tensorrt_llm folders

TensorRT-LLM/tensorrt_llm

The folder structure represents various components and modules of the TensorRT-LLM framework.

auto_parallel

This folder likely contains code related to automatic parallelization of the TensorRT-LLM framework, enabling efficient execution on multiple GPUs or nodes.

commands

This folder contains the build.py command.

hlapi

HLAPI stands for High-Level API. This folder contains higher-level abstractions and interfaces for working with the TensorRT-LLM framework, providing a more user-friendly and simplified API.

layers:

This folder likely contains implementations of various neural network layers and operations used in the TensorRT-LLM framework, such as attention layers, feedforward layers, normalization layers, etc.

models:

This folder may contain pre-trained models or model architectures supported by the TensorRT-LLM framework, such as Transformer-based models like BERT, GPT, or other language models.

plugin:

This folder likely contains custom TensorRT plugins developed for the TensorRT-LLM framework. TensorRT plugins are used to extend the functionality of TensorRT by implementing custom layers or operations that are not natively supported.

quantization:

This folder probably contains code related to quantization techniques used in the TensorRT-LLM framework, such as post-training quantization or quantization-aware training, to reduce the model size and improve inference performance.

runtime:

This folder may contain the runtime components of the TensorRT-LLM framework, responsible for executing the models and managing the inference process.

tools:

This folder likely contains various utility tools and scripts used in the development, testing, and deployment of the TensorRT-LLM framework.

_common.py, _ipc_utils.py, _utils.py: These files likely contain common utility functions, inter-process communication utilities, and other helper functions used throughout the TensorRT-LLM framework.

builder.py:

This file may contain the core functionality for building TensorRT engines from the LLM models, handling the conversion and optimization process.

executor.py:

This file contains the execution logic for running inference on the TensorRT engines, managing memory, and handling input/output data.

functional.py:

This file may contain functional-style operations and transformations used in the TensorRT-LLM framework, similar to the functional API in deep learning frameworks like PyTorch.

graph_rewriting.py:

This file likely contains graph rewriting techniques and optimizations applied to the TensorRT-LLM models to improve performance and efficiency.

logger.py:

This file may contain logging utilities and configurations used for debugging, monitoring, and tracking the execution of the TensorRT-LLM framework.

lora_manager.py:

This file likely contains code related to the management of Low-Rank Adaptation (LoRA) techniques used in the TensorRT-LLM framework for fine-tuning and adapting pre-trained models.

mapping.py:

This file may contain mapping functions or utilities used for transforming data or model parameters between different representations.

module.py, parameter.py:

These files likely contain base classes and abstractions for defining neural network modules and managing model parameters in the TensorRT-LLM framework.

network.py:

This file may contain the core definition and functionality of the TensorRT network used in the TensorRT-LLM framework.

profiler.py:

This file likely contains profiling utilities and tools for measuring and analyzing the performance of the TensorRT-LLM models and framework.

top_model_mixin.py:

This file may contain a mixin class or utility functions for working with top-level models in the TensorRT-LLM framework.

version.py:

This file likely contains version information and metadata for the TensorRT-LLM framework.

These folders and files work together to provide the functionality and capabilities of the TensorRT-LLM framework, enabling efficient deployment and execution of large language models using TensorRT.