#!/usr/bin/env python import argparse import json import logging import math import os import random import sys from pathlib import Path sys.path.insert(0, str(Path(__file__).resolve().parents[1])) import datasets import evaluate import torch from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from datasets import load_dataset from huggingface_hub import HfApi from torch.utils.data import DataLoader from tqdm.auto import tqdm from safetensors.torch import load_file import transformers from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, PretrainedConfig, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import check_min_version from spikingjelly.activation_based import functional check_min_version("4.57.0") logger = get_logger(__name__) task_to_keys = { "cola": ("sentence", None), "mnli": ("premise", "hypothesis"), "mrpc": ("sentence1", "sentence2"), "qnli": ("question", "sentence"), "qqp": ("question1", "question2"), "rte": ("sentence1", "sentence2"), "sst2": ("sentence", None), "stsb": ("sentence1", "sentence2"), "wnli": ("sentence1", "sentence2"), } def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task") parser.add_argument( "--task_name", type=str, default=None, help="The name of the glue task to train on.", choices=list(task_to_keys.keys()), ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--max_length", type=int, default=128, help="The maximum total input sequence length after tokenization.", ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=True, ) parser.add_argument( "--pretrained_checkpoint", type=str, default=None, help="Path to pretrained checkpoint directory containing model.safetensors (e.g., step_10000)", ) parser.add_argument( "--dataset_cache_dir", type=str, default=None, help="Optional Hugging Face datasets cache directory.", ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer.", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--trust_remote_code", type=bool, default=False, help="Whether or not to allow for custom models defined on the Hub.", ) parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help='The integration to report the results and logs to.', ) parser.add_argument( "--ignore_mismatched_sizes", action="store_true", help="Whether or not to enable to load a pretrained model whose head dimensions are different.", ) parser.add_argument( "--T", type=int, default=4, ) args = parser.parse_args() # Sanity checks if args.task_name is None and args.train_file is None and args.validation_file is None: raise ValueError("Need either a task name or a training/validation file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if args.validation_file is not None: extension = args.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def load_model_from_checkpoint(checkpoint_path, config, num_labels): """ Load a sequence classification model from a checkpoint directory containing model.safetensors. Args: checkpoint_path: Checkpoint directory. config: Model configuration. num_labels: Number of labels for the classification task. Returns: Model initialized with matching pretrained weights. """ from spiking_bert.modeling_spiking_bert import BertForSequenceClassification safetensors_path = os.path.join(checkpoint_path, "model.safetensors") if not os.path.exists(safetensors_path): raise FileNotFoundError(f"Cannot find model.safetensors in {checkpoint_path}") logger.info(f"Loading pretrained weights from {safetensors_path}") config.num_labels = num_labels model = BertForSequenceClassification(config) pretrained_state_dict = load_file(safetensors_path) model_state_dict = model.state_dict() matched_keys = [] mismatched_keys = [] for key in pretrained_state_dict.keys(): if key in model_state_dict: if pretrained_state_dict[key].shape == model_state_dict[key].shape: model_state_dict[key] = pretrained_state_dict[key] matched_keys.append(key) else: mismatched_keys.append(key) logger.warning(f"Shape mismatch for {key}: pretrained {pretrained_state_dict[key].shape} vs model {model_state_dict[key].shape}") else: logger.info(f"Key {key} not found in model, skipping...") model.load_state_dict(model_state_dict) logger.info(f"Loaded {len(matched_keys)} matching weights from checkpoint") logger.info(f"Skipped {len(mismatched_keys)} mismatched weights") logger.info(f"Classifier head will be trained from scratch") return model def main(): args = parse_args() # Initialize the accelerator accelerator = ( Accelerator(log_with=args.report_to, project_dir=args.output_dir) if args.with_tracking else Accelerator() ) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # Set seed if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: repo_name = args.hub_model_id if repo_name is None: repo_name = Path(args.output_dir).absolute().name api = HfApi() repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Load datasets if args.task_name is not None: raw_datasets = load_dataset("nyu-mll/glue", args.task_name, cache_dir=args.dataset_cache_dir) else: data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file extension = (args.train_file if args.train_file is not None else args.validation_file).split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files) # Labels if args.task_name is not None: is_regression = args.task_name == "stsb" if not is_regression: label_list = raw_datasets["train"].features["label"].names num_labels = len(label_list) else: num_labels = 1 else: is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"] if is_regression: num_labels = 1 else: label_list = raw_datasets["train"].unique("label") label_list.sort() num_labels = len(label_list) # Load config and tokenizer config = AutoConfig.from_pretrained( args.model_name_or_path, num_labels=num_labels, finetuning_task=args.task_name, trust_remote_code=args.trust_remote_code, ) tokenizer = AutoTokenizer.from_pretrained( args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code ) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token config.pad_token_id = tokenizer.pad_token_id config.T = args.T config._attn_implementation = 'eager' if args.pretrained_checkpoint is not None: logger.info(f"Loading model from pretrained checkpoint: {args.pretrained_checkpoint}") model = load_model_from_checkpoint(args.pretrained_checkpoint, config, num_labels) else: logger.info(f"Loading model from: {args.model_name_or_path}") model = AutoModelForSequenceClassification.