End to end workflow to run a Multimodal model

Support Matrix

The following multimodal model is supported in tensorrtllm_backend:

BLIP2-OPT
LLAVA
VILA
LLaVA OneVision
MLLAMA
Qwen2-VL

For more multimodal models supported in TensorRT-LLM, please visit TensorRT-LLM multimodal examples.

Run Multimodal with single-GPU Tritonserver

Tritonserver setup steps

Make sure that you have initialized the TRT-LLM submodule:

git clone https://github.com/triton-inference-server/tensorrtllm_backend.git && cd tensorrtllm_backend
git lfs install
git submodule update --init --recursive

Start the Triton Server Docker container:

1-1. If you're using Tritonserver from nvcr.io

# Replace <yy.mm> with the version of Triton you want to use.
# The command below assumes the the current directory is the
# TRT-LLM backend root git repository.

docker run --rm -ti --net=host -v `pwd`:/mnt -w /mnt --gpus all nvcr.io/nvidia/tritonserver:\<yy.mm\>-trtllm-python-py3 bash

1-2. If you are using tensorrtllm_backend container:

docker run --rm -ti --net=host -v `pwd`:/mnt -w /mnt --gpus all triton_trt_llm

Build the engine:

2-1. Clone the target model repository

# For BLIP-OPT2
export MODEL_NAME="blip2-opt-2.7b"
git clone https://huggingface.co/Salesforce/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}

# For LLAVA
export MODEL_NAME="llava-1.5-7b-hf"
git clone https://huggingface.co/llava-hf/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}

# For VILA
pip install -r all_models/multimodal/requirements-vila.txt

export MODEL_NAME="vila1.5-3b"
git clone https://huggingface.co/Efficient-Large-Model/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}

export VILA_PATH="tmp/hf_models/VILA"
git clone https://github.com/Efficient-Large-Model/VILA.git ${VILA_PATH}

# For LLaVA OneVision
pip install -r all_models/multimodal/requirements-llava-onevision.txt

export MODEL_NAME="llava-onevision-qwen2-7b-ov-hf"
git clone https://huggingface.co/llava-hf/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}

# For MLLAMA
pip install -r all_models/multimodal/requirements-mllama.txt

export MODEL_NAME="Llama-3.2-11B-Vision"
git clone https://huggingface.co/meta-llama/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}

# For Qwen2-VL
pip install -r all_models/multimodal/requirements-qwen2vl.txt

export MODEL_NAME="Qwen2-VL-7B-Instruct"
git clone https://huggingface.co/Qwen/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}

export

2-2. Build TensorRT-LLM engines

export HF_MODEL_PATH=tmp/hf_models/${MODEL_NAME}
export UNIFIED_CKPT_PATH=tmp/trt_models/${MODEL_NAME}/fp16/1-gpu
export ENGINE_PATH=tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu
export MULTIMODAL_ENGINE_PATH=tmp/trt_engines/${MODEL_NAME}/multimodal_encoder

# For BLIP-OPT2
python tensorrt_llm/examples/opt/convert_checkpoint.py --model_type blip2 \
    --model_dir ${HF_MODEL_PATH} \
    --output_dir ${UNIFIED_CKPT_PATH} \
    --dtype float16

trtllm-build \
    --checkpoint_dir ${UNIFIED_CKPT_PATH} \
    --output_dir ${ENGINE_PATH} \
    --gemm_plugin float16 \
    --max_beam_width 1 \
    --max_batch_size 8 \
    --max_seq_len 1024 \
    --max_input_len 924 \
    --max_multimodal_len 256 # 8 (max_batch_size) * 32 (num_multimodal_features) for BLIP2

python tensorrt_llm/examples/multimodal/build_multimodal_engine.py --model_type blip2 --model_path ${HF_MODEL_PATH} --max_batch_size 8

