multimodal

Multi-Modal

This document shows how to run multimodal pipelines with TensorRT-LLM, e.g. from image+text input modalities to text output.

Multimodal models' LLM part has an additional parameter --max_multimodal_len compared to LLM-only build commands. Under the hood, max_multimodal_len and max_prompt_embedding_table_size are effectively the same concept, i.e., prepended/concatenated embeddings (either multimodal feature embeddings or prompt tuning embeddings) to the LLM input embeddings. The multimodal features from the visual encoder of shape [batch_size, num_visual_features, visual_hidden_dim] is flattened as [batch_size * num_visual_features, visual_hidden_dim] and passed like a prompt embedding table.

We first describe how to run each model on a single GPU. We then provide general guidelines on using tensor parallelism for LLM part of the pipeline.

• BLIP2-T5
• BLIP2-OPT
• LLaVA and VILA
• Nougat
• Enabling tensor parallelism for multi-GPU

BLIP2-T5

1. Download Huggingface weights and convert original checkpoint to TRT-LLM checkpoint format following example in examples/enc_dec/README.md.

   export MODEL_NAME="flan-t5-xl" # also flan-t5-xxl
   git clone https://huggingface.co/google/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}
   
   python ../enc_dec/convert_checkpoint.py --model_type t5 \
       --model_dir tmp/hf_models/${MODEL_NAME} \
       --output_dir tmp/trt_models/${MODEL_NAME}/bfloat16 \
       --tp_size 1 \
       --pp_size 1 \
       --weight_data_type float32 \
       --dtype bfloat16 \
       --max_multimodal_len 256 # 8 (max_batch_size) * 32 (num_visual_features)

2. Build TRT-LLM engine from TRT-LLM checkpoint

   trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/bfloat16/tp1/pp1/encoder \
       --output_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/bfloat16/tp1/encoder \
       --paged_kv_cache disable \
       --moe_plugin disable \
       --enable_xqa disable \
       --use_custom_all_reduce disable \
       --gemm_plugin bfloat16 \
       --bert_attention_plugin bfloat16 \
       --gpt_attention_plugin bfloat16 \
       --remove_input_padding enable \
       --context_fmha disable \
       --max_beam_width 1 \
       --max_batch_size 8 \
       --max_output_len 100 \
       --max_input_len 924 \
       --max_multimodal_len 256 # 8 (max_batch_size) * 32 (num_visual_features)
   
   # Same command for decoder but don't set --max_multimodal_len
   trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/bfloat16/tp1/pp1/decoder \
       --output_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/bfloat16/tp1/decoder \
       --paged_kv_cache disable \
       --moe_plugin disable \
       --enable_xqa disable \
       --use_custom_all_reduce disable \
       --gemm_plugin bfloat16 \
       --bert_attention_plugin bfloat16 \
       --gpt_attention_plugin bfloat16 \
       --remove_input_padding enable \
       --context_fmha disable \
       --max_beam_width 1 \
       --max_batch_size 8 \
       --max_output_len 100 \
       --max_encoder_input_len 924 \
       --max_input_len 1

   NOTE: max_multimodal_len = max_batch_size * num_visual_features, so if you change max_batch_size, max multimodal length MUST be changed accordingly.

   The built T5 engines are located in ./tmp/trt_engines/${MODEL_NAME}/1-gpu/bfloat16/tp1.

3. Build TensorRT engines for visual components

   python build_visual_engine.py --model_type ${MODEL_NAME} --model_path tmp/hf_models/${MODEL_NAME} --max_batch_size 8

   The built engines are located in ./visual_engines/${MODEL_NAME}.

   To run the BLIP2 pipeline with batch size > 1, change --max_batch_size argument to build_visual_engine.py accordingly.

4. Assemble everything into BLIP2 pipeline

   python run.py \
       --max_new_tokens 30 \
       --input_text "Question: which city is this? Answer:" \
       --hf_model_dir tmp/hf_models/${MODEL_NAME} \
       --visual_engine_dir visual_engines/${MODEL_NAME} \
       --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/bfloat16/tp1

BLIP2-OPT

OPT pipeline needs few minor changes from T5 pipeline

1. Convert Huggingface weights to TRT-LLM checkpoint format following examples/opt/README.md.

2. Use trtllm-build command to build TRT-LLM engine for OPT.

3. The full list of commands is as follows:

   export MODEL_NAME="opt-2.7b" # also opt-6.7b
   git clone https://huggingface.co/facebook/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}
   
   python ../opt/convert_checkpoint.py \
       --model_dir tmp/hf_models/${MODEL_NAME} \
       --dtype float16 \
       --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu
   
   trtllm-build \
       --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
       --output_dir trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --gemm_plugin float16 \
       --max_beam_width 1 \
       --max_batch_size 8 \
       --max_multimodal_len 256 \
       --max_input_len 924 \
       --max_output_len 100
   
   python build_visual_engine.py --model_type ${MODEL_NAME} --model_path tmp/hf_models/${MODEL_NAME}
   
   python run.py \
       --max_new_tokens 30 \
       --input_text "Question: which city is this? Answer:" \
       --hf_model_dir tmp/hf_models/${MODEL_NAME} \
       --visual_engine_dir visual_engines/${MODEL_NAME} \
       --llm_engine_dir trt_engines/${MODEL_NAME}/fp16/1-gpu \