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ignore_mismatched_sizes=args.ignore_mismatched_sizes, trust_remote_code=args.trust_remote_code, ) logger.info(model) # Preprocessing the datasets if args.task_name is not None: sentence1_key, sentence2_key = task_to_keys[args.task_name] else: 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 # Label mapping label_to_id = None if ( model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id and args.task_name is not None and not is_regression ): label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()} if sorted(label_name_to_id.keys()) == sorted(label_list): logger.info(f"Using label correspondence: {label_name_to_id}") label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)} else: logger.warning("Model labels don't match dataset labels, ignoring model labels.") elif args.task_name is None and not is_regression: label_to_id = {v: i for i, v in enumerate(label_list)} if label_to_id is not None: model.config.label2id = label_to_id model.config.id2label = {id: label for label, id in config.label2id.items()} elif args.task_name is not None and not is_regression: model.config.label2id = {l: i for i, l in enumerate(label_list)} model.config.id2label = {id: label for label, id in config.label2id.items()} padding = "max_length" if args.pad_to_max_length else False def preprocess_function(examples): texts = ( (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) ) result = tokenizer(*texts, padding=padding, max_length=args.max_length, truncation=True) if "label" in examples: if label_to_id is not None: result["labels"] = [label_to_id[l] for l in examples["label"]] else: result["labels"] = examples["label"] return result with accelerator.main_process_first(): processed_datasets = raw_datasets.map( preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names, desc="Running tokenizer on dataset", ) train_dataset = processed_datasets["train"] eval_dataset = processed_datasets["validation_matched" if args.task_name == "mnli" else "validation"] # Log a few random samples for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # DataLoaders creation if args.pad_to_max_length: data_collator = default_data_collator else: if accelerator.mixed_precision == "fp8": pad_to_multiple_of = 16 elif accelerator.mixed_precision != "no": pad_to_multiple_of = 8 else: pad_to_multiple_of = None data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=pad_to_multiple_of) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size) # Optimizer no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Scheduler overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps, num_training_steps=args.max_train_steps, ) # Prepare with accelerator model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # Recalculate training steps num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # Initialize trackers if args.with_tracking: experiment_config = vars(args) experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("glue_no_trainer", experiment_config) # Get metric if args.task_name is not None: metric = evaluate.load("glue", args.task_name) else: metric = evaluate.load("accuracy") # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Resume from checkpoint if specified if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": checkpoint_path = args.resume_from_checkpoint path = os.path.basename(args.resume_from_checkpoint) else: dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] checkpoint_path = path path = os.path.basename(checkpoint_path) accelerator.print(f"Resumed from checkpoint: {checkpoint_path}") accelerator.load_state(checkpoint_path) training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None completed_steps = starting_epoch * num_update_steps_per_epoch else: resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps starting_epoch = resume_step // len(train_dataloader) completed_steps = resume_step // args.gradient_accumulation_steps resume_step -= starting_epoch * len(train_dataloader) progress_bar.update(completed_steps) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None: active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step) else: active_dataloader = train_dataloader for step, batch in enumerate(active_dataloader): outputs = model(**batch) loss = outputs.loss if args.with_tracking: total_loss += loss.detach().float() loss = loss / args.gradient_accumulation_steps accelerator.backward(loss) if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: optimizer.step() lr_scheduler.step() optimizer.zero_grad() progress_bar.update(1) completed_steps += 1 functional.reset_net(model) if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0 and accelerator.sync_gradients: output_dir = f"step_{completed_steps}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break model.eval() samples_seen = 0 for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) if not is_regression else outputs.logits.squeeze() predictions, references = accelerator.gather((predictions, batch["labels"])) if accelerator.num_processes > 1: if step == len(eval_dataloader) - 1: predictions = predictions[: len(eval_dataloader.dataset) - samples_seen] references = references[: len(eval_dataloader.dataset) - samples_seen] else: samples_seen += references.shape[0] metric.add_batch( predictions=predictions, references=references, ) functional.reset_net(model) eval_metric = metric.compute() logger.info(f"epoch {epoch}: {eval_metric}") if args.with_tracking: accelerator.log( { "accuracy" if args.task_name is not None else "glue": eval_metric, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) api.upload_folder( commit_message=f"Training in progress epoch {epoch}", folder_path=args.output_dir, repo_id=repo_id, repo_type="model", token=args.hub_token, ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: api.upload_folder( commit_message="End of training", folder_path=args.output_dir, repo_id=repo_id, repo_type="model", token=args.hub_token, ) if args.task_name == "mnli": eval_dataset = processed_datasets["validation_mismatched"] eval_dataloader = DataLoader( eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size ) eval_dataloader = accelerator.prepare(eval_dataloader) model.eval() for step, batch in enumerate(eval_dataloader): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) metric.add_batch( predictions=accelerator.gather(predictions), references=accelerator.gather(batch["labels"]), ) functional.reset_net(model) eval_metric = metric.compute() logger.info(f"mnli-mm: {eval_metric}") if args.output_dir is not None: all_results = {f"eval_{k}": v for k, v in eval_metric.items()} with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump(all_results, f) accelerator.wait_for_everyone() accelerator.end_training() if __name__ == "__main__": main()