TensorRT-LLMs/examples/models/contrib/hyperclovax

HyperClovaX

This document shows how to build and run a HyperClovaX model in TensorRT-LLM.

The TensorRT-LLM HyperClovaX implementation is based on the LLaMA model. The implementation can be found in llama/model.py. See the LLaMA example examples/models/core/llama for details.

• HyperClovaX
  • Support Matrix
  • Supported Models
    • HyperClovaX-SEED
  • PyTorch flow
  • TRT flow
    • Convert checkpoint and build TensorRT engine(s)
    • FP8 Post-Training Quantization
    • SmoothQuant
    • Groupwise quantization (AWQ)
      • W4A16 AWQ with FP8 GEMM (W4A8 AWQ)
    • Run Engine

Support Matrix

• FP16
• BF16
• Tensor Parallel
• FP8
• INT8 & INT4 Weight-Only
• INT8 SmoothQuant
• INT4 AWQ & W4A8 AWQ

Supported Models

HyperClovaX-SEED-Text

Download the HuggingFace checkpoints of HyperClovaX-SEED-Text model. We support HyperClovaX-SEED-Text families but here, we only use the HyperCLOVAX-SEED-Text-Instruct-0.5B model for the example.

export MODEL_NAME=HyperCLOVAX-SEED-Text-Instruct-0.5B
git clone https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B hf_models/$MODEL_NAME

Quick Start with PyTorch flow

To quickly run HyperCLOVAX-SEED, you can use examples/pytorch/quickstart_advanced.py:

python ../../../pytorch/quickstart_advanced.py --model_dir hf_models/$MODEL_NAME

The output will be like:

[0] Prompt: 'Hello, my name is', Generated text: ' [name] and I am a [position] at [company name]. I am interested in learning more about the [industry] and would like to discuss this further with you. I would appreciate it if you could provide me with a list of questions to ask you. Here are some questions that I would like to ask'
[1] Prompt: 'The president of the United States is', Generated text: ' the head of the executive branch, which is responsible for the day-to-day administration of the country. The president is the head of the executive branch, which is responsible for the day-to-day administration of the country. The president is the head of the executive branch, which is responsible for the day-to-day administration of the'
[2] Prompt: 'The capital of France is', Generated text: ' Paris, which is the largest city in the country. It is home to the Eiffel Tower, the Louvre Museum, and Notre-Dame Cathedral. Paris is also known for its rich history, cultural heritage, and culinary delights. The city is a hub for art, fashion, and entertainment, and is home'
[3] Prompt: 'The future of AI is', Generated text: " not just about technology, but about how we use it to improve our lives. It's about creating a world where technology and humanity work together to solve complex problems, make decisions, and enhance our quality of life. As we continue to develop and integrate AI into our daily lives, it's essential to consider the ethical implications"

TRT flow

The next section describe how to convert the weights from the HuggingFace (HF) Transformers format to the TensorRT-LLM format. We will use llama's convert_checkpoint.py for HyperClovaX model and then we build the model with trtllm-build.

Convert checkpoint and build TensorRT engine(s)

# Build a single-GPU float16 engine from HF weights.

# Build the HyperClovaX model using a single GPU and FP16.
python ../../core/llama/convert_checkpoint.py \
    --model_dir hf_models/$MODEL_NAME \
    --output_dir trt_models/$MODEL_NAME/fp16/1-gpu \
    --dtype float16

trtllm-build \
    --checkpoint_dir trt_models/$MODEL_NAME/fp16/1-gpu \
    --output_dir trt_engines/$MODEL_NAME/fp16/1-gpu \
    --gemm_plugin auto

# Build the HyperClovaX model using a single GPU and and apply INT8 weight-only quantization.
python ../../core/llama/convert_checkpoint.py \
    --model_dir hf_models/$MODEL_NAME \
    --output_dir trt_models/$MODEL_NAME/int8_wq/1-gpu \
    --use_weight_only \
    --weight_only_precision int8 \
    --dtype float16

trtllm-build \
    --checkpoint_dir trt_models/$MODEL_NAME/int8_wq/1-gpu \
    --output_dir trt_engines/$MODEL_NAME/int8_wq/1-gpu \
    --gemm_plugin auto

# Build the HyperClovaX model using a single GPU and and apply INT4 weight-only quantization.
python ../../core/llama/convert_checkpoint.py \
    --model_dir hf_models/$MODEL_NAME \
    --output_dir trt_models/$MODEL_NAME/int4_wq/1-gpu \
    --use_weight_only \
    --weight_only_precision int4 \
    --dtype float16

trtllm-build \
    --checkpoint_dir trt_models/$MODEL_NAME/int4_wq/1-gpu \
    --output_dir trt_engines/$MODEL_NAME/int4_wq/1-gpu \
    --gemm_plugin auto

# Build the HyperClovaX model using 2-way tensor parallelism and FP16.
python ../../core/llama/convert_checkpoint.py \
    --model_dir hf_models/$MODEL_NAME \
    --output_dir trt_models/$MODEL_NAME/fp16/2-gpu \
    --tp_size 2 \
    --dtype float16

trtllm-build \
    --checkpoint_dir trt_models/$MODEL_NAME/fp16/2-gpu \
    --output_dir trt_engines/$MODEL_NAME/fp16/2-gpu \
    --gemm_plugin auto

FP8 Post-Training Quantization

The examples below uses the NVIDIA Modelopt (AlgorithMic Model Optimization) toolkit for the model quantization process.

