Functionals

The functionals in TensorRT-LLM, such as slice, softmax, softplus, split, sqrt, and others, are designed to offer high-level, efficient, and specialised operations for processing tensors within the TensorRT Large Language Model (LLM) framework.

These functionals encapsulate complex tensor operations, making them accessible through a simplified and standardised interface for model developers.

High-Level Abstraction

Ease of Use: Functionals abstract away the low-level details of tensor operations, allowing developers to focus on higher-level model architecture without delving into the intricacies of each operation.

Standardised Operations: They provide a set of commonly used operations in deep learning models, ensuring consistency and predictability across different implementations.

Efficient Tensor Processing

Optimisation: Each functional is optimised for performance on NVIDIA GPUs, ensuring efficient execution of tensor operations critical for Large Language Models.

Hardware Acceleration: Leveraging TensorRT optimisations, these functionals are designed to maximise the computational capabilities of the underlying hardware, particularly for high-throughput and low-latency inference.

Flexibility and Customization

• Configurable Parameters: Functionals come with various parameters that can be tuned according to the specific needs of the model, offering flexibility in how operations are applied to tensors.

• Adaptability: They can be easily integrated into different parts of neural network architectures, catering to a wide range of applications from simple feed-forward networks to complex transformer models.

Simplified Model Development

Rapid Prototyping: By using these high-level operations, developers can quickly prototype and experiment with different model architectures.

Readability and Maintenance: The use of functionals leads to more readable and maintainable code, as complex tensor operations are encapsulated in simple, descriptive function calls.

Consistency with Established Frameworks

Familiarity: Many of these functionals mirror operations found in popular deep learning frameworks like PyTorch and TensorFlow, making it easier for developers to transition or integrate models with TensorRT-LLM.

Support for Advanced Features

Advanced Tensor Operations

Beyond basic operations, functionals like gpt_attention provide advanced capabilities specifically tailored for state-of-the-art language models, enabling cutting-edge performance and features.

In summary, the functionals in TensorRT-LLM are a collection of high-level, optimised, and flexible operations that simplify and accelerate the development of large language models on NVIDIA GPUs.

They are instrumental in transforming complex tensor manipulations into accessible, efficient, and standardised building blocks for model development.