run_prompt_creation.py

import logging
import os
import shutil
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import torch
from accelerate import Accelerator
from datasets import DatasetDict, load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
)


logger = logging.getLogger(__name__)


@dataclass
class ModelArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    model_name_or_path: str = field(
        metadata={"help": "The name of the model to use (via the transformers library) for the prompt annotation."},
    )
    per_device_eval_batch_size: int = field(
        metadata={"help": "The per-device batch size to use for inference."},
    )
    model_variant: str = field(
        default=None,
        metadata={"help": "If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. "},
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
    )
    torch_dtype: Optional[str] = field(
        default="float16",
        metadata={
            "help": (
                "Floating-point format in which the model weights should be initialized"
                " and the computations run. Choose one of `[float32, float16, bfloat16]`."
            )
        },
    )
    attn_implementation: Optional[str] = field(
        default="sdpa",
        metadata={"help": "Which attn type to use: ['eager', 'sdpa', 'flash_attention_2']"},
    )
    load_in_8bit: Optional[bool] = field(
        default=False, metadata={"help": "Whether to use 8-bit precision for inference."}
    )
    load_in_4bit: Optional[bool] = field(
        default=False, metadata={"help": "Whether to use 4-bit precision for inference."}
    )
    bnb_4bit_quant_type: Optional[str] = field(
        default="nf4", metadata={"help": "precise the quantization type (fp4 or nf4)"}
    )
    use_bnb_nested_quant: Optional[bool] = field(default=False, metadata={"help": "use nested quantization"})
    trust_remote_code: Optional[bool] = field(
        default=False,
        metadata={
            "help": (
                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
                "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
                "execute code present on the Hub on your local machine."
            )
        },
    )
    use_fast_tokenizer: Optional[bool] = field(
        default=True, metadata={"help": "Use fast tokenizer for encoding/decoding input ids"}
    )
    token: Optional[bool] = field(
        default=True,
        metadata={
            "help": "Whether or not to use an authentication token when loading/uploading from the Hugging Face Hub"
        },
    )
    do_sample: Optional[bool] = field(default=True, metadata={"help": "Whether to use sampling mode for generation"})
    temperature: Optional[float] = field(default=0.6, metadata={"help": "Temperature for sampling-based generation"})
    max_new_tokens: Optional[int] = field(
        default=256, metadata={"help": "Maximum number of new tokens during generation"}
    )
    compile_generate: Optional[bool] = field(
        default=False, metadata={"help": "Whether to compile the forward pass (not sampling) in generate."}
    )


@dataclass
class DataArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    output_dir: str = field(
        metadata={
            "help": "Where to save the processed dataset to disk. If unspecified, uses a 'pretty' version of the "
            "original dataset name. E.g. 'facebook/voxpopuli' will be saved under 'voxpopuli'."
        },
    )
    dataset_name: str = field(
        default=None,
        metadata={"help": "The name of the dataset to use (via the datasets library)"},
    )
    dataset_config_name: Optional[str] = field(
        default=None,
        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."},
    )
    dataset_split_name: Optional[str] = field(
        default=None,
        metadata={"help": "The split name of the dataset to use (via the datasets library)."},
    )
    dataset_cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Path to cache directory for saving and loading datasets"},
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={"help": "Maximum number of samples for generation - use for debugging purposes."},
    )
    overwrite_cache: bool = field(
        default=False,
        metadata={"help": "Overwrite the cached training and evaluation sets"},
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    dataloader_num_workers: Optional[int] = field(
        default=0,
        metadata={"help": "The number of processes to use for the dataloader."},
    )
    push_to_hub: Optional[bool] = field(
        default=False,
        metadata={"help": "Whether or not to push the processed dataset to the Hub."},
    )
    hub_dataset_id: Optional[str] = field(
        default=None,
        metadata={"help": "Repository namespace if pushing to the Hugging Face Hub."},
    )
    overwrite_output_dir: Optional[bool] = field(
        default=False,
        metadata={"help": "Overwrite the content of the output directory each time the script is run."},
    )

    def __post_init__(self):
        if self.push_to_hub and self.hub_dataset_id is None:
            raise ValueError("You must specify the `hub_dataset_id` when setting `--push_to_hub=True`")


def get_quantization_config(model_args: ModelArguments) -> Union[BitsAndBytesConfig, None]:
    if model_args.load_in_4bit:
        compute_dtype = torch.float16
        if model_args.torch_dtype not in {"auto", None}:
            compute_dtype = getattr(torch, model_args.torch_dtype)

        quantization_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=compute_dtype,
            bnb_4bit_quant_type=model_args.bnb_4bit_quant_type,
            bnb_4bit_use_double_quant=model_args.use_bnb_nested_quant,
        )
    elif model_args.load_in_8bit:
        quantization_config = BitsAndBytesConfig(
            load_in_8bit=True,
        )
    else:
        quantization_config = None

    return quantization_config


def get_current_device() -> int:
    """Get the current device. For GPU we return the local process index to enable multiple GPU training."""
    return Accelerator().local_process_index if torch.cuda.is_available() else "cpu"


def get_kbit_device_map() -> Union[Dict[str, int], None]:
    """Useful for running inference with quantized models by setting `device_map=get_peft_device_map()`"""
    return {"": get_current_device()} if torch.cuda.is_available() else None


@dataclass
class DataCollatorWithPadding:
    """
    Data collator that will dynamically pad the inputs received to the longest sequence in the batch.
    """

    tokenizer: Any

    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
        # split inputs and labels since they have to be of different lengths and need
        # different padding methods
        input_ids = {"input_ids": [feature["input_ids"] for feature in features]}
        batch = self.tokenizer.pad(input_ids, return_tensors="pt", padding="longest", return_attention_mask=True)
        return batch


# TODO(SG): add accent keyword
PROMPT = """You will be given six descriptive keywords related to an audio sample of a person's speech. These keywords include:
1. The gender (e.g., male, female)
2. The level of reverberation (e.g., very roomy sounding, quite roomy sounding, slightly roomy sounding, moderate reverberation, slightly confined sounding, quite confined sounding, very confined sounding)
3. The amount of noise the sample (e.g., very noisy, quite noisy, slightly noisy, moderate ambient sound, slightly clear, quite clear, very clear)
4. The tone of the speaker's voice (e.g., very monotone, quite monotone, slightly monotone, moderate intonation, slightly expressive, quite expressive, very expressive)
5. The pace of the speaker's delivery (e.g., very slowly, quite slowly, slightly slowly, moderate speed, slightly fast, quite fast, very fast)
6. The pitch of the speaker's voice (e.g., very low pitch, quite low pitch, slightly low pitch, moderate pitch, slightly high pitch, quite high pitch, very high pitch)

Your task is to create a text description using these keywords that accurately describes the speech sample while ensuring the description remains grammatically correct and easy to understand. You should rearrange the keyword order as necessary, and substitute synonymous terms where appropriate. If the amount of noise is 'very noisy' and the level of reverberation is 'very roomy sounding', include terms like 'very bad recording' in the description. Likewise, if the amount of noise is 'very clear' and the level of reverberation is 'very confined sounding', include terms like 'very good recording' in the description. Otherwise, do not add extra details beyond what has been provided, and only return the generated description. 

For example, given the following keywords: 'female', 'slightly roomy sounding', 'slightly noisy', 'very expressive', 'slightly low pitch', 'very slowly', a valid description would be: 'a woman with a deep voice speaks slowly but has an animated delivery in an echoey room with some background noise'.

For the keywords: '[gender]', '[reverberation]', '[noise]', '[speech_monotony]', '[pitch]', '[speaking_rate]', the corresponding description is:"
"""

def main():
    # 1. Parse input arguments
    parser = HfArgumentParser((ModelArguments, DataArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args = parser.parse_args_into_dataclasses()

    # 2. Setup logging
    # Make one log on every process with the configuration for debugging.
    logger.setLevel(logging.INFO)
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    accelerator = Accelerator()

    if data_args.overwrite_output_dir and os.path.exists(data_args.output_dir) and os.path.isdir(data_args.output_dir):
        logger.info("Cleaning output dir from previous run...")
        shutil.rmtree(data_args.output_dir)

    # 3. Load annotated dataset
    logger.info("*** Load annotated dataset ***")
    if data_args.dataset_split_name is not None:
        raw_datasets = DatasetDict()
        data_splits = data_args.dataset_split_name.split("+")
        # load on a split-wise basis
        for split in data_splits:
            raw_datasets[split] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=split,
                cache_dir=model_args.cache_dir,
                token=model_args.token,
                num_proc=data_args.preprocessing_num_workers,
            )
    else:
        # load all splits for annotation
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            num_proc=data_args.preprocessing_num_workers,
        )

    raw_datasets_features = set(raw_datasets[next(iter(raw_datasets))].features.keys())

    if data_args.max_eval_samples is not None:
        for split in raw_datasets:
            raw_datasets[split] = raw_datasets[split].select(range(data_args.max_eval_samples))

    # TODO(SG): add accent
    EXPECTED_COLUMNS = {"gender", "pitch", "noise", "reverberation", "speech_monotony", "speaking_rate"}
    if not EXPECTED_COLUMNS.issubset(raw_datasets_features):
        missing_columns = EXPECTED_COLUMNS - raw_datasets_features
        raise ValueError(
            f"Missing columns {missing_columns} from the dataset features. Got dataset features {raw_datasets_features}"
        )

