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https://github.com/huggingface/accelerate.git
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5002e56704
* Setup 2023 tooling for quality * Result of styling * Simplify inits and remove isort and flake8 from doc * Puts back isort skip flag
233 lines
9.6 KiB
Python
233 lines
9.6 KiB
Python
# Copyright 2022 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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# New Code #
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import evaluate
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import torch
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from datasets import load_dataset
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from torch.optim import AdamW
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from torch.utils.data import DataLoader
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
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from accelerate import Accelerator, DistributedType
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from accelerate.utils import find_executable_batch_size
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########################################################################
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# This is a fully working simple example to use Accelerate,
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# specifically showcasing how to combine both the gradient accumulation
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# and automatic batch size finder utilities of Accelerate to perfrom
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# automatic gradient accumulation
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#
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# This example trains a Bert base model on GLUE MRPC
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# in any of the following settings (with the same script):
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# - single CPU or single GPU
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# - multi GPUS (using PyTorch distributed mode)
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# - (multi) TPUs
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# - fp16 (mixed-precision) or fp32 (normal precision)
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#
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# New additions from the base script can be found quickly by
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# looking for the # New Code # tags
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#
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# To run it in each of these various modes, follow the instructions
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# in the readme for examples:
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# https://github.com/huggingface/accelerate/tree/main/examples
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#
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########################################################################
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EVAL_BATCH_SIZE = 32
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def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
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"""
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Creates a set of `DataLoader`s for the `glue` dataset,
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using "bert-base-cased" as the tokenizer.
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Args:
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accelerator (`Accelerator`):
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An `Accelerator` object
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batch_size (`int`, *optional*):
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The batch size for the train and validation DataLoaders.
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"""
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tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
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datasets = load_dataset("glue", "mrpc")
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def tokenize_function(examples):
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# max_length=None => use the model max length (it's actually the default)
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outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None)
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return outputs
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# Apply the method we just defined to all the examples in all the splits of the dataset
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# starting with the main process first:
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with accelerator.main_process_first():
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tokenized_datasets = datasets.map(
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tokenize_function,
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batched=True,
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remove_columns=["idx", "sentence1", "sentence2"],
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)
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# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
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# transformers library
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tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
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def collate_fn(examples):
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# On TPU it's best to pad everything to the same length or training will be very slow.
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if accelerator.distributed_type == DistributedType.TPU:
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return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt")
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return tokenizer.pad(examples, padding="longest", return_tensors="pt")
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# Instantiate dataloaders.
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train_dataloader = DataLoader(
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tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size
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)
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eval_dataloader = DataLoader(
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tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE
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)
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return train_dataloader, eval_dataloader
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# For testing only
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if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
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from accelerate.test_utils.training import mocked_dataloaders
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get_dataloaders = mocked_dataloaders # noqa: F811
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def training_function(config, args):
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# For testing only
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if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
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config["num_epochs"] = 2
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# Initialize accelerator
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accelerator = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision)
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# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
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lr = config["lr"]
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num_epochs = int(config["num_epochs"])
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seed = int(config["seed"])
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observed_batch_size = int(config["batch_size"])
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metric = evaluate.load("glue", "mrpc")
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# New Code #
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# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
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# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
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# half each time. From this, we can calculate the number of gradient accumulation steps needed
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# and modify the Accelerator object as a result
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@find_executable_batch_size(starting_batch_size=int(observed_batch_size))
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def inner_training_loop(batch_size):
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# Since we need to modify the outside accelerator object, we need to bring it
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# to the local scope
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nonlocal accelerator
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# We can calculate the number of gradient accumulation steps based on the current
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# batch size vs the starting batch size
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num_gradient_accumulation_steps = observed_batch_size // batch_size
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# And then set it in the Accelerator directly:
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accelerator.gradient_accumulation_steps = num_gradient_accumulation_steps
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# Next we need to free all of the stored model references in the Accelerator each time
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accelerator.free_memory()
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# And set the seed so our results are reproducable each reset
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set_seed(seed)
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# Instantiate the model (we build the model here so that the seed also control new weights initialization)
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model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True)
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# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
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# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
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# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
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model = model.to(accelerator.device)
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# Instantiate optimizer
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optimizer = AdamW(params=model.parameters(), lr=lr)
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train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)
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# Instantiate scheduler
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lr_scheduler = get_linear_schedule_with_warmup(
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optimizer=optimizer,
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num_warmup_steps=100,
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num_training_steps=(len(train_dataloader) * num_epochs),
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)
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# Prepare everything
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# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
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# prepare method.
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model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
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model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
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)
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# Now we train the model
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for epoch in range(num_epochs):
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model.train()
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for step, batch in enumerate(train_dataloader):
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# And perform gradient accumulation
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with accelerator.accumulate(model):
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# We could avoid this line since we set the accelerator with `device_placement=True`.
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batch.to(accelerator.device)
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outputs = model(**batch)
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loss = outputs.loss
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accelerator.backward(loss)
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optimizer.step()
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lr_scheduler.step()
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optimizer.zero_grad()
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model.eval()
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for step, batch in enumerate(eval_dataloader):
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# We could avoid this line since we set the accelerator with `device_placement=True`.
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batch.to(accelerator.device)
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with torch.no_grad():
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outputs = model(**batch)
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predictions = outputs.logits.argmax(dim=-1)
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predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
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metric.add_batch(
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predictions=predictions,
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references=references,
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)
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eval_metric = metric.compute()
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# Use accelerator.print to print only on the main process.
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accelerator.print(f"epoch {epoch}:", eval_metric)
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# New Code #
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# And call it at the end with no arguments
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# Note: You could also refactor this outside of your training loop function
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inner_training_loop()
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def main():
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parser = argparse.ArgumentParser(description="Simple example of training script.")
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parser.add_argument(
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"--mixed_precision",
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type=str,
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default=None,
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choices=["no", "fp16", "bf16"],
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help="Whether to use mixed precision. Choose"
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"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
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"and an Nvidia Ampere GPU.",
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)
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parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.")
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args = parser.parse_args()
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# New Code #
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# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
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config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
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training_function(config, args)
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if __name__ == "__main__":
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main()
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