mirror of https://github.com/THUDM/ChatGLM2-6B
create ptuning script
wanghh2000
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921d7e9adc
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2da0265e83
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Fine-tuning the library models for sequence to sequence for P-Tuning v2
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"""
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# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
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# CUDA_VISIBLE_DEVICES=-1 python finetune-p-tuning-v2.py
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# accelerate launch --cpu --num_machines=1 --num_processes=1 --num_cpu_threads_per_process=1 finetune-p-tuning-v2.py
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import logging
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import os
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import sys
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import json
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import numpy as np
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from datasets import load_dataset
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import jieba
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from rouge_chinese import Rouge
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from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
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import torch
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# import transformers
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from transformers import (
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AutoConfig,
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AutoModel,
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AutoTokenizer,
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DataCollatorForSeq2Seq,
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# HfArgumentParser,
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# Seq2SeqTrainingArguments,
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# set_seed,
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)
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from typing import Any, Dict, List, Optional, Tuple, Union
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import torch
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from torch import nn
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from torch.utils.data import Dataset
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from transformers.deepspeed import is_deepspeed_zero3_enabled
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# from trainer import PrefixTrainer
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from transformers.trainer_utils import PredictionOutput
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# from transformers.utils import logging
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import os
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from typing import Optional
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from transformers import Trainer
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import torch
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from transformers.modeling_utils import PreTrainedModel, unwrap_model
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# from transformers.utils import logging
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# from trainer_seq2seq import Seq2SeqTrainer
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# from arguments import ModelArguments, DataTrainingArguments
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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def main():
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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print("device:", device)
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# parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
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# if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# # If we pass only one argument to the script and it's the path to a json file,
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# # let's parse it to get our arguments.
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# model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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# else:
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# model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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# Setup logging
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# logging.basicConfig(
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# format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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# datefmt="%m/%d/%Y %H:%M:%S",
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# handlers=[logging.StreamHandler(sys.stdout)],
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# )
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# if training_args.should_log:
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# # The default of training_args.log_level is passive, so we set log level at info here to have that default.
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# transformers.utils.logging.set_verbosity_info()
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# log_level = training_args.get_process_log_level()
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# logger.setLevel(log_level)
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# datasets.utils.logging.set_verbosity(log_level)
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# transformers.utils.logging.set_verbosity(log_level)
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# transformers.utils.logging.enable_default_handler()
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# transformers.utils.logging.enable_explicit_format()
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# Log on each process the small summary:
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# logger.warning(
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# f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
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# + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
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# )
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# logger.info(f"Training/evaluation parameters {training_args}")
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# Set seed before initializing model.
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# set_seed(training_args.seed)
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# Load dataset
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data_files_path = "/tmp/hub/dataset/shibing624/AdvertiseGen"
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print('data_files_path:', data_files_path)
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data_files = {}
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data_files["train"] = "train.json"
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data_files["validation"] = "dev.json"
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# data_files["test"] = "test.json"
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print('data_files:', data_files)
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raw_datasets = load_dataset(data_files_path, data_files=data_files)
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print("raw_datasets:", raw_datasets)
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# Load pretrained model and tokenizer
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model_name_or_path = '/tmp/hub/chatglm2-6b'
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print('model_name_or_path', model_name_or_path)
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config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
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print('load autoconfig done')
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# soft prompt 长度
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PRE_SEQ_LEN=128
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config.pre_seq_len = PRE_SEQ_LEN
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config.prefix_projection = False
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
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print('load AutoTokenizer done')
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# if model_args.ptuning_checkpoint is not None:
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# # Evaluation
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# # Loading extra state dict of prefix encoder
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# model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)
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# prefix_state_dict = torch.load(os.path.join(model_args.ptuning_checkpoint, "pytorch_model.bin"))
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# new_prefix_state_dict = {}
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# for k, v in prefix_state_dict.items():
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# if k.startswith("transformer.prefix_encoder."):
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# new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
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# model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
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# else:
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# model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)
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model = AutoModel.from_pretrained(model_name_or_path, config=config, trust_remote_code=True)
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#print('load AutoModel done')
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# model = model.quantize(4)
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# if model_args.quantization_bit is not None:
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# print(f"Quantized to {model_args.quantization_bit} bit")
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# model = model.quantize(model_args.quantization_bit)
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# if model_args.pre_seq_len is not None:
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# # P-tuning v2
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# model = model.half()
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# model.transformer.prefix_encoder.float()
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# else:
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# # Finetune
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# model = model.float()
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# P-tuning v2
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model = model.half()
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model.transformer.prefix_encoder.float()
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print('model half done')
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prefix = ""
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# Preprocessing the datasets.
