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Diffstat (limited to 'finetune_bert.py')
| -rw-r--r-- | finetune_bert.py | 718 |
1 files changed, 84 insertions, 634 deletions
diff --git a/finetune_bert.py b/finetune_bert.py index 59c8090..9a8ad46 100644 --- a/finetune_bert.py +++ b/finetune_bert.py @@ -1,663 +1,113 @@ -#!/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. """ -Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset. - -Here is the full list of checkpoints on the hub that can be fine-tuned by this script: -https://huggingface.co/models?filter=text-generation +accelerate launch --mixed_precision bf16 finetune_bert.py --model_direction ltr --learning_rate 5e-5 --output_dir checkpoints/test """ -# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments. -""" -From https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py -""" - -import logging +import argparse import math -import os -import sys -from dataclasses import dataclass, field -from itertools import chain -from typing import Optional -import datasets -import evaluate +import accelerate import torch -from datasets import load_dataset - import transformers -from transformers import ( - CONFIG_MAPPING, - MODEL_FOR_CAUSAL_LM_MAPPING, - AutoConfig, - AutoModelForCausalLM, - AutoTokenizer, - HfArgumentParser, - Trainer, - TrainingArguments, - default_data_collator, - is_torch_xla_available, - set_seed, -) -from transformers.testing_utils import CaptureLogger -from transformers.trainer_utils import get_last_checkpoint -from transformers.utils import check_min_version, send_example_telemetry -from transformers.utils.versions import require_version - - -# Will error if the minimal version of Transformers is not installed. Remove at your own risks. -check_min_version("4.47.0.dev0") - -require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt") - -logger = logging.getLogger(__name__) - - -MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys()) -MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) - - -@dataclass -class ModelArguments: - """ - Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. - """ - # text_direction: str = field( - # - # ) - model_name_or_path: Optional[str] = field( - default=None, - metadata={ - "help": ( - "The model checkpoint for weights initialization. Don't set if you want to train a model from scratch." - ) - }, - ) - # model_type: Optional[str] = field( - # default=None, - # metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, - # ) - config_overrides: Optional[str] = field( - default=None, - metadata={ - "help": ( - "Override some existing default config settings when a model is trained from scratch. Example: " - "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" - ) - }, - ) - 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"} - ) - cache_dir: Optional[str] = field( - default=None, - metadata={"help": "Where do you want 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)."}, - ) - token: str = field( - default=None, - metadata={ - "help": ( - "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " - "generated when running `huggingface-cli login` (stored in `~/.huggingface`)." - ) - }, - ) - trust_remote_code: bool = field( - default=False, - metadata={ - "help": ( - "Whether to trust the execution of code from datasets/models defined on the Hub." - " This option should only be set to `True` for repositories you trust and in which you have read the" - " code, as it will execute code present on the Hub on your local machine." - ) - }, - ) - torch_dtype: Optional[str] = field( - default=None, - metadata={ - "help": ( - "Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the " - "dtype will be automatically derived from the model's weights." - ), - "choices": ["auto", "bfloat16", "float16", "float32"], - }, - ) - low_cpu_mem_usage: bool = field( - default=False, - metadata={ - "help": ( - "It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. " - "set True will benefit LLM loading time and RAM consumption." - ) - }, - ) +from datasets import load_dataset +from torch.utils.data import DataLoader +from tqdm.auto import tqdm +from transformers import get_scheduler - def __post_init__(self): - if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None): - raise ValueError( - "--config_overrides can't be used in combination with --config_name or --model_name_or_path" - ) +from utils import preprocess_datasets, convert_to_torch_dataset, add_attn_hooks, causal_loss_wrapper -@dataclass -class DataTrainingArguments: +def parse_args(): """ - Arguments pertaining to what data we are going to input our model for training and eval. + Re-using HuggingFace arguments when possible (most of the help strings are directly copied). + https://github.com/huggingface/transformers/blob/7bbc62474391aff64f63fcc064c975752d1fa4de/examples/pytorch/language-modeling/run_clm.py#L75 """ - - dataset_name: Optional[str] = field( - default=None, 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)."