mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
655524dd82
* Update TensorRT-LLM --------- Co-authored-by: Bhuvanesh Sridharan <bhuvan.sridharan@gmail.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
406 lines
16 KiB
Python
406 lines
16 KiB
Python
import argparse
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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 numpy as np
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import requests
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import tensorrt as trt
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import torch
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from huggingface_hub import hf_hub_download
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from PIL import Image
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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 torch_to_numpy
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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(
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'--decoder_llm',
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action='store_true',
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help='Whether LLM is decoder-only or an encoder-decoder variant?')
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parser.add_argument('--blip2_encoder',
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action='store_true',
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help='Whether visual encoder is a BLIP2 model')
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parser.add_argument('--nougat',
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action='store_true',
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help='Run nougat pipeline')
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parser.add_argument('--input_text',
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type=str,
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default='Question: which city is this? Answer:',
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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('--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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else:
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raise TypeError("%s is not supported" % dtype)
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class MultiModalModel:
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def __init__(self, args):
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self.args = args
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runtime_rank = tensorrt_llm.mpi_rank()
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device_id = 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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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.args.nougat:
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self.tokenizer = NougatTokenizerFast.from_pretrained(
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self.args.hf_model_dir)
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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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self.tokenizer.pad_token = self.tokenizer.eos_token
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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.args.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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self.args.hf_model_dir.split('/')[-1],
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self.args.llm_engine_dir,
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skip_encoder=self.args.nougat,
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debug_mode=False,
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stream=self.stream)
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if args.nougat:
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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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config = AutoConfig.from_pretrained(self.args.hf_model_dir)
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decoder_start_id = config.decoder_start_token_id
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if decoder_start_id is None:
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decoder_start_id = self.tokenizer.bos_token_id
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decoder_input_ids = torch.IntTensor([[decoder_start_id]
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]).to(self.device)
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batch_size = self.args.batch_size
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self.decoder_input_ids = decoder_input_ids.repeat((batch_size, 1))
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def generate(self, pre_prompt, post_prompt, image, max_new_tokens):
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profiler.start("Generate")
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profiler.start("Vision")
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visual_features, visual_atts = self.get_visual_features(image)
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profiler.stop("Vision")
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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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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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if self.args.decoder_llm and tensorrt_llm.mpi_rank() == 0:
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prompt_table = ptuning_args[0]
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prompt_table = torch.stack([prompt_table])
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np.save('prompt_table.npy', torch_to_numpy(prompt_table))
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tensorrt_llm.mpi_barrier() # Sync before reading prompt_table file
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profiler.start("LLM")
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if self.args.decoder_llm:
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end_id = self.tokenizer.eos_token_id
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if 'opt' in self.args.hf_model_dir and self.args.blip2_encoder:
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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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output_ids = self.model.generate(
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input_ids,
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sampling_config=None,
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prompt_table_path='prompt_table.npy',
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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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top_k=self.args.top_k,
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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 args.nougat:
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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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input_ids = torch.zeros(ids_shape, dtype=torch.int32)
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output_ids = self.model.generate(
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input_ids,
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self.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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# 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):
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visual_features = {'input': image.half()}
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visual_output_info = self.visual_encoder_session.infer_shapes(
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[TensorInfo('input', trt.DataType.HALF, image.shape)])
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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 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.args.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(self, prompt_table, input_ids, input_lengths):
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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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hidden_size = self.model_config.hidden_size * self.runtime_mapping.tp_size
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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 args.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(model_name):
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if "nougat" in model_name:
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filepath = hf_hub_download(
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repo_id="hf-internal-testing/fixtures_docvqa",
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filename="nougat_paper.png",
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repo_type="dataset")
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image = Image.open(filepath)
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else:
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img_url = 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.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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return image
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if __name__ == '__main__':
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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args = parse_arguments()
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tensorrt_llm.logger.set_level(args.log_level)
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runtime_rank = tensorrt_llm.mpi_rank()
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image = load_test_image(args.hf_model_dir)
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if args.blip2_encoder:
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if 'opt-2.7b' in args.hf_model_dir:
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model_type = 'Salesforce/blip2-opt-2.7b'
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else:
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model_type = 'Salesforce/blip2-flan-t5-xl'
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processor = Blip2Processor.from_pretrained(model_type)
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image = processor(image, args.input_text,
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return_tensors="pt")['pixel_values']
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pre_prompt = args.input_text
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post_prompt = None
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elif args.nougat:
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processor = NougatProcessor.from_pretrained(args.hf_model_dir)
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image = processor(image, return_tensors="pt")['pixel_values']
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pre_prompt = args.input_text
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post_prompt = None
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else:
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processor = AutoProcessor.from_pretrained(args.hf_model_dir)
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image = processor(text=args.input_text,
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images=image,
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return_tensors="pt")['pixel_values']
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pre_prompt = "USER:\n"
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post_prompt = args.input_text + " ASSISTANT:"
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# Repeat inputs to match batch size
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pre_prompt = [pre_prompt] * args.batch_size
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post_prompt = [post_prompt] * args.batch_size
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image = image.expand(args.batch_size, -1, -1, -1).contiguous()
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model = MultiModalModel(args)
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image = image.to(model.device)
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num_iters = 100 if args.run_profiling else 1
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for _ in range(num_iters):
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stripped_text = model.generate(pre_prompt, post_prompt, image,
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args.max_new_tokens)
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if runtime_rank == 0:
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logger.info("---------------------------------------------------------")
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if not args.nougat:
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logger.info(f"\n[Q] {args.input_text}")
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logger.info(f"\n[A] {stripped_text}")
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if args.check_accuracy and not args.nougat:
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assert stripped_text[0][0].lower() == 'singapore'
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if args.run_profiling:
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vision_latency = profiler.elapsed_time_in_sec("Vision") / num_iters
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logger.info(
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f'TensorRT vision encoder latency: {vision_latency} sec')
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llm_latency = profiler.elapsed_time_in_sec("LLM") / num_iters
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logger.info(f'TensorRT-LLM LLM latency: {llm_latency} sec')
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generate_latency = profiler.elapsed_time_in_sec(
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"Generate") / num_iters
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logger.info(f'Generate latency: {generate_latency} sec')
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logger.info("---------------------------------------------------------")
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