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https://github.com/hiyouga/LLaMA-Factory.git
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merge model part to the text stream
Former-commit-id: 838eb87a961894b072bf79a60bbda63516670d6f
This commit is contained in:
BUAADreamer
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cb1c66a810
commit
4e032ff95e
@ -169,6 +169,10 @@ class ModelArguments:
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default=False,
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metadata={"help": "For debugging purposes, print the status of the parameters in the model."},
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)
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use_mllm: bool = field(
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default=False,
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metadata={"help": "Whether use Multimodal LLM."},
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)
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def __post_init__(self):
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self.compute_dtype = None
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@ -1,10 +1,9 @@
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from .loader import load_config, load_model, load_tokenizer, load_mm_model
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from .loader import load_config, load_model, load_tokenizer, load_processor
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from .utils.misc import find_all_linear_modules, load_valuehead_params
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__all__ = [
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"load_config",
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"load_model",
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"load_mm_model",
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"load_tokenizer",
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"load_processor",
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"load_valuehead_params",
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@ -1,4 +1,4 @@
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from typing import TYPE_CHECKING
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from typing import TYPE_CHECKING, Union
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import torch
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from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model
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@ -21,11 +21,11 @@ logger = get_logger(__name__)
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def init_adapter(
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config: "PretrainedConfig",
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model: "PreTrainedModel",
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model: Union["PreTrainedModel","AutoModelForVision2Seq"],
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model_args: "ModelArguments",
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finetuning_args: "FinetuningArguments",
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is_trainable: bool,
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) -> "PreTrainedModel":
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) -> Union["PreTrainedModel","AutoModelForVision2Seq"]:
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r"""
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Initializes the adapters.
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@ -195,103 +195,4 @@ def init_adapter(
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if model_args.adapter_name_or_path is not None:
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logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
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return model
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def init_mm_adapter(
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model: "AutoModelForVision2Seq", model_args: "ModelArguments",
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finetuning_args: "FinetuningArguments",
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is_trainable: bool,
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use_clm=True,
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) -> "AutoModelForVision2Seq":
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if finetuning_args.finetuning_type == "lora":
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logger.info("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
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adapter_to_resume = None
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if model_args.adapter_name_or_path is not None:
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is_mergeable = True
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if getattr(model, "quantization_method", None): # merge lora in quantized model is unstable
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assert len(model_args.adapter_name_or_path) == 1, "Quantized model only accepts a single adapter."
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is_mergeable = False
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if is_deepspeed_zero3_enabled():
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assert len(model_args.adapter_name_or_path) == 1, "Cannot use multiple adapters in DeepSpeed ZeRO-3."
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is_mergeable = False
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if (is_trainable and not finetuning_args.create_new_adapter) or (not is_mergeable):
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adapter_to_merge = model_args.adapter_name_or_path[:-1]
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adapter_to_resume = model_args.adapter_name_or_path[-1]
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else:
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adapter_to_merge = model_args.adapter_name_or_path
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for adapter in adapter_to_merge:
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model: "LoraModel" = PeftModel.from_pretrained(
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model, adapter, offload_folder=model_args.offload_folder
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)
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model = model.merge_and_unload()
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if len(adapter_to_merge) > 0:
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logger.info("Merged {} adapter(s).".format(len(adapter_to_merge)))
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if adapter_to_resume is not None: # resume lora training
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model = PeftModel.from_pretrained(
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model, adapter_to_resume, is_trainable=is_trainable, offload_folder=model_args.offload_folder
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)
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if is_trainable and adapter_to_resume is None: # create new lora weights while training
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if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
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target_modules = find_all_linear_modules(model)
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else:
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target_modules = finetuning_args.lora_target
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if finetuning_args.use_llama_pro:
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target_modules = find_expanded_modules(model, target_modules, finetuning_args.num_layer_trainable)
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if (
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finetuning_args.use_dora
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and getattr(model, "quantization_method", None) is not None
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and getattr(model, "quantization_method", None) != QuantizationMethod.BITS_AND_BYTES
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):
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raise ValueError("DoRA is not compatible with PTQ-quantized models.")
