TensorRT-LLMs/examples/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 the LLM part of the pipeline.

• BLIP2
• CogVLM
• Deplot
• Fuyu
• Kosmos-2
• LLaVA, LLaVa-NeXT and VILA
• NeVA
• Nougat
• Phi-3-vision
• Video NeVA
• Enabling tensor parallelism for multi-GPU

BLIP2

This BLIP section covers both BLIP2-OPT and BLIP2-T5, with minor changes needed when switching the LLM backbone.

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

   export MODEL_NAME="blip2-opt-2.7b" # options: blip2-opt-6.7b, blip2-flan-t5-xl, blip2-flan-t5-xxl
   git clone https://huggingface.co/Salesforce/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}
   

   For BLIP2-OPT family,

   python ../opt/convert_checkpoint.py --model_type blip2 \
       --model_dir tmp/hf_models/${MODEL_NAME} \
       --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
       --dtype float16
   

   For BLIP2-T5 family,

   python ../enc_dec/convert_checkpoint.py --model_type blip2 \
       --model_dir tmp/hf_models/${MODEL_NAME} \
       --output_dir tmp/trt_models/${MODEL_NAME}/bfloat16 \
       --tp_size 1 \
       --pp_size 1 \
       --dtype bfloat16
   

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

   For BLIP2-OPT family,

   trtllm-build \
       --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
       --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --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_visual_features)
   

   For BLIP2-T5 family,

   trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/bfloat16/encoder \
       --output_dir tmp/trt_engines/${MODEL_NAME}/bfloat16/encoder \
       --paged_kv_cache disable \
       --moe_plugin disable \
       --enable_xqa 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_input_len 924 \
       --max_multimodal_len 256 # 8 (max_batch_size) * 32 (num_visual_features)
   
   trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/bfloat16/decoder \
       --output_dir tmp/trt_engines/${MODEL_NAME}/bfloat16/decoder \
       --paged_kv_cache disable \
       --moe_plugin disable \
       --enable_xqa 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_seq_len 1024 \
       --max_encoder_input_len 924 \
       --max_input_len 1 # Same command for decoder but don't set --max_multimodal_len
   

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

3. Build TensorRT engines for vision encoders

   python build_visual_engine.py --model_type blip2 --model_path tmp/hf_models/${MODEL_NAME} --max_batch_size 8
   

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

   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

   For BLIP2-OPT family,

   python run.py \
       --max_new_tokens 30 \
       --input_text "Question: which city is this? Answer:" \
       --hf_model_dir tmp/hf_models/${MODEL_NAME} \
       --visual_engine_dir tmp/trt_engines/${MODEL_NAME}/vision_encoder \
       --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu
   

   For BLIP2-T5 family,

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

5. (Optional) 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/${MODEL_NAME}/int4_weightonly/1-gpu \
       --output_dir tmp/trt_engines/${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_seq_len 1024
   

   The built OPT engines lie in tmp/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.

CogVLM

Currently, CogVLM only support bfloat16 precision.

1. Download Huggingface weights

   export MODEL_NAME="cogvlm-chat-hf"
   git clone https://huggingface.co/THUDM/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}
   export TOKENIZER_NAME="vicuna-7b-v1.5"
   git clone https://huggingface.co/lmsys/${TOKENIZER_NAME} tmp/hf_models/${TOKENIZER_NAME}
   

   Because currently onnx doesn't support xops.memory_efficient_attention, we need to modify some source code of the huggingface CogVLM.

   cd tmp/hf_models/${MODEL_NAME}
   sed -i '4s/.*//;40s/.*/        out = self.attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)).transpose(1, 2).contiguous()/;41s/.*//;42s/.*//' visual.py   # It will replace memory_efficient_attention with some basic ops
   

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

   CogVLM uses a Vit encoder as LLM encoder and a modified Llama as decoder.

   python ../cogvlm/convert_checkpoint.py --model_dir tmp/hf_models/${MODEL_NAME}  --output_dir tmp/trt_models/${MODEL_NAME} --dtype bfloat16 --use_prompt_tuning
   
   trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME} \
   --output_dir tmp/trt_engines/${MODEL_NAME}/bf16/1-gpu \
   --gemm_plugin bfloat16 \
   --gpt_attention_plugin bfloat16 \
   --remove_input_padding enable \
   --max_batch_size 48 \
   --max_input_len 2048 \
   --max_seq_len 3076 \
   --paged_kv_cache enable \
   --enable_xqa disable \
   --bert_attention_plugin disable \
   --moe_plugin disable \
   --max_multimodal_len 61440 # 48 (max_batch_size) * 1280 (max_num_visual_features)
   

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

   python build_visual_engine.py --model_type cogvlm --model_path tmp/hf_models/${MODEL_NAME} --max_batch_size 48
   
   python run.py \
   --max_new_tokens 1000 \
   --input_text " [INST] please describe this image in detail [/INST] " \
   --hf_model_dir tmp/hf_models/${TOKENIZER_NAME} \
   --visual_engine_dir tmp/trt_engines/${MODEL_NAME}/vision_encoder \
   --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/bf16/1-gpu \
   --batch_size 1 \
   --top_p 0.4 \
   --top_k 1 \
   --temperature 0.2 \
   --repetition_penalty 1.2 \
   --enable_context_fmha_fp32_acc
   
   CogVLM uses model_runner_cpp by default. To switch to model_runner, set `--use_py_session` in the command mentioned above.
   

