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voice_clone_v3
/
transformers
/examples
/research_projects
/tapex
/run_wikitablequestions_with_tapex.py
#!/usr/bin/env python | |
# coding=utf-8 | |
# Copyright 2022 The Microsoft and 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. | |
""" | |
Fine-tuning the library models for tapex on table-based question answering tasks. | |
Adapted from script: https://github.com/huggingface/transformers/blob/master/examples/pytorch/summarization/run_summarization.py | |
""" | |
import logging | |
import os | |
import sys | |
from collections import defaultdict | |
from dataclasses import dataclass, field | |
from functools import partial | |
from typing import List, Optional | |
import nltk # Here to have a nice missing dependency error message early on | |
import numpy as np | |
import pandas as pd | |
from datasets import load_dataset | |
from filelock import FileLock | |
import transformers | |
from transformers import ( | |
AutoConfig, | |
BartForConditionalGeneration, | |
DataCollatorForSeq2Seq, | |
HfArgumentParser, | |
Seq2SeqTrainer, | |
Seq2SeqTrainingArguments, | |
TapexTokenizer, | |
set_seed, | |
) | |
from transformers.file_utils import is_offline_mode | |
from transformers.trainer_utils import get_last_checkpoint, is_main_process | |
from transformers.utils import check_min_version | |
# Will error if the minimal version of Transformers is not installed. Remove at your own risks. | |
check_min_version("4.17.0.dev0") | |
logger = logging.getLogger(__name__) | |
try: | |
nltk.data.find("tokenizers/punkt") | |
except (LookupError, OSError): | |
if is_offline_mode(): | |
raise LookupError( | |
"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files" | |
) | |
with FileLock(".lock") as lock: | |
nltk.download("punkt", quiet=True) | |
class ModelArguments: | |
""" | |
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. | |
""" | |
model_name_or_path: str = field( | |
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}, | |
) | |
config_name: Optional[str] = field( | |
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} | |
) | |
tokenizer_name: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"Pretrained tokenizer name or path if not the same as model_name. " | |
"By default we use BART-large tokenizer for TAPEX-large." | |
) | |
}, | |
) | |
cache_dir: Optional[str] = field( | |
default=None, | |
metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"}, | |
) | |
use_fast_tokenizer: bool = field( | |
default=True, | |
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, | |
) | |
model_revision: str = field( | |
default="main", | |
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, | |
) | |
use_auth_token: bool = field( | |
default=False, | |
metadata={ | |
"help": ( | |
"Will use the token generated when running `huggingface-cli login` (necessary to use this script " | |
"with private models)." | |
) | |
}, | |
) | |
class DataTrainingArguments: | |
""" | |
Arguments pertaining to what data we are going to input our model for training and eval. | |
""" | |
dataset_name: Optional[str] = field( | |
default="wikitablequestions", metadata={"help": "The name of the dataset to use (via the datasets library)."} | |
) | |
dataset_config_name: Optional[str] = field( | |
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} | |
) | |
train_file: Optional[str] = field( | |
default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."} | |
) | |
validation_file: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"An optional input evaluation data file to evaluate the metrics (rouge) on (a jsonlines or csv file)." | |
) | |
}, | |
) | |
test_file: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": "An optional input test data file to evaluate the metrics (rouge) on (a jsonlines or csv file)." | |
}, | |
) | |
overwrite_cache: bool = field( | |
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} | |
) | |
preprocessing_num_workers: Optional[int] = field( | |
default=None, | |
metadata={"help": "The number of processes to use for the preprocessing."}, | |
) | |
max_source_length: Optional[int] = field( | |
default=1024, | |
metadata={ | |
"help": ( | |
"The maximum total input sequence length after tokenization. Sequences longer " | |
"than this will be truncated, sequences shorter will be padded." | |
) | |
}, | |
) | |
max_target_length: Optional[int] = field( | |
default=128, | |
metadata={ | |
"help": ( | |
"The maximum total sequence length for target text after tokenization. Sequences longer " | |
"than this will be truncated, sequences shorter will be padded." | |
) | |
}, | |
) | |
val_max_target_length: Optional[int] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"The maximum total sequence length for validation target text after tokenization. Sequences longer " | |
"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`." | |
"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used " | |
"during ``evaluate`` and ``predict``." | |
) | |
}, | |
) | |
pad_to_max_length: bool = field( | |
default=False, | |
metadata={ | |
"help": ( | |
"Whether to pad all samples to model maximum sentence length. " | |
"If False, will pad the samples dynamically when batching to the maximum length in the batch. More " | |
"efficient on GPU but very bad for TPU." | |
) | |
}, | |
) | |
max_train_samples: Optional[int] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"For debugging purposes or quicker training, truncate the number of training examples to this " | |
"value if set." | |
) | |
}, | |
) | |
max_eval_samples: Optional[int] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"For debugging purposes or quicker training, truncate the number of evaluation examples to this " | |
"value if set." | |
) | |
}, | |
) | |
