mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
703 lines
30 KiB
Python
703 lines
30 KiB
Python
import argparse
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import json
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import os
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import sys
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from pathlib import Path
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import requests
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# isort: off
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import torch
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import tensorrt as trt
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# isort: on
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from huggingface_hub import hf_hub_download
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from PIL import Image
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from torchvision import transforms
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from transformers import (AutoConfig, AutoProcessor, AutoTokenizer,
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Blip2Processor, NougatProcessor, NougatTokenizerFast)
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import tensorrt_llm
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import tensorrt_llm.profiler as profiler
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from tensorrt_llm import logger
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from tensorrt_llm._utils import str_dtype_to_trt
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from tensorrt_llm.runtime import ModelRunner, Session, TensorInfo
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sys.path.append(str(Path(__file__).parent.parent))
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from enc_dec.run import TRTLLMEncDecModel
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument('--max_new_tokens', type=int, default=30)
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parser.add_argument('--batch_size', type=int, default=1)
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parser.add_argument('--log_level', type=str, default='info')
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parser.add_argument('--visual_engine_dir',
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type=str,
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default=None,
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help='Directory containing visual TRT engines')
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parser.add_argument('--llm_engine_dir',
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type=str,
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default=None,
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help='Directory containing TRT-LLM engines')
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parser.add_argument('--hf_model_dir',
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type=str,
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default=None,
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help="Directory containing tokenizer")
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parser.add_argument('--input_text',
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type=str,
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default=None,
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help='Text prompt to LLM')
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parser.add_argument('--num_beams',
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type=int,
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help="Use beam search if num_beams >1",
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default=1)
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parser.add_argument('--top_k', type=int, default=1)
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parser.add_argument('--top_p', type=float, default=0.0)
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parser.add_argument('--temperature', type=float, default=1.0)
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parser.add_argument('--repetition_penalty', type=float, default=1.0)
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parser.add_argument('--run_profiling',
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action='store_true',
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help='Profile runtime over several iterations')
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parser.add_argument('--check_accuracy',
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action='store_true',
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help='Check correctness of text output')
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return parser.parse_args()
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def trt_dtype_to_torch(dtype):
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if dtype == trt.float16:
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return torch.float16
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elif dtype == trt.float32:
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return torch.float32
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elif dtype == trt.int32:
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return torch.int32
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elif dtype == trt.bfloat16:
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return torch.bfloat16
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else:
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raise TypeError("%s is not supported" % dtype)
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class MultimodalModelRunner:
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def __init__(self, args):
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self.args = args
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self.runtime_rank = tensorrt_llm.mpi_rank()
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device_id = self.runtime_rank % torch.cuda.device_count()
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torch.cuda.set_device(device_id)
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self.device = "cuda:%d" % (device_id)
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self.stream = torch.cuda.Stream(torch.cuda.current_device())
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torch.cuda.set_stream(self.stream)
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# parse model type from visual engine config
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with open(os.path.join(self.args.visual_engine_dir, "config.json"),
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"r") as f:
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config = json.load(f)
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self.model_type = config['builder_config']['model_type']
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self.vision_precision = config['builder_config']['precision']
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if self.model_type == 'pix2struct':
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self.vision_precision = 'float16'
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self.decoder_llm = not (
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't5' in self.model_type
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or self.model_type in ['nougat', 'pix2struct']
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) # BLIP2-T5, pix2struct and Nougat are using encoder-decoder models as LLMs
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self.profiling_iterations = 20
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self.init_image_encoder()
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self.init_tokenizer()
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self.init_llm()
