Spaces:
Runtime error
Runtime error
File size: 4,963 Bytes
383af88 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
"""
Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
SPDX-License-Identifier: MIT
"""
import argparse
import os
import tensorrt_llm
import tensorrt_llm.profiler as profiler
from PIL import Image
from tensorrt_llm import logger
from tensorrt_llm import mpi_rank
from tensorrt_llm.runtime import MultimodalModelRunner
from dolphin_runner import DolphinRunner
from utils import add_common_args
os.environ["TOKENIZERS_PARALLELISM"] = "false"
def print_result(model, input_text, output_text, args):
logger.info("---------------------------------------------------------")
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 == "llava":
for i in range(len(args.image_path.split(args.path_sep))):
assert output_text[i][0].lower() == 'singapore'
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 "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] or "Here is a haiku for the image:\n\n" in output_text[
0][0], f"expected results: 'it would be:.\\nPeter Rabbit is a rabbit.\\nHe lives in a', generated results: '{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] or "not to be found in the external world," in output_text[
0][0], f"expected results: 'to find your passion and pursue it with all your heart.', generated results: '{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 'dog' 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__':
parser = argparse.ArgumentParser()
parser = add_common_args(parser)
args = parser.parse_args()
logger.set_level(args.log_level)
model = DolphinRunner(args)
input_image = Image.open(args.image_path[0]).convert('RGB')
num_iters = args.profiling_iterations if args.run_profiling else 1
for _ in range(num_iters):
output_texts = model.run(args.input_text, [input_image], args.max_new_tokens)
runtime_rank = tensorrt_llm.mpi_rank()
if runtime_rank == 0:
print_result(model, args.input_text, output_texts, args)
|