Examples of running the convert_checkpoint.py script

This process will help you create the arguments that go with the checkpoint.py script

Examples of running the convert_`checkpoint.py` script

The script provides three examples demonstrating different use cases for running the convert_heckpoint.py script.
Each example shows the specific command to run convert_checkpoint.py with the corresponding arguments.
The examples cover scenarios such as converting a checkpoint using a single GPU with FP16 or BF16 precision, and converting a checkpoint with specific model hyperparameters.

1. Convert a checkpoint using a single GPU and FP16:
python convert_checkpoint.py --model_dir ./path/to/model/directory \
                              --output_dir ./tllm_checkpoint_1gpu_fp16 \
                              --dtype float16

2. Convert a checkpoint using a single GPU and BF16:
python convert_checkpoint.py --model_dir ./path/to/model/directory \
                              --output_dir ./tllm_checkpoint_1gpu_bf16 \
                              --dtype bfloat16

3. Convert a checkpoint with specific model hyperparameters:
python convert_checkpoint.py --model_dir ./path/to/model/directory \
                              --output_dir ./path/to/output/directory \
                              --dtype float16 \
                              --n_layer 32 \
                              --n_head 32 \
                              --n_embd 4096

README: Converting Checkpoints with TensorRT-LLM

This guide explains how to use the run_convert_checkpoint.py script to run the convert_checkpoint.py script with transparent configurations using a YAML file.

First download the scripts from Github

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

You will need to manually move the files into the right directory for now

Prerequisites

Python 3.x
TensorRT-LLM
PyYAML (install with pip install pyyaml)

Configuration

Populate the config.yaml file in the same directory as the run_convert_checkpoint.py script. This file will contain the configurations for the convert_checkpoint.py script.
Open the config.yaml file and specify the desired configurations. The file should have the following structure:

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

checkpoint:
  tp_size: 1
  pp_size: 1
  vocab_size: 32000
  n_positions: 2048
  n_layer: 32
  n_head: 32
  n_embd: 4096
  inter_size: 11008
  # Additional checkpoint arguments
  # ...

Adjust the values in the config.yaml file according to your requirements. You can refer to the comments in the file for suggestions and available choices.

Running the Script

Open a terminal and navigate to the directory containing the run_convert_checkpoint.py script and the config.yaml file.
Run the following command:

python3 run_convert_checkpoint.py

The script will read the configurations from the config.yaml file and construct the corresponding command-line arguments for the convert_checkpoint.py script.
The convert_checkpoint.py script will be executed with the specified configurations.

How It Works

The run_convert_checkpoint.py script does the following:

It uses the argparse module to parse the command-line argument --config, which specifies the path to the YAML configuration file (default is config.yaml).
It loads the configurations from the specified YAML file using the yaml.safe_load() function.
It extracts the relevant configuration values from the loaded YAML data, such as model_dir, output_dir, and dtype.
It constructs the command-line arguments for the convert_checkpoint.py script based on the extracted configuration values.
It iterates over the checkpoint arguments in the YAML file and adds them to the command-line arguments list. Boolean values are handled separately, adding only the argument flag if the value is True.
Finally, it uses the subprocess.run() function to execute the convert_checkpoint.py script with the constructed command-line arguments.

By using the run_convert_checkpoint.py script and the config.yaml file, you can easily configure and run the convert_checkpoint.py script without explicitly specifying all the command-line arguments.

This approach provides a more transparent and manageable way to set the configurations for the checkpoint conversion process.

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1. Convert a checkpoint using a single GPU and FP16: python convert_checkpoint.py --model_dir ./path/to/model/directory \ --output_dir ./tllm_checkpoint_1gpu_fp16 \ --dtype float16 2. Convert a checkpoint using a single GPU and BF16: python convert_checkpoint.py --model_dir ./path/to/model/directory \ --output_dir ./tllm_checkpoint_1gpu_bf16 \ --dtype bfloat16 3. Convert a checkpoint with specific model hyperparameters: python convert_checkpoint.py --model_dir ./path/to/model/directory \ --output_dir ./path/to/output/directory \ --dtype float16 \ --n_layer 32 \ --n_head 32 \ --n_embd 4096

model: model_dir: ./path/to/model output_dir: ./path/to/output dtype: float16 checkpoint: tp_size: 1 pp_size: 1 vocab_size: 32000 n_positions: 2048 n_layer: 32 n_head: 32 n_embd: 4096 inter_size: 11008 # Additional checkpoint arguments # ...