Deplot

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

   export MODEL_NAME="deplot"
   git clone https://huggingface.co/google/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}
   
   python ../enc_dec/convert_checkpoint.py --model_type pix2struct \
       --model_dir tmp/hf_models/${MODEL_NAME} \
       --output_dir tmp/trt_models/${MODEL_NAME}/float16 \
       --tp_size 1 \
       --pp_size 1 \
       --dtype float16
   

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

   trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/float16/decoder \
       --output_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/float16/decoder \
       --paged_kv_cache disable \
       --moe_plugin disable \
       --enable_xqa disable \
       --gemm_plugin float16 \
       --bert_attention_plugin float16 \
       --gpt_attention_plugin float16 \
       --remove_input_padding enable \
       --context_fmha disable \
       --max_beam_width 1 \
       --max_batch_size 8 \
       --max_seq_len 2558 \
       --max_encoder_input_len 2048 \
       --max_input_len 1
   

   The built deplot engines are located in tmp/trt_engines/${MODEL_NAME}/1-gpu/float16.

3. Build TensorRT engines for visual components

   python build_visual_engine.py --model_type pix2struct --model_path tmp/hf_models/${MODEL_NAME} --max_batch_size 8
   

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

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

4. Assemble everything into deplot pipeline

   python run.py \
       --max_new_tokens 100 \
       --input_text "" \
       --hf_model_dir tmp/hf_models/${MODEL_NAME} \
       --visual_engine_dir tmp/trt_engines/${MODEL_NAME}/vision_encoder \
       --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/float16
   

Fuyu

1. Download Huggingface weights

   export MODEL_NAME="fuyu-8b"
   git clone https://huggingface.co/adept/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}
   

2. Convert Huggingface weights into TRT-LLM checkpoints and build TRT engines using scripts in examples/gpt. The LLM portion of Fuyu uses a Persimmon model

   python ../gpt/convert_checkpoint.py \
       --model_dir tmp/hf_models/${MODEL_NAME} \
       --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
       --dtype float16 \
       --gpt_variant persimmon
   
   trtllm-build \
       --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
       --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --gemm_plugin float16 \
       --use_fused_mlp=enable \
       --max_batch_size 1 \
       --max_input_len 2048 \
       --max_seq_len 2560 \
       --max_multimodal_len 2048
   

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

   python build_visual_engine.py --model_type fuyu --model_path tmp/hf_models/${MODEL_NAME}
   
   python run.py \
       --hf_model_dir tmp/hf_models/${MODEL_NAME} \
       --visual_engine_dir tmp/trt_engines/${MODEL_NAME}/vision_encoder \
       --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu
   

Kosmos-2

1. Download Huggingface weights

   export MODEL_NAME="kosmos-2"
   git clone https://huggingface.co/microsoft/kosmos-2-patch14-224 tmp/hf_models/${MODEL_NAME}
   

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

   python ../gpt/convert_checkpoint.py \
       --model_dir tmp/hf_models/${MODEL_NAME} \
       --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
       --dtype float16 \
       --gpt_variant ${MODEL_NAME}
   
   trtllm-build \
       --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
       --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --gpt_attention_plugin float16 \
       --gemm_plugin float16 \
       --max_batch_size 1 \
       --max_input_len 512 \
       --max_seq_len 1024 \
       --max_multimodal_len 64 # 1 (max_batch_size) * 64 (num_visual_features)
   

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

   python build_visual_engine.py --model_type kosmos-2 --model_path tmp/hf_models/${MODEL_NAME}
   
   python run.py \
       --hf_model_dir tmp/hf_models/${MODEL_NAME} \
       --visual_engine_dir tmp/trt_engines/${MODEL_NAME}/vision_encoder \
       --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu
   

LLaVA, LLaVa-NeXT and VILA

LLaVA and VILA are both visual language models (VLM) that can be deployed in TensorRT-LLM with many quantization options. LLaVA-NeXT is an extension of LLaVA. TRT-LLM currently supports Mistral-7b and Nous-Hermes-2-Yi-34B variant of LLaVA-NeXT.

