# -*- coding: utf-8 -*- import os import jieba import dataclasses as dc import functools from collections.abc import Callable, Mapping, Sequence from pathlib import Path from typing import Annotated, Any, Union import numpy as np import ruamel.yaml as yaml import torch import typer from datasets import Dataset, Split from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction from peft import PeftConfig, get_peft_config, get_peft_model from rouge_chinese import Rouge from torch import nn from transformers import ( AutoModelForCausalLM, AutoTokenizer, EvalPrediction, GenerationConfig, PreTrainedTokenizer, Seq2SeqTrainingArguments, ) from transformers import DataCollatorForSeq2Seq as _DataCollatorForSeq2Seq from transformers import Seq2SeqTrainer as _Seq2SeqTrainer from datasets import load_dataset, DatasetDict, NamedSplit from typing import Optional # For Ascend NPU, please add this # import torch_npu # from torch_npu.contrib import transfer_to_npu app = typer.Typer(pretty_exceptions_show_locals=False) class DataCollatorForSeq2Seq(_DataCollatorForSeq2Seq): def __call__(self, features, return_tensors=None): output_ids = ( [feature["output_ids"] for feature in features] if "output_ids" in features[0].keys() else None ) if output_ids is not None: max_output_length = max(len(out) for out in output_ids) if self.pad_to_multiple_of is not None: max_output_length = ( (max_output_length + self.pad_to_multiple_of - 1) // self.pad_to_multiple_of * self.pad_to_multiple_of ) for feature in features: remainder = [self.tokenizer.pad_token_id] * ( max_output_length - len(feature["output_ids"]) ) if isinstance(feature["output_ids"], list): feature["output_ids"] = feature["output_ids"] + remainder else: feature["output_ids"] = np.concatenate( [feature["output_ids"], remainder] ).astype(np.int64) return super().__call__(features, return_tensors) class Seq2SeqTrainer(_Seq2SeqTrainer): def prediction_step( self, model: nn.Module, inputs: dict[str, Any], prediction_loss_only: bool, ignore_keys=None, **gen_kwargs, ) -> tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: with torch.no_grad(): # Ensure no gradient computation if self.args.predict_with_generate: output_ids = inputs.pop("output_ids") input_ids = inputs["input_ids"] loss, generated_tokens, labels = super().prediction_step( model, inputs, prediction_loss_only, ignore_keys, **gen_kwargs ) generated_tokens = generated_tokens[:, input_ids.size()[1] :] labels = output_ids del inputs, input_ids, output_ids torch.cuda.empty_cache() return loss, generated_tokens, labels @dc.dataclass class DataConfig(object): train_file: Optional[str] = None val_file: Optional[str] = None test_file: Optional[str] = None num_proc: Optional[int] = None @property def data_format(self) -> str: return Path(self.train_file).suffix @property def data_files(self) -> dict[NamedSplit, str]: return { split: data_file for split, data_file in zip( [Split.TRAIN, Split.VALIDATION, Split.TEST], [self.train_file, self.val_file, self.test_file], ) if data_file is not None } @dc.dataclass class FinetuningConfig(object): data_config: DataConfig max_input_length: int max_output_length: int combine: bool freezeV: bool training_args: Seq2SeqTrainingArguments = dc.field( default_factory=lambda: Seq2SeqTrainingArguments(output_dir="./output") ) peft_config: Optional[PeftConfig] = None def __post_init__(self): if not self.training_args.do_eval or self.data_config.val_file is None: self.training_args.do_eval = False self.training_args.evaluation_strategy = "no" self.data_config.val_file = None else: self.training_args.per_device_eval_batch_size = ( self.training_args.per_device_eval_batch_size or self.training_args.per_device_train_batch_size ) @classmethod def from_dict(cls, **kwargs) -> "FinetuningConfig": training_args = kwargs.get("training_args", None) if training_args is not None and not isinstance( training_args, Seq2SeqTrainingArguments ): gen_config = training_args.get("generation_config") if not isinstance(gen_config, GenerationConfig): training_args["generation_config"] = GenerationConfig(**gen_config) kwargs["training_args"] = Seq2SeqTrainingArguments(**training_args) data_config = kwargs.get("data_config") if not isinstance(data_config, DataConfig): kwargs["data_config"] = DataConfig(**data_config) peft_config = kwargs.get("peft_config", None) if peft_config is not None and not isinstance(peft_config, PeftConfig): kwargs["peft_config"] = get_peft_config(config_dict=peft_config) return cls(**kwargs) @classmethod def from_file(cls, path: Union[str, Path]) -> "FinetuningConfig": path = Path(path) parser = yaml.YAML(typ="safe", pure=True) parser.indent(mapping=2, offset=2, sequence=4) parser.default_flow_style = False kwargs = parser.load(path) return cls.from_dict(**kwargs) def _load_datasets( data_dir: str, data_format: str, data_files: dict[NamedSplit, str], num_proc: Optional[int], ) -> DatasetDict: if data_format == ".jsonl": dataset_dct = load_dataset( data_dir, data_files=data_files, split=None, num_proc=num_proc, ) else: raise NotImplementedError(f"Cannot load dataset in the '{data_format}' format.") return dataset_dct class DataManager(object): def __init__(self, data_dir: str, data_config: DataConfig): self._num_proc = data_config.num_proc self._dataset_dct = _load_datasets( data_dir, data_config.data_format, data_config.data_files, self._num_proc, ) def _get_dataset(self, split: NamedSplit) -> Optional[Dataset]: return self._dataset_dct.get(split, None) def get_dataset( self, split: NamedSplit, process_fn: Callable[[dict[str, Any]], dict[str, Any]], batched: bool = True, remove_orig_columns: bool = True, ) -> Optional[Dataset]: orig_dataset = self._get_dataset(split) if orig_dataset is None: return if remove_orig_columns: remove_columns = orig_dataset.column_names else: remove_columns = None return orig_dataset.map( process_fn, batched=batched, remove_columns=remove_columns, num_proc=self._num_proc, ) def process_message(message): if "tools" in message and message["role"] == "system": for tool in message["tools"]: parameters = tool["function"]["parameters"]["properties"] tool["function"]["parameters"]["properties"] = { k: v for k, v in parameters.items() if v is not None } elif "tools" in message: del message["tools"] return message def process_batch( batch: Mapping[str, Sequence], tokenizer: PreTrainedTokenizer, max_input_length: int, max_output_length: int, combine: bool, ) -> dict[str, list]: batched_conv = batch["messages"] batched_input_ids = [] batched_labels = [] for conv in batched_conv: input_ids = [151331, 151333] loss_masks = [False, False] if combine: new_input_ids = tokenizer.apply_chat_template( conv, tokenize=True, return_dict=False ) input_ids = new_input_ids loss_masks = [False] * len(input_ids) last_assistant_index = len(input_ids) - input_ids[::-1].index(151337) - 1 for j in range(last_assistant_index + 1, len(input_ids)): loss_masks[j] = True else: for message in conv: message = process_message(message) loss_mask_val = ( False if message["role"] in ("system", "user", "observation") else True ) new_input_ids = tokenizer.apply_chat_template( [message], tokenize=True, return_dict=False )[2:] input_ids += new_input_ids loss_masks += [loss_mask_val] * len(new_input_ids) input_ids.append(151336) # EOS for chat loss_masks = [False, *loss_masks] labels = [] for input_id, mask in zip(input_ids, loss_masks): if mask: labels.append(input_id) else: labels.append(-100) max_length = max_input_length + max_output_length + 1 batched_input_ids.append(input_ids[:max_length]) batched_labels.append(labels[:max_length]) del batched_conv, conv, input_ids, loss_masks, new_input_ids, labels torch.cuda.empty_cache() return {"input_ids": batched_input_ids, "labels": batched_labels} def process_batch_eval( batch: Mapping[str, Sequence], tokenizer: PreTrainedTokenizer, max_input_length: int, max_output_length: int, combine: bool, ) -> dict[str, list]: batched_conv = batch["messages"] batched_input_ids = [] batched_output_ids = [] for conv in batched_conv: if combine: new_input_ids = tokenizer.apply_chat_template( conv, tokenize=True, return_dict=False ) input_ids = new_input_ids last_assistant_index = len(input_ids) - input_ids[::-1].index(151337) - 1 output_prompt, output_ids = ( input_ids[:1], input_ids[last_assistant_index:], ) output_ids.append(151336) batched_input_ids.append(input_ids[:max_input_length] + output_prompt[:1]) batched_output_ids.append(output_ids[:max_output_length]) else: input_ids = [151331, 151333] for message in conv: if len(input_ids) >= max_input_length: break else: message = process_message(message) new_input_ids = tokenizer.apply_chat_template( [message], tokenize=True, return_dict=False )[2:] if message["role"] == "assistant": output_prompt, output_ids = ( new_input_ids[:1], new_input_ids[1:], ) output_ids.append(151336) batched_input_ids.append( input_ids[:max_input_length] + output_prompt[:1] ) batched_output_ids.append(output_ids[:max_output_length]) input_ids += new_input_ids del batched_conv, conv, input_ids, new_input_ids, output_prompt, output_ids torch.cuda.empty_cache() return {"input_ids": batched_input_ids, "output_ids": batched_output_ids} def load_tokenizer_and_model( model_dir: str, peft_config: Optional[PeftConfig] = None, ): tokenizer = AutoTokenizer.from_pretrained( model_dir, padding_side="left", trust_remote_code=True ) if peft_config is not None: model = AutoModelForCausalLM.from_pretrained( model_dir, trust_remote_code=True, use_cache=False, torch_dtype=torch.bfloat16, # Must use BFloat 16 ) model = get_peft_model(model, peft_config) model.print_trainable_parameters() else: model = AutoModelForCausalLM.from_pretrained( model_dir, trust_remote_code=True, use_cache=False, torch_dtype=torch.bfloat16, ) return tokenizer, model def compute_metrics(eval_preds: EvalPrediction, tokenizer): batched_pred_ids, batched_label_ids = eval_preds batched_pred_ids[batched_pred_ids == -100] = tokenizer.pad_token_id batched_label_ids[batched_label_ids == -100] = tokenizer.pad_token_id metrics_dct = {"rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []} for pred_ids, label_ids in zip(batched_pred_ids, batched_label_ids): pred_txt = tokenizer.decode(pred_ids).strip() label_txt = tokenizer.decode(label_ids).strip() pred_tokens = list(jieba.cut(pred_txt)) label_tokens = list(jieba.cut(label_txt)) rouge = Rouge() scores = rouge.get_scores(" ".join(pred_tokens), " ".join(label_tokens)) for k, v in scores[0].items(): metrics_dct[k].append(round(v["f"] * 100, 4)) metrics_dct["bleu-4"].append( sentence_bleu( [label_tokens], pred_tokens, smoothing_function=SmoothingFunction().method3, ) ) return {k: np.mean(v) for k, v in metrics_dct.items()} @app.command() def main( data_dir: Annotated[str, typer.Argument(help="")], model_dir: Annotated[ str, typer.Argument( help="A string that specifies the model id of a pretrained model configuration hosted on huggingface.co, or a path to a directory containing a model configuration file." ), ], config_file: Annotated[str, typer.Argument(help="")], auto_resume_from_checkpoint: str = typer.Argument( default="", help="If entered as yes, automatically use the latest save checkpoint. If it is a numerical example 12 15, use the corresponding save checkpoint. If the input is no, restart training", ), ): ft_config = FinetuningConfig.from_file(config_file) tokenizer, model = load_tokenizer_and_model( model_dir, peft_config=ft_config.peft_config ) data_manager = DataManager(data_dir, ft_config.data_config) train_dataset = data_manager.get_dataset( Split.TRAIN, functools.partial( process_batch, tokenizer=tokenizer, combine=ft_config.combine, max_input_length=ft_config.max_input_length, max_output_length=ft_config.max_output_length, ), batched=True, ) print("train_dataset:", train_dataset) val_dataset = data_manager.get_dataset( Split.VALIDATION, functools.partial( process_batch_eval, tokenizer=tokenizer, combine=ft_config.combine, max_input_length=ft_config.max_input_length, max_output_length=ft_config.max_output_length, ), batched=True, ) if val_dataset is not None: print("val_dataset:", val_dataset) test_dataset = data_manager.get_dataset( Split.TEST, functools.partial( process_batch_eval, tokenizer=tokenizer, combine=ft_config.combine, max_input_length=ft_config.max_input_length, max_output_length=ft_config.max_output_length, ), batched=True, ) if test_dataset is not None: print("test_dataset:", test_dataset) ft_config.training_args.generation_config.pad_token_id = 151329 ft_config.training_args.generation_config.eos_token_id = [151329, 151336, 151338] trainer = Seq2SeqTrainer( model=model, args=ft_config.training_args, data_collator=DataCollatorForSeq2Seq( tokenizer=tokenizer, padding="longest", return_tensors="pt", ), train_dataset=train_dataset, eval_dataset=val_dataset, compute_metrics=functools.partial(compute_metrics, tokenizer=tokenizer), ) if auto_resume_from_checkpoint.upper() == "" or auto_resume_from_checkpoint is None: trainer.train() else: output_dir = ft_config.training_args.output_dir dirlist = os.listdir(output_dir) checkpoint_sn = 0 for checkpoint_str in dirlist: if checkpoint_str.find("eckpoint") > 0 and checkpoint_str.find("tmp") == -1: checkpoint = int(checkpoint_str.replace("checkpoint-", "")) if checkpoint > checkpoint_sn: checkpoint_sn = checkpoint if auto_resume_from_checkpoint.upper() == "YES": if checkpoint_sn > 0: model.gradient_checkpointing_enable() model.enable_input_require_grads() checkpoint_directory = os.path.join( output_dir, "checkpoint-" + str(checkpoint_sn) ) print("resume checkpoint from checkpoint-" + str(checkpoint_sn)) trainer.train(resume_from_checkpoint=checkpoint_directory) else: trainer.train() else: if auto_resume_from_checkpoint.isdigit(): if int(auto_resume_from_checkpoint) > 0: checkpoint_sn = int(auto_resume_from_checkpoint) model.gradient_checkpointing_enable() model.enable_input_require_grads() checkpoint_directory = os.path.join( output_dir, "checkpoint-" + str(checkpoint_sn) ) print("resume checkpoint from checkpoint-" + str(checkpoint_sn)) trainer.train(resume_from_checkpoint=checkpoint_directory) else: print( auto_resume_from_checkpoint, "The specified checkpoint sn(" + auto_resume_from_checkpoint + ") has not been saved. Please search for the correct checkpoint in the model output directory", ) if test_dataset is not None: trainer.predict(test_dataset) if __name__ == "__main__": app()