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def init_tokenizer(self):
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if self.model_type == 'nougat':
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self.tokenizer = NougatTokenizerFast.from_pretrained(
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self.args.hf_model_dir)
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elif self.model_type == 'neva':
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from sentencepiece import SentencePieceProcessor
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sp = SentencePieceProcessor(
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os.path.join(self.args.hf_model_dir, 'tokenizer.model'))
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class return_obj:
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def __init__(self, input_ids):
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self.input_ids = input_ids
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def __getitem__(self, name):
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if name in "input_ids":
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return self.input_ids
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else:
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raise AttributeError(
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f"'return_obj' has no item '{name}'")
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# sentencepiece does not follow the same interface as HF
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class HFTokenizerInterface():
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def encode(self, x, return_tensors=None, **kwargs):
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out = sp.encode(x)
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if return_tensors == "pt":
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out = torch.tensor(out)
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return return_obj(out)
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def __call__(self, x, return_tensors=None, **kwargs):
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return self.encode(x, return_tensors, **kwargs)
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def decode(self, x, **kwargs):
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return sp.decode(x.tolist())
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def batch_decode(self, x, **kwargs):
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return self.decode(x, **kwargs)
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self.tokenizer = HFTokenizerInterface()
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self.tokenizer.eos_token_id = sp.eos_id()
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self.tokenizer.bos_token_id = sp.bos_id()
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self.tokenizer.pad_token_id = sp.pad_id()
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else:
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self.tokenizer = AutoTokenizer.from_pretrained(
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self.args.hf_model_dir, use_fast=False, use_legacy=False)
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self.tokenizer.padding_side = "right"
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def init_image_encoder(self):
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vision_encoder_path = os.path.join(self.args.visual_engine_dir,
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'visual_encoder.engine')
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logger.info(f'Loading engine from {vision_encoder_path}')
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with open(vision_encoder_path, 'rb') as f:
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engine_buffer = f.read()
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logger.info(f'Creating session from engine {vision_encoder_path}')
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self.visual_encoder_session = Session.from_serialized_engine(
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engine_buffer)
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def init_llm(self):
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if self.decoder_llm:
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self.model = ModelRunner.from_dir(self.args.llm_engine_dir,
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rank=tensorrt_llm.mpi_rank(),
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debug_mode=False,
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stream=self.stream)
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self.model_config = self.model.session._model_config
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self.runtime_mapping = self.model.session.mapping
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else:
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self.model = TRTLLMEncDecModel.from_engine(
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os.path.basename(self.args.hf_model_dir),
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self.args.llm_engine_dir,
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skip_encoder=self.model_type in ['nougat', 'pix2struct'],
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debug_mode=False,
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stream=self.stream)
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if self.model_type in ['nougat', 'pix2struct']:
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self.model_config = self.model.decoder_model_config
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self.runtime_mapping = self.model.decoder_runtime_mapping
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else:
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self.model_config = self.model.encoder_model_config
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self.runtime_mapping = self.model.encoder_runtime_mapping
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def preprocess(self, warmup, pre_prompt, post_prompt, image,
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attention_mask):
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if self.model_type == 'kosmos-2':
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input_ids = image['input_ids'].clone()
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image_mask = image["image_embeds_position_mask"]
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image = image['pixel_values']
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input_ids += image_mask * (self.model_config.vocab_size - 4)
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input_ids = input_ids.expand(self.args.batch_size,
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*input_ids.shape[1:])
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length = input_ids.shape[1]
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if not warmup:
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profiler.start("Vision")
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visual_features, visual_atts = self.get_visual_features(
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torch.stack(image['image_patches'], dim=0)
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if self.model_type == 'fuyu' else image, attention_mask)
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if not warmup:
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profiler.stop("Vision")
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if self.model_type == 'fuyu':
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visual_features = visual_features.squeeze()
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input_ids = image['input_ids'].to(torch.int32)
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image_patches_indices = image['image_patches_indices'].to(
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torch.int32)
