import os import sys from typing import List import fire import torch import transformers from datasets import load_dataset from typing import List import json def jload(data_path:str)-> List: with open(data_path,'r') as f: data = json.load(f) return data """ Unused imports: import torch.nn as nn import bitsandbytes as bnb """ from peft import ( LoraConfig, get_peft_model, PeftModel, get_peft_model_state_dict, prepare_model_for_int8_training, set_peft_model_state_dict, ) from transformers import LlamaForCausalLM, LlamaTokenizer,EarlyStoppingCallback import json import os.path as osp from typing import Union # os.environ["WANDB_DISABLED"] = "true" class Prompter(object): __slots__ = ("template", "_verbose") def __init__(self, template_name: str = "", verbose: bool = False): self._verbose = verbose if not template_name: # Enforce the default here, so the constructor can be called with '' and will not break. template_name = "alpaca" file_name = osp.join("data/templates", f"{template_name}.json") if not osp.exists(file_name): raise ValueError(f"{file_name} 文件不存在") with open(file_name) as fp: self.template = json.load(fp) if self._verbose: print( f"Using prompt template {file_name}: {self.template['description']}" ) def generate_prompt( self, instruction: str, input: Union[None, str] = None, label: Union[None, str] = None, ) -> str: # returns the full prompt from instruction and optional input # if a label (=response, =output) is provided, it's also appended. if input: res = self.template["prompt_input"].format( instruction=instruction, input=input ) else: res = self.template["prompt_no_input"].format( instruction=instruction ) if label: res = f"{res}{label}" if self._verbose: print(res) return res def get_response(self, output: str) -> str: return output.split(self.template["response_split"])[1].strip() def train( # model/data params base_model: str = "", # the only required argument data_path: str = "data/alapa", output_dir: str = "./lora-alpaca", # training hyperparams batch_size: int = 12, micro_batch_size: int = 4, num_epochs: int = 3, learning_rate: float = 3e-4, cutoff_len: int = 512, val_set_size: int = 200, # lora hyperparams lora_r: int = 64, lora_alpha: int = 128, lora_dropout: float = 0.05, lora_target_modules: List[str] = [ "q_proj", "v_proj", ], cache_dir=None, peft_path='', report_to='none', # llm hyperparams train_on_inputs: bool = False, # if False, masks out inputs in loss add_eos_token: bool = False, group_by_length: bool = False, # faster, but produces an odd training loss curve # wandb params resume_from_checkpoint: str = None, # either training checkpoint or final adapter prompt_template_name: str = "alpaca", # The prompt template to use, will default to alpaca. ): if int(os.environ.get("LOCAL_RANK", 0)) == 0: print( f"Training Alpaca-LoRA model with params:\n" f"base_model: {base_model}\n" f"data_path: {data_path}\n" f"output_dir: {output_dir}\n" f"batch_size: {batch_size}\n" f"micro_batch_size: {micro_batch_size}\n" f"num_epochs: {num_epochs}\n" f"learning_rate: {learning_rate}\n" f"cutoff_len: {cutoff_len}\n" f"val_set_size: {val_set_size}\n" f"lora_r: {lora_r}\n" f"cache_dir: {cache_dir}\n" f"lora_alpha: {lora_alpha}\n" f"lora_dropout: {lora_dropout}\n" f"lora_target_modules: {lora_target_modules}\n" f"train_on_inputs: {train_on_inputs}\n" f"group_by_length: {group_by_length}\n" f"resume_from_checkpoint: {resume_from_checkpoint or False}\n" f"prompt template: {prompt_template_name}\n" f"peft_path: {peft_path}\n" ) assert ( base_model ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'" gradient_accumulation_steps = batch_size // micro_batch_size prompter = Prompter(prompt_template_name) device_map = "auto" world_size = int(os.environ.get("WORLD_SIZE", 1)) ddp = world_size != 1 if ddp: device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} gradient_accumulation_steps = gradient_accumulation_steps // world_size model = LlamaForCausalLM.from_pretrained( base_model, load_in_8bit=False, torch_dtype=torch.float16, device_map=device_map, cache_dir=cache_dir, ) tokenizer = LlamaTokenizer.from_pretrained(base_model) if tokenizer.pad_token_id is None: tokenizer.pad_token_id = ( len(tokenizer) + 1 # unk. we want this to be different from the eos token ) tokenizer.padding_side = "left" # Allow batched inference if model.get_input_embeddings().weight.size(0) != len(tokenizer): print("Resize model embeddings to fit tokenizer") model.resize_token_embeddings(len(tokenizer)) def