mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-03 12:12:50 +08:00
129 lines
5.1 KiB
Python
129 lines
5.1 KiB
Python
from typing import TYPE_CHECKING, Optional, Tuple
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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
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from transformers.integrations import is_deepspeed_zero3_enabled
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from transformers.utils.versions import require_version
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from trl import AutoModelForCausalLMWithValueHead
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from llmtuner.extras.logging import get_logger
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from llmtuner.extras.misc import count_parameters, get_current_device, try_download_model_from_ms
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from llmtuner.model.adapter import init_adapter
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from llmtuner.model.patcher import patch_config, patch_tokenizer, patch_model, patch_valuehead_model
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from llmtuner.model.utils import load_valuehead_params, register_autoclass
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if TYPE_CHECKING:
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from transformers import PreTrainedModel, PreTrainedTokenizer
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from llmtuner.hparams import ModelArguments, FinetuningArguments
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logger = get_logger(__name__)
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require_version("transformers>=4.36.2", "To fix: pip install transformers>=4.36.2")
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require_version("datasets>=2.14.3", "To fix: pip install datasets>=2.14.3")
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require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0")
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require_version("peft>=0.7.0", "To fix: pip install peft>=0.7.0")
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require_version("trl>=0.7.6", "To fix: pip install trl>=0.7.6")
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def load_model_and_tokenizer(
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model_args: "ModelArguments",
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finetuning_args: "FinetuningArguments",
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is_trainable: Optional[bool] = False,
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add_valuehead: Optional[bool] = False
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) -> Tuple["PreTrainedModel", "PreTrainedTokenizer"]:
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r"""
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Loads pretrained model and tokenizer.
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Support both training and inference.
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"""
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try_download_model_from_ms(model_args)
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config_kwargs = {
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"trust_remote_code": True,
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"cache_dir": model_args.cache_dir,
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"revision": model_args.model_revision,
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"token": model_args.hf_hub_token
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}
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.model_name_or_path,
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use_fast=model_args.use_fast_tokenizer,
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split_special_tokens=model_args.split_special_tokens,
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padding_side="right",
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**config_kwargs
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)
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patch_tokenizer(tokenizer)
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config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
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patch_config(config, tokenizer, model_args, config_kwargs, is_trainable)
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model = None
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if is_trainable and model_args.use_unsloth:
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require_version("unsloth==2023.12", "Follow the instructions at: https://github.com/unslothai/unsloth")
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from unsloth import FastLlamaModel, FastMistralModel # type: ignore
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unsloth_kwargs = {
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"model_name": model_args.model_name_or_path,
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"max_seq_length": model_args.model_max_length,
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"dtype": model_args.compute_dtype,
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"load_in_4bit": model_args.quantization_bit == 4,
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"token": model_args.hf_hub_token,
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"device_map": get_current_device(),
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"rope_scaling": getattr(config, "rope_scaling", None)
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}
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if getattr(config, "model_type", None) == "llama":
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model, _ = FastLlamaModel.from_pretrained(**unsloth_kwargs)
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elif getattr(config, "model_type", None) == "mistral":
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model, _ = FastMistralModel.from_pretrained(**unsloth_kwargs)
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else:
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logger.warning("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
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model_args.use_unsloth = False
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if model_args.adapter_name_or_path:
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model_args.adapter_name_or_path = None
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logger.warning("Unsloth does not support loading adapters.")
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if model is None:
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model = AutoModelForCausalLM.from_pretrained(
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model_args.model_name_or_path,
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config=config,
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low_cpu_mem_usage=(not is_deepspeed_zero3_enabled()),
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**config_kwargs
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)
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model = model.to(model_args.compute_dtype) if not getattr(model, "quantization_method", None) else model
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patch_model(model, tokenizer, model_args, is_trainable)
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register_autoclass(config, model, tokenizer)
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model = init_adapter(model, model_args, finetuning_args, is_trainable)
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if add_valuehead:
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model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(model)
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patch_valuehead_model(model)
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if model_args.adapter_name_or_path is not None:
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vhead_path = model_args.adapter_name_or_path[-1]
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else:
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vhead_path = model_args.model_name_or_path
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vhead_params = load_valuehead_params(vhead_path, model_args)
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if vhead_params is not None:
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model.load_state_dict(vhead_params, strict=False)
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logger.info("Loaded valuehead from checkpoint: {}".format(vhead_path))
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if not is_trainable:
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model.requires_grad_(False)
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model.eval()
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else:
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model.train()
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trainable_params, all_param = count_parameters(model)
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logger.info("trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format(
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trainable_params, all_param, 100 * trainable_params / all_param
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))
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if not is_trainable:
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logger.info("This IS expected that the trainable params is 0 if you are using model for inference only.")
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return model, tokenizer
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