# For LLAVA
python tensorrt_llm/examples/llama/convert_checkpoint.py \
    --model_dir ${HF_MODEL_PATH} \
    --output_dir ${UNIFIED_CKPT_PATH} \
    --dtype float16

trtllm-build \
    --checkpoint_dir ${UNIFIED_CKPT_PATH} \
    --output_dir ${ENGINE_PATH} \
    --gemm_plugin float16 \
    --max_batch_size 8 \
    --max_input_len 2048 \
    --max_seq_len 2560 \
    --max_multimodal_len 4608 # 8 (max_batch_size) * 576 (num_multimodal_features) for LLaVA

python tensorrt_llm/examples/multimodal/build_multimodal_engine.py --model_path ${HF_MODEL_PATH} --model_type llava --max_batch_size 8

# For VILA
python tensorrt_llm/examples/llama/convert_checkpoint.py \
    --model_dir ${HF_MODEL_PATH} \
    --output_dir ${UNIFIED_CKPT_PATH} \
    --dtype float16

trtllm-build \
    --checkpoint_dir ${UNIFIED_CKPT_PATH} \
    --output_dir ${ENGINE_PATH} \
    --gemm_plugin float16 \
    --max_batch_size 8 \
    --max_input_len 2048 \
    --max_seq_len 2560 \
    --max_multimodal_len 6272 # 8 (max_batch_size) * 196 (num_multimodal_features) * 4 (max_num_images_per_request)

python tensorrt_llm/examples/multimodal/build_multimodal_engine.py --model_path ${HF_MODEL_PATH} --model_type vila --vila_path ${VILA_PATH} --max_batch_size 32 #max_batch_size * max_num_images_per_request since vila support multiple images inference

# For LLaVA OneVision
python tensorrt_llm/examples/qwen/convert_checkpoint.py \
    --model_dir ${HF_MODEL_PATH} \
    --output_dir ${UNIFIED_CKPT_PATH} \
    --dtype float16

trtllm-build \
    --checkpoint_dir ${UNIFIED_CKPT_PATH} \
    --output_dir ${ENGINE_PATH} \
    --gemm_plugin float16 \
    --max_batch_size 1 \
    --max_input_len  7500 \
    --max_seq_len  7600 \
    --max_multimodal_len 7300 # max_batch_size * num_multimodal_features(depends on the image size or the specified video num frame)

python tensorrt_llm/examples/multimodal/build_multimodal_engine.py --model_path ${HF_MODEL_PATH} --model_type llava_onevision --max_batch_size 16 # max_batch_size * patch for image or frame for video

# For MLLAMA
python tensorrt_llm/examples/mllama/convert_checkpoint.py \
    --model_dir ${HF_MODEL_PATH} \
    --output_dir ${UNIFIED_CKPT_PATH} \
    --dtype bfloat16

trtllm-build \
--checkpoint_dir ${UNIFIED_CKPT_PATH} \
--output_dir ${ENGINE_PATH} \
--gemm_plugin auto \
--max_batch_size 8 \
--max_seq_len 2048 \
--max_num_tokens 4096 \
--max_encoder_input_len 6404

python tensorrt_llm/examples/multimodal/build_multimodal_engine.py --model_path ${HF_MODEL_PATH} --model_type mllama --output_dir ${MULTIMODAL_ENGINE_PATH} --max_batch_size 8 #max_batch_size * max_num_images_per_request

# For Qwen2-VL
python3 ../qwen/convert_checkpoint.py \
    --model_dir ${HF_MODEL_PATH} \
    --output_dir ${UNIFIED_CKPT_PATH} \
    --dtype float16

trtllm-build --checkpoint_dir ${UNIFIED_CKPT_PATH} \
    --output_dir ${ENGINE_PATH} \
    --gemm_plugin=float16 \
    --gpt_attention_plugin=float16 \
    --max_batch_size 4 \
    --max_input_len 2048 \
    --max_seq_len 3072 \
    --max_multimodal_len 1296 #(max_batch_size) * 324 (num_multimodal_features), this's for image_shape=[504,504]

python build_multimodal_engine.py --model_type qwen2_vl --model_path tmp/hf_models/${MODEL_NAME} --output_dir ${MULTIMODAL_ENGINE_PATH}

NOTE:

max_multimodal_len = max_batch_size * num_multimodal_features, so if you change max_batch_size, max_multimodal_len MUST be changed accordingly. For multi-image inference, where a single request could contain multiple images, max_multimodal_len = max_batch_size * num_multimodal_features * max_num_images_per_request

The built visual engines are located in tmp/trt_engines/${MODEL_NAME}/multimodal_encoder.