} + parser = argparse.ArgumentParser() + + # Model + parser.add_argument("--model_direction", type=str, required=True, choices=["ltr", "rtl"], + help="Whether to train a left-to-right or right-to-left LM.") + parser.add_argument("--model_name_or_path", type=str, default="bert-base-uncased", + help="Checkpoint to initialize weights from.") # TODO: option for training from scratch w/ conf + + # Data + parser.add_argument("--dataset_name", type=str, default="Salesforce/wikitext", + help="The name of the dataset to use (via the datasets library).") + parser.add_argument("--dataset_config_name", type=str, default="wikitext-103-v1", + help="The configuration name of the dataset to use (via the datasets library).") + # TODO: block_size, train on shorter seqs? + parser.add_argument( + "--block_size", + type=int, + help="Optional input sequence length after tokenization. " + "The training dataset will be truncated in block of this size for training. " + "Default to the model max input length for single sentence inputs (take into account special tokens)." ) - train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) - validation_file: Optional[str] = field( - default=None, - metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, - ) - 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." - ) - }, - ) - streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"}) - block_size: Optional[int] = field( - default=None, - metadata={ - "help": ( - "Optional input sequence length after tokenization. " - "The training dataset will be truncated in block of this size for training. " - "Default to the model max input length for single sentence inputs (take into account special tokens)." - ) - }, - ) - overwrite_cache: bool = field( - default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} - ) - validation_split_percentage: Optional[int] = field( - default=5, - metadata={ - "help": "The percentage of the train set used as validation set in case there's no validation split" - }, - ) - preprocessing_num_workers: Optional[int] = field( - default=None, - metadata={"help": "The number of processes to use for the preprocessing."}, - ) - keep_linebreaks: bool = field( - default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."} - ) - - def __post_init__(self): - if self.streaming: - require_version("datasets>=2.0.0", "The streaming feature requires `datasets>=2.0.0`") - 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", "txt"], "`train_file` should be a csv, a json or a txt file." - if self.validation_file is not None: - extension = self.validation_file.split(".")[-1] - assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." + # Training + parser.add_argument("--output_dir", type=str, required=True, + help="The output directory where the model predictions and checkpoints will be written.") + parser.add_argument("--per_device_train_batch_size", type=int, default=8) + parser.add_argument("--per_device_eval_batch_size", type=int, default=16) + parser.add_argument("--gradient_accumulation_steps", type=int, default=1) + parser.add_argument("--num_train_epochs", type=int, default=1) + parser.add_argument("--learning_rate", type=float, required=True) + parser.add_argument("--warmup_steps", type=int, default=0) + parser.add_argument("--weight_decay", type=float, default=0.0) + parser.add_argument("--logging_steps", type=int, default=1, + help="Number of update steps between two logs.") + parser.add_argument("--eval_steps", type=int, default=500, + help="Number of update steps between two logs.") + parser.add_argument("--dataloader_num_workers", type=int, default=8) + return parser.parse_args() 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, TrainingArguments)) - 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() + args = parse_args() - # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The - # information sent is the one passed as arguments along with your Python/PyTorch versions. - send_example_telemetry("run_clm", model_args, data_args) + accelerator = accelerate.Accelerator() + model = transformers.AutoModelForMaskedLM.from_pretrained(args.model_name_or_path, attn_implementation="sdpa") + add_attn_hooks(model, args.model_direction) + tokenizer = transformers.AutoTokenizer.from_pretrained(args.model_name_or_path) - # 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)], - ) + # Data + raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) + block_size = args.block_size if args.block_size is not None else model.config.max_position_embeddings + processed_datasets = preprocess_datasets(raw_datasets, tokenizer, block_size) + for split, hf_dataset in processed_datasets.items(): + processed_datasets[split] = convert_to_torch_dataset(hf_dataset) - if training_args.should_log: - # The default of training_args.log_level is passive, so we set log level at info here to have that default. - transformers.utils.logging.set_verbosity_info() + train_loader = DataLoader(processed_datasets["train"], batch_size=args.per_device_train_batch_size, shuffle=True) + val_loader = DataLoader(processed_datasets["validation"], batch_size=args.per_device_eval_batch_size) + # test_loader = DataLoader(processed_datasets["test"], batch_size=args.per_device_eval_batch_size) + model, train_loader, val_loader = accelerator.prepare(model, train_loader, val_loader) - 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: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}" + optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay) + lr_scheduler = transformers.get_scheduler( + name=transformers.SchedulerType.COSINE, + optimizer=optimizer, + num_warmup_steps=args.warmup_steps, #* accelerator.num_processes, + num_training_steps=args.num_train_epochs * math.ceil(len(train_loader) / args.gradient_accumulation_steps), ) - logger.info(f"Training/evaluation parameters {training_args}") - - # 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." - ) - - # Set seed before initializing model. - set_seed(training_args.seed) - - # Get the datasets: you can either provide your own CSV/JSON/TXT 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 CSV/JSON files, this script will use the column called 'text' or the first column if no column called - # 'text' is found. You can easily tweak this behavior (see below). - # - # 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. - raw_datasets = load_dataset( - data_args.dataset_name, - data_args.dataset_config_name, - cache_dir=model_args.cache_dir, - token=model_args.token, - streaming=data_args.streaming, - trust_remote_code=model_args.trust_remote_code, - ) - if "validation" not in raw_datasets.keys(): - raw_datasets["validation"] = load_dataset( - data_args.dataset_name, - data_args.dataset_config_name, - split=f"train[:{data_args.validation_split_percentage}%]", - cache_dir=model_args.cache_dir, - token=model_args.token, - streaming=data_args.streaming, - trust_remote_code=model_args.trust_remote_code, - ) - raw_datasets["train"] = load_dataset( - data_args.dataset_name, - data_args.dataset_config_name, - split=f"train[{data_args.validation_split_percentage}%:]", - cache_dir=model_args.cache_dir, - token=model_args.token, - streaming=data_args.streaming, - trust_remote_code=model_args.trust_remote_code, - ) - else: - data_files = {} - dataset_args = {} - if data_args.train_file is not None: - data_files["train"] = data_args.train_file - if data_args.validation_file is not None: - data_files["validation"] = data_args.validation_file - extension = ( - data_args.train_file.split(".")[-1] - if data_args.train_file is not None - else data_args.validation_file.split(".")[-1] - ) - if extension == "txt": - extension = "text" - dataset_args["keep_linebreaks"] = data_args.keep_linebreaks - raw_datasets = load_dataset( - extension, - data_files=data_files, - cache_dir=model_args.cache_dir, - token=model_args.token, - **dataset_args, - ) - # If no validation data is there, validation_split_percentage will be used to divide the dataset. - if "validation" not in raw_datasets.keys(): - raw_datasets["validation"] = load_dataset( - extension, - data_files=data_files, - split=f"train[:{data_args.validation_split_percentage}%]", - cache_dir=model_args.cache_dir, - token=model_args.token, - **dataset_args, - ) - raw_datasets["train"] = load_dataset( - extension, - data_files=data_files, - split=f"train[{data_args.validation_split_percentage}%:]", - cache_dir=model_args.cache_dir, - token=model_args.token, - **dataset_args, - ) - - # 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. - - # Load pretrained model and tokenizer - # - # Distributed training: - # The .from_pretrained methods guarantee that only one local process can concurrently - # download model & vocab. - - config_kwargs = { - "cache_dir": model_args.cache_dir, - "revision": model_args.model_revision, - "token": model_args.token, - "trust_remote_code": model_args.trust_remote_code, - } - if model_args.config_name: - config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs) - elif model_args.model_name_or_path: - config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs) - else: - config = CONFIG_MAPPING[model_args.model_type]() - logger.warning("You are instantiating a new config instance from scratch.") - if model_args.config_overrides is not None: - logger.info(f"Overriding config: {model_args.config_overrides}") - config.update_from_string(model_args.config_overrides) - logger.info(f"New config: {config}") - - tokenizer_kwargs = { - "cache_dir": model_args.cache_dir, - "use_fast": model_args.use_fast_tokenizer, - "revision": model_args.model_revision, - "token": model_args.token, - "trust_remote_code": model_args.trust_remote_code, - } - if model_args.tokenizer_name: - tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs) - elif model_args.model_name_or_path: - tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs) - else: - raise ValueError( - "You are instantiating a new tokenizer from scratch. This is not supported by this script. " - "You can do it from another script, save it, and load it from here, using --tokenizer_name." - ) - - if model_args.model_name_or_path: - torch_dtype = ( - model_args.torch_dtype - if model_args.torch_dtype in ["auto", None] - else getattr(torch, model_args.torch_dtype) - ) - model = AutoModelForCausalLM.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, - token=model_args.token, - trust_remote_code=model_args.trust_remote_code, - torch_dtype=torch_dtype, - low_cpu_mem_usage=model_args.low_cpu_mem_usage, - ) - else: - model = AutoModelForCausalLM.from_config(config, trust_remote_code=model_args.trust_remote_code) - n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values()) - logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params") - - # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch - # on a small vocab and want a smaller embedding size, remove this test. - embedding_size = model.get_input_embeddings().weight.shape[0] - if len(tokenizer) > embedding_size: - model.resize_token_embeddings(len(tokenizer)) - - # Preprocessing the datasets. - # First we tokenize all the texts. - if training_args.do_train: - column_names = list(raw_datasets["train"].features) - else: - column_names = list(raw_datasets["validation"].features) - text_column_name = "text" if "text" in column_names else column_names[0] - - # since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function - tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base") - - def tokenize_function(examples): - with CaptureLogger(tok_logger) as cl: - output = tokenizer(examples[text_column_name]) - # clm input could be much much longer than block_size - if "Token indices sequence length is longer than the" in cl.out: - tok_logger.warning( - "^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits" - " before being passed to the model." - ) - return output - - with training_args.main_process_first(desc="dataset map tokenization"): - if not data_args.streaming: - tokenized_datasets = raw_datasets.map( - tokenize_function, - batched=True, - num_proc=data_args.preprocessing_num_workers, - remove_columns=column_names, - load_from_cache_file=not data_args.overwrite_cache, - desc="Running