summarize.py script in Llama folder

The summarize_long.py file is a Python script that demonstrates how to use the TensorRT-LLM framework to perform text summarization using the LLaMA model.

It compares the performance of the TensorRT-LLM implementation with the Hugging Face (HF) implementation of the LLaMA model.

To execute the summarize_long.py script and use your serialized LLaMA model, follow these steps:

1. Ensure that you have the necessary dependencies installed in your TensorRT-LLM Docker container. The script requires the datasets and transformers libraries, which should be listed in the requirements.txt file.

2. Make sure you have the serialized LLaMA model checkpoint files in the appropriate directory. In your case, it seems like the serialized model is located in the tllm_checkpoint_1gpu_bf16 directory.

3. Open the summarize_long.py script and locate the parse_args() function. This function defines the command-line arguments that the script accepts. You may need to modify some of the default values to match your setup. For example:

   • Set the hf_model_location argument to the path of your Hugging Face LLaMA model checkpoint, if you want to compare with the HF implementation.

   • Set the dataset_path argument to the path where you want to cache the dataset used for testing.

   • Set the engine_dir argument to the directory containing your serialized TensorRT-LLM model checkpoint, which in your case is tllm_checkpoint_1gpu_bf16.

   • Adjust other arguments such as max_attention_window_size, max_input_len, batch_size, num_beams, output_len, etc., according to your requirements.

4. Run the summarize_long.py script using the following command:

python summarize_long.py --test_trt_llm --engine_dir ./tllm_checkpoint_1gpu_bf16

This command will execute the script and use the TensorRT-LLM implementation with your serialized LLaMA model checkpoint located in the tllm_checkpoint_1gpu_bf16 directory.

You can also add additional arguments to control the behavior of the script. For example, you can add --test_hf to compare with the Hugging Face implementation, or --check_accuracy to check the accuracy of the TensorRT-LLM implementation against a specified threshold.

Now, let's analyze the summarize_long.py script in more detail

• The script starts by importing the necessary libraries and modules, including TensorRT-LLM-specific modules and profiling tools.

• The parse_args() function defines the command-line arguments that the script accepts, allowing you to configure various aspects of the summarization task, such as model locations, dataset paths, hyperparameters, etc.

• The TRTLLaMA() function sets up the TensorRT-LLM model based on the provided configuration files and loads the serialized model checkpoint.

• The get_long_texts() function retrieves long text samples from the OpenWebText-10k dataset that fall within a specified token length range.

• The prepare_prompt() function prepares the input prompt for summarization by cleaning and formatting the text.

• The summarize_hf() function performs text summarization using the Hugging Face implementation of the LLaMA model.

• The summarize_tensorrt_llm() function performs text summarization using the TensorRT-LLM implementation of the LLaMA model.

• The main() function is the entry point of the script. It loads the tokenizer, retrieves the test data, and performs summarization using either the TensorRT-LLM implementation, the Hugging Face implementation, or both, depending on the provided command-line arguments.

• If both TensorRT-LLM and Hugging Face implementations are tested, the script compares the generated summaries using the ROUGE metric and logs the results.

The script provides a comprehensive example of how to use the TensorRT-LLM framework for text summarization tasks, demonstrating the integration with popular NLP libraries and datasets, as well as the comparison with the Hugging Face implementation.

Keep in mind that the script assumes the availability of certain files and directories, such as the serialized model checkpoint, configuration files, and dataset cache. Make sure to set up your environment accordingly before running the script.