Multi-GPU Evaluation in Accelerate for Large Language Models

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Now, if you’re like us, you might be wondering: “Why bother with multiple GPUs when I can just use one and save myself the hassle of setting up a distributed training environment?” Well, bro, let me tell you there are several reasons why multi-GPU evaluation is worth considering.

First, it allows us to evaluate our models faster than ever before! With multiple GPUs working in parallel, we can process more data and get results quicker than if we were using a single GPU. This means less waiting around for your computer to finish running tests, which is always a plus.

Secondly, it’s great for testing the scalability of our models! By evaluating on multiple GPUs simultaneously, we can see how well our model performs as we increase the number of resources available to it. This information can be incredibly valuable when deciding whether or not to invest in more expensive hardware for training and deployment purposes.

And finally (and perhaps most importantly), multi-GPU evaluation is just plain fun! There’s something satisfying about watching your computer churn through data at lightning speed, especially if you’re working on a particularly complex or challenging model. Plus, it can be a great way to impress your colleagues and show off your technical skills.

So how do we go about setting up multi-GPU evaluation in Accelerate? Well, first things first make sure that all of your GPUs are properly configured and connected to your computer. This may involve installing additional drivers or software packages depending on the specific hardware you’re using.

Next, create a new script for running multi-GPU evaluations. In this script, we’ll use Accelerate’s built-in support for distributed training to split our data across multiple GPUs and evaluate each one separately. Here’s an example code snippet that should get you started: 

# Import the necessary libraries
import accelerate as acc
from transformers import AutoTokenizer, TFBertForSequenceClassification

# Load the model and tokenizer from Hugging Face Hub
model = TFBertForSequenceClassification.from_pretrained("bert-base-uncased")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# Define a function for evaluating on each GPU separately
def evaluate(data):
    # Load the data onto the current device using Accelerate's distributed training API
    with acc.device_placement():
        # Tokenize the input data using the tokenizer
        inputs = tokenizer(data["input"], padding=True, truncation=True)
        # Convert the label data into a tensor
        labels = torch.tensor([d['label'] for d in data['labels']])
        
        # Evaluate the model on each batch of input and label data
        # Use the model to predict the outputs for the input data
        outputs = model(inputs[0], attention_mask=[x.ne(tokenizer.pad_token_id).any() for x in inputs])
        # Calculate the loss using the predicted outputs and the actual labels
        loss = torch.nn.functional.cross_entropy(outputs, labels)
        
        # Calculate the accuracy and return it as a dictionary with the current device's ID as the key
        # Use the predicted outputs to calculate the accuracy and return it as a dictionary
        acc = (torch.max(outputs, dim=1)[1] == labels).float().mean()
        return {"accuracy": acc}

# Define a list of data to evaluate on each GPU separately
# Create a list of dictionaries containing the input and label data for each batch
data_list = [{"input": input_text, "labels": label_ids} for input_text, label_ids in dataset["train"][:10]]

# Use Accelerate's distributed training API to split the data across multiple GPUs and run evaluations on each one separately
# Use the run function from Accelerate to run the evaluate function on each batch of data in the data list
results = acc.run(evaluate, data=data_list)

And that’s it! With just a few lines of code, you can easily set up multi-GPU evaluation for your large language models using Accelerate in Python. So give it a try we promise it’ll be worth your while!

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