main.py

import os
import argparse
import random
import torch
import gradio as gr
import transformers
import logging
import sys
from datasets import Dataset
from transformers import LlamaForCausalLM, LlamaTokenizer, GenerationConfig
from peft import PeftConfig, prepare_model_for_int8_training, LoraConfig, get_peft_model, PeftModel

logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
model = None
tokenizer = None
peft_model = None

def dummy_word():
    word_list = ['hello', 'llama', 'happy', 'hours', 'PhD', 'CS']
    word1 = random.choice(word_list)
    word2 = random.choice(word_list)
    return word1 + '-' + word2

def maybe_load_models():
    global model
    global tokenizer

    if model is None:
        model = LlamaForCausalLM.from_pretrained(
            "decapoda-research/llama-7b-hf",
            load_in_8bit=True,
            torch_dtype=torch.float16,
            device_map="auto",
        )

    if tokenizer is None:
        tokenizer = LlamaTokenizer.from_pretrained(
            "decapoda-research/llama-7b-hf",
        )

def reset_models():
    global model
    global tokenizer

    del model
    del tokenizer

    model = None
    tokenizer = None

def load_trained_llama(model_name):
  peft_model_id = model_name
  config = PeftConfig.from_pretrained(peft_model_id)
  model = LlamaForCausalLM.from_pretrained(config.base_model_name_or_path)
  model = PeftModel.from_pretrained(model, peft_model_id)
  tokenizer = LlamaTokenizer.from_pretrained(config.base_model_name_or_path)
  model.cuda()
  return model, tokenizer

def generate_text(
    model_name, 
    text, 
    temperature, 
    top_p, 
    top_k, 
    repeat_penalty,
    max_new_tokens,
    progress=gr.Progress(track_tqdm=True)
):
    global model
    global tokenizer
    if model_name and model_name != "None":
        model, tokenizer = load_trained_llama(model_name)
    else: 
        #for finetuned llama without CoT: see sallywww/insft50e_llama7b 
        model, tokenizer = load_trained_llama("sallywww/CoT_llama")
    model.eval()
    inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = model.generate(input_ids=inputs["input_ids"].cuda(), \
        temperature=temperature, \
        top_p=top_p, \
        top_k=top_k, \
        max_new_tokens=max_new_tokens)
        invariants = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
    return invariants


def tokenize_and_train(
    training_text,
    max_seq_length,
    micro_batch_size,
    gradient_accumulation_steps,
    epochs,
    learning_rate,
    lora_r,
    lora_alpha,
    lora_dropout,
    model_name,
    progress=gr.Progress(track_tqdm=True)
):
    global model
    global tokenizer

    reset_models()
    maybe_load_models()

    tokenizer.pad_token_id = 0

    paragraphs = training_text.split("<end of text>")
    print("Number of samples: " + str(len(paragraphs)))
        
    def tokenize(item):
        result = tokenizer(
            item["text"],
            truncation=True,
            max_length=max_seq_length,
            padding="max_length",
        )
        return {
            "input_ids": result["input_ids"][:-1],
            "attention_mask": result["attention_mask"][:-1],
        }

    def to_dict(text):
        return {"text": text}

    paragraphs = [to_dict(x) for x in paragraphs]
    data = Dataset.from_list(paragraphs)
    data = data.shuffle().map(lambda x: tokenize(x))

    model = prepare_model_for_int8_training(model)

    model = get_peft_model(model, LoraConfig(
        r=lora_r,
        lora_alpha=lora_alpha,
        target_modules=["q_proj", "v_proj"],
        lora_dropout=lora_dropout,
        bias="none",
        task_type="CAUSAL_LM",
    ))

    output_dir = f"lora-{model_name}"

    print("Training...")

    training_args = transformers.TrainingArguments(
        # Set the batch size for training on each device (GPU, CPU, or TPU).
        per_device_train_batch_size=micro_batch_size, 

        # Number of steps for gradient accumulation. This is useful when the total 
        # batch size is too large to fit in GPU memory. The effective batch size 
        # will be the product of 'per_device_train_batch_size' and 'gradient_accumulation_steps'.
        gradient_accumulation_steps=gradient_accumulation_steps,  

        # Number of warmup steps for the learning rate scheduler. During these steps, 
        # the learning rate increases linearly from 0 to its initial value. Warmup helps
        #  to reduce the risk of very large gradients at the beginning of training, 
        # which could destabilize the model.
        # warmup_steps=100, 

