mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-10-15 16:18:10 +08:00
240 lines
11 KiB
Python
240 lines
11 KiB
Python
import os
|
|
import math
|
|
import torch
|
|
from types import MethodType
|
|
from typing import TYPE_CHECKING, Optional, Tuple
|
|
|
|
from transformers import (
|
|
AutoConfig,
|
|
AutoModelForCausalLM,
|
|
AutoTokenizer,
|
|
BitsAndBytesConfig,
|
|
PretrainedConfig,
|
|
PreTrainedModel,
|
|
PreTrainedTokenizerBase
|
|
)
|
|
from transformers.models.llama import modeling_llama as LlamaModule
|
|
from transformers.utils.versions import require_version
|
|
from trl import AutoModelForCausalLMWithValueHead
|
|
|
|
try:
|
|
from transformers.integrations import is_deepspeed_zero3_enabled
|
|
except ImportError: # https://github.com/huggingface/transformers/releases/tag/v4.33.1
|
|
from transformers.deepspeed import is_deepspeed_zero3_enabled
|
|
|
|
from llmtuner.extras.logging import get_logger
|
|
from llmtuner.extras.misc import count_parameters, get_current_device, infer_optim_dtype, try_download_model_from_ms
|
|
from llmtuner.extras.packages import is_flash_attn2_available
|
|
from llmtuner.extras.patches import llama_patch as LlamaPatches
|
|
from llmtuner.hparams import FinetuningArguments
|
|
from llmtuner.model.adapter import init_adapter
|
|
from llmtuner.model.utils import load_valuehead_params, prepare_model_for_training
|
|
|
|
if TYPE_CHECKING:
|
|
from transformers import PreTrainedTokenizer
|
|
from llmtuner.hparams import ModelArguments
|
|
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
|
|
require_version("transformers>=4.31.0,<4.35.0", "To fix: pip install \"transformers>=4.31.0,<4.35.0\"")
|
|
require_version("datasets>=2.14.0", "To fix: pip install datasets>=2.14.0")
|
|
require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0")
|
|
require_version("peft>=0.6.0", "To fix: pip install peft>=0.6.0")
|
|
require_version("trl>=0.7.4", "To fix: pip install trl>=0.7.4")
|
|
|
|
|
|
def load_model_and_tokenizer(
|
|
model_args: "ModelArguments",
|
|
finetuning_args: "FinetuningArguments",
|
|
is_trainable: Optional[bool] = False,
|
|
add_valuehead: Optional[bool] = False
|
|
) -> Tuple[PreTrainedModel, "PreTrainedTokenizer"]:
|
|
r"""
|
|
Loads pretrained model and tokenizer.
|
|
|
|
Support both training and inference.
|
|
"""
|
|
|
|
try_download_model_from_ms(model_args)
|
|
|
|
config_kwargs = {
|
|
"trust_remote_code": True,
|
|
"cache_dir": model_args.cache_dir,
|
|
"revision": model_args.model_revision,
|
|
"token": model_args.hf_hub_token
|
|
}
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_args.model_name_or_path,
|
|
use_fast=model_args.use_fast_tokenizer,
|
|
split_special_tokens=model_args.split_special_tokens,
|
|
padding_side="right", # training with left-padded tensors in fp16 precision may cause overflow
|
|
**config_kwargs
|
|
)
|
|
|
|
if finetuning_args.finetuning_type != "lora" and model_args.checkpoint_dir is not None:
|
|
logger.info("Use `model_name_or_path` to specify the model trained with full/freeze method.")
