423A35C7/LLaMA-Factory
1
0
Fork 0
mirror of https://github.com/hiyouga/LLaMA-Factory.git synced 2025-12-17 04:10:36 +08:00
Code Issues Packages Projects Releases Wiki Activity

format style

Browse Source
This commit is contained in:
hiyouga
2024-01-20 20:15:56 +08:00
parent f6d6e00337
commit 638234ceee
73 changed files with 1492 additions and 2325 deletions
Download Patch File Download Diff File Expand all files Collapse all files

32
src/llmtuner/train/sft/workflow.py
View File

@@ -1,6 +1,7 @@
# Inspired by: https://github.com/huggingface/transformers/blob/v4.34.1/examples/pytorch/summarization/run_summarization.py
from typing import TYPE_CHECKING, Optional, List
from typing import TYPE_CHECKING, List, Optional
from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments
from ...data import get_dataset, split_dataset
@@ -15,7 +16,8 @@ from ...train.utils import create_modelcard_and_push
if TYPE_CHECKING:
from transformers import TrainerCallback
from ...hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
from ...hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
def run_sft(
@@ -24,29 +26,31 @@ def run_sft(
training_args: "Seq2SeqTrainingArguments",
finetuning_args: "FinetuningArguments",
generating_args: "GeneratingArguments",
callbacks: Optional[List["TrainerCallback"]] = None
callbacks: Optional[List["TrainerCallback"]] = None,
):
model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train)
dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="sft")
if training_args.predict_with_generate:
tokenizer.padding_side = "left" # use left-padding in generation
tokenizer.padding_side = "left" # use left-padding in generation
if getattr(model, "is_quantized", False) and not training_args.do_train:
setattr(model, "_hf_peft_config_loaded", True) # hack here: make model compatible with prediction
setattr(model, "_hf_peft_config_loaded", True) # hack here: make model compatible with prediction
data_collator = DataCollatorForSeq2Seq(
tokenizer=tokenizer,
pad_to_multiple_of=8 if tokenizer.padding_side == "right" else None, # for shift short attention
label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
pad_to_multiple_of=8 if tokenizer.padding_side == "right" else None, # for shift short attention
label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id,
)
# Override the decoding parameters of Seq2SeqTrainer
training_args_dict = training_args.to_dict()
training_args_dict.update(dict(
generation_max_length=training_args.generation_max_length or data_args.cutoff_len,
generation_num_beams=data_args.eval_num_beams or training_args.generation_num_beams
))
training_args_dict.update(
dict(
generation_max_length=training_args.generation_max_length or data_args.cutoff_len,
generation_num_beams=data_args.eval_num_beams or training_args.generation_num_beams,
)
)
training_args = Seq2SeqTrainingArguments(**training_args_dict)
# Initialize our Trainer
@@ -57,7 +61,7 @@ def run_sft(
data_collator=data_collator,
callbacks=callbacks,
compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None,
**split_dataset(dataset, data_args, training_args)
**split_dataset(dataset, data_args, training_args),
)
# Keyword arguments for `model.generate`
@@ -79,7 +83,7 @@ def run_sft(
# Evaluation
if training_args.do_eval:
metrics = trainer.evaluate(metric_key_prefix="eval", **gen_kwargs)
if training_args.predict_with_generate: # eval_loss will be wrong if predict_with_generate is enabled
if training_args.predict_with_generate: # eval_loss will be wrong if predict_with_generate is enabled
metrics.pop("eval_loss", None)
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
@@ -87,7 +91,7 @@ def run_sft(
# Predict
if training_args.do_predict:
predict_results = trainer.predict(dataset, metric_key_prefix="predict", **gen_kwargs)
if training_args.predict_with_generate: # predict_loss will be wrong if predict_with_generate is enabled
if training_args.predict_with_generate: # predict_loss will be wrong if predict_with_generate is enabled
predict_results.metrics.pop("predict_loss", None)
trainer.log_metrics("predict", predict_results.metrics)
trainer.save_metrics("predict", predict_results.metrics)