Converting Checkpoints

The convert_checkpoint.py script is a component in the TensorRT-LLM process of converting a pre-trained language model, such as LLaMA, into an optimised format suitable for inference on GPUs using TensorRT.

The script takes a pre-trained model checkpoint and converts it into a format that can be loaded and optimised by TensorRT-LLM.

Here's a detailed analysis of how the script works and its role in the TensorRT-LLM process:

Command-line Arguments

• The script accepts various command-line arguments to configure the conversion process.

• Key arguments include --model_dir (path to the pre-trained model directory), --output_dir (path to save the converted checkpoint), and --dtype (data type for the converted model, e.g., float16).

• Other arguments control parallelism, quantization, and model-specific settings.

Model Loading

• The script supports loading models from different sources, such as a Hugging Face model directory (--model_dir) or a meta checkpoint directory (--meta_ckpt_dir).

• It uses the Hugging Face Transformers library to load the pre-trained model and its configuration.

• The preload_model function is responsible for loading the model based on the specified directory and device (CPU or GPU).

Model Conversion

• The script converts the pre-trained model into a format compatible with TensorRT-LLM.

• It creates an instance of the LLaMAForCausalLM class, which represents the LLaMA model architecture in TensorRT-LLM.

• The conversion process involves initializing the model with the specified data type, mapping (tensor parallelism and pipeline parallelism), and quantization settings.

• The from_hugging_face method of LLaMAForCausalLM is used to convert the Hugging Face model to TensorRT-LLM format.

Quantization

• The script supports various quantization options to reduce the model size and improve inference performance.

• Quantization settings are determined based on the command-line arguments, such as --use_weight_only, --weight_only_precision, --smoothquant, --per_channel, and --per_token.

• The args_to_quantization function maps the command-line arguments to the corresponding quantization configuration (QuantConfig).

• Quantization algorithms like QuantAlgo.W8A16, QuantAlgo.W4A16, and QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN are used based on the specified settings.

Parallelism

• The script supports tensor parallelism and pipeline parallelism to distribute the model across multiple GPUs.

• The --tp_size and --pp_size arguments control the parallelism settings.

• The Mapping class is used to define the mapping of the model across GPUs based on the parallelism settings.

Saving the Converted Checkpoint

• After the model conversion and quantization, the script saves the converted checkpoint to the specified output directory (--output_dir).

• The save_checkpoint method of LLaMAForCausalLM is used to save the converted model weights and configuration.

Multi-threading

• The script supports multi-threaded execution to speed up the conversion process when using multiple GPUs.

• The execute function is used to distribute the conversion tasks across multiple threads based on the specified number of workers (--workers).

Key considerations when using this script

1. Ensure that the pre-trained model is compatible with the LLaMA architecture and can be loaded using the Hugging Face Transformers library.

2. Choose the appropriate data type (--dtype) based on the desired precision and performance trade-off. Float16 (FP16) is commonly used for faster inference with minimal accuracy loss.

3. Consider the available GPU memory and select the appropriate parallelism settings (--tp_size and --pp_size) to distribute the model across multiple GPUs if necessary.

4. Experiment with different quantization settings to achieve the desired balance between model size, inference speed, and accuracy. Weight-only quantization (--use_weight_only) and SmoothQuant (--smoothquant) are popular options.

5. Ensure that the output directory (--output_dir) has sufficient space to store the converted checkpoint.

6. If using multi-threading (--workers), ensure that the system has enough resources to handle the parallel execution.

Overall, the convert_checkpoint.py script plays a vital role in the TensorRT-LLM process by converting pre-trained language models into a format optimised for inference on GPUs using TensorRT.

It provides flexibility in model loading, quantization, parallelism, and saves the converted checkpoint for further optimisation and deployment.