Prepare Tritonserver configs

cp all_models/inflight_batcher_llm/ multimodal_ifb -r
# Override the ensemble and creates new multimodal_encoders directories for multimodal
cp all_models/multimodal/ensemble multimodal_ifb -r
cp all_models/multimodal/multimodal_encoders multimodal_ifb -r

python3 tools/fill_template.py -i multimodal_ifb/tensorrt_llm/config.pbtxt triton_backend:tensorrtllm,triton_max_batch_size:8,decoupled_mode:False,max_beam_width:1,engine_dir:${ENGINE_PATH},enable_kv_cache_reuse:False,batching_strategy:inflight_fused_batching,max_queue_delay_microseconds:0,enable_chunked_context:False,encoder_input_features_data_type:${ENCODER_INPUT_FEATURES_DTYPE},logits_datatype:TYPE_FP32,cross_kv_cache_fraction:0.5

python3 tools/fill_template.py -i multimodal_ifb/preprocessing/config.pbtxt tokenizer_dir:${HF_MODEL_PATH},triton_max_batch_size:8,preprocessing_instance_count:1,multimodal_model_path:${MULTIMODAL_ENGINE_PATH},engine_dir:${ENGINE_PATH},max_num_images:1,max_queue_delay_microseconds:20000

python3 tools/fill_template.py -i multimodal_ifb/postprocessing/config.pbtxt tokenizer_dir:${HF_MODEL_PATH},triton_max_batch_size:8,postprocessing_instance_count:1

python3 tools/fill_template.py -i multimodal_ifb/ensemble/config.pbtxt triton_max_batch_size:8,logits_datatype:TYPE_FP32

python3 tools/fill_template.py -i multimodal_ifb/tensorrt_llm_bls/config.pbtxt triton_max_batch_size:8,decoupled_mode:False,bls_instance_count:1,accumulate_tokens:False,tensorrt_llm_model_name:tensorrt_llm,multimodal_encoders_name:multimodal_encoders,logits_datatype:TYPE_FP32

# Newly added for multimodal
python3 tools/fill_template.py -i multimodal_ifb/multimodal_encoders/config.pbtxt triton_max_batch_size:8,multimodal_model_path:${MULTIMODAL_ENGINE_PATH},encoder_input_features_data_type:${ENCODER_INPUT_FEATURES_DTYPE},hf_model_path:${HF_MODEL_PATH},max_queue_delay_microseconds:20000

NOTE:

You can set the decoupled_mode option to True to use streaming mode.

You can set the accumulate_tokens option to True in streaming mode to call the postprocessing model with all accumulated tokens.

You can set the enable_kv_cache_reuse option to True to enable kv cache reuse. Requests with the same image/prompt table/input tokens will reuse the KV cache, which will help reduce latency. The specific performance improvement depends on the length of reuse.

You can set the max_num_images to the max number of images per request. The value should be the same as the max_num_images_per_request value used at build the engine step above.

Set ${ENCODER_INPUT_FEATURES_DTYPE} to TYPE_BF16 for mllama, and TYPE_FP16 for other models. cross_kv_cache_fraction is used to determine the paged kv cache memory pool size of enc-dec models. For such case, we distinguish free_fraction * (1 - cross_kv_cache_fraction) to self attention kv caches, and free_fraction * cross_kv_cache_fraction to cross attention kv caches.