    # 4. Load pre-trained model
    logger.info("*** Load pretrained model ***")
    torch_dtype = (
        model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
    )
    quantization_config = get_quantization_config(model_args)

    model = AutoModelForCausalLM.from_pretrained(
        model_args.model_name_or_path,
        revision=model_args.model_revision,
        variant=model_args.model_variant,
        trust_remote_code=model_args.trust_remote_code,
        attn_implementation=model_args.attn_implementation,
        torch_dtype=torch_dtype,
        device_map=get_kbit_device_map() if quantization_config is not None else None,
        quantization_config=quantization_config,
        low_cpu_mem_usage=True,
        token=model_args.token,
    ).eval()

    if model_args.compile_generate:
        if not callable(getattr(model, "_setup_cache", None)):
            raise ValueError(
                f"Static k/v cache is not compatible with the model {model.__class__.__name__}. Set `--compile_generate=False"
                "for dynamic k/v cache"
            )
        model.generation_config.cache_implementation = "static"
        model._forward = model.forward
        compiled_forward = torch.compile(model.forward)

        def compiled(func, input_ids, **kwargs):
            return func(input_ids, **kwargs)

        def call(input_ids, **kwargs):
            if input_ids.shape[-1] == 1:
                return compiled(compiled_forward, input_ids, **kwargs)

            return model._forward(input_ids, **kwargs)

        model.forward = call

    tokenizer = AutoTokenizer.from_pretrained(
        model_args.model_name_or_path,
        revision=model_args.model_revision,
        trust_remote_code=model_args.trust_remote_code,
        use_fast=model_args.use_fast_tokenizer,
        padding_side="left",
    )
    if tokenizer.pad_token_id is None:
        tokenizer.pad_token_id = tokenizer.bos_token_id
        model.generation_config.pad_token_id = model.generation_config.eos_token_id

    def prepare_dataset(sample):
        sample_prompt = PROMPT
        for key in EXPECTED_COLUMNS:
            sample_prompt = sample_prompt.replace(f"[{key}]", sample[key])
        sample_prompt = [{"role": "user", "content": sample_prompt}]
        token_ids = tokenizer.apply_chat_template(sample_prompt)
        sample["input_ids"] = token_ids
        return sample

    with accelerator.main_process_first():
        vectorized_datasets = raw_datasets.map(
            prepare_dataset, num_proc=data_args.preprocessing_num_workers, desc="Preparing prompts"
        )

    # Prepare everything with our `accelerator`
    model = accelerator.prepare(model)
    data_collator = DataCollatorWithPadding(tokenizer)

    def generate_step(batch):
        output_ids = accelerator.unwrap_model(model).generate(
            batch["input_ids"],
            attention_mask=batch["attention_mask"],
            do_sample=model_args.do_sample,
            temperature=model_args.temperature,
            max_new_tokens=model_args.max_new_tokens,
        )
        output_ids = accelerator.pad_across_processes(output_ids, dim=1, pad_index=tokenizer.pad_token_id)
        return output_ids

    for split in vectorized_datasets:
        data_loader = DataLoader(
            vectorized_datasets[split],
            batch_size=model_args.per_device_eval_batch_size,
            collate_fn=data_collator,
            num_workers=data_args.dataloader_num_workers,
            pin_memory=True,
        )
        data_loader = accelerator.prepare(data_loader)

        all_generated_ids = []
        for batch in tqdm(data_loader, disable=not accelerator.is_local_main_process):
            generated_ids = generate_step(batch)
            generated_ids = accelerator.gather_for_metrics(generated_ids)
            all_generated_ids.extend(generated_ids.cpu())

        def postprocess_dataset(sample, idx):
            prompt_text = tokenizer.decode(sample["input_ids"], skip_special_tokens=True)
            generated_text = tokenizer.decode(all_generated_ids[idx], skip_special_tokens=True)
            sample["text_description"] = generated_text[len(prompt_text) :]
            return sample

        if accelerator.is_main_process:
            vectorized_datasets[split] = vectorized_datasets[split].map(
                postprocess_dataset,
                num_proc=data_args.preprocessing_num_workers,
                desc="Postprocessing dataset",
                remove_columns=["input_ids"],
                with_indices=True,
            )

    if accelerator.is_main_process:
        vectorized_datasets.save_to_disk(data_args.output_dir)
        if data_args.push_to_hub:
            vectorized_datasets.push_to_hub(
                data_args.hub_dataset_id,
                config_name=data_args.dataset_config_name if data_args.dataset_config_name is not None else "default",
                token=model_args.token,
            )

    accelerator.end_training()


if __name__ == "__main__":
    main()