From 2da0265e8384b09ab4770ed39d5c6b1ca2a35ae3 Mon Sep 17 00:00:00 2001 From: wanghh2000 Date: Thu, 28 Dec 2023 09:14:18 +0800 Subject: [PATCH] create ptuning script --- ptuning/finetune-p-tuning-v2.py | 695 ++++++++++++++++++++++++++++++++ 1 file changed, 695 insertions(+) create mode 100644 ptuning/finetune-p-tuning-v2.py diff --git a/ptuning/finetune-p-tuning-v2.py b/ptuning/finetune-p-tuning-v2.py new file mode 100644 index 0000000..55f079c --- /dev/null +++ b/ptuning/finetune-p-tuning-v2.py @@ -0,0 +1,695 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2021 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Fine-tuning the library models for sequence to sequence for P-Tuning v2 +""" +# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments. + +# CUDA_VISIBLE_DEVICES=-1 python finetune-p-tuning-v2.py + +# accelerate launch --cpu --num_machines=1 --num_processes=1 --num_cpu_threads_per_process=1 finetune-p-tuning-v2.py + +import logging +import os +import sys +import json + +import numpy as np +from datasets import load_dataset +import jieba +from rouge_chinese import Rouge +from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction +import torch + +# import transformers +from transformers import ( + AutoConfig, + AutoModel, + AutoTokenizer, + DataCollatorForSeq2Seq, + # HfArgumentParser, + # Seq2SeqTrainingArguments, + # set_seed, +) + +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +from torch import nn +from torch.utils.data import Dataset + +from transformers.deepspeed import is_deepspeed_zero3_enabled +# from trainer import PrefixTrainer +from transformers.trainer_utils import PredictionOutput +# from transformers.utils import logging + +import os +from typing import Optional +from transformers import Trainer + +import torch +from transformers.modeling_utils import PreTrainedModel, unwrap_model +# from transformers.utils import logging + +# from trainer_seq2seq import Seq2SeqTrainer + +# from arguments import ModelArguments, DataTrainingArguments + +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + +def main(): + device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + print("device:", device) + # parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments)) + # if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): + # # If we pass only one argument to the script and it's the path to a json file, + # # let's parse it to get our arguments. + # model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) + # else: + # model_args, data_args, training_args = parser.parse_args_into_dataclasses() + + # Setup logging + # logging.basicConfig( + # format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + # datefmt="%m/%d/%Y %H:%M:%S", + # handlers=[logging.StreamHandler(sys.stdout)], + # ) + + # if training_args.should_log: + # # The default of training_args.log_level is passive, so we set log level at info here to have that default. + # transformers.utils.logging.set_verbosity_info() + + # log_level = training_args.get_process_log_level() + # logger.setLevel(log_level) + # datasets.utils.logging.set_verbosity(log_level) + # transformers.utils.logging.set_verbosity(log_level) + # transformers.utils.logging.enable_default_handler() + # transformers.utils.logging.enable_explicit_format() + + # Log on each process the small summary: + # logger.warning( + # f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + # + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" + # ) + # logger.info(f"Training/evaluation parameters {training_args}") + + # Set seed before initializing model. + # set_seed(training_args.seed) + + # Load dataset + data_files_path = "/tmp/hub/dataset/shibing624/AdvertiseGen" + print('data_files_path:', data_files_path) + data_files = {} + data_files["train"] = "train.json" + data_files["validation"] = "dev.json" + # data_files["test"] = "test.json" + print('data_files:', data_files) + raw_datasets = load_dataset(data_files_path, data_files=data_files) + print("raw_datasets:", raw_datasets) + + # Load pretrained model and tokenizer + model_name_or_path = '/tmp/hub/chatglm2-6b' + print('model_name_or_path', model_name_or_path) + config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True) + print('load autoconfig done') + # soft prompt 长度 + PRE_SEQ_LEN=128 + config.pre_seq_len = PRE_SEQ_LEN + config.prefix_projection = False + + tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True) + print('load AutoTokenizer done') + + # if model_args.ptuning_checkpoint is not None: + # # Evaluation + # # Loading extra state dict of prefix encoder + # model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True) + # prefix_state_dict = torch.load(os.path.join(model_args.ptuning_checkpoint, "pytorch_model.bin")) + # new_prefix_state_dict = {} + # for k, v in prefix_state_dict.items(): + # if k.startswith("transformer.prefix_encoder."): + # new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v + # model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict) + # else: + # model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True) + + model = AutoModel.from_pretrained(model_name_or_path, config=config, trust_remote_code=True) + #print('load AutoModel done') + # model = model.quantize(4) + + # if model_args.quantization_bit is not None: + # print(f"Quantized to {model_args.quantization_bit} bit") + # model = model.quantize(model_args.quantization_bit) + # if model_args.pre_seq_len is not None: + # # P-tuning v2 + # model = model.half() + # model.transformer.prefix_encoder.float() + # else: + # # Finetune + # model = model.float() + + # P-tuning v2 + model = model.half() + model.transformer.prefix_encoder.float() + print('model half done') + + prefix = "" + + # Preprocessing the datasets. + # We need to tokenize inputs and targets. + # if training_args.do_train: + # column_names = raw_datasets["train"].column_names + # elif training_args.do_eval: + # column_names = raw_datasets["validation"].column_names + # elif training_args.do_predict: + # column_names = raw_datasets["test"].column_names + # else: + # logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.") + # return + column_names = raw_datasets["train"].column_names + + # Get the column names for input/target. + prompt_column = 'content' + response_column = 'summary' + history_column = None + + # Temporarily set max_target_length for training. + max_source_length = 64 + max_target_length = 128 + ignore_pad_token_for_loss = True + + def preprocess_function_eval(examples): + inputs, targets = [], [] + for i in range(len(examples[prompt_column])): + if examples[prompt_column][i] and examples[response_column][i]: + query = examples[prompt_column][i] + history = examples[history_column][i] if history_column is not None else None + prompt = tokenizer.build_prompt(query, history) + inputs.append(prompt) + targets.append(examples[response_column][i]) + + inputs = [prefix + inp for inp in inputs] + model_inputs = tokenizer(inputs, max_length=max_source_length, truncation=True, padding=True) + labels = tokenizer(text_target=targets, max_length=max_target_length, truncation=True) + + if ignore_pad_token_for_loss: + labels["input_ids"] = [[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]] + + model_inputs["labels"] = labels["input_ids"] + return model_inputs + + def preprocess_function_train(examples): + max_seq_length = max_source_length + max_target_length + 1 + model_inputs = { "input_ids": [], "labels": [] } + for i in range(len(examples[prompt_column])): + if examples[prompt_column][i] and examples[response_column][i]: + query, answer = examples[prompt_column][i], examples[response_column][i] + + history = examples[history_column][i] if history_column is not None else None + prompt = tokenizer.build_prompt(query, history) + + prompt = prefix + prompt + a_ids = tokenizer.encode(text=prompt, add_special_tokens=True, truncation=True, max_length=max_source_length) + b_ids = tokenizer.encode(text=answer, add_special_tokens=False, truncation=True, max_length=max_target_length) + + context_length = len(a_ids) + input_ids = a_ids + b_ids + [tokenizer.eos_token_id] + labels = [tokenizer.pad_token_id] * context_length + b_ids + [tokenizer.eos_token_id] + + pad_len = max_seq_length - len(input_ids) + input_ids = input_ids + [tokenizer.pad_token_id] * pad_len + labels = labels + [tokenizer.pad_token_id] * pad_len + if ignore_pad_token_for_loss: + labels = [(l if l != tokenizer.pad_token_id else -100) for l in labels] + + model_inputs["input_ids"].append(input_ids) + model_inputs["labels"].append(labels) + + return