tokenizer on dataset", - ) - else: - tokenized_datasets = raw_datasets.map( - tokenize_function, - batched=True, - remove_columns=column_names, - ) - if hasattr(config, "max_position_embeddings"): - max_pos_embeddings = config.max_position_embeddings - else: - # Define a default value if the attribute is missing in the config. - max_pos_embeddings = 1024 - - if data_args.block_size is None: - block_size = tokenizer.model_max_length - if block_size > max_pos_embeddings: - logger.warning( - f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). " - f"Using block_size={min(1024, max_pos_embeddings)} instead. You can change that default value by passing --block_size xxx." - ) - if max_pos_embeddings > 0: - block_size = min(1024, max_pos_embeddings) - else: - block_size = 1024 - else: - if data_args.block_size > tokenizer.model_max_length: - logger.warning( - f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model " - f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}." - ) - block_size = min(data_args.block_size, tokenizer.model_max_length) - - # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size. - def group_texts(examples): - # Concatenate all texts. - concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} - total_length = len(concatenated_examples[list(examples.keys())[0]]) - # We drop the small remainder, and if the total_length < block_size we exclude this batch and return an empty dict. - # We could add padding if the model supported it instead of this drop, you can customize this part to your needs. - total_length = (total_length // block_size) * block_size - # Split by chunks of max_len. - result = { - k: [t[i : i + block_size] for i in range(0, total_length, block_size)] - for k, t in concatenated_examples.items() - } - result["labels"] = result["input_ids"].copy() - return result - - # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder - # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower - # to preprocess. - # - # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: - # https://huggingface.co/docs/datasets/process#map + loss_fn = causal_loss_wrapper(args.model_direction) - with training_args.main_process_first(desc="grouping texts together"): - if not data_args.streaming: - lm_datasets = tokenized_datasets.map( - group_texts, - batched=True, - num_proc=data_args.preprocessing_num_workers, - load_from_cache_file=not data_args.overwrite_cache, - desc=f"Grouping texts in chunks of {block_size}", - ) - else: - lm_datasets = tokenized_datasets.map( - group_texts, - batched=True, - ) + model.train() + optimizer.zero_grad() - if training_args.do_train: - if "train" not in tokenized_datasets: - raise ValueError("--do_train requires a train dataset") - train_dataset = lm_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(range(max_train_samples)) + for epoch in range(args.num_train_epochs): + for step, batch in enumerate(tqdm(train_loader)): + labels = batch.pop("labels") + outputs = model(**batch) + loss = loss_fn(outputs.logits, labels) + loss.backward() - if training_args.do_eval: - if "validation" not in tokenized_datasets: - raise ValueError("--do_eval requires a validation dataset") - eval_dataset = lm_datasets["validation"] - if data_args.max_eval_samples is not None: - max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) - eval_dataset = eval_dataset.select(range(max_eval_samples)) + if (step + 1) % 50 == 1: + print(f"{loss.item()=}") - def preprocess_logits_for_metrics(logits, labels): - if isinstance(logits, tuple): - # Depending on the model and config, logits may contain extra tensors, - # like past_key_values, but logits always come first - logits = logits[0] - return logits.argmax(dim=-1) - - metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir) - - def compute_metrics(eval_preds): - preds, labels = eval_preds - # preds have the same shape as the labels, after the argmax(-1) has been calculated - # by preprocess_logits_for_metrics but we need to shift the labels - labels = labels[:, 1:].reshape(-1) - preds = preds[:, :-1].reshape(-1) - return metric.compute(predictions=preds, references=labels) - - # Initialize our Trainer - trainer = Trainer( - 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, - processing_class=tokenizer, - # Data collator will default to DataCollatorWithPadding, so we change it. - data_collator=default_data_collator, - compute_metrics=compute_metrics if training_args.do_eval and not is_torch_xla_available() else None, - preprocess_logits_for_metrics=preprocess_logits_for_metrics - if training_args.do_eval and not is_torch_xla_available() - else 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) - 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 - if training_args.do_eval: - logger.info("*** Evaluate ***") - - metrics = trainer.evaluate() - - 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)) - try: - perplexity = math.exp(metrics["eval_loss"]) - except OverflowError: - perplexity = float("inf") - metrics["perplexity"] = perplexity - - trainer.log_metrics("eval", metrics) - trainer.save_metrics("eval", metrics) - - kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"} - if data_args.dataset_name is not None: - kwargs["dataset_tags"] = data_args.dataset_name - if data_args.dataset_config_name is not None: - kwargs["dataset_args"] = data_args.dataset_config_name - kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" - else: - kwargs["dataset"] = data_args.dataset_name - - 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 (step + 1) % args.gradient_accumulation_steps == 0: + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() if __name__ == "__main__": - main()
\ No newline at end of file + main() |