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peft_kwargs = {
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"r": finetuning_args.lora_rank,
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"target_modules": target_modules,
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"lora_alpha": finetuning_args.lora_alpha,
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"lora_dropout": finetuning_args.lora_dropout,
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"use_rslora": finetuning_args.use_rslora,
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"modules_to_save": finetuning_args.additional_target,
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}
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if model_args.use_unsloth:
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from unsloth import FastLanguageModel # type: ignore
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unsloth_peft_kwargs = {
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"model": model,
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"max_seq_length": model_args.model_max_length,
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"use_gradient_checkpointing": "unsloth",
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}
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model = FastLanguageModel.get_peft_model(**peft_kwargs, **unsloth_peft_kwargs)
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else:
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if use_clm:
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lora_config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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inference_mode=False,
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use_dora=finetuning_args.use_dora,
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**peft_kwargs,
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)
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else:
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lora_config = LoraConfig(
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inference_mode=False,
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use_dora=finetuning_args.use_dora,
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**peft_kwargs,
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)
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model = get_peft_model(model, lora_config)
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if (not finetuning_args.pure_bf16) and (not finetuning_args.use_badam):
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for param in filter(lambda p: p.requires_grad, model.parameters()):
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param.data = param.data.to(torch.float32)
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if model_args.adapter_name_or_path is not None:
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logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
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return model
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return model
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@ -1,11 +1,11 @@
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from typing import TYPE_CHECKING, Any, Dict
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from typing import TYPE_CHECKING, Any, Dict, Union
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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, AutoProcessor, AutoModelForVision2Seq
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from trl import AutoModelForCausalLMWithValueHead
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from ..extras.logging import get_logger
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from ..extras.misc import count_parameters, try_download_model_from_ms
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from .adapter import init_adapter, init_mm_adapter
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from .adapter import init_adapter
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from .patcher import patch_config, patch_model, patch_tokenizer, patch_valuehead_model
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from .utils.misc import load_valuehead_params, register_autoclass
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from .utils.mod import convert_pretrained_model_to_mod, load_mod_pretrained_model
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@ -106,12 +106,12 @@ def load_config(model_args: "ModelArguments") -> "PretrainedConfig":
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def load_model(
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tokenizer: "PreTrainedTokenizer",
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model_args: "ModelArguments",
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finetuning_args: "FinetuningArguments",
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is_trainable: bool = False,
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add_valuehead: bool = False,
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) -> "PreTrainedModel":
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tokenizer: "PreTrainedTokenizer",
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model_args: "ModelArguments",
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finetuning_args: "FinetuningArguments",
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is_trainable: bool = False,
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add_valuehead: bool = False,
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) -> Union["PreTrainedModel", "AutoModelForVision2Seq"]:
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r"""
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Loads pretrained model.
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"""
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@ -134,7 +134,10 @@ def load_model(
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if model_args.mixture_of_depths == "load":
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model = load_mod_pretrained_model(**init_kwargs)
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else:
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model = AutoModelForCausalLM.from_pretrained(**init_kwargs)
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if model_args.use_mllm:
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model = AutoModelForVision2Seq.from_pretrained(**init_kwargs)
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else:
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model = AutoModelForCausalLM.from_pretrained(**init_kwargs)
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if model_args.mixture_of_depths == "convert":
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model = convert_pretrained_model_to_mod(model, config, model_args)
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@ -182,56 +185,4 @@ def load_model(
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)
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)
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return model
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def load_mm_model(
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processor: "AutoProcessor",
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model_args: "ModelArguments",
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finetuning_args: "FinetuningArguments",
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is_trainable: bool = False,
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add_valuehead: bool = False,
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use_clm=True,
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) -> "AutoModelForVision2Seq":
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r"""
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Loads pretrained model. Must after load_tokenizer.
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"""
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tokenizer = processor.tokenizer
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init_kwargs = _get_init_kwargs(model_args)
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config = AutoConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs)
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patch_config(config, tokenizer, model_args, init_kwargs, is_trainable)
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model = None
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if model is None:
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init_kwargs["config"] = config
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init_kwargs["pretrained_model_name_or_path"] = model_args.model_name_or_path
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model: "AutoModelForVision2Seq" = AutoModelForVision2Seq.from_pretrained(**init_kwargs)
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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_mm_adapter(model, model_args, finetuning_args, is_trainable, use_clm)
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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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if is_trainable:
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param_stats = "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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else:
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param_stats = "all params: {:d}".format(all_param)
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logger.info(param_stats)
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if model_args.print_param_status:
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for name, param in model.named_parameters():
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print(
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"name: {}, dtype: {}, device: {}, trainable: {}".format(
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name, param.dtype, param.device, param.requires_grad
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)
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)
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return model
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return model
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@ -4,7 +4,7 @@ from typing import TYPE_CHECKING, List, Optional
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from ...data import split_dataset, get_mm_dataset
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from ...extras.misc import get_logits_processor
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from ...extras.ploting import plot_loss
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from ...model import load_tokenizer, load_processor, load_mm_model
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from ...model import load_tokenizer, load_processor, load_model
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from ..utils import create_modelcard_and_push
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from .metric import ComputeMetrics
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from .trainer import CustomSeq2SeqTrainer
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@ -29,10 +29,7 @@ def run_sft_mm(
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CHAT_TEMPLATE = """{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. {% for message in messages %}{% if message['role'] == 'user' %}USER: {% else %}ASSISTANT: {% endif %}{% for item in message['content'] %}{% if item['type'] == 'text' %}{{ item['text'] }}{% elif item['type'] == 'image' %}<image>{% endif %}{% endfor %}{% if message['role'] == 'user' %} {% else %}{{eos_token}}{% endif %}{% endfor %}{% if add_generation_prompt %}ASSISTANT: {% endif %}"""
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tokenizer.chat_template = CHAT_TEMPLATE
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processor.tokenizer = tokenizer
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use_clm = True
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if "blip" in model_args.model_name_or_path:
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use_clm = False
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model = load_mm_model(processor, model_args, finetuning_args, training_args.do_train, use_clm=use_clm)
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model = load_model(processor.tokenizer, model_args, finetuning_args, training_args.do_train)
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dataset = get_mm_dataset(processor, model_args, data_args, training_args, stage="sft")
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if getattr(model, "is_quantized", False) and not training_args.do_train:
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setattr(model, "_hf_peft_config_loaded", True) # hack here: make model compatible with prediction
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