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LLM-Recipes (Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs - Paper)

This project is a fork of the LLama recipes repository, which contains a collection of recipes and scripts for training and fine-tuning Large Language Models (LLMs). For comprehensive documentation and additional usage examples, please refer to the LLama recipes repository.

This README file specifically focuses on distillation runs, which involve transferring knowledge from a teacher model to a student model using distillation techniques based on the paper "Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs".

HuggingFace implementation in progress...

Run Distillation Process

For distillation, several parameters can be set:

--model_name: The ID of the student model (HuggingFace repository ID).
--lr: Learning rate for the training process.
--num_epochs: Number of epochs for training.
--batch_size_training: Batch size for training.
--val_batch_size: Batch size for validation.
--dataset.file: Path to the dataset file.
--output_dir: Directory to save the output.
--distillation: Activate distillation.
--distillation_config.model_name: The ID of the teacher model (HuggingFace repository ID).
--distillation_config.enable_fsdp: Enable Fully Sharded Data Parallelism (FSDP).
--distillation_config.pure_bf16: Use pure BF16 precision.
--distillation_config.distil_factor: Factor for distillation loss.
--save_step: Interval for saving checkpoints during training.
--encoder_decoder: Specify this parameter if the student model follows an encoder-decoder architecture.

Example

Below is an example command for running the distillation process:

llm-recipes/finetuning.py --model_name EleutherAI/pythia-410m-deduped --dataset.file datasets/loader/squad.py --lr 1e-6 --num_epochs 5 --batch_size_training 4 --val_batch_size 4 --output_dir train/output/path --distillation_config.model_name meta-llama/Llama-2-7b-chat-hf --distillation --distillation_config.enable_fsdp --distillation_config.pure_bf16 --distillation_config.distil_factor 1.5 --save_step 100

In this example, the values for the parameters are replaced as follows:

model_name: EleutherAI/pythia-410m-deduped
teacher_model_name: meta-llama/Llama-2-7b-chat-hf
lr: 1e-6
num_epochs: 5
batch_size_training: 4
val_batch_size: 4
distil_factor: 1.5
dataset: llm-distillation/datasets/loader/squad.py

Dataset File

In order to recalculate teacher logits correctly, the parameters used to generate them must be exactly the same as those used to create the dataset with the LLM-Distillation library.

Example:

import os
import sys
from datasets import load_from_disk

sys.path.append(f"{os.getenv('HOME')}/llm-distillation")
from prompt.prompt import create_chat_prompt
from prompt.prompt import create_prompt

def tokenize(item, tokenizer, encoder_decoder=False):
    is_chat = True if 'chat' in tokenizer.name_or_path.lower() or "instruct" in tokenizer.name_or_path.lower() else False
    task = "qa"

    if tokenizer.name_or_path == "meta-llama/Llama-2-7b-chat-hf":
        shot = 1
        title = False
    elif tokenizer.name_or_path == "mistralai/Mistral-7B-Instruct-v0.2":
        shot = 3
        title = item['title']
    elif tokenizer.name_or_path == "tiiuae/falcon-7b-instruct":
        shot = 4
        title = False

    if is_chat:
        prompt = create_chat_prompt(
            task, shot,
            title = title,
            context = item['context'],
            question = item['question'],
            sys_user = True if "mistralai/Mistral-7B-Instruct-v0.2" in tokenizer.name_or_path else False,
            chat_template = tokenizer.apply_chat_template
        )
    else:
        prompt = create_prompt(
            task, 0, 
            context = item['context'],
            question = item['question'],
        )

    context_tokens = tokenizer.encode(f"{tokenizer.bos_token} {prompt}", add_special_tokens=False)
    if not encoder_decoder:
        if 'chat' in tokenizer.name_or_path.lower() or "instruct" in tokenizer.name_or_path.lower():
            context_tokens = tokenizer.encode(f"{prompt}", add_special_tokens=False)
            if tokenizer.name_or_path == "tiiuae/falcon-7b-instruct":
                answer_tokens = tokenizer.encode(f" {item['answers_generated']}", add_special_tokens=False)
            else:
                answer_tokens = tokenizer.encode(f"{item['answers_generated']}", add_special_tokens=False)
        else:
            context_tokens = tokenizer.encode(f"{tokenizer.bos_token}{prompt}", add_special_tokens=False)
            answer_tokens = tokenizer.encode(f" {item['answers_generated']}{tokenizer.eos_token}", add_special_tokens=False)

        prompt_tokens = context_tokens+answer_tokens
        labels_tokens = (len(context_tokens)*[-100,])+answer_tokens

        combined_tokens = {
            "input_ids": prompt_tokens,
            "labels": labels_tokens
        }
        return dict(combined_tokens, attention_mask=[1]*len(combined_tokens["input_ids"]))
    else:
        input_ids = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt")[0]
        labels = tokenizer.encode(item['answers_generated'], add_special_tokens=True, return_tensors="pt")[0]

        return {
            "input_ids": input_ids,
            "labels": labels,
            "attention_mask": [1]*len(input_ids)
        }

def get_split(dataset_config, tokenizer, split):
    dataset = load_from_disk(f"{os.getenv('HOME')}/llm-distillation/datasets/hf/{dataset_config.generated_by.split('/')[-1]}-squad")
    dataset = dataset[split]
    if dataset_config.training_size < 1: dataset = dataset.select(range(int(len(dataset)*dataset_config.training_size)))
    dataset = dataset.map(lambda item: tokenize(item, tokenizer, dataset_config.encoder_decoder), remove_columns=list(dataset.features))
    return dataset

Citation

@misc{boizard2024crosstokenizer,
      title={Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs}, 
      author={Nicolas Boizard and Kevin El Haddad and Céline Hudelot and Pierre Colombo},
      year={2024},
      eprint={2402.12030},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}