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# We need to tokenize inputs and targets.
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# if training_args.do_train:
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# column_names = raw_datasets["train"].column_names
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# elif training_args.do_eval:
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# column_names = raw_datasets["validation"].column_names
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# elif training_args.do_predict:
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# column_names = raw_datasets["test"].column_names
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# else:
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# logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
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# return
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column_names = raw_datasets["train"].column_names
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# Get the column names for input/target.
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prompt_column = 'content'
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response_column = 'summary'
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history_column = None
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# Temporarily set max_target_length for training.
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max_source_length = 64
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max_target_length = 128
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ignore_pad_token_for_loss = True
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def preprocess_function_eval(examples):
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inputs, targets = [], []
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for i in range(len(examples[prompt_column])):
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if examples[prompt_column][i] and examples[response_column][i]:
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query = examples[prompt_column][i]
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history = examples[history_column][i] if history_column is not None else None
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prompt = tokenizer.build_prompt(query, history)
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inputs.append(prompt)
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targets.append(examples[response_column][i])
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inputs = [prefix + inp for inp in inputs]
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model_inputs = tokenizer(inputs, max_length=max_source_length, truncation=True, padding=True)
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labels = tokenizer(text_target=targets, max_length=max_target_length, truncation=True)
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if ignore_pad_token_for_loss:
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labels["input_ids"] = [[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]]
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model_inputs["labels"] = labels["input_ids"]
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return model_inputs
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def preprocess_function_train(examples):
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max_seq_length = max_source_length + max_target_length + 1
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model_inputs = { "input_ids": [], "labels": [] }
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for i in range(len(examples[prompt_column])):
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if examples[prompt_column][i] and examples[response_column][i]:
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query, answer = examples[prompt_column][i], examples[response_column][i]
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history = examples[history_column][i] if history_column is not None else None
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prompt = tokenizer.build_prompt(query, history)
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prompt = prefix + prompt
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a_ids = tokenizer.encode(text=prompt, add_special_tokens=True, truncation=True, max_length=max_source_length)
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b_ids = tokenizer.encode(text=answer, add_special_tokens=False, truncation=True, max_length=max_target_length)
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context_length = len(a_ids)
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input_ids = a_ids + b_ids + [tokenizer.eos_token_id]
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labels = [tokenizer.pad_token_id] * context_length + b_ids + [tokenizer.eos_token_id]
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pad_len = max_seq_length - len(input_ids)
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input_ids = input_ids + [tokenizer.pad_token_id] * pad_len
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labels = labels + [tokenizer.pad_token_id] * pad_len
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if ignore_pad_token_for_loss:
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labels = [(l if l != tokenizer.pad_token_id else -100) for l in labels]
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model_inputs["input_ids"].append(input_ids)
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model_inputs["labels"].append(labels)
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return model_inputs
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def print_dataset_example(example):
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print('******************* print_dataset_example ******************************')
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print("input_ids:", example["input_ids"])
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print("inputs:", tokenizer.decode(example["input_ids"]))
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print("label_ids:", example["labels"])
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print("labels:", tokenizer.decode(example["labels"]))
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max_train_samples = 5
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do_train = True
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if do_train:
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train_dataset = raw_datasets["train"]
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max_train_samples = min(len(train_dataset), max_train_samples)
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train_dataset = train_dataset.select(range(max_train_samples))
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train_dataset = train_dataset.map(
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preprocess_function_train,
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batched=True,
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num_proc=10,
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remove_columns=column_names,
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load_from_cache_file=False,
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desc="Running tokenizer on train dataset",
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)
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print_dataset_example(train_dataset[0])
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max_eval_samples = 5
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do_eval = True
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if do_eval:
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eval_dataset = raw_datasets["validation"]
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max_eval_samples = min(len(eval_dataset), max_eval_samples)
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eval_dataset = eval_dataset.select(range(max_eval_samples))
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eval_dataset = eval_dataset.map(
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preprocess_function_eval,
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batched=True,
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num_proc=5,
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remove_columns=column_names,
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load_from_cache_file=False,
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desc="Running tokenizer on validation dataset",
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)
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print_dataset_example(eval_dataset[0])
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# if training_args.do_predict:
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# max_target_length = data_args.val_max_target_length
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# if "test" not in raw_datasets:
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# raise ValueError("--do_predict requires a test dataset")
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# predict_dataset = raw_datasets["test"]
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# if data_args.max_predict_samples is not None:
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# max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
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# predict_dataset = predict_dataset.select(range(max_predict_samples))
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# with training_args.main_process_first(desc="prediction dataset map pre-processing"):
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# predict_dataset = predict_dataset.map(
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# preprocess_function_eval,
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# batched=True,
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# num_proc=data_args.preprocessing_num_workers,
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# remove_columns=column_names,
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# load_from_cache_file=not data_args.overwrite_cache,
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# desc="Running tokenizer on prediction dataset",
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# )
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# print_dataset_example(predict_dataset[0])
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# Data collator
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label_pad_token_id = -100 if ignore_pad_token_for_loss else tokenizer.pad_token_id
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data_collator = DataCollatorForSeq2Seq(
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tokenizer,
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model=model,
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label_pad_token_id=label_pad_token_id,
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pad_to_multiple_of=None,
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padding=False
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)
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print("data_collator done")
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# # Metric
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# def compute_metrics(eval_preds):
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# preds, labels = eval_preds
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# if isinstance(preds, tuple):
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# preds = preds[0]
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# decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
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# if ignore_pad_token_for_loss:
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# # Replace -100 in the labels as we can't decode them.