max_predict_samples: Optional[int] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"For debugging purposes or quicker training, truncate the number of prediction examples to this " | |
"value if set." | |
) | |
}, | |
) | |
num_beams: Optional[int] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"Number of beams to use for evaluation. This argument will be passed to ``model.generate``, " | |
"which is used during ``evaluate`` and ``predict``." | |
) | |
}, | |
) | |
ignore_pad_token_for_loss: bool = field( | |
default=True, | |
metadata={ | |
"help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not." | |
}, | |
) | |
def __post_init__(self): | |
if self.dataset_name is None and self.train_file is None and self.validation_file is None: | |
raise ValueError("Need either a dataset name or a training/validation file.") | |
else: | |
if self.train_file is not None: | |
extension = self.train_file.split(".")[-1] | |
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." | |
if self.validation_file is not None: | |
extension = self.validation_file.split(".")[-1] | |
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." | |
if self.val_max_target_length is None: | |
self.val_max_target_length = self.max_target_length | |
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. | |
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() | |
# Detecting last checkpoint. | |
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." | |
) | |
# 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)], | |
) | |
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) | |
# 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}" | |
) | |
# Set the verbosity to info of the Transformers logger (on main process only): | |
if is_main_process(training_args.local_rank): | |
transformers.utils.logging.set_verbosity_info() | |
logger.info(f"Training/evaluation parameters {training_args}") | |
# Set seed before initializing model. | |
set_seed(training_args.seed) | |
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below) | |
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ | |
# (the dataset will be downloaded automatically from the datasets Hub). | |
# | |
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table. | |
# | |
# In distributed training, the load_dataset function guarantee that only one local process can concurrently | |
# download the dataset. | |
if data_args.dataset_name is not None: | |
# Downloading and loading a dataset from the hub. | |
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir) | |
else: | |
data_files = {} | |
if data_args.train_file is not None: | |
data_files["train"] = data_args.train_file | |
extension = data_args.train_file.split(".")[-1] | |
if data_args.validation_file is not None: | |
data_files["validation"] = data_args.validation_file | |
extension = data_args.validation_file.split(".")[-1] | |
if data_args.test_file is not None: | |
data_files["test"] = data_args.test_file | |
extension = data_args.test_file.split(".")[-1] | |
datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir) | |
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at | |
# https://huggingface.co/docs/datasets/loading_datasets.html. | |
# Load pretrained model and tokenizer | |
# | |
# Distributed training: | |
# The .from_pretrained methods guarantee that only one local process can concurrently | |
# download model & vocab. | |
config = AutoConfig.from_pretrained( | |
model_args.config_name if model_args.config_name else model_args.model_name_or_path, | |
cache_dir=model_args.cache_dir, | |
revision=model_args.model_revision, | |
use_auth_token=True if model_args.use_auth_token else None, | |
) | |
# IMPORTANT: the initial BART model's decoding is penalized by no_repeat_ngram_size, and thus | |
# we should disable it here to avoid problematic generation | |
config.no_repeat_ngram_size = 0 | |
config.max_length = 1024 | |
config.early_stopping = False | |
# load tapex tokenizer | |
tokenizer = TapexTokenizer.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=model_args.use_fast_tokenizer, | |
revision=model_args.model_revision, | |
use_auth_token=True if model_args.use_auth_token else None, | |
add_prefix_space=True, | |
) | |
# load Bart based Tapex model (default tapex-large) | |
model = BartForConditionalGeneration.from_pretrained( | |
model_args.model_name_or_path, | |
from_tf=bool(".ckpt" in model_args.model_name_or_path), | |
config=config, | |
cache_dir=model_args.cache_dir, | |
revision=model_args.model_revision, | |
use_auth_token=True if model_args.use_auth_token else None, | |
) | |
if model.config.decoder_start_token_id is None: | |
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined") | |
# Preprocessing the datasets. | |
# We need to tokenize inputs and targets. | |
if training_args.do_train: | |
column_names = datasets["train"].column_names | |
elif training_args.do_eval: | |
column_names = datasets["validation"].column_names | |
elif training_args.do_predict: | |
column_names = datasets["test"].column_names | |
else: | |
logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.") | |
return | |
# Temporarily set max_target_length for training. | |
max_target_length = data_args.max_target_length | |
padding = "max_length" if data_args.pad_to_max_length else False | |
if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"): | |
logger.warning( | |
"label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for" | |
f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory" | |
) | |
def preprocess_tableqa_function(examples, is_training=False): | |
""" | |
The is_training FLAG is used to identify if we could use the supervision | |
to truncate the table content if it is required. | |
""" | |
questions = [question.lower() for question in examples["question"]] | |
example_tables = examples["table"] | |
tables = [ | |
pd.DataFrame.from_records(example_table["rows"], columns=example_table["header"]) | |