4. INT8/INT4 weight-only quantization for OPT can be enabled using commands as follows (take INT4 as an example, while INT8 is the default precision for weight-only quantization):

   python ../opt/convert_checkpoint.py \
       --model_dir tmp/hf_models/${MODEL_NAME} \
       --dtype float16 \
       --output_dir tmp/trt_models/${MODEL_NAME}/int4_weightonly/1-gpu \
       --use_weight_only \
       --weight_only_precision int4
   
   trtllm-build \
       --checkpoint_dir tmp/trt_models/${MODEL_NAME}/int4_weightonly/1-gpu \
       --output_dir trt_engines/${MODEL_NAME}/int4_weightonly/1-gpu \
       --gemm_plugin float16 \
       --max_beam_width 1 \
       --max_batch_size 8 \
       --max_multimodal_len 256 \
       --max_input_len 924 \
       --max_output_len 100

   The built OPT engines lie in trt_engines/${MODEL_NAME}/int4_weightonly/1-gpu. You should use this directory as --llm_engine_dir argument to run.py

   NOTE: INT8/INT4 option is not supported for BLIP2-T5, because quantization support has not been added for encoder-decoder models yet.

LLaVA and VILA

LLaVA and VILA are both visual language models (VLM) that can be deployed in TensorRT-LLM with many quantization options.

1. Download Huggingface model weights. These models have both visual and LLM components unlike BLIP2 example which downloads only LLM components from Huggingface.

   For LLaVA,

       export MODEL_NAME="llava-1.5-7b-hf" # also llava-1.5-13b-hf
       git clone https://huggingface.co/llava-hf/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}

   For VILA, we need a few more steps until it is added to HF model zoo

       # clone original VILA repo
       export VILA_PATH="tmp/hf_models/VILA"
       git clone https://github.com/Efficient-Large-Model/VILA.git ${VILA_PATH}
   
       # download VILA checkpoints
       export MODEL_NAME="vila-7B" # also vila-2.7B, vila-13B
       git clone https://huggingface.co/Efficient-Large-Model/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}
   
       # turn off delay_load to allow model component access
       sed -i 's/delay_load=True/delay_load=False/g' ${VILA_PATH}/llava/model/llava_arch.py
       # line manipulation to enable AWQ. otherwise need to replace HF's llama implementation
       sed -i '/vision_tower = self.get_vision_tower()/a \        attention_mask = torch.ones_like(input_ids, dtype=torch.bool)' ${VILA_PATH}/llava/model/llava_arch.py
       sed -i 's/seqlens_in_batch=sorted_seqlens_in_batch/#seqlens_in_batch=sorted_seqlens_in_batch/g' ${VILA_PATH}/llava/model/language_model/llava_llama.py

2. Generate TRT-LLM engine for LLaMA following example in examples/llama/README.md

   python ../llama/convert_checkpoint.py \
       --model_dir tmp/hf_models/${MODEL_NAME} \
       --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
       --dtype float16
   
   trtllm-build \
       --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
       --output_dir trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --gemm_plugin float16 \
       --use_fused_mlp \
       --max_batch_size 1 \
       --max_input_len 2048 \
       --max_output_len 512 \
       --max_multimodal_len 576 # 1 (max_batch_size) * 576 (num_visual_features)

   Note: do not use --use_fused_mlp flag in quantization mode.

3. Build TensorRT engines for visual components

   python build_visual_engine.py --model_path tmp/hf_models/${MODEL_NAME} --model_type llava # for LLaVA
   python build_visual_engine.py --model_path tmp/hf_models/${MODEL_NAME} --model_type vila --vila_path ${VILA_PATH} # for VILA

   python run.py \
       --max_new_tokens 30 \
       --hf_model_dir tmp/hf_models/${MODEL_NAME} \
       --visual_engine_dir visual_engines/${MODEL_NAME} \
       --llm_engine_dir trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --input_text "Question: which city is this? Answer:" # or "Please describe the traffic condition." for VILA

   Note: use --run_profiling for performance measurement, use --check_accuracy for accuracy check.

