The Variational Autoencoder (VAE) shown here is an optimized implementation of the architecture first described in Variational Autoencoders for Collaborative Filtering and can be used for recommendation tasks. The main differences between this model and the original one are the performance optimizations, such as using sparse matrices, mixed precision, larger mini-batches and multiple GPUs. These changes enabled us to achieve a significantly higher speed while maintaining the same accuracy. Because of our fast implementation, we've also been able to carry out an extensive hyperparameter search to slightly improve the accuracy metrics.
When using Variational Autoencoder for Collaborative Filtering (VAE-CF), you can quickly train a recommendation model for the collaborative filtering task. The required input data consists of pairs of user-item IDs for each interaction between a user and an item. With a trained model, you can run inference to predict what items is a new user most likely to interact with.
This model is trained with mixed precision using Tensor Cores on NVIDIA Volta, Turing and Ampere GPUs. Therefore, researchers can get results 1.9x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
This implementation has been initially developed as an educational project at the University of Warsaw by Albert Cieślak, Michał Filipiuk, Frederic Grabowski and Radosław Rowicki.
Figure 1. The architecture of the VAE-CF model
The Variational Autoencoder is a neural network that provides collaborative filtering based on implicit feedback. Specifically, it provides product recommendations based on user and item interactions. The training data for this model should contain a sequence of (user ID, item ID) pairs indicating that the specified user has interacted with the specified item.
The model consists of two parts: the encoder and the decoder. The encoder transforms the vector, which contains the interactions for a specific user, into a n-dimensional variational distribution. We can then use this variational distribution to obtain a latent representation of a user. This latent representation is then fed into the decoder. The result is a vector of item interaction probabilities for a particular user.
The following features were implemented in this model:
The following features are supported by this model:
|Horovod Multi-GPU (NCCL)||Yes|
|Automatic mixed precision (AMP)||Yes|
Horovod: Horovod is a distributed training framework for TensorFlow, Keras, PyTorch and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the Horovod: Official repository.
Multi-GPU training with Horovod: Our model uses Horovod to implement efficient multi-GPU training with NCCL. For details, see example sources in this repository or see the TensorFlow tutorial.
Mixed precision is the combined use of different numerical precisions in a computational method. Mixed precision training offers significant computational speedup by performing operations in half-precision format, while storing minimal information in single-precision to retain as much information as possible in critical parts of the network. Since the introduction of Tensor Cores in Volta, and following with both the Turing and Ampere architectures, significant training speedups are experienced by switching to mixed precision -- up to 3x overall speedup on the most arithmetically intense model architectures. Using mixed precision training requires two steps:
This can now be achieved using Automatic Mixed Precision (AMP) for TensorFlow to enable the full mixed precision methodology in your existing TensorFlow model code. AMP enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically. The TensorFlow framework code makes all necessary model changes internally.
In TF-AMP, the computational graph is optimized to use as few casts as necessary and maximize the use of FP16, and the loss scaling is automatically applied inside of supported optimizers. AMP can be configured to work with the existing tf.contrib loss scaling manager by disabling the AMP scaling with a single environment variable to perform only the automatic mixed-precision optimization. It accomplishes this by automatically rewriting all computation graphs with the necessary operations to enable mixed precision training and automatic loss scaling.
For information about:
Mixed precision is enabled in TensorFlow by using the Automatic Mixed Precision (TF-AMP) extension which casts variables to half-precision upon retrieval, while storing variables in single-precision format. Furthermore, to preserve small gradient magnitudes in backpropagation, a loss scaling step must be included when applying gradients. In TensorFlow, loss scaling can be applied statically by using simple multiplication of loss by a constant value or automatically, by TF-AMP. Automatic mixed precision makes all the adjustments internally in TensorFlow, providing two benefits over manual operations. First, programmers need not modify network model code, reducing development and maintenance effort. Second, using AMP maintains forward and backward compatibility with all the APIs for defining and running TensorFlow models.
To enable mixed precision, you can simply add the values to the environmental variables inside your training script:
Enable TF-AMP graph rewrite:
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = '1'
Enable Automated Mixed Precision:
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
To enable mixed precision in VAE-CF, run the
main.py script with the
TensorFloat-32 (TF32) is the new math mode in NVIDIA A100 GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs.
TF32 Tensor Cores can speed up networks using FP32, typically with no loss of accuracy. It is more robust than FP16 for models which require high dynamic range for weights or activations.
For more information, refer to the TensorFloat-32 in the A100 GPU Accelerates AI Training, HPC up to 20x blog post.
TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.