# coding=utf-8 # Copyright 2022 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. import argparse from typing import List import numpy as np import torch from torch.optim import AdamW from torch.utils.data import DataLoader import evaluate from accelerate import Accelerator, DistributedType from datasets import DatasetDict, load_dataset # New Code # # We'll be using StratifiedKFold for this example from sklearn.model_selection import StratifiedKFold from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed ######################################################################## # This is a fully working simple example to use Accelerate, # specifically showcasing how to perform Cross Validation, # and builds off the `nlp_example.py` script. # # This example trains a Bert base model on GLUE MRPC # in any of the following settings (with the same script): # - single CPU or single GPU # - multi GPUS (using PyTorch distributed mode) # - (multi) TPUs # - fp16 (mixed-precision) or fp32 (normal precision) # # To help focus on the differences in the code, building `DataLoaders` # was refactored into its own function. # New additions from the base script can be found quickly by # looking for the # New Code # tags # # To run it in each of these various modes, follow the instructions # in the readme for examples: # https://github.com/huggingface/accelerate/tree/main/examples # ######################################################################## MAX_GPU_BATCH_SIZE = 16 EVAL_BATCH_SIZE = 32 # New Code # # We need a different `get_dataloaders` function that will build dataloaders by index def get_fold_dataloaders( accelerator: Accelerator, dataset: DatasetDict, train_idxs: List[int], valid_idxs: List[int], batch_size: int = 16 ): """ Gets a set of train, valid, and test dataloaders for a particular fold Args: accelerator (`Accelerator`): The main `Accelerator` object train_idxs (list of `int`): The split indices for the training dataset valid_idxs (list of `int`): The split indices for the validation dataset batch_size (`int`): The size of the minibatch. Default is 16 """ tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") datasets = DatasetDict( { "train": dataset["train"].select(train_idxs), "validation": dataset["train"].select(valid_idxs), "test": dataset["validation"], } ) def tokenize_function(examples): # max_length=None => use the model max length (it's actually the default) outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None) return outputs # Apply the method we just defined to all the examples in all the splits of the dataset # starting with the main process first: with accelerator.main_process_first(): tokenized_datasets = datasets.map( tokenize_function, batched=True, remove_columns=["idx", "sentence1", "sentence2"], ) # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the # transformers library tokenized_datasets = tokenized_datasets.rename_column("label", "labels") def collate_fn(examples): # On TPU it's best to pad everything to the same length or training will be very slow. if accelerator.distributed_type == DistributedType.TPU: return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt") return tokenizer.pad(examples, padding="longest", return_tensors="pt") # Instantiate dataloaders. train_dataloader = DataLoader( tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size ) eval_dataloader = DataLoader( tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE ) test_dataloader = DataLoader( tokenized_datasets["test"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE ) return train_dataloader, eval_dataloader, test_dataloader def training_function(config, args): # New Code # test_predictions = [] # Download the dataset datasets = load_dataset("glue", "mrpc") # Create our splits kfold = StratifiedKFold(n_splits=int(args.num_folds)) # Initialize accelerator accelerator = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision) # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs lr = config["lr"] num_epochs = int(config["num_epochs"]) seed = int(config["seed"]) batch_size = int(config["batch_size"]) metric = evaluate.load("glue", "mrpc") # If the batch size is too big we use gradient accumulation gradient_accumulation_steps = 1 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU: gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE batch_size = MAX_GPU_BATCH_SIZE set_seed(seed) # New Code # # Create our folds: folds = kfold.split(np.zeros(datasets["train"].num_rows), datasets["train"]["label"]) test_references = [] # Iterate over them for i, (train_idxs, valid_idxs) in enumerate(folds): train_dataloader, eval_dataloader, test_dataloader = get_fold_dataloaders( accelerator, datasets, train_idxs, valid_idxs, ) # Instantiate the model (we build the model here so that the seed also control new weights initialization) model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True) # We could avoid this line since the accelerator is set with `device_placement=True` (default value). # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that). model = model.to(accelerator.device) # Instantiate optimizer optimizer = AdamW(params=model.parameters(), lr=lr) # Instantiate scheduler lr_scheduler = get_linear_schedule_with_warmup( optimizer=optimizer, num_warmup_steps=100, num_training_steps=(len(train_dataloader) * num_epochs) // gradient_accumulation_steps, ) # Prepare everything # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the # prepare method. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # Now we train the model for epoch in range(num_epochs): model.train() for step, batch in enumerate(train_dataloader): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device) outputs = model(**batch) loss = outputs.loss loss = loss / gradient_accumulation_steps accelerator.backward(loss) if step % gradient_accumulation_steps == 0: optimizer.step() lr_scheduler.step() optimizer.zero_grad() model.eval() for step, batch in enumerate(eval_dataloader): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device) with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits.argmax(dim=-1) predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"])) metric.add_batch( predictions=predictions, references=references, ) eval_metric = metric.compute() # Use accelerator.print to print only on the main process. accelerator.print(f"epoch {epoch}:", eval_metric) # New Code # # We also run predictions on the test set at the very end fold_predictions = [] for step, batch in enumerate(test_dataloader): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device) with torch.no_grad(): outputs = model(**batch) predictions = outputs.logits predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"])) fold_predictions.append(predictions.cpu()) if i == 0: # We need all of the test predictions test_references.append(references.cpu()) # Use accelerator.print to print only on the main process. test_predictions.append(torch.cat(fold_predictions, dim=0)) # We now need to release all our memory and get rid of the current model, optimizer, etc accelerator.free_memory() # New Code # # Finally we check the accuracy of our folded results: test_references = torch.cat(test_references, dim=0) preds = torch.stack(test_predictions, dim=0).sum(dim=0).div(int(args.num_folds)).argmax(dim=-1) test_metric = metric.compute(predictions=preds, references=test_references) accelerator.print("Average test metrics from all folds:", test_metric) def main(): parser = argparse.ArgumentParser(description="Simple example of training script.") parser.add_argument( "--mixed_precision", type=str, default="no", choices=["no", "fp16", "bf16"], help="Whether to use mixed precision. Choose" "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." "and an Nvidia Ampere GPU.", ) parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.") # New Code # parser.add_argument("--num_folds", type=int, default=3, help="The number of splits to perform across the dataset") args = parser.parse_args() config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16} training_function(config, args) if __name__ == "__main__": main()