4. (Optional) INT8/INT4 weight-only quantization for LLaMA can be enabled as follows (take INT4 as an example, while INT8 is the default precision for weight-only quantization):

   python ../llama/convert_checkpoint.py \
       --model_dir tmp/hf_models/${MODEL_NAME} \
       --dtype float16 \
       --output_dir tmp/trt_models/${MODEL_NAME}/int4_weightonly/1-gpu \
       --use_weight_only \
       --weight_only_precision int4
   
   trtllm-build \
       --checkpoint_dir tmp/trt_models/${MODEL_NAME}/int4_weightonly/1-gpu \
       --output_dir trt_engines/${MODEL_NAME}/int4_weightonly/1-gpu \
       --gemm_plugin float16 \
       --max_batch_size 1 \
       --max_input_len 1024 \
       --max_output_len 100 \
       --max_multimodal_len 576

   The built engines lie in trt_engines/${MODEL_NAME}/int4_weightonly/1-gpu. You should use this directory as --llm_engine_dir argument to run.py

5. (Optional) One can also use LLaVA/VILA with other quantization options, like SmoothQuant and INT4 AWQ, that are supported by LLaMA. Instructions in LLaMA README to enable SmoothQuant and INT4 AWQ can be re-used to generate quantized TRT engines for LLM component of LLaVA/VILA.

   For example,

   python ../quantization/quantize.py \
        --model_dir tmp/hf_models/${MODEL_NAME} \
        --output_dir tmp/trt_models/${MODEL_NAME}/int4_awq/1-gpu \
        --dtype float16 \
        --qformat int4_awq \
        --calib_size 32
   
    trtllm-build \
        --checkpoint_dir tmp/trt_models/${MODEL_NAME}/int4_awq/1-gpu \
        --output_dir trt_engines/${MODEL_NAME}/int4_awq/1-gpu \
        --gemm_plugin float16 \
        --max_batch_size 1 \
        --max_input_len 1024 \
        --max_output_len 100 \
        --max_multimodal_len 576

Nougat

1. Download Huggingface weights

   export MODEL_NAME="nougat-base" # also nougat-small
   git clone https://huggingface.co/facebook/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}

2. Convert Huggingface weights into TRT-LLM checkpoints and build TRT engines using scripts in examples/enc_dec

   Nougat uses mBART architecture but replaces the LLM encoder with a Swin Transformer encoder. To achieve this, we add an extra --nougat flag (over mBART example) to convert_checkpoint.py in examples/enc_dec and trtllm-build.

   python ../enc_dec/convert_checkpoint.py --model_type bart \
       --model_dir tmp/hf_models/${MODEL_NAME} \
       --output_dir tmp/trt_models/${MODEL_NAME}/bfloat16 \
       --tp_size 1 \
       --pp_size 1 \
       --weight_data_type float32 \
       --dtype bfloat16 \
       --nougat
   
   trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/bfloat16/tp1/pp1/decoder \
       --output_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/bfloat16/tp1/decoder \
       --paged_kv_cache disable \
       --moe_plugin disable \
       --enable_xqa disable \
       --use_custom_all_reduce disable \
       --gemm_plugin bfloat16 \
       --bert_attention_plugin bfloat16 \
       --gpt_attention_plugin bfloat16 \
       --remove_input_padding enable \
       --max_beam_width 1 \
       --max_batch_size 1 \
       --max_output_len 100 \
       --max_input_len 1 \
       --max_encoder_input_len 588 # 1 (max_batch_size) * 588 (num_visual_features)

3. Generate TensorRT engines for visual components and combine everything into final pipeline.

   python build_visual_engine.py --model_type nougat --model_path tmp/hf_models/${MODEL_NAME}
   
   python run.py \
       --hf_model_dir tmp/hf_models/${MODEL_NAME} \
       --visual_engine_dir visual_engines/${MODEL_NAME} \
       --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/bfloat16/tp1 \

   Note: Nougat models usually do not need a text prompt.

Enabling tensor parallelism for multi-GPU

The LLM part of the pipeline can be run on multiple GPUs using tensor parallelism. The visual encoder will be replicated on each GPU and operate in a data parallel fashion.

To enable tensor parallelism, both weight conversion step (from Huggingface to FT format) and engine building step should use additional arguments. Finally run.py should be prefixed with mpirun -n NUM_GPUS --allow-run-as-root.

The full set of commands to enable 2-way tensor parallelism for LLaVA is:

```bash
export MODEL_NAME="llava-1.5-7b-hf"

python ../llama/convert_checkpoint.py \
    --model_dir tmp/hf_models/${MODEL_NAME} \
    --output_dir tmp/trt_models/${MODEL_NAME}/fp16/2-gpu \
    --dtype float16 --tp_size 2

trtllm-build \
    --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/2-gpu \
    --output_dir trt_engines/${MODEL_NAME}/fp16/2-gpu \
    --gemm_plugin float16 \
    --max_batch_size 1 \
    --max_input_len 2048 \
    --max_output_len 512 \
    --max_multimodal_len 576

python build_visual_engine.py --model_type llava --model_path tmp/hf_models/${MODEL_NAME}

mpirun -n 2 --allow-run-as-root \
    python run.py \
    --max_new_tokens 30 \
    --hf_model_dir tmp/hf_models/${MODEL_NAME} \
    --visual_engine_dir visual_engines/${MODEL_NAME} \
    --llm_engine_dir trt_engines/${MODEL_NAME}/fp16/2-gpu \
```