        # The total number of training steps. The training process will end once this 
        # number is reached, even if not all the training epochs are completed.
        # max_steps=1500, 

        # The total number of epochs (complete passes through the training data) 
        # to perform during the training process.
        num_train_epochs=epochs,  

        # The initial learning rate to be used during training.
        learning_rate=learning_rate, 

        # Enables mixed precision training using 16-bit floating point numbers (FP16). 
        # This can speed up training and reduce GPU memory consumption without 
        # sacrificing too much model accuracy.
        fp16=True,  

        # The frequency (in terms of steps) of logging training metrics and statistics 
        # like loss, learning rate, etc. In this case, it logs after every 20 steps.
        logging_steps=20, 

        # The output directory where the trained model, checkpoints, 
        # and other training artifacts will be saved.
        output_dir=output_dir, 

        # The maximum number of checkpoints to keep. When this limit is reached, 
        # the oldest checkpoint will be deleted to save a new one. In this case, 
        # a maximum of 3 checkpoints will be kept.
        save_total_limit=100,  
    )


    trainer = transformers.Trainer(
        # The pre-trained model that you want to fine-tune or train from scratch. 
        # 'model' should be an instance of a Hugging Face Transformer model, such as BERT, GPT-2, T5, etc.
        model=model, 

        # The dataset to be used for training. 'data' should be a PyTorch Dataset or 
        # a compatible format, containing the input samples and labels or masks (if required).
        train_dataset=data, 

        # The TrainingArguments instance created earlier, which contains various 
        # hyperparameters and configurations for the training process.
        args=training_args, 

        # A callable that takes a batch of samples and returns a batch of inputs for the model. 
        # This is used to prepare the input samples for training by batching, padding, and possibly masking.
        data_collator=transformers.DataCollatorForLanguageModeling( 
            tokenizer,  
            # Whether to use masked language modeling (MLM) during training. 
            # MLM is a training technique used in models like BERT, where some tokens in the 
            # input are replaced by a mask token, and the model tries to predict the 
            # original tokens. In this case, MLM is set to False, indicating that it will not be used.
            mlm=False, 
        ),
    )

    result = trainer.train(resume_from_checkpoint=False)

    model.save_pretrained(output_dir)
    
    reset_models()

    return result


with gr.Blocks(
    css="#refresh-button { max-width: 32px }", 
    title="PEFT LLaMA Finetuner") as demo:
    
    with gr.Tab("Finetuning"):

        with gr.Column():
            training_text = gr.Textbox(lines=12, label="Training Data", info="Each sequence must be separated by <end of text>")
            max_seq_length = gr.Slider(
                minimum=1, maximum=4096, value=512,
                label="Max Sequence Length", 
                info="The maximum length of each sample text sequence. Sequences longer than this will be truncated."
            )

        with gr.Row():
            with gr.Column():
                micro_batch_size = gr.Slider(
                    minimum=1, maximum=100, value=1, 
                    label="Micro Batch Size", 
                    info="The number of examples in each mini-batch for gradient computation. A smaller micro_batch_size reduces memory usage but may increase training time."
                )

                gradient_accumulation_steps = gr.Slider(
                    minimum=1, maximum=10, value=1, 
                    label="Gradient Accumulation Steps", 
                    info="The number of steps to accumulate gradients before updating model parameters. This can be used to simulate a larger effective batch size without increasing memory usage."
                )

                epochs = gr.Slider(
                    minimum=1, maximum=100, value=1, 
                    label="Epochs",
                    info="The number of times to iterate over the entire training dataset. A larger number of epochs may improve model performance but also increase the risk of overfitting.")

                learning_rate = gr.Slider(
                    minimum=0.00001, maximum=0.01, value=3e-4,
                    label="Learning Rate",
                    info="The initial learning rate for the optimizer. A higher learning rate may speed up convergence but also cause instability or divergence. A lower learning rate may require more steps to reach optimal performance but also avoid overshooting or oscillating around local minima."
                )

            with gr.Column():
                lora_r = gr.Slider(
                    minimum=1, maximum=16, value=8, 
                    label="LoRA R",
                    info="LoRA rank parameter: this controls the dimensionality of the rank decomposition matrices. A larger lora_r increases the expressiveness and flexibility of LoRA but also increases the number of trainable parameters and memory usage."
                )

                lora_alpha = gr.Slider(
                    minimum=1, maximum=128, value=16, 
                    label="LoRA Alpha",
                    info="LoRA scaling parameter: this controls how much LoRA affects the original pre-trained model weights. A larger lora_alpha amplifies the impact of LoRA but may also distort or override the pre-trained knowledge."
                )
                
                lora_dropout = gr.Slider(
                    minimum=0, maximum=1, value=0.01,
                    label="LoRA Dropout",
                    info="LoRA dropout probability: this controls the fraction of LoRA parameters that are set to zero during training. A larger lora_dropout increases the regularization effect of LoRA but also increases the risk of underfitting."
                )

                with gr.Column():
                    model_name = gr.Textbox(
                        lines=1, label="LoRA Model Name", value=dummy_word()
                    )

                    with gr.Row():
                        train_btn = gr.Button(
                            "Train", variant="primary", label="Train", 
                        )