model_inputs + + def print_dataset_example(example): + print('******************* print_dataset_example ******************************') + print("input_ids:", example["input_ids"]) + print("inputs:", tokenizer.decode(example["input_ids"])) + print("label_ids:", example["labels"]) + print("labels:", tokenizer.decode(example["labels"])) + + max_train_samples = 5 + do_train = True + if do_train: + train_dataset = raw_datasets["train"] + max_train_samples = min(len(train_dataset), max_train_samples) + train_dataset = train_dataset.select(range(max_train_samples)) + train_dataset = train_dataset.map( + preprocess_function_train, + batched=True, + num_proc=10, + remove_columns=column_names, + load_from_cache_file=False, + desc="Running tokenizer on train dataset", + ) + print_dataset_example(train_dataset[0]) + + max_eval_samples = 5 + do_eval = True + if do_eval: + eval_dataset = raw_datasets["validation"] + max_eval_samples = min(len(eval_dataset), max_eval_samples) + eval_dataset = eval_dataset.select(range(max_eval_samples)) + eval_dataset = eval_dataset.map( + preprocess_function_eval, + batched=True, + num_proc=5, + remove_columns=column_names, + load_from_cache_file=False, + desc="Running tokenizer on validation dataset", + ) + print_dataset_example(eval_dataset[0]) + + # if training_args.do_predict: + # max_target_length = data_args.val_max_target_length + # if "test" not in raw_datasets: + # raise ValueError("--do_predict requires a test dataset") + # predict_dataset = raw_datasets["test"] + # if data_args.max_predict_samples is not None: + # max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples) + # predict_dataset = predict_dataset.select(range(max_predict_samples)) + # with training_args.main_process_first(desc="prediction dataset map pre-processing"): + # predict_dataset = predict_dataset.map( + # preprocess_function_eval, + # batched=True, + # num_proc=data_args.preprocessing_num_workers, + # remove_columns=column_names, + # load_from_cache_file=not data_args.overwrite_cache, + # desc="Running tokenizer on prediction dataset", + # ) + # print_dataset_example(predict_dataset[0]) + + # Data collator + label_pad_token_id = -100 if ignore_pad_token_for_loss else tokenizer.pad_token_id + data_collator = DataCollatorForSeq2Seq( + tokenizer, + model=model, + label_pad_token_id=label_pad_token_id, + pad_to_multiple_of=None, + padding=False + ) + print("data_collator done") + # # Metric + # def compute_metrics(eval_preds): + # preds, labels = eval_preds + # if isinstance(preds, tuple): + # preds = preds[0] + # decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) + # if ignore_pad_token_for_loss: + # # Replace -100 in the labels as we can't decode them. + # labels = np.where(labels != -100, labels, tokenizer.pad_token_id) + # decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) + + # score_dict = { + # "rouge-1": [], + # "rouge-2": [], + # "rouge-l": [], + # "bleu-4": [] + # } + # for pred, label in zip(decoded_preds, decoded_labels): + # hypothesis = list(jieba.cut(pred)) + # reference = list(jieba.cut(label)) + # rouge = Rouge() + # scores = rouge.get_scores(' '.join(hypothesis) , ' '.join(reference)) + # result = scores[0] + + # for k, v in result.items(): + # score_dict[k].append(round(v["f"] * 100, 4)) + # bleu_score = sentence_bleu([list(label)], list(pred), smoothing_function=SmoothingFunction().method3) + # score_dict["bleu-4"].append(round(bleu_score * 100, 4)) + + # for k, v in score_dict.items(): + # score_dict[k] = float(np.mean(v)) + # return score_dict + + # Override the decoding parameters of Seq2SeqTrainer + # training_args.generation_max_length = ( + # training_args.generation_max_length + # if training_args.generation_max_length is not None + # else data_args.val_max_target_length + # ) + # training_args.generation_num_beams = ( + # data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams + # ) + # Initialize our Trainer + trainer = Seq2SeqTrainer( + model=model, + # args=training_args, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + tokenizer=tokenizer, + data_collator=data_collator, + # compute_metrics=compute_metrics if training_args.predict_with_generate else None, + save_changed=PRE_SEQ_LEN is not None + ) + print('build trainer