run.py

import argparse
import os

from utils import add_common_args

import tensorrt_llm
import tensorrt_llm.profiler as profiler
from tensorrt_llm import logger
from tensorrt_llm.runtime import MultimodalModelRunner


def print_result(model, input_text, output_text, args):
    logger.info("---------------------------------------------------------")
    if model.model_type != 'nougat':
        logger.info(f"\n[Q] {input_text}")
    for i in range(len(output_text)):
        logger.info(f"\n[A]: {output_text[i]}")

    if args.num_beams == 1:
        output_ids = model.tokenizer(output_text[0][0],
                                     add_special_tokens=False)['input_ids']
        logger.info(f"Generated {len(output_ids)} tokens")

    if args.check_accuracy:
        if model.model_type != 'nougat':
            if model.model_type == "vila":
                for i in range(len(args.image_path.split(args.path_sep))):
                    if i % 2 == 0:
                        assert output_text[i][0].lower(
                        ) == "the image captures a bustling city intersection teeming with life. from the perspective of a car's dashboard camera, we see"
                    else:
                        assert output_text[i][0].lower(
                        ) == "the image captures the iconic merlion statue in singapore, a renowned worldwide landmark. the merlion, a mythical"
            elif model.model_type == 'fuyu':
                assert output_text[0][0].lower() == '4'
            elif model.model_type == "pix2struct":
                assert "characteristic | cat food, day | cat food, wet | cat treats" in output_text[
                    0][0].lower()
            elif model.model_type in [
                    'blip2', 'neva', 'phi-3-vision', 'llava_next'
            ]:
                assert 'singapore' in output_text[0][0].lower()
            elif model.model_type == 'video-neva':
                assert 'robot' in output_text[0][0].lower()
            elif model.model_type == 'kosmos-2':
                assert 'snowman' in output_text[0][0].lower()
            elif model.model_type == "mllama":
                if "<|image|><|begin_of_text|>If I had to write a haiku for this one" in input_text:
                    assert "it would be:.\\nPeter Rabbit is a rabbit.\\nHe lives in a" in output_text[
                        0][0]
                elif "The key to life is" in input_text:
                    assert "to find your passion and pursue it with all your heart." in output_text[
                        0][0]
            elif model.model_type == 'llava_onevision':
                if args.video_path is None:
                    assert 'singapore' in output_text[0][0].lower()
                else:
                    assert 'the video is funny because the child\'s actions are' in output_text[
                        0][0].lower()
            elif model.model_type == "qwen2_vl":
                assert 'woman' in output_text[0][0].lower()
            else:
                assert output_text[0][0].lower() == 'singapore'

    if args.run_profiling:
        msec_per_batch = lambda name: 1000 * profiler.elapsed_time_in_sec(
            name) / args.profiling_iterations
        logger.info('Latencies per batch (msec)')
        logger.info('TRT vision encoder: %.1f' % (msec_per_batch('Vision')))
        logger.info('TRTLLM LLM generate: %.1f' % (msec_per_batch('LLM')))
        logger.info('Multimodal generate: %.1f' % (msec_per_batch('Generate')))

    logger.info("---------------------------------------------------------")


if __name__ == '__main__':
    os.environ["TOKENIZERS_PARALLELISM"] = "false"
    parser = argparse.ArgumentParser()
    parser = add_common_args(parser)
    args = parser.parse_args()
    logger.set_level(args.log_level)

    model = MultimodalModelRunner(args)
    raw_image = model.load_test_image()

    num_iters = args.profiling_iterations if args.run_profiling else 1
    for _ in range(num_iters):
        input_text, output_text = model.run(args.input_text, raw_image,
                                            args.max_new_tokens)

    runtime_rank = tensorrt_llm.mpi_rank()
    if runtime_rank == 0:
        print_result(model, input_text, output_text, args)