Mixed Precision Training in cuTENSOR

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Alright, the latest craze in deep learning: Mixed Precision Training (MPT) using NVIDIA’s cuTENSOR library! If you haven’t heard of it yet, don’t worry we’re here to help you out.

To kick things off, why you should care about mixed precision training in the first place. Well, for starters, it can significantly improve your model’s performance and reduce your training time by a factor of two or more! That’s right with MPT, you can train your models faster than ever before without sacrificing accuracy.

But how does this magic work? Let me break it down for you in simple terms: instead of using single-precision floating point numbers (which are typically used in deep learning), we use a combination of single and half precision numbers to perform our calculations. This may sound like madness, but trust us it’s worth it!

So how do we get started with MPT? First, you need to make sure that your GPU supports mixed precision training (which most modern GPUs do). Then, you can enable MPT in cuDNN and cuTENSOR by setting a few environment variables. Here’s an example: 

# Set the GPU to use for training
# Note: CUDA_VISIBLE_DEVICES is used to specify which GPU to use for training
# This is useful when multiple GPUs are available and you want to select a specific one
export CUDA_VISIBLE_DEVICES=0 

# Enable debugging messages from NCCL (optional)
# Note: NCCL_DEBUG is used to enable debugging messages from NCCL library
# This is useful for troubleshooting any issues related to NCCL
export NCCL_DEBUG=INFO 

# Set the mixed precision level (O1 is recommended)
# Note: AMPLITE_LEVEL is used to specify the mixed precision level for training
# O1 is recommended as it provides a good balance between speed and accuracy
export AMPLITE_LEVEL=O1

Once you’ve set those variables, you can load your model and start training as usual. But wait there’s more! In order to get the best performance out of MPT, we need to make sure that our data is properly formatted for half precision numbers. This means converting all of our input tensors (and any intermediate calculations) to half precision before passing them through cuDNN and cuTENSOR.

Luckily, NVIDIA provides a handy tool called `amp` that can automatically convert your model’s operations to mixed precision for you! Here’s an example of how to use it: 

# Import the necessary libraries
import torch
from torch.cuda import amp

# Load the model and data as usual
model = MyAwesomeModel() # Create an instance of the model
data = load_my_awesome_dataset() # Load the dataset for training

# Wrap the model in an AMP context manager to enable mixed precision training
with amp.autocast():
    # Automatically convert operations to mixed precision
    # This allows for faster training on GPUs with cuDNN and cuTENSOR
    # as it uses half precision instead of single precision
    # which can significantly reduce memory usage and improve performance
    # especially for large models and datasets
    # Train the model on the dataset using cuDNN and cuTENSOR
    model.train(data)

And that’s it you’re now training with mixed precision! You can adjust the `AMPLITE_LEVEL` variable to fine-tune your performance, but for most cases, O1 is a good starting point.

With this guide, you’re ready to take on the world of deep learning and train faster than ever before.

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