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input_ids = input_ids.expand(self.args.batch_size,
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*input_ids.shape[1:])
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image_patches_indices = image_patches_indices.expand(
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self.args.batch_size, *image_patches_indices.shape[1:])
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input_ids = self.ptuning_setup_fuyu(input_ids,
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image_patches_indices)
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input_ids = torch.stack(input_ids, dim=0).to('cpu')
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length = input_ids.shape[1]
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elif self.model_type == 'kosmos-2':
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visual_features = visual_features.squeeze()
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else:
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pre_input_ids = self.tokenizer(pre_prompt,
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return_tensors="pt",
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padding=True).input_ids
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if post_prompt[0] is not None:
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post_input_ids = self.tokenizer(post_prompt,
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return_tensors="pt",
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padding=True).input_ids
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length = pre_input_ids.shape[1] + post_input_ids.shape[
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1] + visual_atts.shape[1]
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else:
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post_input_ids = None
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length = pre_input_ids.shape[1] + visual_atts.shape[1]
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input_lengths = torch.IntTensor([length] * args.batch_size).to(
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torch.int32)
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if self.model_type in ['fuyu', 'kosmos-2']:
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return input_ids, input_lengths, [visual_features], visual_features
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input_ids, ptuning_args = self.setup_fake_prompts(
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visual_features, pre_input_ids, post_input_ids, input_lengths)
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return input_ids, input_lengths, ptuning_args, visual_features
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def generate(self, pre_prompt, post_prompt, image, decoder_input_ids,
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max_new_tokens, attention_mask, warmup):
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if not warmup:
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profiler.start("Generate")
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input_ids, input_lengths, ptuning_args, visual_features = self.preprocess(
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warmup, pre_prompt, post_prompt, image, attention_mask)
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if warmup: return None
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profiler.start("LLM")
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if self.decoder_llm:
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end_id = self.tokenizer.eos_token_id
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if 'opt' in self.model_type and 'blip2' in self.model_type:
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# For BLIP2-OPT, model outputs a "\n" at the end.
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# we avoid it by using newline as the end token
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end_id = self.tokenizer.encode("\n",
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add_special_tokens=False)[0]
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ptuning_args[0] = torch.stack([ptuning_args[0]])
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output_ids = self.model.generate(
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input_ids,
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sampling_config=None,
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prompt_table=ptuning_args[0],
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max_new_tokens=max_new_tokens,
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end_id=end_id,
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pad_id=self.tokenizer.pad_token_id
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if self.tokenizer.pad_token_id is not None else
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self.tokenizer.all_special_ids[0],
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top_k=self.args.top_k,
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top_p=self.args.top_p,
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temperature=self.args.temperature,
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repetition_penalty=self.args.repetition_penalty,
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num_beams=self.args.num_beams,
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output_sequence_lengths=False,
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return_dict=False)
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else:
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if self.model_type in ['nougat', 'pix2struct']:
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# Trim encoder input_ids to match visual features shape
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ids_shape = (self.args.batch_size, visual_features.shape[1])
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if self.model_type == 'nougat':
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input_ids = torch.zeros(ids_shape, dtype=torch.int32)
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elif self.model_type == 'pix2struct':
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input_ids = torch.ones(ids_shape, dtype=torch.int32)
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output_ids = self.model.generate(
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input_ids,
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decoder_input_ids,
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max_new_tokens,
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num_beams=self.args.num_beams,
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bos_token_id=self.tokenizer.bos_token_id,
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pad_token_id=self.tokenizer.pad_token_id,
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eos_token_id=self.tokenizer.eos_token_id,
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debug_mode=False,
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prompt_embedding_table=ptuning_args[0],
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prompt_tasks=ptuning_args[1],
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prompt_vocab_size=ptuning_args[2],
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attention_mask=attention_mask)
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# Reset input_lengths to match decoder_input_ids
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input_lengths = torch.ones(input_lengths.shape,
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dtype=input_lengths.dtype)
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profiler.stop("LLM")
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if tensorrt_llm.mpi_rank() == 0:
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# Extract a list of tensors of shape beam_width x output_ids.