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# labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
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# decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
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# score_dict = {
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# "rouge-1": [],
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# "rouge-2": [],
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# "rouge-l": [],
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# "bleu-4": []
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# }
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# for pred, label in zip(decoded_preds, decoded_labels):
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# hypothesis = list(jieba.cut(pred))
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# reference = list(jieba.cut(label))
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# rouge = Rouge()
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# scores = rouge.get_scores(' '.join(hypothesis) , ' '.join(reference))
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# result = scores[0]
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# for k, v in result.items():
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# score_dict[k].append(round(v["f"] * 100, 4))
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# bleu_score = sentence_bleu([list(label)], list(pred), smoothing_function=SmoothingFunction().method3)
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# score_dict["bleu-4"].append(round(bleu_score * 100, 4))
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# for k, v in score_dict.items():
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# score_dict[k] = float(np.mean(v))
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# return score_dict
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# Override the decoding parameters of Seq2SeqTrainer
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# training_args.generation_max_length = (
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# training_args.generation_max_length
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# if training_args.generation_max_length is not None
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# else data_args.val_max_target_length
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# )
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# training_args.generation_num_beams = (
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# data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
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# )
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# Initialize our Trainer
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trainer = Seq2SeqTrainer(
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model=model,
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# args=training_args,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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tokenizer=tokenizer,
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data_collator=data_collator,
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# compute_metrics=compute_metrics if training_args.predict_with_generate else None,
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save_changed=PRE_SEQ_LEN is not None
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)
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print('build trainer done')
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# Training
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if do_train:
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checkpoint = False
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# if training_args.resume_from_checkpoint is not None:
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# checkpoint = training_args.resume_from_checkpoint
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# elif last_checkpoint is not None:
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# checkpoint = last_checkpoint
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model.gradient_checkpointing_enable()
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model.enable_input_require_grads()
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logger.info("begin trainning")
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train_result = trainer.train(resume_from_checkpoint=checkpoint)
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# trainer.save_model() # Saves the tokenizer too for easy upload
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logger.info("done trainning")
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metrics = train_result.metrics
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max_train_samples = len(train_dataset)
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metrics["train_samples"] = min(max_train_samples, len(train_dataset))
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trainer.log_metrics("train", metrics)
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trainer.save_metrics("train", metrics)
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trainer.save_state()
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logger.info("save state")
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# # Evaluation
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# results = {}
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# max_seq_length = data_args.max_source_length + data_args.max_target_length + 1
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# if training_args.do_eval:
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# logger.info("*** Evaluate ***")
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# metrics = trainer.evaluate(metric_key_prefix="eval", do_sample=True, top_p=0.7, max_length=max_seq_length, temperature=0.95)
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# max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
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# metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
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# trainer.log_metrics("eval", metrics)
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# trainer.save_metrics("eval", metrics)
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# if training_args.do_predict:
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# logger.info("*** Predict ***")
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# 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)
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# metrics = predict_results.metrics
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# max_predict_samples = (
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# data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
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# )
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# metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
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# trainer.log_metrics("predict", metrics)
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# trainer.save_metrics("predict", metrics)
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# if trainer.is_world_process_zero():
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# if training_args.predict_with_generate:
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# predictions = tokenizer.batch_decode(
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# predict_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
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# )
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# predictions = [pred.strip() for pred in predictions]
|
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# 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.
|
||||
|
||||
<Tip>
|
||||
|
||||
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.
|
||||
|
||||
</Tip>
|
||||
|
||||
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()
|
||||
Loading…
Reference in New Issue