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support DPO training (2305.18290)

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Former-commit-id: 6d98de148e4af63a7028dfaeb6cf86eb56a4488f
This commit is contained in:
hiyouga
2023-08-11 03:02:53 +08:00
parent 72dfd74005
commit ca719a8697
33 changed files with 513 additions and 192 deletions
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2
src/llmtuner/tuner/sft/trainer.py
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@@ -79,7 +79,7 @@ class Seq2SeqPeftTrainer(PeftTrainer):
padded_tensor = pad_token_id * torch.ones_like(tgt_tensor)
padded_tensor[:, -src_tensor.shape[-1]:] = src_tensor # adopt left-padding
return padded_tensor.contiguous()
return padded_tensor.contiguous() # in contiguous memory
def save_predictions(
self,

15
src/llmtuner/tuner/sft/workflow.py
View File

@@ -5,7 +5,7 @@ from transformers import DataCollatorForSeq2Seq
from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
from llmtuner.extras.constants import IGNORE_INDEX
from llmtuner.extras.misc import get_logits_processor
from llmtuner.extras.misc import get_logits_processor, get_stopping_criteria
from llmtuner.extras.ploting import plot_loss
from llmtuner.tuner.core import load_model_and_tokenizer
from llmtuner.tuner.sft.metric import ComputeMetrics
@@ -13,7 +13,7 @@ from llmtuner.tuner.sft.trainer import Seq2SeqPeftTrainer
if TYPE_CHECKING:
from transformers import Seq2SeqTrainingArguments, TrainerCallback
from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
def run_sft(
@@ -21,6 +21,7 @@ def run_sft(
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
finetuning_args: "FinetuningArguments",
generating_args: "GeneratingArguments",
callbacks: Optional[List["TrainerCallback"]] = None
):
dataset = get_dataset(model_args, data_args)
@@ -50,13 +51,9 @@ def run_sft(
)
# Keyword arguments for `model.generate`
gen_kwargs = {
"do_sample": True,
"top_p": 0.7,
"max_new_tokens": data_args.max_target_length + 1,
"temperature": 0.95,
"logits_processor": get_logits_processor()
}
gen_kwargs = generating_args.to_dict()
gen_kwargs["logits_processor"] = get_logits_processor()
gen_kwargs["stopping_criteria"] = get_stopping_criteria(tokenizer.additional_special_tokens_ids)
# Training
if training_args.do_train: