finetune.py

# this code is modified from lora_alpaca https://github.com/tloen/alpaca-lora under Apache-2.0 license
import os
from typing import List

import fire
import torch
import transformers
from datasets import load_dataset
from transformers import BertTokenizerFast

"""
Unused imports:
import torch.nn as nn
import bitsandbytes as bnb
"""

from peft import (
    LoraConfig,
    get_peft_model,
    get_peft_model_state_dict,
    prepare_model_for_int8_training,
    set_peft_model_state_dict,
)
from transformers import LlamaForCausalLM, LlamaTokenizer

from utils.prompter import Prompter

def train(
    # model/data params
    base_model: str = "",  # the only required argument
    data_path: str = "",
    output_dir: str = "",
    # training hyperparams
    batch_size: int = 128,
    micro_batch_size: int = 4,
    num_epochs: int = 3,
    learning_rate: float = 3e-4,
    cutoff_len: int = 256,
    val_set_size: int = 2000,
    # lora hyperparams
    lora_r: int = 8,
    lora_alpha: int = 16,
    lora_dropout: float = 0.05,
    lora_target_modules: List[str] = [
        "q_proj",
        "v_proj",
    ],
    # llm hyperparams
    train_on_inputs: bool = True,  # if False, masks out inputs in loss
    add_eos_token: bool = False,
    group_by_length: bool = False,  # faster, but produces an odd training loss curve
    # wandb params
    wandb_project: str = "gama",
    wandb_run_name: str = "",
    wandb_watch: str = "false",  # options: false | gradients | all
    wandb_log_model: str = "false",  # options: false | true
    resume_from_checkpoint: str = None,  # either training checkpoint or final adapter
    prompt_template_name: str = "alpaca_short",  # The prompt template to use, will default to alpaca.
    save_steps: int = 100,
    trainable_params = 'all'
):
    if int(os.environ.get("LOCAL_RANK", 0)) == 0:
        print(
            f"Training Alpaca-LoRA model with params:\n"
            f"base_model: {base_model}\n"
            f"data_path: {data_path}\n"
            f"output_dir: {output_dir}\n"
            f"batch_size: {batch_size}\n"
            f"micro_batch_size: {micro_batch_size}\n"
            f"num_epochs: {num_epochs}\n"
            f"learning_rate: {learning_rate}\n"
            f"cutoff_len: {cutoff_len}\n"
            f"val_set_size: {val_set_size}\n"
            f"lora_r: {lora_r}\n"
            f"lora_alpha: {lora_alpha}\n"
            f"lora_dropout: {lora_dropout}\n"
            f"lora_target_modules: {lora_target_modules}\n"
            f"train_on_inputs: {train_on_inputs}\n"
            f"add_eos_token: {add_eos_token}\n"
            f"group_by_length: {group_by_length}\n"
            f"wandb_project: {wandb_project}\n"
            f"wandb_run_name: {wandb_run_name}\n"
            f"wandb_watch: {wandb_watch}\n"
            f"wandb_log_model: {wandb_log_model}\n"
            f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
            f"prompt template: {prompt_template_name}\n"
        )
    assert (
        base_model
    ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"

    # trick to load checkpoints correctly from HF
    if '/fs/nexus-projects/brain_project/acl_sk_24/GAMA/src/Llama-2-7b-chat-hf-qformer' not in base_model:
        # start from a different model with original vicuna
        # temporally first load the original vicuna, then load the actual checkpoint
        start_model = base_model # need to point to a specific bin file that contains state dict.
        # TODO: change to your vicuna_tltr path
        base_model = '/fs/nexus-projects/brain_project/acl_sk_24/GAMA/src/Llama-2-7b-chat-hf-qformer'
        print('Will load from {:s} later, for implementation purpose, first load from {:s}'.format(start_model, base_model))
    else:
        start_model = None

    gradient_accumulation_steps = batch_size // micro_batch_size
    prompter = Prompter(prompt_template_name)

    device_map = "auto"
    world_size = int(torch.cuda.device_count())
    ddp = world_size != 1
    if ddp:
        device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
        gradient_accumulation_steps = gradient_accumulation_steps // world_size

    use_wandb = len(wandb_project) > 0 or (
        "WANDB_PROJECT" in os.environ and len(os.environ["WANDB_PROJECT"]) > 0
    )
    # Only overwrite environ if wandb param passed
    if len(wandb_project) > 0:
        os.environ["WANDB_PROJECT"] = wandb_project
    if len(wandb_watch) > 0:
        os.environ["WANDB_WATCH"] = wandb_watch
    if len(wandb_log_model) > 0:
        os.environ["WANDB_LOG_MODEL"] = wandb_log_model
    
    # base_model = '/fs/nexus-projects/brain_project/acl_sk_24/GAMA/src/Llama-2-7b-chat-hf-qformer'

    model = LlamaForCausalLM.from_pretrained(
        base_model,
        load_in_8bit=False,
        # torch_dtype=torch.float16,
        device_map=device_map,
    )

    tokenizer = LlamaTokenizer.from_pretrained(base_model)
    
    tokenizer.pad_token = tokenizer.eos_token

    tokenizer.padding_side = "left"  # Allow batched inference

    bert_tokenizer = BertTokenizerFast.from_pretrained("google-bert/bert-base-uncased")

    def tokenize(prompt, add_eos_token=True):
        result = tokenizer(
            prompt,
            truncation=True,
            max_length=cutoff_len,
            padding=False,
            return_tensors=None,
        )
        if (
            result["input_ids"][-1] != tokenizer.eos_token_id
            and len(result["input_ids"]) < cutoff_len
            and add_eos_token
        ):
            result["input_ids"].append(tokenizer.eos_token_id)
            result["attention_mask"].append(1)

        result["labels"] = result["input_ids"].copy()
        return result
    
    def flatten_c(example):
        if 'tokenized_full_prompt' in example:
            example.update(example['tokenized_full_prompt'])  # Merge 'c' into the root
            del example['tokenized_full_prompt']              # Remove 'c' from the example
        return example

    def generate_and_tokenize_prompt(data_point):
        
        full_prompt = prompter.generate_prompt(
            data_point["instruction"],
            data_point["input"],
            data_point["output"]
        )
        tokenized_full_prompt = tokenize(full_prompt)
        if not train_on_inputs:
            user_prompt = prompter.generate_prompt(
                data_point["instruction"], data_point["input"]
            )
            tokenized_user_prompt = tokenize(
                user_prompt, add_eos_token=add_eos_token
            )
            user_prompt_len = len(tokenized_user_prompt["input_ids"])

            if add_eos_token:
                user_prompt_len -= 1

            tokenized_full_prompt["labels"] = [
                -100
            ] * user_prompt_len + tokenized_full_prompt["labels"][
                user_prompt_len:
            ]  # could be sped up, probably
        tokenizer_input_bert = []
        # print(tokenized_full_prompt)
        return tokenized_full_prompt
        # return {'tokenized_full_prompt': tokenized_full_prompt, 'tokenizer_input_bert':tokenizer_input_bert}


    config = LoraConfig(
        r=lora_r,
        lora_alpha=lora_alpha,
        target_modules=lora_target_modules,
        lora_dropout=lora_dropout,
        bias="none",
        task_type="CAUSAL_LM",
    )
    model = get_peft_model(model, config)

    # print(model)

    # for audio params, lora always trainable, llama always frozen
    for name, param in model.named_parameters():
        if trainable_params == 'all':
            if "audio" in name:
                param.requires_grad = True
        if trainable_params == 'proj':
            if "audio_proj" in name:
                param.requires_grad = True
        if trainable_params == 'qformer':
            if "audio_aggregator_layer_1" in name or "audio_aggregator_layer_2" in name or "audio_proj_qformer" in name or "audio_proj_audioenc" in name or "audio_proj_norm_qformer" in name or "audio_proj_norm_audioenc" in name:
                param.requires_grad = True 
        if trainable_params == 'qformer_all':
            if "audio_aggregator_layer_1" in name or "audio_aggregator_layer_2" in name or "audio_proj_qformer" in name or "audio_proj_audioenc" in name or "audio_proj_norm_qformer" in name or "audio_proj_norm_audioenc" in name or 'audio_encoder' in name or 'Qformer'  in name or 'query_tokens'  in name or 'qformer_proj_norm' in name:
                param.requires_grad = True 

    if data_path.endswith(".json") or data_path.endswith(".jsonl"):
        data = load_dataset("json", data_files=data_path)
    else:
        data = load_dataset(data_path)

    if resume_from_checkpoint:
        # Check the available weights and load them
        checkpoint_name = os.path.join(
            resume_from_checkpoint, "pytorch_model.bin"
        )  # Full checkpoint
        if not os.path.exists(checkpoint_name):
            checkpoint_name = os.path.join(
                resume_from_checkpoint, "adapter_model.bin"
            )  # only LoRA model - LoRA config above has to fit
            resume_from_checkpoint = (
                False  # So the trainer won't try loading its state
            )
        # The two files above have a different name depending on how they were saved, but are actually the same.
        if os.path.exists(checkpoint_name):
            state_dict = torch.load(checkpoint_name, map_location='cpu')
            msg = model.load_state_dict(state_dict, strict=False)
        else:
            print(f"Checkpoint {checkpoint_name} not found")

    # # now load from real checkpoint
    if start_model != None and (resume_from_checkpoint == None or resume_from_checkpoint == False):
        state_dict = torch.load(start_model, map_location='cpu')
        msg = model.load_state_dict(state_dict, strict=False)
        # print('load checkpoint', msg)

    model.print_trainable_parameters()  # Be more transparent about the % of trainable params.

    if val_set_size > 0:
        train_val = data["train"].train_test_split(
            test_size=val_set_size, shuffle=True, seed=42
        )
        train_data = (
            train_val["train"].shuffle().map(generate_and_tokenize_prompt)
        )
        val_data = (
            train_val["test"].shuffle().map(generate_and_tokenize_prompt)
        )
    else:
        train_data = data["train"].shuffle().map(generate_and_tokenize_prompt)
        val_data = None

    # train_data = train_data.map(flatten_c)
    
    if not ddp and torch.cuda.device_count() > 1:
        # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available
        model.is_parallelizable = True
        model.model_parallel = True
    
    from transformers import TrainerCallback

    class PrecisionLoggingCallback(TrainerCallback):
        def on_log(self, args, state, control, logs=None, **kwargs):
            # Modify this method to log the loss with more decimal points
            if logs is not None and 'loss' in logs:
                # Assuming 'logs' is a dictionary that contains the loss
                high_precision_loss = format(logs['loss'], '.10f')  # Adjust the '.4f' for more or fewer decimals
                # print(f"High Precision Loss: {high_precision_loss}")

    trainer = transformers.Trainer(
        model=model,
        train_dataset=train_data,
        eval_dataset=val_data,
        callbacks=[PrecisionLoggingCallback],
        args=transformers.TrainingArguments(
            per_device_train_batch_size=micro_batch_size,
            gradient_accumulation_steps=gradient_accumulation_steps,
            warmup_steps=100,
            num_train_epochs=num_epochs,
            learning_rate=learning_rate,
            bf16=True,
            logging_steps=10,
            optim="adamw_torch",
            evaluation_strategy="no",
            save_strategy="steps",
            eval_steps=None,
            save_steps=save_steps,
            dataloader_num_workers=8,
            output_dir=output_dir,
            save_total_limit=50,
            load_best_model_at_end=False,
            ddp_find_unused_parameters=True,
            group_by_length=group_by_length,
            report_to="wandb" if use_wandb else None,
            run_name=wandb_run_name if use_wandb else None,
            remove_unused_columns=False        ),
        data_collator=transformers.DataCollatorForSeq2Seq(
            tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
        ),
    )
    model.config.use_cache = False
    trainer.train(resume_from_checkpoint=resume_from_checkpoint)

    model.save_pretrained(output_dir)

if __name__ == "__main__":
    fire.Fire(train)