Launch Tritonserver
```
python3 scripts/launch_triton_server.py --world_size 1 --model_repo=multimodal_ifb/ --tensorrt_llm_model_name tensorrt_llm,multimodal_encoders --multimodal_gpu0_cuda_mem_pool_bytes 300000000
```
NOTE: If there is an error associated with 'MPI_Init_thread', please do export PMIX_MCA_gds=hash'

When launching the server, since the prompt_embedding_table is in GPU memory, we need to set the CUDA pool memory for inter-step communication. For example, when we have a shape of (1, 576, 4096) promp_embedding table, we would need 300MB of CUDA pool memory, so we set 30MB to have some GPU buffers. (2(fp16=>2bytes) * 576 * 4096 * 8(max_batch_size) = 18,874,368)

Also, the tensorrt_llm initialization assumes using another GPU, we need to initialize it but not use them.

Send requests

Send request with decoupled_mode set to False

python tools/multimodal/client.py --text 'Question: which city is this? Answer:' --image 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png' --request-output-len 16 --model_type blip2

[beam 0 ]:
Question: which city is this? Answer: singapore
[INFO] Latency: 41.942 ms

Send request with decoupled_mode set to True

python tools/multimodal/client.py --text 'Question: which city is this? Answer:' --image 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png' --request-output-len 16 --model_type blip2 --streaming

[beam 0 ]:   sing
[beam 0 ]:  apore
[beam 0 ]:
[INFO] Latency: 43.441 ms

Send request to the tensorrt_llm_bls model

python tools/multimodal/client.py --text 'Question: which city is this? Answer:' --image 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png' --request-output-len 16 --model_type blip2 --use_bls

[beam 0 ]:
Question: which city is this? Answer: singapore
[INFO] Latency: 44.152 ms

Send request to the tensorrt_llm_bls model with accumulate_tokens set to True

python tools/multimodal/client.py --text 'Question: which city is this? Answer:' --image 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png' --request-output-len 16 --model_type blip2 --use_bls --streaming

[beam 0 ]:   sing
[beam 0 ]:   singapore
[beam 0 ]:   singapore
[INFO] Latency: 45.48 ms

Send request with enable_kv_cache_reuse set to True

python tools/multimodal/client.py --text 'Question: which city is this? Answer:' --image 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png' --request-output-len 16 --model_type blip2 --prompt_table_extra_id ${id}

[beam 0 ]:
Question: which city is this? Answer: singapore
[INFO] Latency: 42.514 ms

Send request with multiple images per request

wget -O av.png https://raw.githubusercontent.com/Efficient-Large-Model/VILA/main/demo_images/av.png

python tools/multimodal/client.py --text '<image>\n<image>\n Please elaborate what you see in the images?' --image av.png,'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png' --request-output-len 68 --model_type vila --hf_model_dir ${HF_MODEL_PATH}

[beam 0 ]:
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:  \n \n Please elaborate what you see in the images? ASSISTANT: The first image shows a busy street scene with a car driving through a crosswalk, surrounded by pedestrians and traffic lights. The second image captures a beautiful sunset with the iconic Merlion statue spouting water into the bay, with the Singapore Flyer and the city skyline in the background.

[INFO] Latency: 403.879 ms

Send request with curl The triton server supports curl requests with an image url in the payload. For example here is a request sent to a Llama-3.2-11B-Vision (mLLama) model:

curl -X POST localhost:8000/v2/models/ensemble/generate_stream \
-d '{"id": "42", "text_input": "<|image|>If I had to write a haiku for this one", "image_url_input": "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png", "parameters": {"max_tokens": 16, "beam_width": 1, "end_id": 128001, "pad_id": 128004, "top_k": 1, "top_p": 0, "stream": false, "temperature": 0}}'

# response
data: {"batch_index":0,"context_logits":0.0,"cum_log_probs":0.0,"generation_logits":0.0,"id":"42","model_name":"ensemble","model_version":"1","output_log_probs":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"sequence_end":false,"sequence_id":0,"sequence_index":0,"sequence_start":false,"text_output":"If I had to write a haiku for this one, it would be:.\\nMerlion spouts water.\\nMarina"}

You can also send requests with base64 encoded images. Just replace the url above with data:image/jpeg;base64,<base64_encoded_image>.