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 LLaVA-NeXT,

      export MODEL_NAME="llava-v1.6-mistral-7b-hf" #for 34b variant "llava-v1.6-34b-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

       # install the following dependency
       pip install -r requirements-vila.txt
   
       # 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="vila1.5-3b"
       git clone https://huggingface.co/Efficient-Large-Model/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}
   

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
   
   # for LLaVA
   trtllm-build \
       --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
       --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --gemm_plugin float16 \
       --use_fused_mlp=enable \
       --max_batch_size 1 \
       --max_input_len 2048 \
       --max_seq_len 2560 \
       --max_multimodal_len 576 # 1 (max_batch_size) * 576 (num_visual_features)
   
   # for LLaVA-NeXT
   trtllm-build \
       --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
       --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --gpt_attention_plugin float16 \
       --gemm_plugin float16 \
       --use_fused_mlp=enable \
       --max_batch_size 1 \
       --max_input_len 4096 \
       --max_seq_len 5120 \
       --max_num_tokens 4096 \  # 1 (max_batch_size) * 4096 (max_input_len)
       --max_multimodal_len 4096 # 1 (max_batch_size) * 4096 (max_input_len)
   
   # for VILA
   trtllm-build \
       --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
       --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --gemm_plugin float16 \
       --use_fused_mlp=enable \
       --max_batch_size 1 \
       --max_input_len 2048 \
       --max_seq_len 2560 \
       --max_multimodal_len 4096 # 1 (max_batch_size) * 4096 (num_visual_features)
   

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/${MODEL_NAME} --model_type llava_next --model_path tmp/hf_models/${MODEL_NAME} --max_batch_size 5 # 1 (max_batch_size) * 5 (because LLAVA-NeXT visual encoder can have at most 5 patches)  # for LLaVA-NeXT
   
   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/${MODEL_NAME} \
       --visual_engine_dir tmp/trt_engines/${MODEL_NAME}/vision_encoder \
       --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --input_text "Question: which city is this? Answer:" # for LLaVA and for LLaVA-NeXT
   

   For VILA, you can use either local file or web url as input images. Suppose you have a local image av.png downloaded from https://github.com/Efficient-Large-Model/VILA/blob/main/demo_trt_llm/av.png and the url of merlion.png

   wget -O av.png https://raw.githubusercontent.com/Efficient-Large-Model/VILA/main/demo_images/av.png
   
   python run.py  \
       --max_new_tokens 100 \
       --hf_model_dir tmp/hf_models/${MODEL_NAME} \
       --visual_engine_dir tmp/trt_engines/${MODEL_NAME}/vision_encoder \
       --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --image_path=av.png,https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png \
       --input_text="<image>\n<image>\n Please elaborate what you see in the images?" \
       --batch_size=1 # for VILA mode 1
   
   python run.py  \
       --max_new_tokens 100 \
       --hf_model_dir tmp/hf_models/${MODEL_NAME} \
       --visual_engine_dir tmp/trt_engines/${MODEL_NAME}/vision_encoder \
       --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --image_path=av.png,https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png \
       --input_text="<image>\n Please elaborate what you see in the images?" \
       --batch_size=2 # for VILA mode 2
   

   Note that VILA can support different modes in terms of batching:

   • Mode 1: if you want to query N images as a whole using a prompt, --batch_size=1 should be used (which is the default value). Example is given above.
   • Mode 2: if you want to query N images individually using the same prompt (replicated), --batch_size=N should be used. Don't forget to set the --max_batch_size and --max_multimodal_len during engine building.

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

4. (Optional) Different quantization methods supported in LLaMA can be applied to LLaVA/VILA as well, such as INT4/INT8 weight-only, SmoothQuant, and INT4 Activation-Aware Quantization (AWQ). Detailed instructions can be found in LLaMA README.

   For example,

   # INT4 weight only
   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
   
   # INT4 AWQ
   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
   

   Then follow the same trtllm-build and run.py steps as before. NOTE: for trtllm-build command, do not use --use_fused_mlp=enable in these quantization modes.

NeVA

NeVA is a groundbreaking addition to the NeMo Multimodal ecosystem. This model seamlessly integrates large language-centric models with a vision encoder, that can be deployed in TensorRT-LLM.

1. Generate TRT-LLM engine for NVGPT following example in examples/gpt/README.md. To adhere to the NVGPT conventions of the conversion script, some layer keys have to be remapped using --nemo_rename_key.

