#!/usr/bin/env python # coding=utf-8 # Copyright 2020 The HuggingFace Inc. 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. """ Finetuning the library models for sequence classification on GLUE.""" # You can also adapt this script on your own text classification task. Pointers for this are left as comments. print('The script has began') import itertools import logging import os import random import sys import time from dataclasses import dataclass, field from typing import Optional import shutil import datasets import numpy as np from datasets import load_dataset, load_metric import torch import transformers from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, PretrainedConfig, Trainer, TrainingArguments, default_data_collator, set_seed, TrainerCallback, ) from transformers import BertForSequenceClassification from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version from transformers.utils.versions import require_version from src import getTokenizedLabelDescriptions from src import getLabelModel from src import SemSupDataset from src import AutoModelForMultiLabelClassification from src import multilabel_metrics from src import task_to_keys, task_to_label_keys, dataset_to_numlabels from src import DataTrainingArguments, ModelArguments, CustomTrainingArguments from src import dataset_classification_type from src import BertForSemanticEmbedding from src import read_yaml_config from transformers import AdamW, get_linear_schedule_with_warmup from torch.utils.data import DataLoader import os require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") logger = logging.getLogger(__name__) def setup_logging(training_args): # 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)], ) 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}") def get_last_check(training_args): last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) return last_checkpoint def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. print('Main Function is Called!!!', sys.argv) parser = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)) if len(sys.argv) > 1 and sys.argv[1].startswith('--local_rank'): extra_args = {'local_rank' : sys.argv[1].split('=')[1]} argv = sys.argv[0:1] + sys.argv[2:] else: argv = sys.argv extra_args = {} print(len(argv) == 3 and argv[1].endswith(".yml")) if len(argv) == 2 and 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(argv[1])) elif len(argv) == 3 and argv[1].endswith(".yml"): model_args, data_args, training_args = parser.parse_dict(read_yaml_config(os.path.abspath(argv[1]), output_dir = argv[2], extra_args = extra_args)) print('training args', training_args) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() setup_logging(training_args) if training_args.seed == -1: training_args.seed = np.random.randint(0, 100000000) print(training_args.seed) last_checkpoint = get_last_check(training_args) set_seed(training_args.seed) if data_args.dataset_name is not None and not data_args.load_from_local: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: # Loading a dataset from your local files. # CSV/JSON training and evaluation files are needed. data_files = {"train": data_args.train_file, "validation": data_args.validation_file} # Get the test dataset: you can provide your own CSV/JSON test file (see below) # when you use `do_predict` without specifying a GLUE benchmark task. if training_args.do_predict: if data_args.test_file is not None: data_files["test"] = data_args.test_file else: raise ValueError("Need a test file for `do_predict`.") for key in data_files.keys(): logger.info(f"load a local file for {key}: {data_files[key]}") if data_args.train_file.endswith(".csv"): # Loading a dataset from local csv files raw_datasets = load_dataset( "csv", data_files=data_files, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) else: # Loading a dataset from local json files print('df are', data_files, model_args.cache_dir) raw_datasets = load_dataset( "json", data_files=data_files, cache_dir=model_args.cache_dir, use_auth_token=True if model_args.use_auth_token else None, ) # See more about loading any type of standard or custom dataset at # https://huggingface.co/docs/datasets/loading_datasets.html. # Labels if data_args.task_name is not None: label_key = task_to_label_keys[data_args.task_name] if training_args.scenario == 'unseen_labels': label_list = [x.strip() for x in open(data_args.all_labels).readlines()] train_labels = list(set([item for sublist in raw_datasets['train'][label_key] for item in sublist])) if data_args.test_labels is not None: test_labels = [x.strip() for x in open(data_args.test_labels).readlines()] else: test_labels = list(set([item