Send request with video input

python tools/multimodal/client.py --text "Why is this video funny?" --video sample_demo_1.mp4 --video_num_frames 8 --request-output-len 30 --model_type llava_onevision  --end-id 151645

[beam 0 ]:
user
Why is this video funny?assistant
The video is funny because the child's actions are playful and exaggerated, as if they are reading the book with great enthusiasm.
[INFO] Latency: 507.537 ms

NOTE: Please ignore any exception thrown with the output. It's a known issue to be fixed.

When enable_kv_cache_reuse is set to true, the prompt_table_extra_id must be specified in the requests. The prompt_table_extra_id is a unique identifier representing the image (or prompt table), the same image uses the same id. The data type is uint64, and the minimum value is 1.

Kill the server

pkill tritonserver

Supported image input types

When programmatically preparing your own request for the server, note that ensemble:

image_input: a float16 5D tensor of shape [batch_size, num_images, num_channels, height, width] or [batch_size, num_images, height, width, num_channels] representing a batch of images already processed (via transformers AutoProcessor) for the vision encoder.
image_bytes_input: a uint8 5D tensor of shape [batch_size, num_images, num_channels, height, width] or [batch_size, num_images, height, width, num_channels] representing a batch of raw images.
image_url_input: a list of strings of shape [batch_size, num_images] representing a batch of image urls.

You may populate only one of these image inputs in a request. We suggest you use image_bytes_input when using grpc requests and image_url_input when sending http requests. For grpc requests where the client can preprocess images to reduce load on the server, use image_input. Note that tensorrt_llm_bls only supports image_input.

Long multimodal context, FP8 KV cache and tensor parallelism

Follow these steps to enable chunked context inference (using LLaVA as an example) with FP8 KV cache and 2-way tensor parallelism. Ensure you convert the checkpoint using --tp_size 2 and build the model with --use_paged_context_fmha enable and --use_fp8_context_fmha enable. Set the chunked context to true in the Tritonserver configuration file. The chunk size is determined by the max_num_tokens flag when building the engine, which defaults to 8192. When launching the server, you need to change the --world_size to match your tensor parallelism size.

Build the engine

    export MODEL_NAME="llava-1.5-7b-hf"
    export HF_MODEL_PATH=tmp/hf_models/${MODEL_NAME}

    # Convert checkpoint
    # For fp16 KV cache
    export UNIFIED_CKPT_PATH=tmp/trt_models/${MODEL_NAME}/fp8/2-gpu
    export ENGINE_PATH=tmp/trt_engines/${MODEL_NAME}/fp8/2-gpu
    export MULTIMODAL_ENGINE_PATH=tmp/trt_engines/${MODEL_NAME}/multimodal_encoder
    python tensorrt_llm/examples/llama/convert_checkpoint.py \
        --model_dir ${HF_MODEL_PATH} \
        --output_dir ${UNIFIED_CKPT_PATH} \
        --dtype float16 \
        --tp_size 2

    # For fp8 KV cache
    export UNIFIED_CKPT_PATH=tmp/trt_models/${MODEL_NAME}/fp8/2-gpu
    export ENGINE_PATH=tmp/trt_engines/${MODEL_NAME}/fp8/2-gpu
    export MULTIMODAL_ENGINE_PATH=tmp/trt_engines/${MODEL_NAME}/multimodal_encoder
    python ./tensorrt_llm/examples/quantization/quantize.py \
                                --model_dir ${HF_MODEL_PATH} \
                                --dtype float16 \
                                --qformat fp8 \
                                --kv_cache_dtype fp8 \
                                --output_dir ${UNIFIED_CKPT_PATH} \
                                --calib_size 512 \
                                --tp_size 2

    # Build the llm engine
    # --use_paged_context_fmha and --use_fp8_context_fmha are defaultly enabled
    # include --max_num_tokens to set the chunk size
    trtllm-build \
        --checkpoint_dir ${UNIFIED_CKPT_PATH} \
        --output_dir ${ENGINE_PATH} \
        --gemm_plugin auto \
        --max_batch_size 8 \
        --max_input_len 2048 \
        --max_seq_len 2560 \
        --max_multimodal_len 4608 # 8 (max_batch_size) * 576 (num_multimodal_features) for LLaVA

