TensorRT-LLMs/tensorrt_llm/bench/utils/data.py

import json
from functools import partial
from typing import Any, Dict, List, TextIO, Tuple

from transformers import AutoTokenizer, PreTrainedTokenizer

from tensorrt_llm.bench.dataclasses.general import (DatasetMetadata,
                                                    InferenceRequest)
from tensorrt_llm.bench.dataclasses.statistics import PercentileStats
from tensorrt_llm.inputs import (INPUT_FORMATTER_MAP, default_image_loader,
                                 default_video_loader)


def prepare_multimodal_inputs(model_dir: str,
                              model_type: str,
                              modality: str,
                              prompts: List[str],
                              media: List[str],
                              image_data_format: str = "pt",
                              num_frames: int = 8) -> List[Dict[str, Any]]:

    inputs = []
    if modality == "image":
        inputs = default_image_loader(prompts, media, image_data_format)
    elif modality == "video":
        inputs = default_video_loader(prompts, media, image_data_format,
                                      num_frames)
    else:
        raise ValueError(f"Unsupported modality: {modality}")

    inputs = INPUT_FORMATTER_MAP[model_type](model_dir, inputs)

    return inputs


def initialize_tokenizer(model_name: str) -> PreTrainedTokenizer:
    """Initialize a tokenizer.

    Args:
        model_name (str): The name of the HuggingFace model to pull a
        tokenizer from.

    Returns:
        PreTrainedTokenizer: An initialized HuggingFace tokenizer.
    """
    # Initialize the tokenizer specific to the model that we are planning
    # to benchmark.
    tokenizer = AutoTokenizer.from_pretrained(model_name,
                                              padding_side="left",
                                              trust_remote_code=True)
    if tokenizer.pad_token_id is None:
        tokenizer.add_special_tokens({"pad_token": "[PAD]"})

    return tokenizer


def create_dataset_from_stream(
    tokenizer: PreTrainedTokenizer,
    stream: TextIO,
    max_input_length: int = 0,
    max_output_length: int = 0,
    num_requests: int = 0,
    model_dir: str = None,
    model_type: str = None,
    modality: str = None,
    max_input_seq_len_for_multimodal: int = 4096,
) -> Tuple[DatasetMetadata, List[InferenceRequest]]:
    """Generate metadata and a list of requests to drive benchmarking.

    Args:
        tokenizer (PreTrainedTokenizer): HuggingFace tokenizer.
        stream (TextIO): Stream of input requests.
        max_input_length (int, optional): Maximum input length to cap prompts to. Defaults to 0.
        max_output_length (int, optional): Maximum output length to cap prompts to.. Defaults to 0.
        num_requests (int, optional): Number of requests to limit to. Defaults to 0.

    Returns:
        Tuple[DatasetMetadata, List[InferenceRequest]]: A tuple containing a dataclass of dataset
        statistics and a list of inference requests for benchmarking.
    """
    # Initialize dataset list, and metadata tracking variables.
    dataset = []
    max_requests = num_requests if num_requests > 0 else float("inf")

    # If we're limiting the input length to a certain size, then set up
    # a partial to truncate the data down to size. Otherwise, just use the
    # unmodified tokenizer callable.
    tokenize = (partial(
        tokenizer,
        padding="max_length",
        max_length=max_input_length,
        truncation=True,
    ) if max_input_length > 0 else tokenizer)

    # If we need to limit the output length, fill in a partial callable
    # for max, otherwise a lambda that just returns x with no bounds.
    output_limiter = (partial(max, max_output_length)
                      if max_output_length > 0 else lambda x: x)

    # For each line in the standard input, parse out the JSON string we expect
    # to see.
    # Note the := walrus -- we're assigning and checking the condition.
    all_isl = []
    all_osl = []
    all_seq_len = []
    while (line := stream.readline()) and len(dataset) < max_requests:
        # We expect the data to come in as a JSON string.
        # For example:
        # {"prompt": "Generate an infinite response to the following:
        # There once was a man who.", "output_tokens": 1000}
        # Each line should be a complete JSON dictionary with no indentation
        # or newline characters.
        data = json.loads(line)
        if modality is not None:
            # Multimodal data
            assert modality in [
                "image", "video"
            ], f"Modality must be one of ['image', 'video'] but got {modality}."

            prompt = data.get("prompt")  # cannot be None
            media_paths = data.get("media_paths", None)
            inputs = prepare_multimodal_inputs(
                model_dir,
                model_type,
                modality,
                prompts=[prompt],
                media=media_paths)  # list of dicts
            logits = None  # cannot tokenize multi-modal data, handled by preprocessor
            prompt = inputs[0]
        else:
            logits = data.get("input_ids", data.get("logits", None))
            prompt = data.get("prompt", None)
            # If the request comes in with logits, just use the provided.
            # Otherwise we need to tokenize it.
            logits = tokenize(prompt)["input_ids"] if logits is None else logits
        task_id = data["task_id"]
        osl = data["output_tokens"]

        request = InferenceRequest(
            task_id=task_id,
            prompt=prompt,
            output_tokens=output_limiter(osl),
            input_ids=logits,
        )
        all_osl.append(osl)
        if modality is not None:
            cur_isl = max_input_seq_len_for_multimodal  # NOTE: actual sequence length is unknown until the model is run
            all_isl.append(cur_isl)
            all_seq_len.append(cur_isl + osl)
        else:
            all_isl.append(len(logits))
            all_seq_len.append(len(logits) + osl)
        dataset.append(request)

    isl_stats = PercentileStats.from_iterable(all_isl)
    osl_stats = PercentileStats.from_iterable(all_osl)
    seq_len_stats = PercentileStats.from_iterable(all_seq_len)

    # Fill in basic dataset metrics here
    metadata = DatasetMetadata(
        isl_stats=isl_stats,
        osl_stats=osl_stats,
        seq_len_stats=seq_len_stats,
        num_requests=len(dataset),
    )

    return metadata, dataset


def update_metadata_for_multimodal(metadata, statistics) -> DatasetMetadata:
    """Update the metadata from benchmark statistics. Only used for multimodal models.

    Args:
        metadata (DatasetMetadata): The metadata to update.
        statistics (StatsKeeper): The statistics to update the metadata with.

    Returns:
        DatasetMetadata: The updated metadata.
    """
    all_isl = []
    all_osl = []
    all_seq_len = []
    for request in statistics.requests.values():
        all_isl.append(request.num_input_tokens)
        all_osl.append(request.num_total_output_tokens)
        all_seq_len.append(request.num_input_tokens +
                           request.num_total_output_tokens)
    isl_stats = PercentileStats.from_iterable(all_isl)
    osl_stats = PercentileStats.from_iterable(all_osl)
    seq_len_stats = PercentileStats.from_iterable(all_seq_len)
    metadata.isl_stats = isl_stats
    metadata.osl_stats = osl_stats
    metadata.seq_len_stats = seq_len_stats

    return metadata