for sublist in raw_datasets['validation'][label_key] for item in sublist])) else: label_list = list(set(itertools.chain(*[ [item for sublist in raw_datasets[split_key][label_key] for item in sublist] for split_key in raw_datasets.keys()] ))) num_labels = len(label_list) label_list.sort() # For consistency print('Debugging: num_labels: ', num_labels) print('Debugging: label_list[:50]: ', label_list[:50]) else: # Trying to have good defaults here, don't hesitate to tweak to your needs. # A useful fast method: # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique label_list = raw_datasets["train"].unique("label") label_list.sort() # Let's sort it for determinism num_labels = len(label_list) # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if model_args.semsup: label_model, label_tokenizer = getLabelModel(data_args, model_args) config = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path, # num_labels=num_labels, finetuning_task=data_args.task_name, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) config.model_name_or_path = model_args.model_name_or_path config.problem_type = dataset_classification_type[data_args.task_name] config.negative_sampling = model_args.negative_sampling config.semsup = model_args.semsup config.encoder_model_type = model_args.encoder_model_type config.arch_type = model_args.arch_type config.coil = model_args.coil config.token_dim = model_args.token_dim config.colbert = model_args.colbert if config.semsup: config.label_hidden_size = label_model.config.hidden_size print('Label hidden size is ', label_model.config.hidden_size) temp_label_id = {v: i for i, v in enumerate(label_list)} tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=True,#model_args.use_fast_tokenizer, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) # Preprocessing the raw_datasets if data_args.task_name is not None: sentence1_key, sentence2_key = task_to_keys[data_args.task_name] else: # Again, we try to have some nice defaults but don't hesitate to tweak to your use case. non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"] if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names: sentence1_key, sentence2_key = "sentence1", "sentence2" else: if len(non_label_column_names) >= 2: sentence1_key, sentence2_key = non_label_column_names[:2] else: sentence1_key, sentence2_key = non_label_column_names[0], None # Padding strategy if data_args.pad_to_max_length: padding = "max_length" else: # We will pad later, dynamically at batch creation, to the max sequence length in each batch padding = False # Some models have set the order of the labels to use, so let's make sure we do use it. def model_init(): model = BertForSemanticEmbedding(config) num_frozen_layers = model_args.num_frozen_layers if num_frozen_layers > 0: try: for param in model.encoder.bert.embeddings.parameters(): param.requires_grad = False for param in model.encoder.bert.pooler.parameters(): param.requires_grad = False for layer in model.encoder.bert.encoder.layer[:num_frozen_layers]: for param in layer.parameters(): param.requires_grad = False except: for param in model.encoder.embeddings.parameters(): param.requires_grad = False for param in model.encoder.pooler.parameters(): param.requires_grad = False for layer in model.encoder.encoder.layer[:num_frozen_layers]: for param in layer.parameters(): param.requires_grad = False # Place the label model inside the main model if model_args.semsup: model.label_model = label_model model.label_tokenizer = label_tokenizer if model_args.tie_weights: for i in range(9): if num_frozen_layers >= 9: try: model.label_model.encoder.layer[i] = model.encoder.bert.encoder.layer[i] except: model.label_model.encoder.layer[i] = model.encoder.encoder.layer[i] else: for param in model.label_model.encoder.layer[i].parameters(): param.requires_grad = False for param in model.label_model.embeddings.parameters(): param.requires_grad = False for param in model.label_model.pooler.parameters(): param.requires_grad = False else: label_frozen_layers = model_args.label_frozen_layers if label_frozen_layers > 0: print(model.label_model) for param in model.label_model.embeddings.parameters(): param.requires_grad = False for param in model.label_model.pooler.parameters(): param.requires_grad = False for layer in model.label_model.encoder.layer[:label_frozen_layers]: for param in layer.parameters(): param.requires_grad = False model.config.label2id = {l: i for i, l in enumerate(label_list)} model.config.id2label = {id: label for label, id in config.label2id.items()} return model model = model_init() if model_args.pretrained_model_path != '': model.load_state_dict(torch.load(model_args.pretrained_model_path, map_location = list(model.parameters())[0].device)) if model_args.pretrained_label_model_path != '': model.label_model.load_state_dict(torch.load(model_args.pretrained_label_model_path, map_location = list(model.parameters())[0].device)) id2label = model.config.id2label label_to_id = model.config.label2id if data_args.max_seq_length > tokenizer.model_max_length: logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) def preprocess_function(examples): # Tokenize the texts args = ( (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) ) result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True) # Map labels to IDs (not necessary for GLUE tasks) if label_to_id is not None and label_key in examples: # check if multi-label problem if isinstance(examples[label_key][0], list): # Multi-Label, create one-hot encoding labels = [[label_to_id[l] for l in examples[label_key][i]] for i in range(len(examples[label_key]))] result["label"] = [[1 if j in labels[i] else 0 for j in range(num_labels)] for i in range(len(labels))] else: result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]] # Labels keyword should not be present(it may contain string) try: del input['labels'] except: ... return result try: if data_args.test_descriptions_file == '': data_args.test_descriptions_file = data_args.descriptions_file except: data_args.test_descriptions_file = data_args.descriptions_file print('Running with_transform') raw_datasets = raw_datasets.with_transform(preprocess_function) class_descs_tokenized = None if model_args.semsup and data_args.large_dset and os.path.exists(data_args.tokenized_descs_file): if data_args.tokenized_descs_file.endswith('npy'): class_descs_tokenized = np.load(data_args.tokenized_descs_file, allow_pickle=True) if training_args.do_train: if "train" not in raw_datasets: raise ValueError("--do_train requires a train dataset") train_dataset = raw_datasets["train"] if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(np.random.choice(len(train_dataset), max_train_samples)) if model_args.semsup: train_dataset = SemSupDataset(train_dataset, data_args, data_args.descriptions_file, label_to_id, id2label, label_tokenizer, return_desc_embeddings = True, sampleRandom = data_args.contrastive_learning_samples, cl_min_positive_descs= data_args.cl_min_positive_descs, seen_labels = None if training_args.scenario == 'seen' else train_labels, add_label_name = model_args.add_label_name, max_descs_per_label = data_args.max_descs_per_label, use_precomputed_embeddings = model_args.use_precomputed_embeddings, bm_short_file = data_args.bm_short_file, ignore_pos_labels_file = data_args.ignore_pos_labels_file, class_descs_tokenized = class_descs_tokenized) else: train_dataset = SemSupDataset(train_dataset, data_args, data_args.descriptions_file, label_to_id, id2label, None, useSemSup = False, add_label_name = model_args.add_label_name) if training_args.do_eval: if "validation" not in raw_datasets and "validation_matched" not in raw_datasets: raise ValueError("--do_eval requires a validation dataset") eval_dataset = raw_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"] choice_indexes = None if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) if data_args.random_sample_seed != -1: l = len(eval_dataset) np.random.seed(data_args.random_sample_seed) choice_indexes = np.random.choice(l, max_eval_samples, replace = False).tolist() choice_indexes = [x for x in choice_indexes] import pickle pickle.dump(choice_indexes, open('choice_indexes.pkl','wb')) eval_dataset = eval_dataset.select(choice_indexes) np.random.seed() else: choice_indexes = None eval_dataset = eval_dataset.select(range(max_eval_samples)) if model_args.semsup: eval_dataset = SemSupDataset(eval_dataset, data_args, data_args.test_descriptions_file, label_to_id, id2label, label_tokenizer, return_desc_embeddings=True, seen_labels = None if training_args.scenario == 'seen' else test_labels, add_label_name = model_args.add_label_name, max_descs_per_label = data_args.max_descs_per_label, use_precomputed_embeddings = model_args.use_precomputed_embeddings, class_descs_tokenized = class_descs_tokenized, isTrain = False, choice_indexes = choice_indexes) if training_args.do_predict: if "test" not in raw_datasets and "test_matched" not in raw_datasets: raise ValueError("--do_predict requires a test dataset") predict_dataset = raw_datasets["test_matched" if data_args.task_name == "mnli" else "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)) compute_metrics = multilabel_metrics(data_args, model.config.id2label, model.config.label2id, {}, training_args) if data_args.pad_to_max_length: data_collator = default_data_collator elif training_args.fp16: data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) else: data_collator = None # Initialize our Trainer print('Initializing Optimizers') from torch.optim import AdamW # from transformers import AdamW if model_args.use_custom_optimizer: decay_cond = lambda x: x[0].lower().find('layernorm.weight')!