TensorRT-LLM Build Process Documentation

Introduction

This documentation explains the process of building a TensorRT engine for large language models (LLMs) using the TensorRT-LLM framework.

The build process involves configuring the build settings, running the buildrun.py script, and leveraging the capabilities of TensorRT-LLM to optimse and accelerate the model inference.

Prerequisites

Before starting the build process, ensure that you have the following prerequisites:

• NVIDIA GPU with CUDA support

• CUDA toolkit and GPU drivers properly installed

• TensorRT-LLM framework installed

• PyTorch with CUDA support installed

• YAML and argparse Python packages installed

First get the files to help - you willl hve to manually move these files into the main folder

git clone https://github.com/Continuum-Labs-HQ/tensorrt-continuum.git

Build Process

Step 1: Populate the buildconfig.yaml File

The first step in the build process is to populate the buildconfig.yaml file with the desired build configurations. This file serves as a centralized place to specify various settings related to the model, checkpoint, and build process.

The buildconfig.yaml file consists of three main sections:

1. Model Configuration: This section allows you to specify the paths to the pretrained model directory (model_dir), the output directory for the built engine (output_dir), and the data type for the model (dtype).

2. Checkpoint Configuration: In this section, you can configure settings related to the model checkpoint, such as the checkpoint directory (checkpoint_dir), tensor parallelism size (tp_size), and pipeline parallelism size (pp_size).

3. Build Configuration: The build configuration section enables you to set various parameters for the build process, including the maximum input sequence length (max_input_len), maximum output sequence length (max_output_len), maximum batch size (max_batch_size), and maximum beam width (max_beam_width).

Here's an example of a buildconfig.yaml file:

model:
  model_dir: ./path/to/model
  output_dir: ./path/to/output
  dtype: float16

checkpoint:
  checkpoint_dir: ./path/to/checkpoint
  tp_size: 1
  pp_size: 1

build:
  max_input_len: 256
  max_output_len: 256
  max_batch_size: 8
  max_beam_width: 1

Step 2: Run the buildrun.py Script

Once the buildconfig.yaml file is populated with the desired configurations, the next step is to run the buildrun.py script. This script reads the buildconfig.yaml file, parses the configurations, and passes them as command-line arguments to the trtllm-build command.

The buildrun.py script performs the following tasks:

1. It defines a function called parse_buildconfig that takes the path to the buildconfig.yaml file as input. This function reads the YAML file, extracts the relevant settings from the model, checkpoint, and build configurations, and constructs a list of command-line arguments based on the settings.

2. The main function uses the argparse module to parse the command-line arguments passed to the buildrun.py script. It expects a --config argument that specifies the path to the buildconfig.yaml file.

3. Inside the main function, the parse_buildconfig function is called with the provided buildconfig.yaml file path. It returns a list of command-line arguments.

4. The trtllm-build command is constructed by concatenating the base command with the parsed command-line arguments.

5. Finally, the subprocess.run function is used to execute the trtllm-build command with the provided arguments.

To run the buildrun.py script, use the following command:

python buildrun.py --config buildconfig.yaml

Step 3: TensorRT-LLM Build Process

Once the trtllm-build command is executed, TensorRT-LLM takes over the build process.

It leverages the power of TensorRT, a high-performance deep learning inference optimizer and runtime, to optimize and accelerate the model inference.

TensorRT-LLM performs several key steps during the build process:

1. Model Parsing: TensorRT-LLM parses the model architecture and converts it into an internal representation suitable for optimization. It analyzes the model's layers, operations, and data flow to create an optimized execution plan.

2. Tensor Fusion: TensorRT-LLM identifies opportunities for tensor fusion, where multiple operations can be combined into a single kernel. This helps reduce memory transfers and improves overall performance.

3. Precision Calibration: Based on the specified data type (dtype) in the buildconfig.yaml file, TensorRT-LLM performs precision calibration. It can convert the model's weights and activations to lower precision formats like FP16 or INT8, which can significantly reduce memory bandwidth and improve inference speed while maintaining acceptable accuracy.

4. Memory Optimization: TensorRT-LLM optimizes memory usage by reusing memory buffers whenever possible. It minimizes memory allocations and deallocations, leading to more efficient memory utilization.

5. Kernel Auto-Tuning: TensorRT-LLM automatically tunes the CUDA kernels for optimal performance on the target GPU. It explores different kernel configurations and selects the most efficient ones based on the specific hardware characteristics.

6. Engine Serialization: Once the optimizations are complete, TensorRT-LLM serializes the built engine into a file. This serialized engine can be deserialized later for fast and efficient inference.

Throughout the build process, TensorRT-LLM applies various optimizations and techniques to maximize the performance and efficiency of the model inference.

Conclusion

The TensorRT-LLM build process simplifies the conversion of large language models into optimized TensorRT engines. By populating the buildconfig.yaml file with the desired configurations and running the buildrun.py script, users can leverage the power of TensorRT-LLM to accelerate model inference on NVIDIA GPUs.

TensorRT-LLM's build process incorporates advanced optimizations such as tensor fusion, precision calibration, memory optimization, and kernel auto-tuning. These optimizations enable efficient utilization of GPU resources and significantly improve inference speed and throughput.

By following the steps outlined in this documentation and providing the necessary configurations, users can easily build high-performance TensorRT engines for their large language models using the TensorRT-LLM framework.