done') + + # Training + if do_train: + checkpoint = False + # if training_args.resume_from_checkpoint is not None: + # checkpoint = training_args.resume_from_checkpoint + # elif last_checkpoint is not None: + # checkpoint = last_checkpoint + model.gradient_checkpointing_enable() + model.enable_input_require_grads() + logger.info("begin trainning") + train_result = trainer.train(resume_from_checkpoint=checkpoint) + # trainer.save_model() # Saves the tokenizer too for easy upload + logger.info("done trainning") + metrics = train_result.metrics + max_train_samples = len(train_dataset) + metrics["train_samples"] = min(max_train_samples, len(train_dataset)) + + trainer.log_metrics("train", metrics) + trainer.save_metrics("train", metrics) + trainer.save_state() + logger.info("save state") + + # # Evaluation + # results = {} + # max_seq_length = data_args.max_source_length + data_args.max_target_length + 1 + # if training_args.do_eval: + # logger.info("*** Evaluate ***") + # metrics = trainer.evaluate(metric_key_prefix="eval", do_sample=True, top_p=0.7, max_length=max_seq_length, temperature=0.95) + # max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset) + # metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset)) + + # trainer.log_metrics("eval", metrics) + # trainer.save_metrics("eval", metrics) + + # if training_args.do_predict: + # logger.info("*** Predict ***") + # predict_results = trainer.predict(predict_dataset, metric_key_prefix="predict", max_length=max_seq_length, do_sample=True, top_p=0.7, temperature=0.95) + # metrics = predict_results.metrics + # max_predict_samples = ( + # data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset) + # ) + # metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset)) + + # trainer.log_metrics("predict", metrics) + # trainer.save_metrics("predict", metrics) + + # if trainer.is_world_process_zero(): + # if training_args.predict_with_generate: + # predictions = tokenizer.batch_decode( + # predict_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True + # ) + # predictions = [pred.strip() for pred in predictions] + # labels = tokenizer.batch_decode( + # predict_results.label_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True + # ) + # labels = [label.strip() for label in labels] + # output_prediction_file = os.path.join(training_args.output_dir, "generated_predictions.txt") + # with open(output_prediction_file, "w", encoding="utf-8") as writer: + # for p, l in zip(predictions, labels): + # res = json.dumps({"labels": l, "predict": p}, ensure_ascii=False) + # writer.write(f"{res}\n") + # return results + +WEIGHTS_NAME = "pytorch_model.bin" +TRAINING_ARGS_NAME = "training_args.bin" + +class PrefixTrainer(Trainer): + def __init__(self, *args, save_changed=False, **kwargs): + self.save_changed = save_changed + super().__init__(*args, **kwargs) + + def _save(self, output_dir: Optional[str] = None, state_dict=None): + # If we are executing this function, we are the process zero, so we don't check for that. + output_dir = output_dir if output_dir is not None else self.args.output_dir + os.makedirs(output_dir, exist_ok=True) + logger.info(f"Saving model checkpoint to {output_dir}") + # Save a trained model and configuration using `save_pretrained()`. + # They can then be reloaded using `from_pretrained()` + if not isinstance(self.model, PreTrainedModel): + if isinstance(unwrap_model(self.model), PreTrainedModel): + if state_dict is None: + state_dict = self.model.state_dict() + unwrap_model(self.model).save_pretrained(output_dir, state_dict=state_dict) + else: + logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.") + if state_dict is None: + state_dict = self.model.state_dict() + torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME)) + else: + if self.save_changed: + print("Saving PrefixEncoder") + state_dict = self.model.state_dict() + filtered_state_dict = {} + for k, v in self.model.named_parameters(): + if v.requires_grad: + filtered_state_dict[k] = state_dict[k] + self.model.save_pretrained(output_dir, state_dict=filtered_state_dict) + else: + print("Saving the whole model") + self.model.save_pretrained(output_dir, state_dict=state_dict) + if self.tokenizer is not None: + self.tokenizer.save_pretrained(output_dir) + + # Good practice: save