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output_beams_list = [
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self.tokenizer.batch_decode(
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output_ids[batch_idx, :, input_lengths[batch_idx]:],
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skip_special_tokens=True)
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for batch_idx in range(self.args.batch_size)
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]
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stripped_text = [[
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output_beams_list[batch_idx][beam_idx].strip()
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for beam_idx in range(self.args.num_beams)
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] for batch_idx in range(self.args.batch_size)]
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profiler.stop("Generate")
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return stripped_text
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else:
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profiler.stop("Generate")
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return None
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def get_visual_features(self, image, attention_mask):
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visual_features = {
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'input':
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image.to(
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tensorrt_llm._utils.str_dtype_to_torch(self.vision_precision))
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}
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if attention_mask is not None:
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visual_features['attention_mask'] = attention_mask
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tensor_info = [
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TensorInfo('input', str_dtype_to_trt(self.vision_precision),
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image.shape)
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]
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if attention_mask is not None:
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tensor_info.append(
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TensorInfo('attention_mask', trt.DataType.INT32,
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attention_mask.shape))
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visual_output_info = self.visual_encoder_session.infer_shapes(
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tensor_info)
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visual_outputs = {
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t.name: torch.empty(tuple(t.shape),
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dtype=trt_dtype_to_torch(t.dtype),
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device=image.device)
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for t in visual_output_info
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}
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ok = self.visual_encoder_session.run(visual_features, visual_outputs,
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self.stream.cuda_stream)
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assert ok, "Runtime execution failed for vision encoder session"
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self.stream.synchronize()
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image_embeds = visual_outputs['output']
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image_atts = torch.ones(image_embeds.size()[:-1],
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dtype=torch.long).to(image.device)
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return image_embeds, image_atts
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def setup_fake_prompts(self, visual_features, pre_input_ids, post_input_ids,
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input_lengths):
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# Assemble fake prompts which points to image embedding actually
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fake_prompt_id = torch.arange(
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self.model_config.vocab_size, self.model_config.vocab_size +
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visual_features.shape[0] * visual_features.shape[1])
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fake_prompt_id = fake_prompt_id.reshape(visual_features.shape[0],
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visual_features.shape[1])
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if 'cogvlm' in self.model_type:
