diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..6a0a11b --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +tmp_trainer diff --git a/README.md b/README.md index 4aab786..a2cc9fc 100644 --- a/README.md +++ b/README.md @@ -339,6 +339,8 @@ model = load_model_on_gpus("THUDM/chatglm2-6b", num_gpus=2) 如果你觉得我们的工作有帮助的话，请考虑引用下列论文，ChatGLM2-6B 的论文会在近期公布，敬请期待～ +123 + ``` @article{zeng2022glm, title={Glm-130b: An open bilingual pre-trained model}, diff --git a/finetune/finetune-opt-lora.py b/finetune/finetune-opt-lora.py new file mode 100644 index 0000000..01e4ee8 --- /dev/null +++ b/finetune/finetune-opt-lora.py @@ -0,0 +1,82 @@ +import torch + +device = torch.device("cpu") +if torch.cuda.is_available(): + device = torch.device("cuda") +elif torch.backends.mps.is_available(): + device = torch.device("mps") +else: + device = torch.device("cpu") +print("device:", device) + +checkpoint = "/Users/hhwang/models/opt-350m" +checkpoint = "/Users/hhwang/models/opt-125m" + +prompt = "No matter how plain a woman may be" +print('***************** before lora finetune *********************') +from transformers import AutoModelForCausalLM, AutoTokenizer +tokenizer = AutoTokenizer.from_pretrained(checkpoint, use_fast=False) +model = AutoModelForCausalLM.from_pretrained(checkpoint) +batch = tokenizer(prompt, return_tensors='pt') +output_tokens = model.generate(**batch, max_new_tokens=50) +print('prompt:', prompt) +print('result:', tokenizer.decode(output_tokens[0], skip_special_tokens=True)) + +print('***************** begin lora finetune *********************') +from peft import LoraConfig, TaskType +from peft import get_peft_model +print(model) +lora_config = LoraConfig( + r=16, + target_modules=["q_proj", "v_proj"], + task_type=TaskType.CAUSAL_LM, + lora_alpha=32, + lora_dropout=0.05 +) +lora_model = get_peft_model(model, lora_config) +lora_model.print_trainable_parameters() + + +from datasets import load_dataset +dataset = load_dataset("/Users/hhwang/models/dataset/english_quotes") +dataset = dataset.map(lambda samples: tokenizer(samples['quote']), batched=True) +train_ds = dataset['train'].select(range(100)) + +from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling +trainer = Trainer( + model=lora_model, + train_dataset=train_ds, + args=TrainingArguments( + num_train_epochs=1, + per_device_train_batch_size=4, + gradient_accumulation_steps=4, + warmup_steps=3, + max_steps=10, + learning_rate=2e-4, + # fp16=True, # only works on cuda + logging_steps=1, + output_dir='outputs' + ), + data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False) +) +lora_model.config.use_cache = False # silence the warnings. Please re-enable for inference! + +print('begin train') +trainer.train() +print('done train') + +lora_checkpoint = "/tmp/outputs/opt-350m-lora" +lora_model.save_pretrained(lora_checkpoint) +print('Save', lora_checkpoint) + +print('***************** after lora finetune *********************') +from peft import PeftModel, PeftConfig +config = PeftConfig.from_pretrained(lora_checkpoint) +# print(config) +model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path) +lora_model = PeftModel.from_pretrained(model, lora_checkpoint) +batch = tokenizer(prompt, return_tensors='pt') +output_tokens = lora_model.generate(**batch, max_new_tokens=50) +print('prompt:', prompt) +print('result:', tokenizer.decode(output_tokens[0], skip_special_tokens=True)) + diff --git a/finetune/finetune-opt.py b/finetune/finetune-opt.py new file mode 100644 index 0000000..009b934 --- /dev/null +++ b/finetune/finetune-opt.py @@ -0,0 +1,58 @@ +# https://github.com/jesusoctavioas/Finetune_opt_bnb_peft + +from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling +import torch + +# import os +# os.environ["TOKENIZERS_PARALLELISM"] = "false" + +device = torch.device("cpu") +if torch.cuda.is_available(): + device = torch.device("cuda") +elif torch.backends.mps.is_available(): + device = torch.device("mps") +else: + device = torch.device("cpu") +print("device:", device) + +checkpoint = "/Users/hhwang/models/opt-125m" +checkpoint = "/Users/hhwang/models/opt-350m" +# checkpoint = "/Users/hhwang/models/gpt2" + +print('checkpoint:', checkpoint) +tokenizer = AutoTokenizer.from_pretrained(checkpoint, use_fast=False) +tokenizer.add_special_tokens({'pad_token': '[PAD]'}) +# model = AutoModelForCausalLM.from_pretrained(checkpoint, load_in_8bit=True, device_map='auto') +model = AutoModelForCausalLM.from_pretrained(checkpoint) + +batch = tokenizer("Two things are infinite: ", return_tensors='pt') +output_tokens = model.generate(**batch, max_new_tokens=50) +print('result:', tokenizer.decode(output_tokens[0], skip_special_tokens=True)) + +from datasets import load_dataset +# data = load_dataset("Abirate/english_quotes") +dataset = load_dataset("/Users/hhwang/models/dataset/english_quotes") +print('dataset', dataset) +dataset = dataset.map(lambda samples: tokenizer(samples['quote']), batched=True) +train_ds = dataset['train'].select(range(100)) +print('train_ds', train_ds) + +trainer = Trainer( + model=model, + train_dataset=train_ds, + args=TrainingArguments( + per_device_train_batch_size=4, + gradient_accumulation_steps=4, + warmup_steps=3, + max_steps=10, + learning_rate=2e-4, + # fp16=True, # only works on cuda + logging_steps=1, + output_dir='outputs' + ), + data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False) +) +model.config.use_cache = False # silence the warnings. Please re-enable for inference! +print('begin train') +trainer.train() +print('done train') \ No newline at end of file diff --git a/finetune/finetune-p-tuning-v2.py b/finetune/finetune-p-tuning-v2.py new file mode 100644 index 0000000..11539cd --- /dev/null +++ b/finetune/finetune-p-tuning-v2.py @@ -0,0 +1,720 @@ +#!/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=4 --num_cpu_threads_per_process=1 finetune-p-tuning-v2.py +# accelerate launch --num_machines=1 --num_processes=1 --gpu_ids=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(): + # print(torch.backends.mps.is_available()) + # print(torch.backends.mps.is_built()) + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda") + elif torch.backends.mps.is_available(): + device = torch.device("mps") + else: + device = 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, do not work for accelerate + model = model.half() + model.transformer.prefix_encoder.float() + + # finetune, work for accelerate + # model = model.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=5, + 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, + # save_changed=PRE_SEQ_LEN is not None + # ) + + trainer = Trainer( + model, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + tokenizer=tokenizer, + data_collator=data_collator, + compute_metrics=compute_metrics, + ) + 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() + print("begin trainning") + # train_result = trainer.train(resume_from_checkpoint=checkpoint) + train_result = trainer.train() + # trainer.save_model() # Saves the tokenizer too for easy upload + print("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() + print("save state") + # trainer.save_model("tmp_trainer/ptuning") + print("save model") + + # # 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() diff --git a/finetune/finetune-t5-lora.py b/finetune/finetune-t5-lora.py new file mode 100644 index 0000000..851735d --- /dev/null +++ b/finetune/finetune-t5-lora.py @@ -0,0 +1,106 @@ + +import torch +device = torch.device("cpu") + +checkpoint = "/Users/hhwang/models/t5-small" +checkpoint = "/Users/hhwang/models/flan-t5-small" +prompt = "translate English to German: That is good" +print('********* before finetune ***********') +from transformers import AutoTokenizer, AutoModelForSeq2SeqLM +tokenizer = AutoTokenizer.from_pretrained(checkpoint,use_fast=False) +model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) +# print(model.config) +inputs = tokenizer.encode(prompt, return_tensors="pt") +outputs = model.generate(inputs, max_new_tokens=20) +print('prompt:', prompt) +print('result: ',tokenizer.batch_decode(outputs)) + +print('***************** begin lora finetune *********************') +from peft import LoraConfig, TaskType +from peft import get_peft_model + +lora_config = LoraConfig( + r=16, + target_modules=["q", "v"], + task_type=TaskType.SEQ_2_SEQ_LM, + lora_alpha=32, + lora_dropout=0.05 +) +# print(model) +lora_model = get_peft_model(model, lora_config) +lora_model.print_trainable_parameters() + +data = [ + {"question": "今天天真好", "answer": "那一起打篮球去吧"}, + {"question": prompt, "answer": "Not bad"} +] + +def preprocess_function(examples): + inputs = tokenizer(examples["question"], max_length=32, truncation=True) + labels = tokenizer(examples["answer"], max_length=32, truncation=True) + inputs["labels"] = labels["input_ids"] + return inputs + +from datasets import Dataset, load_dataset +dataset = Dataset.from_list(data) +dataset = dataset.map(preprocess_function, batched=True, remove_columns=dataset.column_names) +print(dataset) + +from transformers import DataCollatorForSeq2Seq +data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model) +# print(data_collator([dataset[0], dataset[1]])) + +from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments +training_args = Seq2SeqTrainingArguments( + output_dir="checkpoints", + overwrite_output_dir=True, + use_cpu=True, + do_train=True, + do_eval=True, + learning_rate=1e-3, + lr_scheduler_type="constant", + per_device_train_batch_size=16, + per_device_eval_batch_size=16, + num_train_epochs=100, + weight_decay=0.01, + save_steps=10, + save_total_limit=5, + logging_first_step=True, + logging_steps=1, + # logging_dir="./", + eval_steps=1, + evaluation_strategy="steps", + load_best_model_at_end=True +) + +trainer = Seq2SeqTrainer( + model=lora_model, + args=training_args, + train_dataset=dataset, + eval_dataset=dataset, + data_collator=data_collator, + # compute_metrics=compute_metrics +) +lora_model.config.use_cache = False # silence the warnings. Please re-enable for inference! + +print('begin train') +trainer.train() +print('done train') + +lora_checkpoint = "/tmp/outputs/t5-small-lora" +lora_model.save_pretrained(lora_checkpoint) +print('Save', lora_checkpoint) + +print('***************** after lora finetune *********************') +from peft import PeftModel, PeftConfig +config = PeftConfig.from_pretrained(lora_checkpoint) +# print(config) +model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path) +lora_model = PeftModel.from_pretrained(model, lora_checkpoint) +# inputs = tokenizer.encode(prompt, return_tensors="pt") +input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cpu") +# outputs = lora_model.generate(inputs) +outputs = lora_model.generate(input_ids=input_ids,max_length=100, temperature=0.7, do_sample=True) +# print('result: ',tokenizer.batch_decode(outputs)) +print('prompt:', prompt) +print('result: ',tokenizer.decode(outputs[0])) diff --git a/finetune/finetune-t5.py b/finetune/finetune-t5.py new file mode 100644 index 0000000..fdafd19 --- /dev/null +++ b/finetune/finetune-t5.py @@ -0,0 +1,78 @@ +from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainer, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq + +import torch +device = torch.device("cpu") + +checkpoint = "/Users/hhwang/models/t5-small" +# checkpoint = "/Users/hhwang/models/flan-t5-small" + +print('********* before finetune ***********') +tokenizer = AutoTokenizer.from_pretrained(checkpoint,use_fast=False) +model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) +# print(model.config) +inputs = tokenizer.encode("translate English to Chinese: That is good", return_tensors="pt") +outputs = model.generate(inputs, max_new_tokens=20) +print('result: ',tokenizer.batch_decode(outputs)) + +data = [ + {"question": "今天天真好", "answer": "那一起打篮球去吧"}, + {"question": "translate English to Chinese: That is good", "answer": "Not bad"} +] + +def preprocess_function(examples): + inputs = tokenizer(examples["question"], max_length=32, truncation=True) + labels = tokenizer(examples["answer"], max_length=32, truncation=True) + inputs["labels"] = labels["input_ids"] + return inputs + +from datasets import Dataset, load_dataset +dataset = Dataset.from_list(data) +dataset = dataset.map(preprocess_function, batched=True, remove_columns=dataset.column_names) +print(dataset) + +data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model) +# print(data_collator([dataset[0], dataset[1]])) + +training_args = Seq2SeqTrainingArguments( + output_dir="checkpoints", + overwrite_output_dir=True, + use_cpu=True, + do_train=True, + do_eval=True, + learning_rate=1e-3, + lr_scheduler_type="constant", + per_device_train_batch_size=16, + per_device_eval_batch_size=16, + num_train_epochs=10, + weight_decay=0.01, + save_steps=10, + save_total_limit=5, + logging_first_step=True, + logging_steps=1, + # logging_dir="./", + eval_steps=1, + evaluation_strategy="steps", + load_best_model_at_end=True +) + +trainer = Seq2SeqTrainer( + model=model, + args=training_args, + train_dataset=dataset, + eval_dataset=dataset, + data_collator=data_collator, + # compute_metrics=compute_metrics +) + +print('begin train') +trainer.train() +print('done train') + +finetune_mode = "/tmp/outputs/t5-small" +trainer.save_model(finetune_mode) + +print('********* after finetune ***********') +prompt = "translate English to Chinese: That is good" +model = AutoModelForSeq2SeqLM.from_pretrained(finetune_mode) +generator = pipeline("summarization", model=model, tokenizer=tokenizer) +print(generator(prompt)) diff --git a/finetune/gpt-opt-use.py b/finetune/gpt-opt-use.py new file mode 100644 index 0000000..1ec7a95 --- /dev/null +++ b/finetune/gpt-opt-use.py @@ -0,0 +1,138 @@ + +from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoModel, AutoModelForCausalLM +from transformers import pipeline + +import torch +device = torch.device("cpu") + +checkpoint = "bigscience/mt0-large" +checkpoint = "/Users/hhwang/models/gpt2" +# checkpoint = "/Users/hhwang/models/opt-125m" +# checkpoint = "/Users/hhwang/models/opt-350m" + +# tokenizer = AutoTokenizer.from_pretrained(checkpoint) +# model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) +# inputs = tokenizer.encode("Write a short story", return_tensors="pt") +# outputs = model.generate(inputs) +# print(tokenizer.decode(outputs[0])) + +# case 1 +# pipe = pipeline(task='text-generation', model=checkpoint) +# print(pipe) +# result = pipe("tell me a joke") +# print('result: ',result) + +# case 2 +print('********* case 2 ***********') +from transformers import GPT2Tokenizer, GPT2LMHeadModel +tokenizer = GPT2Tokenizer.from_pretrained(checkpoint) +model = GPT2LMHeadModel.from_pretrained(checkpoint) +text = "Replace me by any text you'd like." +encoded_input = tokenizer.encode(text, return_tensors='pt') +outputs = model.generate(encoded_input, max_length=50, num_return_sequences=1) +print('outputs:', outputs) +print(outputs.shape) +generated_texts = tokenizer.batch_decode(outputs, skip_special_tokens=True) +for i, generated_text in enumerate(generated_texts): + print(f"Generated text {i + 1}: {generated_text}") + +# case 3 +print('********* case 3 ***********') +from transformers import GPT2Tokenizer, GPT2Model +tokenizer = GPT2Tokenizer.from_pretrained(checkpoint) +model = GPT2Model.from_pretrained(checkpoint) +print('config', model.config) +# print('model', model) +text = "Replace me by any text you'd like." +encoded_input = tokenizer(text, return_tensors='pt') +outputs = model(**encoded_input) +# print(outputs) +last_hidden_states = outputs.last_hidden_state +print('last_hidden_states', last_hidden_states) +print(last_hidden_states.shape) +print(len(last_hidden_states[0][0])) +import torch.nn as nn +lm_head = nn.Linear(model.config.n_embd, model.config.vocab_size, bias=False) +lm_logits = lm_head(last_hidden_states) +print('lm_logits', lm_logits) +print(lm_logits.shape) + +# case 4 +# print('********* case 4 ***********') +# tokenizer = AutoTokenizer.from_pretrained(checkpoint) +# model = AutoModel.from_pretrained(checkpoint) +# encoded_input = tokenizer.encode("Write a short story", return_tensors="pt") +# model = model.eval() +# print('config', model.config) +# print('model', model) +# print('inputs', encoded_input) +# outputs = model(encoded_input) +# print(outputs) + +# case 5 +# print('********* case 5 ***********') +# tokenizer = AutoTokenizer.from_pretrained(checkpoint) +# model = AutoModelForCausalLM.from_pretrained(checkpoint) +# inputs = tokenizer.encode("Write a short story", return_tensors="pt") +# outputs = model.generate(inputs) +# print('result: ',tokenizer.batch_decode(outputs)) + +# case 6 +# print('********* case 6 ***********') +# from transformers import GPT2Tokenizer, OPTForCausalLM +# model = OPTForCausalLM.from_pretrained(checkpoint) +# tokenizer = GPT2Tokenizer.from_pretrained(checkpoint) +# prompt = "Anti Vaccine Movemenet" +# inputs = tokenizer(prompt, return_tensors="pt").input_ids + +# gen_tokens = model.generate(inputs,do_sample=True,temperature=0.9,max_length=100) +# gen_text = tokenizer.batch_decode(gen_tokens)[0] +# print('gen_text', gen_text) +# generate_ids = model.generate(inputs,max_length=2000,early_stopping= True,do_sample=True,min_length=2000,top_k=125,top_p=0.92,temperature= 0.85,repetition_penalty=1.5,num_return_sequences=3) +# for i, sample_output in enumerate(generate_ids): +# result = tokenizer.decode(sample_output, skip_special_tokens=True, clean_up_tokenization_spaces=True) +# print(result) + +# case 7 +# print('********* case 7 ***********') +# generator = pipeline('text-generation', model=checkpoint, device="cpu") +# text_inputs = ["tell me joke", "How do you", "Would you help", "I like apple", "This is something"] +# sent_gen = generator(text_inputs, max_length=50, num_return_sequences=2, repetition_penalty=1.3, top_k = 20) +# #返回的sent_gen 形如#[[{'generated_text':"..."},{}],[{},{}]] +# for i in sent_gen: +# print(i) + +# case 8 +# print('********* case 8 ***********') +# from transformers import GPT2Tokenizer, GPT2LMHeadModel, TextGenerationPipeline +# tokenizer = GPT2Tokenizer.from_pretrained(checkpoint) +# model = GPT2LMHeadModel.from_pretrained(checkpoint) +# text_generator = TextGenerationPipeline(model, tokenizer, batch_size=3, device="cpu") +# text_generator.tokenizer.pad_token_id = model.config.eos_token_id +# text_inputs = ["tell me joke", "How do you", "Would you help", "I like apple", "This is something"] +# gen = text_generator(text_inputs, max_length=50, repetition_penalty=10.0, do_sample=True, num_beams=5, top_k=10) +# for sent in gen: +# gen_seq = sent[0]["generated_text"] +# print("") +# print(gen_seq) + +# case 9 +# print('********* case 9 ***********') +# from transformers import AutoTokenizer, AutoModelWithLMHead +# import torch +# tokenizer = AutoTokenizer.from_pretrained(checkpoint) +# model = AutoModelWithLMHead.from_pretrained(checkpoint) +# config=model.config +# # print('config', config) +# print(model) +# device = 'cuda' if torch.cuda.is_available() else 'cpu' +# model = model.to(device) +# texts = ["tell me joke", "How do you", "Would you help", "I like apple", "This is something"] +# #用batch输入的时候一定要设置padding +# tokenizer.pad_token = tokenizer.eos_token +# encoding = tokenizer(texts, return_tensors='pt', padding=True).to(device) +# with torch.no_grad(): +# generated_ids = model.generate(**encoding, max_length=50, do_sample=True, top_k=20, repetition_penalty=3.0) +# generated_texts = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) +# for l in generated_texts: +# print(l) \ No newline at end of file diff --git a/finetune/t5-use.py b/finetune/t5-use.py new file mode 100644 index 0000000..d3a6eb7 --- /dev/null +++ b/finetune/t5-use.py @@ -0,0 +1,24 @@ +from transformers import AutoTokenizer, AutoModelForSeq2SeqLM + +import torch +device = torch.device("cpu") + +checkpoint = "/Users/hhwang/models/t5-small" +checkpoint = "/Users/hhwang/models/flan-t5-small" + +print('********* case 1 ***********') +# tokenizer = AutoTokenizer.from_pretrained(checkpoint) +# model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) +# # print(model.config) +# inputs = tokenizer.encode("translate English to German: That is good", return_tensors="pt") +# outputs = model.generate(inputs, max_new_tokens=20) +# print('result: ',tokenizer.batch_decode(outputs)) + +print('********* case 2 ***********') + +from transformers import pipeline +tokenizer = AutoTokenizer.from_pretrained(checkpoint) +model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) +prompt = "translate English to German: That is good?" +generator = pipeline("summarization", model=model, tokenizer=tokenizer) +print(generator(prompt))