rename package
Former-commit-id: a07ff0c083558cfe6f474d13027642d3052fee08
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hiyouga
225
src/llamafactory/model/adapter.py
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225
src/llamafactory/model/adapter.py
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import re
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from typing import TYPE_CHECKING
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import torch
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from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model
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from transformers.integrations import deepspeed_config, is_deepspeed_zero3_enabled
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from transformers.modeling_utils import is_fsdp_enabled
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from ..extras.logging import get_logger
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from .utils.misc import find_all_linear_modules, find_expanded_modules
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from .utils.quantization import QuantizationMethod
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from .utils.unsloth import get_unsloth_peft_model, load_unsloth_peft_model
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if TYPE_CHECKING:
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from transformers import PretrainedConfig, PreTrainedModel
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from ..hparams import FinetuningArguments, ModelArguments
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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_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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r"""
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Initializes the adapters.
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Support full-parameter, freeze and LoRA training.
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Note that the trainable parameters must be cast to float32.
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"""
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if (not is_trainable) and model_args.adapter_name_or_path is None:
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logger.info("Adapter is not found at evaluation, load the base model.")
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return model
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if finetuning_args.finetuning_type != "lora" and getattr(model, "quantization_method", None):
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raise ValueError("You can only use lora for quantized models.")
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if deepspeed_config() is not None or is_fsdp_enabled() or finetuning_args.pure_bf16 or finetuning_args.use_badam:
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logger.info("DeepSpeed/FSDP/PureBF16/BAdam detected, remaining trainable params in half precision.")
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cast_trainable_params_to_fp32 = False
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else:
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logger.info("Upcasting trainable params to float32.")
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cast_trainable_params_to_fp32 = True
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if finetuning_args.finetuning_type == "full" and is_trainable:
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logger.info("Fine-tuning method: Full")
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if cast_trainable_params_to_fp32:
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model = model.float()
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if model_args.visual_inputs and hasattr(model, "vision_tower"): # freeze vision model
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model.vision_tower.requires_grad_(False)
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if finetuning_args.finetuning_type == "freeze" and is_trainable:
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logger.info("Fine-tuning method: Freeze")
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num_layers = (
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getattr(model.config, "num_hidden_layers", None)
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or getattr(model.config, "num_layers", None)
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or getattr(model.config, "n_layer", None)
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)
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if not num_layers:
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raise ValueError("Current model does not support freeze tuning.")
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if finetuning_args.use_llama_pro:
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if num_layers % finetuning_args.freeze_trainable_layers != 0:
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raise ValueError(
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"`num_layers` {} should be divisible by `num_layer_trainable` {}.".format(
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num_layers, finetuning_args.freeze_trainable_layers
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)
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)
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stride = num_layers // finetuning_args.freeze_trainable_layers
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trainable_layer_ids = range(stride - 1, num_layers + stride - 1, stride)
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elif finetuning_args.freeze_trainable_layers > 0: # fine-tuning the last n layers if num_layer_trainable > 0
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trainable_layer_ids = range(max(0, num_layers - finetuning_args.freeze_trainable_layers), num_layers)
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else: # fine-tuning the first n layers if num_layer_trainable < 0
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trainable_layer_ids = range(min(-finetuning_args.freeze_trainable_layers, num_layers))
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hidden_modules = set()
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non_hidden_modules = set()
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for name, _ in model.named_parameters():
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if ".0." in name:
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hidden_modules.add(name.split(".0.")[-1].split(".")[0])
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elif ".1." in name: # MoD starts from layer 1
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hidden_modules.add(name.split(".1.")[-1].split(".")[0])
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if re.search(r"\.\d+\.", name) is None:
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non_hidden_modules.add(name.split(".")[-2])
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trainable_layers = []
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for module_name in finetuning_args.freeze_trainable_modules:
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if module_name != "all" and module_name not in hidden_modules:
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raise ValueError(
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"Module {} is not found, please choose from {}".format(module_name, ", ".join(hidden_modules))
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)
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for idx in trainable_layer_ids:
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trainable_layers.append(".{:d}.{}".format(idx, module_name if module_name != "all" else ""))
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if finetuning_args.freeze_extra_modules:
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for module_name in finetuning_args.freeze_extra_modules:
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if module_name not in non_hidden_modules:
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raise ValueError(
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"Module {} is not found, please choose from {}".format(
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module_name, ", ".join(non_hidden_modules)
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)
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)
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trainable_layers.append(module_name)
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for name, param in model.named_parameters():
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if any(trainable_layer in name for trainable_layer in trainable_layers):
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if cast_trainable_params_to_fp32:
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param.data = param.data.to(torch.float32)
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else:
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param.requires_grad_(False)
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logger.info("Set trainable layers: {}".format(",".join(map(str, trainable_layer_ids))))
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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 model_args.use_unsloth:
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assert len(model_args.adapter_name_or_path) == 1, "Unsloth model only accepts a single adapter."
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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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if model_args.use_unsloth:
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model = load_unsloth_peft_model(config, model_args, is_trainable=is_trainable)
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else:
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model = PeftModel.from_pretrained(
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model,
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adapter_to_resume,
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is_trainable=is_trainable,
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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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if model_args.resize_vocab and finetuning_args.additional_target is None:
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input_embeddings = model.get_input_embeddings()
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output_embeddings = model.get_output_embeddings()
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module_names = set()
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for name, module in model.named_modules():
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if module in [input_embeddings, output_embeddings]:
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module_names.add(name.split(".")[-1])
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finetuning_args.additional_target = module_names
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logger.warning("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))
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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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model = get_unsloth_peft_model(model, model_args, peft_kwargs)
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else:
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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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model = get_peft_model(model, lora_config)
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if cast_trainable_params_to_fp32:
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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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