   export MODEL_NAME="neva"
   python ../gpt/convert_checkpoint.py \
   --nemo_ckpt_path ./${MODEL_NAME}.nemo \
   --dtype bfloat16 \
   --output_dir tmp/trt_models/${MODEL_NAME} \
   --nemo_rename_key model:model.language_model \
       attention.linear_qkv.layer_norm_bias:input_layernorm.bias \
       attention.linear_qkv.layer_norm_weight:input_layernorm.weight \
       mlp.linear_fc1.layer_norm_bias:post_attention_layernorm.bias \
       mlp.linear_fc1.layer_norm_weight:post_attention_layernorm.weight \
       linear_qkv:query_key_value \
       linear_fc1:dense_h_to_4h \
       linear_fc2:dense_4h_to_h \
       linear_proj:dense \
       decoder:encoder
   
   trtllm-build \
       --checkpoint_dir tmp/trt_models/${MODEL_NAME} \
       --output_dir tmp/trt_engines/${MODEL_NAME}/bf16/1-gpu \
       --gpt_attention_plugin bfloat16 \
       --gemm_plugin bfloat16 \
       --max_batch_size 1 \
       --max_input_len 2048 \
       --max_seq_len 2560 \
       --max_multimodal_len 729 # 1 (max_batch_size) * 729 (num_visual_features)
   

2. Build TensorRT engines for visual components

   python build_visual_engine.py --model_path ./${MODEL_NAME}.nemo --model_type neva
   

   python run.py \
       --max_new_tokens 30 \
       --hf_model_dir tmp/trt_models/${MODEL_NAME} \
       --visual_engine_dir tmp/trt_engines/${MODEL_NAME}/vision_encoder \
       --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/bf16/1-gpu \
       --input_text "Question: which city is this? Answer:"
   

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

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 \
       --dtype bfloat16 \
       --nougat
   
   trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/bfloat16/decoder \
       --output_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/bfloat16/decoder \
       --paged_kv_cache disable \
       --moe_plugin disable \
       --enable_xqa 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_seq_len 101 \
       --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/${MODEL_NAME} \
       --visual_engine_dir tmp/trt_engines/${MODEL_NAME}/vision_encoder \
       --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/bfloat16
   

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

Phi-3-vision

1. Download Huggingface weights

   export MODEL_NAME="Phi-3-vision-128k-instruct"
   git clone https://huggingface.co/microsoft/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}
   

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

   python ../phi/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/${MODEL_NAME}/fp16/1-gpu \
       --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \
       --gpt_attention_plugin float16 \
       --gemm_plugin float16 \
       --max_batch_size 1 \
       --max_input_len 4096 \
       --max_seq_len 4608 \
       --max_multimodal_len 4096
   

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

   python build_visual_engine.py --model_type phi-3-vision --model_path tmp/hf_models/${MODEL_NAME}
   
   python run.py \
       --hf_model_dir tmp/hf_models/${MODEL_NAME} \
       --visual_engine_dir tmp/trt_engines/${MODEL_NAME}/vision_encoder \
       --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/ \
       --image_path=https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png
   

Video NeVA

Video NeVA is a groundbreaking addition to the NeMo Multimodal ecosystem that could work with video modality. This model seamlessly integrates large language-centric models with a vision encoder, that can be deployed in TensorRT-LLM.

1. Generate TRT-LLM engine for Nemotron model following example in examples/nemotron/README.md. To adhere to the NVGPT conventions of the conversion script. This will be used as our base LM for inference.

   pip install decord # used for loading video
   
   python3 ../quantization/quantize.py \
       --nemo_ckpt_path /path/to/nemotron/model.nemo \
       --dtype bfloat16 \
       --batch_size 64 \
       --qformat full_prec \
       --output_dir nemotron-3/trt_ckpt/bf16/1-gpu
   
   
   trtllm-build \
       --checkpoint_dir nemotron-3/trt_ckpt/bf16/1-gpu \
       --output_dir tmp/trt_engines/nemotron-3/bf16/1-gpu \
       --gpt_attention_plugin bfloat16 \
       --gemm_plugin bfloat16 \
       --max_batch_size 1 \
       --max_input_len 4096 \
       --max_seq_len 4352 \
       --max_multimodal_len 3072 # 1 (max_batch_size) * (12 num_frames) * (256 image_token_len)
   

2. Build TensorRT engines for visual components

   python build_visual_engine.py --model_path /path/to/video/neva/projector.nemo --model_type video-neva --output_dir tmp/trt_engines/nemotron-3/visual_encoder
   

   python run.py \
       --max_new_tokens 30 \
       --hf_model_dir nemotron-3/trt_ckpt/bf16/1-gpu \
       --visual_engine_dir tmp/trt_engines/nemotron-3/visual_encoder \
       --llm_engine_dir tmp/trt_engines/nemotron-3/bf16/1-gpu \
       --input_text "Question: what is in the video? Answer:" \
       --video_path /path/to/your/local/video/file
   

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

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/${MODEL_NAME}/fp16/2-gpu \
    --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/2-gpu \
    --gemm_plugin float16 \
    --max_batch_size 1 \
    --max_input_len 2048 \
    --max_seq_len 2560 \
    --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/${MODEL_NAME} \
    --visual_engine_dir tmp/trt_engines/${MODEL_NAME}/vision_encoder \
    --llm_engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/2-gpu \
```