|
|
model_to_load = model_args.checkpoint_dir[0]
|
|
else:
|
|
model_to_load = model_args.model_name_or_path
|
|
|
|
config = AutoConfig.from_pretrained(model_to_load, **config_kwargs)
|
|
|
|
# Fix tokenizer (for ChatGLM2 and ChatGLM3)
|
|
if getattr(config, "model_type", None) == "chatglm":
|
|
tokenizer._pad = MethodType(PreTrainedTokenizerBase._pad, tokenizer)
|
|
|
|
# Set model dtype
|
|
if model_args.compute_dtype is None: # priority: bf16 > fp16 > fp32
|
|
model_args.compute_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))
|
|
setattr(config, "torch_dtype", model_args.compute_dtype)
|
|
|
|
# Fix config (for Qwen)
|
|
if getattr(config, "model_type", None) == "qwen":
|
|
for dtype_name, dtype in [("fp16", torch.float16), ("bf16", torch.bfloat16), ("fp32", torch.float32)]:
|
|
setattr(config, dtype_name, getattr(config, "torch_dtype", None) == dtype)
|
|
|
|
# Set RoPE scaling
|
|
if model_args.rope_scaling is not None:
|
|
if not hasattr(config, "rope_scaling"):
|
|
logger.warning("Current model does not support RoPE scaling.")
|
|
else:
|
|
if is_trainable:
|
|
if model_args.rope_scaling == "dynamic":
|
|
logger.warning(
|
|
"Dynamic NTK may not work well with fine-tuning. "
|
|
"See: https://github.com/huggingface/transformers/pull/24653"
|
|
)
|
|
|
|
current_max_length = getattr(config, "max_position_embeddings", None)
|
|
if current_max_length and model_args.model_max_length > current_max_length:
|
|
scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
|
|
else:
|
|
logger.warning("Input length is smaller than max length. Consider increase input length.")
|
|
scaling_factor = 1.0
|
|
else:
|
|
scaling_factor = 2.0
|
|
|
|
setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
|
|
logger.info("Using {} scaling strategy and setting scaling factor to {}".format(
|
|
model_args.rope_scaling, scaling_factor
|
|
))
|
|
|
|
# Set FlashAttention-2
|
|
if model_args.flash_attn:
|
|
if getattr(config, "model_type", None) == "llama":
|
|
if is_flash_attn2_available():
|
|
LlamaModule.LlamaAttention = LlamaPatches.LlamaFlashAttention2
|
|
LlamaModule.LlamaModel._prepare_decoder_attention_mask = LlamaPatches._prepare_decoder_attention_mask
|
|
logger.info("Using FlashAttention-2 for faster training and inference.")
|
|
else:
|
|
logger.warning("FlashAttention-2 is not installed.")
|
|
elif getattr(config, "model_type", None) in ["qwen", "Yi"]:
|
|
logger.info("Current model automatically enables FlashAttention if installed.")
|
|
else:
|
|
logger.warning("Current model does not support FlashAttention.")
|
|
elif is_trainable and model_args.shift_attn and getattr(config, "model_type", None) == "llama":
|
|
LlamaModule.LlamaAttention = LlamaPatches.LlamaShiftShortAttention
|
|
logger.warning("Using `--flash_attn` for faster training in large context length.")
|
|
|
|
# Set shift short attention (S^2-Attn)
|
|
if is_trainable and model_args.shift_attn:
|
|
if getattr(config, "model_type", None) == "llama":
|
|
setattr(config, "group_size_ratio", 0.25)
|
|
logger.info("Using shift short attention with group_size_ratio=1/4.")
|
|
else:
|
|
logger.warning("Current model does not support shift short attention.")