First make sure Modelopt toolkit is installed (see examples/quantization/README.md)

# Build the HyperClovaX model using a single GPU and and apply FP8 quantization.
python ../../../quantization/quantize.py \
    --model_dir hf_models/$MODEL_NAME \
    --dtype float16 \
    --qformat fp8 \
    --kv_cache_dtype fp8 \
    --output_dir trt_models/$MODEL_NAME/fp8/1-gpu

trtllm-build \
    --checkpoint_dir trt_models/$MODEL_NAME/fp8/1-gpu \
    --output_dir trt_engines/$MODEL_NAME/fp8/1-gpu \
    --gemm_plugin auto

SmoothQuant

The examples below uses the NVIDIA Modelopt (AlgorithMic Model Optimization) toolkit for the model quantization process.

First make sure Modelopt toolkit is installed (see examples/quantization/README.md)

# Build the HyperClovaX model using a single GPU and and apply INT8 SmoothQuant.
python ../../../quantization/quantize.py \
    --model_dir hf_models/$MODEL_NAME \
    --dtype float16 \
    --qformat int8_sq \
    --output_dir trt_models/$MODEL_NAME/int8_sq/1-gpu

trtllm-build \
    --checkpoint_dir trt_models/$MODEL_NAME/int8_sq/1-gpu \
    --output_dir trt_engines/$MODEL_NAME/int8_sq/1-gpu \
    --gemm_plugin auto

Groupwise quantization (AWQ)

The examples below uses the NVIDIA Modelopt (AlgorithMic Model Optimization) toolkit for the model quantization process.

First make sure Modelopt toolkit is installed (see examples/quantization/README.md)

# Build the HyperClovaX model using a single GPU and and apply INT4 AWQ.
python ../../../quantization/quantize.py \
    --model_dir hf_models/$MODEL_NAME \
    --dtype float16 \
    --qformat int4_awq \
    --output_dir trt_models/$MODEL_NAME/int4_awq/1-gpu

trtllm-build \
    --checkpoint_dir trt_models/$MODEL_NAME/int4_awq/1-gpu \
    --output_dir trt_engines/$MODEL_NAME/int4_awq/1-gpu \
    --gemm_plugin auto

W4A16 AWQ with FP8 GEMM (W4A8 AWQ)

For Hopper GPUs, TRT-LLM also supports employing FP8 GEMM for accelerating linear layers. This mode is noted with w4a8_awq for Modelopt and TRT-LLM, in which both weights and activations are converted from W4A16 to FP8 for GEMM calculation.

Please make sure your system contains a Hopper GPU before trying the commands below.

# Build the HyperClovaX model using a single GPU and and apply W4A8 AWQ.
python ../../../quantization/quantize.py \
    --model_dir hf_models/$MODEL_NAME \
    --dtype float16 \
    --qformat w4a8_awq \
    --output_dir trt_models/$MODEL_NAME/w4a8_awq/1-gpu

trtllm-build \
    --checkpoint_dir trt_models/$MODEL_NAME/w4a8_awq/1-gpu \
    --output_dir trt_engines/$MODEL_NAME/w4a8_awq/1-gpu \
    --gemm_plugin auto

Run Engine

Test your engine with the run.py script:

python3 ../../../run.py \
    --input_text "When did the first world war end?" \
    --max_output_len=100 \
    --tokenizer_dir hf_models/$MODEL_NAME \
    --engine_dir trt_engines/$MODEL_NAME/fp16/1-gpu

# Run with 2 GPUs
mpirun -n 2 --allow-run-as-root \
    python3 ../../../run.py \
    --input_text "When did the first world war end?" \
    --max_output_len=100 \
    --tokenizer_dir hf_models/$MODEL_NAME \
    --engine_dir trt_engines/$MODEL_NAME/fp16/2-gpu

python ../../../summarize.py \
    --test_trt_llm \
    --data_type fp16 \
    --hf_model_dir hf_models/$MODEL_NAME \
    --engine_dir trt_engines/$MODEL_NAME/fp16/1-gpu

For more examples see examples/models/core/llama/README.md