tokenize(prompt, add_eos_token=True): # there's probably a way to do this with the tokenizer settings # but again, gotta move fast result = tokenizer( prompt, truncation=True, max_length=cutoff_len, padding=False, return_tensors=None, ) if ( result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < cutoff_len and add_eos_token ): result["input_ids"].append(tokenizer.eos_token_id) result["attention_mask"].append(1) result["labels"] = result["input_ids"].copy() return result def generate_and_tokenize_prompt(data_point): full_prompt = prompter.generate_prompt( data_point["instruction"], data_point["input"], data_point["output"], ) tokenized_full_prompt = tokenize(full_prompt) if not train_on_inputs: user_prompt = prompter.generate_prompt( data_point["instruction"], data_point["input"] ) tokenized_user_prompt = tokenize( user_prompt, add_eos_token=add_eos_token ) user_prompt_len = len(tokenized_user_prompt["input_ids"]) if add_eos_token: user_prompt_len -= 1 tokenized_full_prompt["labels"] = [ -100 ] * user_prompt_len + tokenized_full_prompt["labels"][ user_prompt_len: ] # could be sped up, probably return tokenized_full_prompt # model = prepare_model_for_int8_training(model) config = LoraConfig( r=lora_r, lora_alpha=lora_alpha, target_modules=lora_target_modules, lora_dropout=lora_dropout, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) if data_path.endswith(".json") or data_path.endswith(".jsonl"): data = load_dataset("json", data_files=data_path) # data = jload(data_path) else: data = load_dataset(data_path) if resume_from_checkpoint: # Check the available weights and load them checkpoint_name = os.path.join( resume_from_checkpoint, "pytorch_model.bin" ) # Full checkpoint if not os.path.exists(checkpoint_name): checkpoint_name = os.path.join( resume_from_checkpoint, "adapter_model.bin" ) # only LoRA model - LoRA config above has to fit resume_from_checkpoint = ( False # So the trainer won't try loading its state ) # The two files above have a different name depending on how they were saved, but are actually the same. if os.path.exists(checkpoint_name): print(f"Restarting from {checkpoint_name}") adapters_weights = torch.load(checkpoint_name) set_peft_model_state_dict(model, adapters_weights) else: print(f"Checkpoint {checkpoint_name} not found") if peft_path: adapters_weights = torch.load(f"{peft_path}/adapter_model.bin") set_peft_model_state_dict(model, adapters_weights) model.print_trainable_parameters() # Be more transparent about the % of trainable params. if val_set_size > 0: train_val = data["train"].train_test_split( test_size=val_set_size, shuffle=True, seed=42 ) train_data = ( train_val["train"].shuffle().map(generate_and_tokenize_prompt) ) val_data = ( train_val["test"].shuffle().map(generate_and_tokenize_prompt) ) else: train_data = data["train"].shuffle().map(generate_and_tokenize_prompt) val_data = None if not ddp and torch.cuda.device_count() > 1: # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available model.is_parallelizable = True model.model_parallel = True trainer = transformers.Trainer( model=model, train_dataset=train_data, eval_dataset=val_data, args=transformers.TrainingArguments( per_device_train_batch_size=micro_batch_size, gradient_accumulation_steps=gradient_accumulation_steps, warmup_steps=10, num_train_epochs=num_epochs, learning_rate=learning_rate, fp16=True, sharded_ddp=True, logging_steps=200, optim="adamw_torch", evaluation_strategy="steps" if val_set_size > 0 else "no", save_strategy="steps", eval_steps=200 if val_set_size > 0 else None, save_steps=200, logging_strategy='steps', output_dir=output_dir, save_total_limit=5, load_best_model_at_end=True if val_set_size > 0 else False, ddp_find_unused_parameters=False if ddp else None, group_by_length=group_by_length, report_to=report_to, ), data_collator=transformers.DataCollatorForSeq2Seq( tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True ), ) model.config.use_cache = False old_state_dict = model.state_dict model.state_dict = ( lambda self, *_, **__: get_peft_model_state_dict( self, old_state_dict() ) ).__get__(model, type(model)) if torch.__version__ >= "2" and sys.platform != "win32": model = torch.compile(model) trainer.train(resume_from_checkpoint=None) model.save_pretrained(output_dir) trainer.save_model(output_dir) print( "\n If there's a warning about missing keys above, please disregard :)" ) if __name__ == "__main__": fire.Fire(train)