|
|
|
|
# Quantization configurations (using gptq or awq)
|
|
if getattr(config, "quantization_config", None):
|
|
if model_args.quantization_bit is not None: # remove bnb quantization
|
|
model_args.quantization_bit = None
|
|
config_kwargs["device_map"] = {"": get_current_device()}
|
|
quantization_config = getattr(config, "quantization_config", None)
|
|
logger.info("Loading {}-bit quantized model.".format(quantization_config.get("bits", -1)))
|
|
|
|
# Quantization configurations (using bitsandbytes library)
|
|
if model_args.quantization_bit is not None:
|
|
if is_deepspeed_zero3_enabled():
|
|
raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")
|
|
|
|
if model_args.quantization_bit == 8:
|
|
require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
|
|
config_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
|
|
|
|
if model_args.quantization_bit == 4:
|
|
require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
|
|
config_kwargs["quantization_config"] = BitsAndBytesConfig(
|
|
load_in_4bit=True,
|
|
bnb_4bit_compute_dtype=model_args.compute_dtype,
|
|
bnb_4bit_use_double_quant=model_args.double_quantization,
|
|
bnb_4bit_quant_type=model_args.quantization_type
|
|
)
|
|
|
|
config_kwargs["device_map"] = {"": get_current_device()}
|
|
logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
|
|
|
|
# Load pre-trained models (without valuehead)
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
model_to_load,
|
|
config=config,
|
|
torch_dtype=model_args.compute_dtype,
|
|
low_cpu_mem_usage=(not is_deepspeed_zero3_enabled()),
|
|
**config_kwargs
|
|
)
|
|
|
|
# Disable custom generate method (for Qwen and Baichuan2)
|
|
if isinstance(model, PreTrainedModel) and "GenerationMixin" not in str(model.generate.__func__):
|
|
model.generate = MethodType(PreTrainedModel.generate, model)
|
|
|
|
# Fix LM head (for ChatGLM2 and ChatGLM3)
|
|
if getattr(config, "model_type", None) == "chatglm":
|
|
setattr(model, "lm_head", model.transformer.output_layer)
|
|
setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])
|
|
|
|
# Register auto class to save the custom code files
|
|
if isinstance(config, PretrainedConfig) and "AutoConfig" in getattr(config, "auto_map", {}):
|
|
config.__class__.register_for_auto_class()
|
|
if isinstance(model, PreTrainedModel) and "AutoModelForCausalLM" in getattr(config, "auto_map", {}):
|
|
model.__class__.register_for_auto_class()
|
|
if isinstance(tokenizer, PreTrainedTokenizerBase) and "AutoTokenizer" in tokenizer.init_kwargs.get("auto_map", {}):
|
|
tokenizer.__class__.register_for_auto_class()
|
|
|
|
# Initialize adapters
|
|
model = prepare_model_for_training(model=model, finetuning_args=finetuning_args) if is_trainable else model
|
|
model = init_adapter(model, model_args, finetuning_args, is_trainable)
|
|
|
|
# Prepare model with valuehead for RLHF
|
|
if add_valuehead:
|
|
model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(model)
|
|
ignore_modules = [name for name, _ in model.named_parameters() if "pretrained_model" in name]
|
|
setattr(model, "_keys_to_ignore_on_save", ignore_modules)
|
|
setattr(model, "tie_weights", MethodType(lambda _: None, model)) # use empty method
|
|
vhead_path = (
|
|
model_args.checkpoint_dir[-1] if model_args.checkpoint_dir is not None else model_args.model_name_or_path
|
|
)
|
|
vhead_params = load_valuehead_params(vhead_path, model_args)
|
|
if vhead_params is not None:
|
|
model.load_state_dict(vhead_params, strict=False)
|
|
logger.info("Loaded valuehead from checkpoint: {}".format(vhead_path))
|
|
|
|
# Prepare model for inference
|
|
if not is_trainable:
|
|
model.requires_grad_(False) # fix all model params
|
|
model = model.to(model_args.compute_dtype) if model_args.quantization_bit is None else model
|
|
model.eval()
|
|
else:
|
|
model.train()
|
|
|
|
trainable_params, all_param = count_parameters(model)
|
|
logger.info("trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format(
|
|
trainable_params, all_param, 100 * trainable_params / all_param
|
|
))
|
|
|
|
if not is_trainable:
|
|
logger.info("This IS expected that the trainable params is 0 if you are using model for inference only.")
|
|
|
|
return model, tokenizer
|