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input_ids = torch.cat(
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[pre_input_ids[:, 0:1], fake_prompt_id, pre_input_ids[:, 1:]],
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dim=1).contiguous().to(torch.int32)
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else:
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if post_input_ids is not None:
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input_ids = [pre_input_ids, fake_prompt_id, post_input_ids]
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else:
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input_ids = [fake_prompt_id, pre_input_ids]
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input_ids = torch.cat(input_ids, dim=1).contiguous().to(torch.int32)
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if self.decoder_llm or self.runtime_mapping.is_first_pp_rank():
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ptuning_args = self.ptuning_setup(visual_features, input_ids,
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input_lengths)
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else:
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ptuning_args = [None, None, None]
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return input_ids, ptuning_args
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def ptuning_setup_fuyu(self, input_ids, image_patches_indices):
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res_input_ids = []
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for cur_input_ids, cur_image_patches_indices in zip(
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input_ids, image_patches_indices):
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# Truncate input_ids to the length of image_patches_indices
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cur_image_patches_indices = cur_image_patches_indices[:len(
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cur_input_ids)]
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# Get ids of the image_patches
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non_zero_mask = cur_image_patches_indices != -1
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# Replace input_ids with image_patches_indices values (where the patches are placed)
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cur_input_ids = cur_input_ids.masked_scatter(
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non_zero_mask,
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cur_image_patches_indices[non_zero_mask] +
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self.model_config.vocab_size,
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)
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res_input_ids.append(cur_input_ids)
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return res_input_ids
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def ptuning_setup(self, prompt_table, input_ids, input_lengths):
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hidden_size = self.model_config.hidden_size * self.runtime_mapping.tp_size
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if prompt_table is not None:
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task_vocab_size = torch.tensor(
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[prompt_table.shape[1]],
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dtype=torch.int32,
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).cuda()
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prompt_table = prompt_table.view(
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(prompt_table.shape[0] * prompt_table.shape[1],
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prompt_table.shape[2]))
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assert prompt_table.shape[
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1] == hidden_size, "Prompt table dimensions do not match hidden size"
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prompt_table = prompt_table.cuda().to(
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dtype=tensorrt_llm._utils.str_dtype_to_torch(
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self.model_config.dtype))
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else:
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prompt_table = torch.empty([1, hidden_size]).cuda()
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task_vocab_size = torch.zeros([1]).cuda()
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if self.model_config.remove_input_padding:
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tasks = torch.zeros([torch.sum(input_lengths)],
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dtype=torch.int32).cuda()