    # Build the multimodal engine
    python tensorrt_llm/examples/multimodal/build_multimodal_engine.py --model_path ${HF_MODEL_PATH} --model_type llava --max_batch_size 8 --output_dir ${MULTIMODAL_ENGINE_PATH}

Prepare the Tritonserver config file Prepare the Tritonserver config file with enable_chunked_context set to True. Also, to further utilize the free memory, we can set kv_cache_free_gpu_mem_fraction to 0.9.

cp all_models/inflight_batcher_llm/ multimodal_ifb -r
# Override the ensemble and creates new multimodal_encoders directories for multimodal
cp all_models/multimodal/ensemble multimodal_ifb -r
cp all_models/multimodal/multimodal_encoders multimodal_ifb -r

# Changes the enable_chunked_context to True, and set kv_cache_free_gpu_mem_fraction to 0.9
python3 tools/fill_template.py -i multimodal_ifb/tensorrt_llm/config.pbtxt triton_backend:tensorrtllm,triton_max_batch_size:8,decoupled_mode:False,max_beam_width:1,engine_dir:${ENGINE_PATH},enable_kv_cache_reuse:False,batching_strategy:inflight_fused_batching,max_queue_delay_microseconds:0,enable_chunked_context:True,encoder_input_features_data_type:${ENCODER_INPUT_FEATURES_DTYPE},logits_datatype:TYPE_FP32,kv_cache_free_gpu_mem_fraction:0.9

python3 tools/fill_template.py -i multimodal_ifb/preprocessing/config.pbtxt tokenizer_dir:${HF_MODEL_PATH},triton_max_batch_size:8,preprocessing_instance_count:1,multimodal_model_path:${MULTIMODAL_ENGINE_PATH},engine_dir:${ENGINE_PATH},max_num_images:1,max_queue_delay_microseconds:20000

python3 tools/fill_template.py -i multimodal_ifb/postprocessing/config.pbtxt tokenizer_dir:${HF_MODEL_PATH},triton_max_batch_size:8,postprocessing_instance_count:1

python3 tools/fill_template.py -i multimodal_ifb/ensemble/config.pbtxt triton_max_batch_size:8,logits_datatype:TYPE_FP32

python3 tools/fill_template.py -i multimodal_ifb/tensorrt_llm_bls/config.pbtxt triton_max_batch_size:8,decoupled_mode:False,bls_instance_count:1,accumulate_tokens:False,tensorrt_llm_model_name:tensorrt_llm,multimodal_encoders_name:multimodal_encoders,logits_datatype:TYPE_FP32

# Newly added for multimodal
python3 tools/fill_template.py -i multimodal_ifb/multimodal_encoders/config.pbtxt triton_max_batch_size:8,multimodal_model_path:${MULTIMODAL_ENGINE_PATH},encoder_input_features_data_type:${ENCODER_INPUT_FEATURES_DTYPE},hf_model_path:${HF_MODEL_PATH},max_queue_delay_microseconds:20000

Launch the server

# Change --world_size to your tp size
python3 scripts/launch_triton_server.py --world_size 2 --model_repo=multimodal_ifb/ --tensorrt_llm_model_name tensorrt_llm,multimodal_encoders--multimodal_gpu0_cuda_mem_pool_bytes 300000000

When you launch the server, you will see logs similar to the following. In theory, now you can process long multimodal context up to the "max tokens in paged KV cache" value, and the context prefill phase will be done in chunk sizes.

[TensorRT-LLM][INFO] Memory usage when calculating max tokens in paged kv cache: total: 93.10 GiB, available: 85.57 GiB
...
[TensorRT-LLM][INFO] [MemUsageChange] Allocated 77.02 GiB for max tokens in paged KV cache (315488).

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End to end workflow to run a Multimodal model

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End to end workflow to run a Multimodal model

Support Matrix

Run Multimodal with single-GPU Tritonserver

Tritonserver setup steps

Send requests

Kill the server

Supported image input types

Long multimodal context, FP8 KV cache and tensor parallelism