for example_table in example_tables | |
] | |
# using wikitablequestion's answer set | |
answers = examples["answers"] | |
# IMPORTANT: we cannot pass by answers during evaluation, answers passed during training are used to | |
# truncate large tables in the train set! | |
if is_training: | |
model_inputs = tokenizer( | |
table=tables, | |
query=questions, | |
answer=answers, | |
max_length=data_args.max_source_length, | |
padding=padding, | |
truncation=True, | |
) | |
else: | |
model_inputs = tokenizer( | |
table=tables, query=questions, max_length=data_args.max_source_length, padding=padding, truncation=True | |
) | |
labels = tokenizer( | |
answer=[", ".join(answer) for answer in answers], | |
max_length=max_target_length, | |
padding=padding, | |
truncation=True, | |
) | |
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore | |
# padding in the loss. | |
if padding == "max_length" and data_args.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 | |
# in training, we can use the answer as extra information to truncate large tables | |
preprocess_tableqa_function_training = partial(preprocess_tableqa_function, is_training=True) | |
if training_args.do_train: | |
if "train" not in datasets: | |
raise ValueError("--do_train requires a train dataset") | |
train_dataset = datasets["train"] | |
if data_args.max_train_samples is not None: | |
train_dataset = train_dataset.select(range(data_args.max_train_samples)) | |
train_dataset = train_dataset.map( | |
preprocess_tableqa_function_training, | |
batched=True, | |
num_proc=data_args.preprocessing_num_workers, | |
remove_columns=column_names, | |
load_from_cache_file=not data_args.overwrite_cache, | |
) | |
if training_args.do_eval: | |
max_target_length = data_args.val_max_target_length | |
if "validation" not in datasets: | |
raise ValueError("--do_eval requires a validation dataset") | |
eval_dataset = datasets["validation"] | |
if data_args.max_eval_samples is not None: | |
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples)) | |
eval_dataset = eval_dataset.map( | |
preprocess_tableqa_function, | |
batched=True, | |
num_proc=data_args.preprocessing_num_workers, | |
remove_columns=column_names, | |
load_from_cache_file=not data_args.overwrite_cache, | |
) | |
if training_args.do_predict: | |
max_target_length = data_args.val_max_target_length | |
if "test" not in datasets: | |
raise ValueError("--do_predict requires a test dataset") | |
predict_dataset = datasets["test"] | |
if data_args.max_predict_samples is not None: | |
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples)) | |
predict_dataset = predict_dataset.map( | |
preprocess_tableqa_function, | |
batched=True, | |
num_proc=data_args.preprocessing_num_workers, | |
remove_columns=column_names, | |
load_from_cache_file=not data_args.overwrite_cache, | |
) | |
# Data collator | |
label_pad_token_id = -100 if data_args.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=8 if training_args.fp16 else None, | |
) | |
def postprocess_text(preds, labels): | |
preds = [pred.strip() for pred in preds] | |
labels = [label.strip() for label in labels] | |
return preds, labels | |
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 data_args.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) | |
# Some simple post-processing | |
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) | |
delimiter = ", " | |
# define example evaluation | |
def evaluate_example(predict_str: str, ground_str: str): | |
predict_spans = predict_str.split(delimiter) | |
ground_spans = ground_str.split(delimiter) | |
predict_values = defaultdict(lambda: 0) | |
ground_values = defaultdict(lambda: 0) | |
for span in predict_spans: | |
try: | |
predict_values[float(span)] += 1 | |
except ValueError: | |
predict_values[span.strip()] += 1 | |
for span in ground_spans: | |
try: | |
ground_values[float(span)] += 1 | |
except ValueError: | |
ground_values[span.strip()] += 1 | |
_is_correct = predict_values == ground_values | |
return _is_correct | |
def get_denotation_accuracy(predictions: List[str], references: List[str]): | |
assert len(predictions) == len(references) | |
correct_num = 0 | |
for predict_str, ground_str in zip(predictions, references): | |
is_correct = evaluate_example(predict_str.lower(), ground_str.lower()) | |
if is_correct: | |
correct_num += 1 | |
return correct_num / len(predictions) | |
accuracy = get_denotation_accuracy(decoded_preds, decoded_labels) | |
result = {"denotation_accuracy": accuracy} | |
return result | |
# Initialize our Trainer | |
trainer = Seq2SeqTrainer( | |
model=model, | |
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, | |
tokenizer=tokenizer, | |
data_collator=data_collator, | |
compute_metrics=compute_metrics if training_args.predict_with_generate else None, | |
) | |
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) | |
trainer.save_model() # Saves the tokenizer too for easy upload | |
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.log_metrics("train", metrics) | |
trainer.save_metrics("train", metrics) | |
trainer.save_state() | |
# Evaluation | |
results = {} | |
if training_args.do_eval: | |
logger.info("*** Evaluate ***") | |
metrics = trainer.evaluate( | |
max_length=data_args.val_max_target_length, num_beams=data_args.num_beams, metric_key_prefix="eval" | |
) | |
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=data_args.val_max_target_length, | |
num_beams=data_args.num_beams, | |
) | |
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] | |
output_prediction_file = os.path.join(training_args.output_dir, "tapex_predictions.txt") | |
with open(output_prediction_file, "w") as writer: | |
writer.write("\n".join(predictions)) | |
return results | |
def _mp_fn(index): | |
# For xla_spawn (TPUs) | |
main() | |
if __name__ == "__main__": | |
main() | |