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if self.decoder_llm: tasks = tasks.unsqueeze(0)
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else:
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tasks = torch.zeros(input_ids.shape, dtype=torch.int32).cuda()
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return [prompt_table, tasks, task_vocab_size]
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def load_test_image(self):
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if "vila" in self.model_type:
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img_url = 'https://github.com/Efficient-Large-Model/VILA/raw/main/demo_images/av.png'
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image = Image.open(requests.get(img_url,
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stream=True).raw).convert('RGB')
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elif "nougat" in self.model_type:
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filepath = hf_hub_download(
|
|
repo_id="hf-internal-testing/fixtures_docvqa",
|
|
filename="nougat_paper.png",
|
|
repo_type="dataset")
|
|
image = Image.open(filepath)
|
|
elif "fuyu" in self.model_type:
|
|
filepath = hf_hub_download(repo_id="adept/fuyu-8b",
|
|
filename="skateboard.png",
|
|
repo_type='model')
|
|
image = Image.open(filepath)
|
|
elif "kosmos" in self.model_type:
|
|
img_url = 'https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.png'
|
|
image = Image.open(requests.get(img_url,
|
|
stream=True).raw).convert('RGB')
|
|
elif "pix2struct" in self.model_type:
|
|
img_url = 'https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/multi_col_40963.png'
|
|
image = Image.open(requests.get(img_url,
|
|
stream=True).raw).convert('RGB')
|
|
else:
|
|
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
|
|
image = Image.open(requests.get(img_url,
|
|
stream=True).raw).convert('RGB')
|
|
|
|
return image
|
|
|
|
def setup_inputs(self, input_text, raw_image):
|
|
attention_mask = None
|
|
if 'blip2' in self.model_type:
|
|
processor = Blip2Processor.from_pretrained(self.model_type)
|
|
image = processor(raw_image, input_text,
|
|
return_tensors="pt")['pixel_values']
|
|
|
|
if input_text is None:
|
|
input_text = "Question: which city is this? Answer:"
|
|
|
|
pre_prompt = input_text
|
|
post_prompt = None
|
|
elif 'nougat' in self.model_type:
|
|
processor = NougatProcessor.from_pretrained(self.args.hf_model_dir)
|
|
image = processor(raw_image, return_tensors="pt")['pixel_values']
|
|
|
|
# Nougat doesn't need text prompt (mBART use single token to start generation), just leave a dummy one here
|
|
if input_text is None:
|
|
input_text = "Question: which city is this? Answer:"
|
|
|
|
pre_prompt = input_text
|
|
post_prompt = None
|
|
elif 'cogvlm' in self.model_type:
|
|
image_size = 490
|
|
dtype = torch.bfloat16
|
|
transform = transforms.Compose([
|
|
transforms.Resize(
|
|
(image_size, image_size),
|
|
interpolation=transforms.InterpolationMode.BICUBIC),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize((0.48145466, 0.4578275, 0.40821073),
|
|
(0.26862954, 0.26130258, 0.27577711)),
|
|
])
|
|
image = transform(raw_image).to(dtype).unsqueeze(0)
|
|
|
|
if input_text is None:
|
|
input_text = " [INST] which city is this? [/INST] "
|
|
pre_prompt = input_text
|
|
post_prompt = None
|
|
elif self.model_type == "pix2struct":
|
|
image_processor = AutoProcessor.from_pretrained(args.hf_model_dir)
|
|
if input_text is None:
|
|
input_text = ""
|
|
inputs = image_processor(
|
|
images=raw_image,
|
|
text=input_text,
|
|
return_tensors="pt",
|
|
)
|
|
image = inputs['flattened_patches']
|
|
image = image.expand(self.args.batch_size, -1, -1).contiguous()
|
|
attention_mask = inputs['attention_mask'].to(self.device).to(
|
|
torch.int)
|
|
attention_mask = attention_mask.expand(args.batch_size,
|
|
-1).contiguous()
|
|
pre_prompt = ""
|
|
post_prompt = None
|
|
elif 'neva' in self.model_type:
|
|
image_size = 384
|
|
dtype = torch.float32
|
|
transform = transforms.Compose([
|
|
transforms.Resize(
|
|
(image_size, image_size),
|
|
interpolation=transforms.InterpolationMode.BICUBIC),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
|
|
])
|
|
image = transform(raw_image).to(dtype).unsqueeze(0)
|
|
|
|
if input_text is None:
|
|
input_text = "Hi! What is in this image?"
|
|
|
|
pre_prompt = "<extra_id_0>System\n\n<extra_id_1>User\n"
|
|
post_prompt = f"\n{input_text}\n<extra_id_1>Assistant\n"
|
|
elif self.model_type in ['llava', 'vila', 'fuyu', 'kosmos-2']:
|
|
# LLaVA and VILA
|
|
if self.model_type == "llava":
|
|
pre_prompt = "USER:\n"
|
|
if input_text is None:
|
|
input_text = "Question: which city is this? Answer:"
|
|
elif self.model_type == "vila":
|
|
pre_prompt = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: "
|
|
if input_text is None:
|
|
