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hiyouga
2024-01-20 20:15:56 +08:00
parent f6d6e00337
commit 638234ceee
73 changed files with 1492 additions and 2325 deletions
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64
src/llmtuner/hparams/parser.py
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@@ -1,10 +1,11 @@
import logging
import os
import sys
import torch
import logging
import datasets
import transformers
from typing import Any, Dict, Optional, Tuple
import datasets
import torch
import transformers
from transformers import HfArgumentParser, Seq2SeqTrainingArguments
from transformers.trainer_utils import get_last_checkpoint
@@ -19,24 +20,12 @@ from .model_args import ModelArguments
logger = get_logger(__name__)
_TRAIN_ARGS = [
ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments
]
_TRAIN_CLS = Tuple[
ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments
]
_INFER_ARGS = [
ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
]
_INFER_CLS = Tuple[
ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
]
_EVAL_ARGS = [
ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments
]
_EVAL_CLS = Tuple[
ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments
]
_TRAIN_ARGS = [ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments]
_TRAIN_CLS = Tuple[ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments]
_INFER_ARGS = [ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]
_INFER_CLS = Tuple[ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments]
_EVAL_ARGS = [ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments]
_EVAL_CLS = Tuple[ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments]
def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
@@ -77,7 +66,7 @@ def _verify_model_args(model_args: "ModelArguments", finetuning_args: "Finetunin
if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
if finetuning_args.finetuning_type != "lora":
raise ValueError("Multiple adapters are only available for LoRA tuning.")
if model_args.quantization_bit is not None:
raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
@@ -181,18 +170,22 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
training_args_dict = training_args.to_dict()
training_args_dict.update(dict(resume_from_checkpoint=last_checkpoint))
training_args = Seq2SeqTrainingArguments(**training_args_dict)
logger.info("Resuming training from {}. Change `output_dir` or use `overwrite_output_dir` to avoid.".format(
training_args.resume_from_checkpoint
))
logger.info(
"Resuming training from {}. Change `output_dir` or use `overwrite_output_dir` to avoid.".format(
training_args.resume_from_checkpoint
)
)
if (
finetuning_args.stage in ["rm", "ppo"]
and finetuning_args.finetuning_type == "lora"
and training_args.resume_from_checkpoint is not None
):
logger.warning("Add {} to `adapter_name_or_path` to resume training from checkpoint.".format(
training_args.resume_from_checkpoint
))
logger.warning(
"Add {} to `adapter_name_or_path` to resume training from checkpoint.".format(
training_args.resume_from_checkpoint
)
)
# postprocess model_args
model_args.compute_dtype = (
@@ -201,10 +194,15 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
model_args.model_max_length = data_args.cutoff_len
# Log on each process the small summary:
logger.info("Process rank: {}, device: {}, n_gpu: {}\n distributed training: {}, compute dtype: {}".format(
training_args.local_rank, training_args.device, training_args.n_gpu,
bool(training_args.local_rank != -1), str(model_args.compute_dtype)
))
logger.info(
"Process rank: {}, device: {}, n_gpu: {}\n distributed training: {}, compute dtype: {}".format(
training_args.local_rank,
training_args.device,
training_args.n_gpu,
bool(training_args.local_rank != -1),
str(model_args.compute_dtype),
)
)
logger.info(f"Training/evaluation parameters {training_args}")
# Set seed before initializing model.