=-1 or x[0].lower().find('bias')!=-1 if model_args.semsup and not data_args.hyper_search: main_decay_params = list(map(lambda x: x[1], filter(lambda x: x[1].requires_grad and decay_cond(x) and (x[0][12:], x[1]) not in model.label_model.named_parameters() , model.named_parameters()))) main_no_decay_params = list(map(lambda x: x[1],filter(lambda x: x[1].requires_grad and not decay_cond(x) and (x[0][12:], x[1]) not in model.label_model.named_parameters(), model.named_parameters()))) label_decay_params = list(map(lambda x: x[1], filter(lambda x: x[1].requires_grad and decay_cond(x) , model.label_model.named_parameters()))) label_no_decay_params = list(map(lambda x: x[1],filter(lambda x: x[1].requires_grad and not decay_cond(x), model.label_model.named_parameters()))) if model_args.tie_weights: label_decay_params = list(set(label_decay_params).difference(main_decay_params)) label_no_decay_params = list(set(label_no_decay_params).difference(main_no_decay_params)) optimizer = AdamW([ {'params': main_decay_params, 'weight_decay': 1e-2}, {'params': main_no_decay_params, 'weight_decay': 0}, {'params': label_decay_params, 'weight_decay': 1e-2, 'lr' : training_args.output_learning_rate}, {'params': label_no_decay_params, 'weight_decay': 0, 'lr' : training_args.output_learning_rate} ], lr = training_args.learning_rate, eps= 1e-6) ... else: decay_params = list(map(lambda x: x[1], filter(lambda x: decay_cond(x), model.named_parameters()))) no_decay_params = list(map(lambda x: x[1],filter(lambda x: not decay_cond(x), model.named_parameters()))) optimizer = optim.AdamW([ {'params': decay_params, 'weight_decay': 1e-2}, {'params': no_decay_params, 'weight_decay': 0}], lr = training_args.learning_rate, eps= 1e-6) trainer = Trainer( model=model, model_init= None, args=training_args, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, compute_metrics=compute_metrics, tokenizer=tokenizer, data_collator=data_collator, optimizers = (optimizer if model_args.use_custom_optimizer else None, None) , ) # Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) metrics = train_result.metrics max_train_samples = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) ) metrics["train_samples"] = min(max_train_samples, len(train_dataset)) trainer.save_model() # Saves the tokenizer too for easy upload trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("*** Evaluate ***") # Loop to handle MNLI double evaluation (matched, mis-matched) tasks = [data_args.task_name] eval_datasets = [eval_dataset] if data_args.task_name == "mnli": tasks.append("mnli-mm") eval_datasets.append(raw_datasets["validation_mismatched"]) combined = {} for eval_dataset, task in zip(eval_datasets, tasks): metrics = trainer.evaluate(eval_dataset=eval_dataset) 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)) if task == "mnli-mm": metrics = {k + "_mm": v for k, v in metrics.items()} if task is not None and "mnli" in task: combined.update(metrics) trainer.save_metrics("eval", combined if task is not None and "mnli" in task else metrics) trainer.log_metrics("eval", metrics) if training_args.do_predict: logger.info("*** Predict ***") # Loop to handle MNLI double evaluation (matched, mis-matched) tasks = [data_args.task_name] predict_datasets = [predict_dataset] if data_args.task_name == "mnli": tasks.append("mnli-mm") predict_datasets.append(raw_datasets["test_mismatched"]) for predict_dataset, task in zip(predict_datasets, tasks): # Removing the `label` columns because it contains -1 and Trainer won't like that. predict_dataset = predict_dataset.remove_columns("label") predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions predictions = np.argmax(predictions, axis=1) output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt") if trainer.is_world_process_zero(): with open(output_predict_file, "w") as writer: logger.info(f"***** Predict results {task} *****") writer.write("index\tprediction\n") for index, item in enumerate(predictions): item = label_list[item] writer.write(f"{index}\t{item}\n") kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"} if data_args.task_name is not None: kwargs["language"] = "en" kwargs["dataset_tags"] = "glue" kwargs["dataset_args"] = data_args.task_name kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}" if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()