LLaMA-2 of LLMs: all py files for source code interpretation (including example_text_completion.py/example_chat_completion.py+model.py/generation.py/tokenizer.py)
Table of contents
# 1.0, main function uses pre-trained model to generate text
# 1.1. First create a generator object through the Llama.build method to generate text.
# 1.2. Tips for defining generated text: free text generation, text continuation
# 1.0, main function uses pre-trained model to generate text
# 1.1. First create a generator object through the Llama.build method to generate text.
2.2. Source code interpretation (generation.py file)
2.3. Source code interpretation (tokenizer.py file)
1. Interpretation of llama2 source code—inference function—(example_text_completion.py/example_chat_completion.py)
1. Source code interpretation (example_text_completion.py file) uses the pre-trained language model to generate text tasks based on text prompts.
Use pre-trained models and word segmenters to generate text. Users can configure the generation method through command line parameters.
Source code address : https://github.com/facebookresearch/llama/blob/main/example_text_completion.py
Run script command
torchrun --nproc_per_node 1 example_text_completion.py \
--ckpt_dir llama-2-7b/\
--tokenizer_path tokenizer.model \
--max_seq_len 128 --max_batch_size 4
# 1.0 , main function uses pre-trained model to generate text
ckpt_dir (str): Directory where pre-trained model checkpoint files are stored.
tokenizer_path (str): Path to the tokenizer model used for text encoding/decoding.
temperature (float, optional): Temperature value used to control randomness in the generation process. Default is 0.6.
top_p (float, optional): The top-p sampling parameter used to control the generated diversity. Default is 0.9.
max_seq_len (int, optional): Maximum sequence length for input prompts. The default is 128.
max_gen_len (int, optional): Maximum length of the generated sequence. The default is 64.
max_batch_size (int, optional): Maximum batch size for generating sequences. Default is 4.
# 1. 1. First create a generator object through the Llama.build method to generate text.
# 1. 2. Define tips for generating text: free text generation, text continuation
# The code defines a list containing multiple text prompts. Represents the text generated by the user request model. These prompts include normal free-generated prompts, prompts with examples, and some prompts that require model continuation text.
# 1. 3. Use the text_completion method of the generator to generate text for each prompt, and pass in the text prompt list prompts and other parameters.
# 1. 4. The for loop traverses prompts and corresponding generated results, and prints them out to display the generated text content.
2. Source code interpretation (example_chat_completion.py file) uses the pre-trained language model to implement the dialogue chat task based on a list of multiple groups of conversations based on three roles.
Source code address : https://github.com/facebookresearch/llama/blob/main/example_chat_completion.py
# 1.0, main function uses pre-trained model to generate text
ckpt_dir (str): Directory path containing pre-trained model checkpoint files.
tokenizer_path (str): Path to the tokenizer model file used for text encoding/decoding.
temperature (float, optional): Temperature value used to control randomness in the generation process. The default value is 0.6.
top_p (float, optional): The top-p sampling parameter that controls the generated diversity. The default value is 0.9.
max_seq_len (int, optional): Maximum sequence length for input prompts. The default value is 512.
max_batch_size (int, optional): Maximum batch size for generated sequences. The default value is 8.
max_gen_len (int, optional): Maximum length of generated sequence. If None, set to the model's maximum sequence length. The default value is None.
# 1.1. First create a generator object through the Llama.build method to generate text.
# 1.2. Define prompts for generating text: Create a list of multiple sets of conversations based on three characters
# Each conversation is a list containing multiple messages, including roles ("user", "assistant", "system") and message content
# 1.3. Use the text_completion method of the generator to generate text for each dialog, passing in the dialog list and other parameters.
# 1.4. The for loop traverses the dialog and the corresponding generated results, and prints out the messages of each character and the generated replies.
2. Interpretation of llama2 source code—model/tokenizer/dialogue chat function—(model.py/generation.py/tokenizer.py)
2.1. Source code interpretation (model.py file) implements a Transformer model ( multi-head attention mechanism + feedforward neural network + rotation embedding )
Source code address : https://github.com/facebookresearch/llama/blob/main/llama/model.py
LLaMA-2 of LLMs: Source code interpretation (model.py file) The modular idea implements a complete Transformer model (multi-head attention mechanism + feedforward neural network, RMSNorm + RoPE + parallel computing + caching mechanism to improve efficiency)
2.2. Source code interpretation (generation.py file)
Source code address : https://github.com/facebookresearch/llama/blob/main/llama/generation.py
LLaMA-2 of LLMs: source code interpretation (generation.py file) - Llama class implements text generation function based on pre-trained model (implementation of text completion/multi-round dialogue generation based on single-round prompts) = build function to build Llama instance + init function Initialize the model and vocabulary object + generate function to generate a text sequence based on the prompt text + sample_top_p auxiliary function to implement the core sampling strategy top-P to control randomness
2.3. Source code interpretation (tokenizer.py file)
Source code address : https://github.com/facebookresearch/llama/blob/main/llama/tokenizer.py