more fixes
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@@ -22,7 +22,7 @@ from transformers import (
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from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
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from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
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from axolotl.utils.bench import log_gpu_memory_usage
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from axolotl.utils.bench import log_gpu_memory_usage
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from axolotl.utils.distributed import barrier, is_main_process, zero_first
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from axolotl.utils.distributed import is_main_process, zero_first
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if TYPE_CHECKING:
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if TYPE_CHECKING:
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from axolotl.utils.trainer import AxolotlTrainingArguments
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from axolotl.utils.trainer import AxolotlTrainingArguments
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@@ -203,7 +203,6 @@ def bench_eval_callback_factory(trainer, tokenizer):
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metrics: Dict[str, float], # pylint: disable=unused-argument
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metrics: Dict[str, float], # pylint: disable=unused-argument
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**kwargs, # pylint: disable=unused-argument
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**kwargs, # pylint: disable=unused-argument
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):
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):
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if is_main_process():
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data_loader = trainer.get_eval_dataloader(bench_dataset)
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data_loader = trainer.get_eval_dataloader(bench_dataset)
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source_max_len = trainer.data_collator.max_length
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source_max_len = trainer.data_collator.max_length
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trainer.data_collator.max_length = args.bench_source_max_len
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trainer.data_collator.max_length = args.bench_source_max_len
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@@ -231,9 +230,7 @@ def bench_eval_callback_factory(trainer, tokenizer):
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results = {"bench_loss": loss_bench / len(data_loader)}
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results = {"bench_loss": loss_bench / len(data_loader)}
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subject = bench_dataset["subject"]
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subject = bench_dataset["subject"]
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subjects: dict = {s: {"refs": [], "preds": []} for s in set(subject)}
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subjects: dict = {s: {"refs": [], "preds": []} for s in set(subject)}
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for s, p, r in zip( # pylint: disable=invalid-name
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for s, p, r in zip(subject, preds, refs): # pylint: disable=invalid-name
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subject, preds, refs
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):
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subjects[s]["preds"].append(p)
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subjects[s]["preds"].append(p)
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subjects[s]["refs"].append(r)
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subjects[s]["refs"].append(r)
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subject_scores = []
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subject_scores = []
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@@ -243,13 +240,10 @@ def bench_eval_callback_factory(trainer, tokenizer):
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predictions=subjects[subject]["preds"],
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predictions=subjects[subject]["preds"],
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)["accuracy"]
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)["accuracy"]
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if not pd.isna(subject_score):
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if not pd.isna(subject_score):
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results[
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results[f"bench_{bench_split}_accuracy_{subject}"] = subject_score
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f"bench_{bench_split}_accuracy_{subject}"
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] = subject_score
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subject_scores.append(subject_score)
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subject_scores.append(subject_score)
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results[f"bench_{bench_split}_accuracy"] = np.mean(subject_scores)
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results[f"bench_{bench_split}_accuracy"] = np.mean(subject_scores)
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trainer.log(results)
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trainer.log(results)
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trainer.data_collator.max_length = source_max_len
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trainer.data_collator.max_length = source_max_len
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barrier()
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return BenchEvalCallback
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return BenchEvalCallback
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@@ -147,7 +147,7 @@ class AxolotlTrainingArguments(TrainingArguments):
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},
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},
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)
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)
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bench_source_max_len: int = field(
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bench_source_max_len: int = field(
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default=2048, metadata={"help": "Maximum source sequence length for mmlu."}
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default=2048, metadata={"help": "Maximum source sequence length for bench."}
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)
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)
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@@ -540,9 +540,9 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
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)
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)
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if cfg.do_bench_eval:
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if cfg.do_bench_eval:
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training_arguments_kwargs["do_mmlu_eval"] = cfg.do_bench_eval
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training_arguments_kwargs["do_bench_eval"] = cfg.do_bench_eval
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if cfg.bench_dataset:
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if cfg.bench_dataset:
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training_arguments_kwargs["mmlu_dataset"] = cfg.bench_dataset
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training_arguments_kwargs["bench_dataset"] = cfg.bench_dataset
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training_args = AxolotlTrainingArguments( # pylint: disable=unexpected-keyword-arg
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training_args = AxolotlTrainingArguments( # pylint: disable=unexpected-keyword-arg
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max_steps=total_num_steps if cfg.max_steps else -1,
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max_steps=total_num_steps if cfg.max_steps else -1,
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