input_text = "Please describe the traffic condition."
|
|
elif self.model_type == 'fuyu':
|
|
pre_prompt = "Describe this image:"
|
|
if input_text is None:
|
|
input_text = "Answer the following VQAv2 question based on the image: How many people are in the image?\n"
|
|
elif self.model_type == "kosmos-2":
|
|
pre_prompt = ""
|
|
if input_text is None:
|
|
input_text = "<grounding>An image of"
|
|
|
|
if self.model_type not in ['fuyu', 'kosmos-2']:
|
|
post_prompt = input_text + " ASSISTANT:"
|
|
else:
|
|
post_prompt = None
|
|
|
|
if self.model_type == "vila":
|
|
sys.path.append(self.args.hf_model_dir + "/../VILA")
|
|
from llava.model import LlavaLlamaForCausalLM
|
|
model = LlavaLlamaForCausalLM.from_pretrained(
|
|
self.args.hf_model_dir, torch_dtype=torch.float16)
|
|
vision_tower = model.get_vision_tower()
|
|
image_processor = vision_tower.image_processor
|
|
image = image_processor(images=raw_image,
|
|
return_tensors="pt")['pixel_values']
|
|
else:
|
|
processor = AutoProcessor.from_pretrained(
|
|
self.args.hf_model_dir)
|
|
if self.model_type in ['fuyu', 'kosmos-2']:
|
|
image = processor(text=input_text,
|
|
images=raw_image,
|
|
return_tensors='pt')
|
|
else:
|
|
image = processor(text=input_text,
|
|
images=raw_image,
|
|
return_tensors="pt")['pixel_values']
|
|
|
|
# Repeat inputs to match batch size
|
|
pre_prompt = [pre_prompt] * self.args.batch_size
|
|
post_prompt = [post_prompt] * self.args.batch_size
|
|
if self.model_type not in ['fuyu', 'pix2struct', 'kosmos-2']:
|
|
image = image.expand(args.batch_size, -1, -1, -1).contiguous()
|
|
image = image.to(self.device)
|
|
|
|
# Generate decoder_input_ids for enc-dec models
|
|
# Custom prompts can be added as:
|
|
# decoder_input_ids = model.tokenizer(decoder_prompt).input_ids
|
|
if self.decoder_llm:
|
|
decoder_input_ids = None
|
|
else:
|
|
config = AutoConfig.from_pretrained(args.hf_model_dir)
|
|
decoder_start_id = config.decoder_start_token_id # T5
|
|
if decoder_start_id is None:
|
|
decoder_start_id = config.decoder.bos_token_id # Nougat
|
|
|
|
decoder_input_ids = torch.IntTensor([[decoder_start_id]])
|
|
decoder_input_ids = decoder_input_ids.repeat((args.batch_size, 1))
|
|
|
|
return input_text, pre_prompt, post_prompt, image, decoder_input_ids, attention_mask
|
|
|
|
def run(self, input_text, input_image, max_new_tokens):
|
|
input_text, pre_prompt, post_prompt, processed_image, decoder_input_ids, attention_mask = model.setup_inputs(
|
|
input_text, input_image)
|
|
|
|
model.generate(pre_prompt,
|
|
post_prompt,
|
|
processed_image,
|
|
decoder_input_ids,
|
|
max_new_tokens,
|
|
attention_mask=attention_mask,
|
|
warmup=True)
|
|
num_iters = self.profiling_iterations if self.args.run_profiling else 1
|
|
for _ in range(num_iters):
|
|
output_text = model.generate(pre_prompt,
|
|
post_prompt,
|
|
processed_image,
|
|
decoder_input_ids,
|
|
max_new_tokens,
|
|
attention_mask=attention_mask,
|
|
warmup=False)
|
|
if self.runtime_rank == 0:
|
|
self.print_result(input_text, output_text)
|
|
return output_text
|
|
|
|
def print_result(self, input_text, output_text):
|
|
logger.info("---------------------------------------------------------")
|
|
if self.model_type != 'nougat':
|
|
logger.info(f"\n[Q] {input_text}")
|
|
logger.info(f"\n[A] {output_text[0]}")
|
|
|
|
if args.num_beams == 1:
|
|
output_ids = self.tokenizer(output_text[0][0],
|
|
add_special_tokens=False)['input_ids']
|
|
logger.info(f"Generated {len(output_ids)} tokens")
|
|
|
|
if self.args.check_accuracy:
|
|
for i in range(self.args.batch_size - 1):
|
|
if not (output_text[i] == output_text[i + 1]):
|
|
logger.info(f"Output {i} and {i + 1} do not match")
|
|
assert False
|
|
if self.model_type != 'nougat':
|
|
if self.model_type == "vila":
|
|
assert output_text[0][0].lower(
|
|
) == 'the traffic condition in the image is quite busy, with multiple cars and bicycles sharing the road. there are also pedestrians walking on'
|
|
elif self.model_type == 'fuyu':
|
|
assert output_text[0][0].lower() == '4'
|
|
elif self.model_type == "pix2struct":
|
|
assert "characteristic | cat food, day | cat food, wet | cat treats" in output_text[
|
|
0][0].lower()
|
|
elif self.model_type == 'neva':
|
|
assert 'singapore' in output_text[0][0].lower()
|
|
elif self.model_type == 'kosmos-2':
|
|
assert 'snowman' in output_text[0][0].lower()
|
|
else:
|
|
assert output_text[0][0].lower() == 'singapore'
|
|
|
|
if self.args.run_profiling:
|
|
msec_per_batch = lambda name: 1000 * profiler.elapsed_time_in_sec(
|
|
name) / self.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"
|
|
args = parse_arguments()
|
|
tensorrt_llm.logger.set_level(args.log_level)
|
|
|
|
model = MultimodalModelRunner(args)
|
|
|
|
raw_image = model.load_test_image()
|
|
text_output = model.run(args.input_text, raw_image, args.max_new_tokens)
|