                        abort_button = gr.Button(
                            "Abort", label="Abort", 
                        )
    
        output_text = gr.Text("Training Status")

        train_progress = train_btn.click(
            fn=tokenize_and_train,
            inputs=[
                training_text,
                max_seq_length,
                micro_batch_size,
                gradient_accumulation_steps,
                epochs,
                learning_rate,
                lora_r,
                lora_alpha,
                lora_dropout,
                model_name
            ],
            outputs=output_text
        )

        abort_button.click(None, None, None, cancels=[train_progress])

    with gr.Tab("Inference"):
        with gr.Row():
            with gr.Column():
                with gr.Row():
                        lora_model = gr.Dropdown(
                            label="LoRA Model",
                        )
                        refresh_models_list = gr.Button(
                            "Reload Models",
                            elem_id="refresh-button"
                        )
                inference_text = gr.Textbox(lines=7, label="Input Text")   
            inference_output = gr.Textbox(lines=12, label="Output Text")
        with gr.Row():
            with gr.Column():
                #  temperature, top_p, top_k, repeat_penalty, max_new_tokens
                temperature = gr.Slider(
                    minimum=0, maximum=1.99, value=0.7, step=0.01,
                    label="Temperature",
                    info="Controls the 'temperature' of the softmax distribution during sampling. Higher values (e.g., 1.0) make the model generate more diverse and random outputs, while lower values (e.g., 0.1) make it more deterministic and focused on the highest probability tokens."
                )

                top_p = gr.Slider(
                    minimum=0, maximum=1, value=0.2, step=0.01,
                    label="Top P",
                    info="Sets the nucleus sampling threshold. In nucleus sampling, only the tokens whose cumulative probability exceeds 'top_p' are considered  for sampling. This technique helps to reduce the number of low probability tokens considered during sampling, which can lead to more diverse and coherent outputs."
                )

                top_k = gr.Slider(
                    minimum=0, maximum=200, value=50, step=1,
                    label="Top K",
                    info="Sets the number of top tokens to consider during sampling. In top-k sampling, only the 'top_k' tokens with the highest probabilities are considered for sampling. This method can lead to more focused and coherent outputs by reducing the impact of low probability tokens."
                )

                repeat_penalty = gr.Slider(
                    minimum=0, maximum=1.5, value=0., step=0.01,
                    label="Repeat Penalty",
                    info="Applies a penalty to the probability of tokens that have already been generated, discouraging the model from repeating the same words or phrases. The penalty is applied by dividing the token probability by a factor based on the number of times the token has appeared in the generated text."
                )

                max_new_tokens = gr.Slider(
                    minimum=0, maximum=4096, value=50, step=1,
                    label="Max New Tokens",
                    info="Limits the maximum number of tokens generated in a single iteration."
                )
            with gr.Column():
                with gr.Row():
                    generate_btn = gr.Button(
                        "Generate", variant="primary", label="Generate", 
                    )

                    inference_abort_button = gr.Button(
                        "Abort", label="Abort", 
                    )
            
        inference_progress = generate_btn.click(
            fn=generate_text,
            inputs=[
                lora_model,
                inference_text,
                temperature,
                top_p,
                top_k,
                repeat_penalty,
                max_new_tokens
            ],
            outputs=inference_output,
        )

        lora_model.change(
            fn=reset_models
        )

        def update_models_list():
            return gr.Dropdown.update(choices=["None"] + [
                d for d in os.listdir() if os.path.isdir(d) and d.startswith('lora-')
            ], value="None")

        refresh_models_list.click(
            update_models_list,  
            inputs=None, 
            outputs=lora_model,
        )

    

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="PEFT LLaMA Finetuner")
    parser.add_argument("-s", "--share", action="store_true", help="Enable sharing of the Gradio interface")
    args = parser.parse_args()
    demo.queue().launch(share=args.share)