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refactor model_dtype, fix PPO trainer

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Former-commit-id: 3e17ee5afbcb823a7c9a2f91864b3750cd79edb4
This commit is contained in:
hiyouga
2023-10-11 23:16:01 +08:00
parent a2d08ce961
commit 3198a7e5f4
10 changed files with 104 additions and 119 deletions
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55
src/llmtuner/tuner/core/parser.py
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@@ -8,16 +8,6 @@ from transformers import HfArgumentParser, Seq2SeqTrainingArguments
from transformers.utils.versions import require_version
from transformers.trainer_utils import get_last_checkpoint
try:
from transformers.utils import is_torch_bf16_gpu_available, is_torch_npu_available, is_torch_cuda_available
is_fp16_available = is_torch_cuda_available()
is_bf16_available = is_torch_bf16_gpu_available()
is_npu_available = is_torch_npu_available()
except ImportError:
is_fp16_available = torch.cuda.is_available()
is_bf16_available = torch.cuda.is_bf16_supported()
is_npu_available = False
from llmtuner.extras.logging import get_logger
from llmtuner.hparams import (
ModelArguments,
@@ -31,17 +21,6 @@ from llmtuner.hparams import (
logger = get_logger(__name__)
def _infer_dtype() -> torch.dtype:
if is_npu_available:
return torch.float16
elif is_bf16_available:
return torch.bfloat16
elif is_fp16_available:
return torch.float16
else:
return torch.float32
def _parse_args(parser: HfArgumentParser, args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
if args is not None:
return parser.parse_dict(args)
@@ -178,12 +157,15 @@ def get_train_args(
if not finetuning_args.resume_lora_training:
raise ValueError("Quantized model cannot create new LoRA weight. Merge them first.")
if model_args.quantization_bit is not None and (not training_args.do_train):
logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
if training_args.do_train and model_args.quantization_bit is not None and (not model_args.upcast_layernorm):
logger.warning("We recommend enable `upcast_layernorm` in quantized training.")
if training_args.do_train and (not training_args.fp16) and (not training_args.bf16):
logger.warning("We recommend enable mixed precision training.")
if (not training_args.do_train) and model_args.quantization_bit is not None:
logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
# postprocess data_args
if data_args.max_samples is not None and data_args.streaming:
logger.warning("`max_samples` is incompatible with `streaming`. Disabling max_samples.")
@@ -206,10 +188,9 @@ def get_train_args(
and os.path.isdir(training_args.output_dir)
and not training_args.overwrite_output_dir
):
require_version("transformers>=4.31.0", "Resuming training requires transformers>=4.31.0.")
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError("Output directory already exists and is not empty. Use `overwrite_output_dir`.")
raise ValueError("Output directory already exists and is not empty. Please set `overwrite_output_dir`.")
if last_checkpoint is not None:
training_args_dict = training_args.to_dict()
@@ -220,26 +201,7 @@ def get_train_args(
)
# postprocess model_args
if training_args.bf16:
if not is_bf16_available:
raise ValueError("Current device does not support bf16 training.")
model_args.compute_dtype = torch.bfloat16
elif training_args.fp16:
model_args.compute_dtype = torch.float16
else:
model_args.compute_dtype = _infer_dtype()
if model_args.layernorm_dtype == "bf16":
if not is_bf16_available:
raise ValueError("Current device does not support bf16 type.")
model_args.layernorm_dtype = torch.bfloat16
elif model_args.layernorm_dtype == "fp16":
model_args.layernorm_dtype = torch.float16
elif model_args.layernorm_dtype == "fp32":
model_args.layernorm_dtype = torch.float32
else:
model_args.layernorm_dtype = model_args.compute_dtype
model_args.compute_dtype = torch.bfloat16 if training_args.bf16 else (torch.float16 if training_args.fp16 else None)
model_args.model_max_length = data_args.cutoff_len
# Log on each process the small summary:
@@ -278,7 +240,4 @@ def get_infer_args(
if model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1:
raise ValueError("Quantized model only accepts a single checkpoint. Merge them first.")
# auto-detect cuda capability
model_args.compute_dtype = _infer_dtype()
return model_args, data_args, finetuning_args, generating_args