your training arguments together with the trained model + torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME)) + + +class Seq2SeqTrainer(PrefixTrainer): + def evaluate( + self, + eval_dataset: Optional[Dataset] = None, + ignore_keys: Optional[List[str]] = None, + metric_key_prefix: str = "eval", + **gen_kwargs + ) -> Dict[str, float]: + """ + Run evaluation and returns metrics. + + The calling script will be responsible for providing a method to compute metrics, as they are task-dependent + (pass it to the init `compute_metrics` argument). + + You can also subclass and override this method to inject custom behavior. + + Args: + eval_dataset (`Dataset`, *optional*): + Pass a dataset if you wish to override `self.eval_dataset`. If it is an [`~datasets.Dataset`], columns + not accepted by the `model.forward()` method are automatically removed. It must implement the `__len__` + method. + ignore_keys (`List[str]`, *optional*): + A list of keys in the output of your model (if it is a dictionary) that should be ignored when + gathering predictions. + metric_key_prefix (`str`, *optional*, defaults to `"eval"`): + An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named + "eval_bleu" if the prefix is `"eval"` (default) + max_length (`int`, *optional*): + The maximum target length to use when predicting with the generate method. + num_beams (`int`, *optional*): + Number of beams for beam search that will be used when predicting with the generate method. 1 means no + beam search. + gen_kwargs: + Additional `generate` specific kwargs. + + Returns: + A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The + dictionary also contains the epoch number which comes from the training state. + """ + + gen_kwargs = gen_kwargs.copy() + if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None: + gen_kwargs["max_length"] = self.args.generation_max_length + gen_kwargs["num_beams"] = ( + gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.args.generation_num_beams + ) + self._gen_kwargs = gen_kwargs + + return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix) + + def predict( + self, + test_dataset: Dataset, + ignore_keys: Optional[List[str]] = None, + metric_key_prefix: str = "test", + **gen_kwargs + ) -> PredictionOutput: + """ + Run prediction and returns predictions and potential metrics. + + Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method + will also return metrics, like in `evaluate()`. + + Args: + test_dataset (`Dataset`): + Dataset to run the predictions on. If it is a [`~datasets.Dataset`], columns not accepted by the + `model.forward()` method are automatically removed. Has to implement the method `__len__` + ignore_keys (`List[str]`, *optional*): + A list of keys in the output of your model (if it is a dictionary) that should be ignored when + gathering predictions. + metric_key_prefix (`str`, *optional*, defaults to `"eval"`): + An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named + "eval_bleu" if the prefix is `"eval"` (default) + max_length (`int`, *optional*): + The maximum target length to use when predicting with the generate method. + num_beams (`int`, *optional*): + Number of beams for beam search that will be used when predicting with the generate method. 1 means no + beam search. + gen_kwargs: + Additional `generate` specific kwargs. + + + + If your predictions or labels have different sequence lengths (for instance because you're doing dynamic + padding in a token classification task) the predictions will be padded (on the right) to allow for + concatenation into one array. The padding index is -100. + + + + Returns: *NamedTuple* A namedtuple with the following keys: + + - predictions (`np.ndarray`): The predictions on `test_dataset`. + - label_ids (`np.ndarray`, *optional*): The labels (if the dataset contained some). + - metrics (`Dict[str, float]`, *optional*): The potential dictionary of metrics (if the dataset contained + labels). + """ + + gen_kwargs = gen_kwargs.copy() + if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None: + gen_kwargs["max_length"] = self.args.generation_max_length + gen_kwargs["num_beams"] = ( + gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.args.generation_num_beams + ) + self._gen_kwargs = gen_kwargs + + + return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix) + + def prediction_step( + self, + model: nn.Module, + inputs: Dict[str, Union[torch.Tensor, Any]], + prediction_loss_only: bool, + ignore_keys: Optional[List[str]] = None, + ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: + """ + Perform an evaluation step on `model` using `inputs`. + + Subclass and override to inject custom behavior. + + Args: + model (`nn.Module`): + The model to evaluate. + inputs (`Dict[str, Union[torch.Tensor, Any]]`): + The inputs and targets of the model. + + The dictionary will be unpacked before being fed to the model. Most models expect the targets under the + argument `labels`. Check your model's documentation for all accepted arguments. + prediction_loss_only (`bool`): + Whether or not to return the loss only. + + Return: + Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, logits and + labels (each being optional). + """ + + if not self.args.predict_with_generate or prediction_loss_only: + return super().prediction_step( + model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys + ) + + has_labels = "labels" in inputs + inputs = self._prepare_inputs(inputs) + + # XXX: adapt synced_gpus for fairscale as well + gen_kwargs = self._gen_kwargs.copy() + if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None: + gen_kwargs["max_length"] = self.model.config.max_length + gen_kwargs["num_beams"] = ( + gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.model.config.num_beams + ) + default_synced_gpus = True if is_deepspeed_zero3_enabled() else False + gen_kwargs["synced_gpus"] = ( + gen_kwargs["synced_gpus"] if gen_kwargs.get("synced_gpus") is not None else default_synced_gpus + ) + + if "attention_mask" in inputs: + gen_kwargs["attention_mask"] = inputs.get("attention_mask", None) + if "position_ids" in inputs: + gen_kwargs["position_ids"] = inputs.get("position_ids", None) + if "global_attention_mask" in inputs: + gen_kwargs["global_attention_mask"] = inputs.get("global_attention_mask", None) + + # prepare generation inputs + # some encoder-decoder models can have varying encoder's and thus + # varying model input names + if hasattr(self.model, "encoder") and self.model.encoder.main_input_name != self.model.main_input_name: + generation_inputs = inputs[self.model.encoder.main_input_name] + else: + generation_inputs = inputs[self.model.main_input_name] + + gen_kwargs["input_ids"] = generation_inputs + generated_tokens = self.model.generate(**gen_kwargs) + generated_tokens = generated_tokens[:, generation_inputs.size()[-1]:] + + # in case the batch is shorter than max length, the output should be padded + if gen_kwargs.get("max_length") is not None and generated_tokens.shape[-1] < gen_kwargs["max_length"]: + generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"]) + elif gen_kwargs.get("max_new_tokens") is not None and generated_tokens.shape[-1] < ( + gen_kwargs["max_new_tokens"] + 1 + ): + generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_new_tokens"] + 1) + + loss = None + + if self.args.prediction_loss_only: + return (loss, None, None) + + if has_labels: + labels = inputs["labels"] + if gen_kwargs.get("max_length") is not None and labels.shape[-1] < gen_kwargs["max_length"]: + labels = self._pad_tensors_to_max_len(labels, gen_kwargs["max_length"]) + elif gen_kwargs.get("max_new_tokens") is not None and labels.shape[-1] < ( + gen_kwargs["max_new_tokens"] + 1 + ): + labels = self._pad_tensors_to_max_len(labels, (gen_kwargs["max_new_tokens"] + 1)) + else: + labels = None + + return (loss, generated_tokens, labels) + + def _pad_tensors_to_max_len(self, tensor, max_length): + if self.tokenizer is not None and hasattr(self.tokenizer, "pad_token_id"): + # If PAD token is not defined at least EOS token has to be defined + pad_token_id = ( + self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id + ) + else: + if self.model.config.pad_token_id is not None: + pad_token_id = self.model.config.pad_token_id + else: + raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors") + + padded_tensor = pad_token_id * torch.ones( + (tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device + ) + padded_tensor[:, : tensor.shape[-1]] = tensor + return padded_tensor + + +if __name__ == "__main__": + main()