# Copyright 2022 The HuggingFace 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 import os # New Code # import evaluate import torch from datasets import load_dataset from torch.optim import AdamW from torch.utils.data import DataLoader from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed from accelerate import Accelerator from accelerate.utils import find_executable_batch_size ######################################################################## # This is a fully working simple example to use Accelerate, # specifically showcasing how to combine both the gradient accumulation # and automatic batch size finder utilities of Accelerate to perfrom # automatic gradient accumulation # # 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) # # 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 # ######################################################################## EVAL_BATCH_SIZE = 32 def get_dataloaders(accelerator: Accelerator, batch_size: int = 16): """ Creates a set of `DataLoader`s for the `glue` dataset, using "bert-base-cased" as the tokenizer. Args: accelerator (`Accelerator`): An `Accelerator` object batch_size (`int`, *optional*): The batch size for the train and validation DataLoaders. """ tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") datasets = load_dataset("glue", "mrpc") 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): # When using mixed precision we want round multiples of 8/16 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 return tokenizer.pad( examples, padding="longest", pad_to_multiple_of=pad_to_multiple_of, 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 ) return train_dataloader, eval_dataloader # For testing only if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1": from accelerate.test_utils.training import mocked_dataloaders get_dataloaders = mocked_dataloaders # noqa: F811 def training_function(config, args): # For testing only if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1": config["num_epochs"] = 2 # 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"]) observed_batch_size = int(config["batch_size"]) metric = evaluate.load("glue", "mrpc") # New Code # # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in # half each time. From this, we can calculate the number of gradient accumulation steps needed # and modify the Accelerator object as a result @find_executable_batch_size(starting_batch_size=int(observed_batch_size)) def inner_training_loop(batch_size): # Since we need to modify the outside accelerator object, we need to bring it # to the local scope nonlocal accelerator # We can calculate the number of gradient accumulation steps based on the current # batch size vs the starting batch size num_gradient_accumulation_steps = observed_batch_size // batch_size # And then set it in the Accelerator directly: accelerator.gradient_accumulation_steps = num_gradient_accumulation_steps # Next we need to free all of the stored model references in the Accelerator each time accelerator.free_memory() # And set the seed so our results are reproducable each reset set_seed(seed) # 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) train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size) # Instantiate scheduler lr_scheduler = get_linear_schedule_with_warmup( optimizer=optimizer, num_warmup_steps=100, num_training_steps=(len(train_dataloader) * num_epochs), ) # 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): # And perform gradient accumulation with accelerator.accumulate(model): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device) outputs = model(**batch) loss = outputs.loss accelerator.backward(loss) 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 # # And call it at the end with no arguments # Note: You could also refactor this outside of your training loop function inner_training_loop() accelerator.end_training() def main(): parser = argparse.ArgumentParser(description="Simple example of training script.") parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16", "fp8"], 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.") args = parser.parse_args() # New Code # # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 256} training_function(config, args) if __name__ == "__main__": main()