 18 additions & 6 deletions encodeval/eval_tasks/abstract_eval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,8 @@ class EvalConfig:
task_type: Task type identifier (SC, SR, TC, or IR).
loss_fn: Optional callable for the training loss function.
loss_kwargs: Optional keyword arguments for the loss function.
finetuning_learning_rate: Learning rate used during fine-tuning, used to
build result directories.
"""

model_class: Callable = None
Expand All @@ -40,6 +42,7 @@ class EvalConfig:
task_type: Literal["SC", "SR", "TC", "IR"] = None
loss_fn: Callable = None
loss_kwargs: Dict = None
finetuning_learning_rate: str = None

def __post_init__(self):
"""
Expand All @@ -53,10 +56,19 @@ def __post_init__(self):
self.model_dtype = self.model_kwargs.pop("dtype")
self.device = self.model_kwargs.pop("device")

base_output_dir = self.tr_args_kwargs.get("output_dir", "")
base_model_name = os.environ["EVAL_MODEL_PATH"].split("/")[-1]
ft_lr = None
if self.finetuning_learning_rate is not None:
ft_lr = str(self.finetuning_learning_rate).replace(".", "_").replace("-", "_").replace("+", "p")
model_name = f"{base_model_name}_ftlr_{ft_lr}" if ft_lr else base_model_name

# Handle loading fine-tuned model from disk if specified
ft_model_config_dir = self.model_kwargs.pop("ft_model_config_dir", None)
if ft_model_config_dir is not None:
ft_model_path = f"{os.environ['EVAL_MODEL_PATH']}/evaluation/weights/{self.task_type}/{ft_model_config_dir}"
ft_model_path = (
f"{base_output_dir}/{model_name}/evaluation/weights/{self.task_type}/{ft_model_config_dir}"
)
print(f"Loading fine-tuned model at {ft_model_path}")
if "pretrained_model_name_or_path" in self.model_kwargs:
self.model_kwargs["pretrained_model_name_or_path"] = ft_model_path
Expand Down Expand Up @@ -144,15 +156,15 @@ def __post_init__(self):
self.dataset_name = ""

# Prepare output/log directories
model_name = os.environ["EVAL_MODEL_PATH"].split("/")[-1]
output_dir = self.tr_args.output_dir
output_dir = base_output_dir
output_subdir = (
f"{self.task_type}/{self.dataset_name}/{ft_model_config_dir.replace('/', '_')}/{output_subdir}"
if ft_model_config_dir is not None else f"{self.task_type}/{self.dataset_name}/{output_subdir}"
)
self.tr_args.output_dir = f"{os.environ['EVAL_MODEL_PATH']}/evaluation/weights/{output_subdir}"
self.tr_args.logging_dir = f"{os.environ['EVAL_MODEL_PATH']}/evaluation/logs/{output_subdir}"
self.results_dir = f"{output_dir}/{model_name}/{output_subdir}"
model_results_root = f"{output_dir}/{model_name}"
self.tr_args.output_dir = f"{model_results_root}/evaluation/weights/{output_subdir}"
self.tr_args.logging_dir = f"{model_results_root}/evaluation/logs/{output_subdir}"
self.results_dir = f"{model_results_root}/{output_subdir}"

# Clear old logs if logging directory is not empty
if os.path.exists(self.tr_args.logging_dir) and len(os.listdir(self.tr_args.logging_dir)) > 0:
Expand Down