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Megatron Model Optimization and Deployment

Installation

We recommend that users follow TensorRT-LLM's official installation guide to build it from source and proceed with a containerized environment (docker.io/tensorrt_llm/release:latest):

git clone https://github.com/NVIDIA/TensorRT-LLM.git
cd TensorRT-LLM
git checkout v0.10.0
make -C docker release_build

TROUBLE SHOOTING: rather than copying each folder separately in docker/Dockerfile.multi, you may need to copy the entire dir as COPY ./ /src/tensorrt_llm since a git submodule is called later which requires .git to continue.

Once the container is built, install nvidia-modelopt and additional dependencies for sharded checkpoint support:

pip install "nvidia-modelopt[all]~=0.13.0" --extra-index-url https://pypi.nvidia.com
pip install zarr tensorstore==0.1.45

TensorRT-LLM quantization functionalities are currently packaged in nvidia-modelopt. You can find more documentation about nvidia-modelopt here.

Support Matrix

The following matrix shows the current support for the PTQ + TensorRT-LLM export flow.

model fp16 int8_sq fp8 int4_awq
nextllm-2b x x x
nemotron3-8b x x
nemotron3-15b x x
llama2-text-7b x x x TP2
llama2-chat-70b x x x TP4

Our PTQ + TensorRT-LLM flow has native support on MCore GPTModel with a mixed layer spec (native ParallelLinear and Transformer-Engine Norm (TENorm). Note that this is not the default mcore gpt spec. You can still load the following checkpoint formats with some remedy:

GPTModel sharded remedy arguments
megatron.legacy.model --export-legacy-megatron
TE-Fused (default mcore gpt spec) --export-te-mcore-model
TE-Fused (default mcore gpt spec) x

TROUBLE SHOOTING: If you are trying to load an unpacked .nemo sharded checkpoint, then typically you will need to adding additional_sharded_prefix="model." to modelopt_load_checkpoint() since NeMo has an additional model. wrapper on top of the GPTModel.

NOTE: flag --export-legacy-megatron may not work on all legacy checkpoint versions.

Examples

NOTE: we only provide a simple text generation script to test the generated TensorRT-LLM engines. For a production-level API server or enterprise support, see NeMo and TensorRT-LLM's backend for NVIDIA Triton Inference Server.

Minitron-8B FP8 Quantization and TensorRT-LLM Deployment

First download the nemotron checkpoint from https://huggingface.co/nvidia/Minitron-8B-Base, extract the sharded checkpoint from the .nemo tarbal and fix the tokenizer file name.

NOTE: The following cloning method uses ssh, and assume you have registered the ssh-key in Hugging Face. If you are want to clone with https, then git clone https://huggingface.co/nvidia/Minitron-8B-Base with an access token.

git lfs install
git clone git@hf.co:nvidia/Minitron-8B-Base
cd Minitron-8B-Base/nemo
tar -xvf minitron-8b-base.nemo
cd ../..

Now launch the PTQ + TensorRT-LLM export script,

bash examples/inference/quantization/ptq_trtllm_minitron_8b ./Minitron-8B-Base None

By default, cnn_dailymail is used for calibration. The GPTModel will have quantizers for simulating the quantization effect. The checkpoint will be saved optionally (with quantizers as additional states) and can be restored for further evaluation or quantization-aware training. TensorRT-LLM checkpoint and engine are exported to /tmp/trtllm_ckpt and built in /tmp/trtllm_engine by default.

The script expects ${CHECKPOINT_DIR} (./Minitron-8B-Base/nemo) to have the following structure:

NOTE: The .nemo checkpoint after extraction (including examples below) should all have the following strucure.

├── model_weights
│   ├── common.pt
│   ...
│
├── model_config.yaml
│...

NOTE: The script is using TP=8. Change $TP in the script if your checkpoint has a different tensor model parallelism.

Then build TensorRT engine and run text generation example using the newly built TensorRT engine

export trtllm_options=" \
    --checkpoint_dir /tmp/trtllm_ckpt \
    --output_dir /tmp/trtllm_engine \
    --max_input_len 2048 \
    --max_output_len 512 \
    --max_batch_size 8 "

trtllm-build ${trtllm_options}

python examples/inference/quantization/trtllm_text_generation.py --tokenizer nvidia/Minitron-8B-Base

mistral-12B FP8 Quantization and TensorRT-LLM Deployment

First download the nemotron checkpoint from https://huggingface.co/nvidia/Mistral-NeMo-12B-Base, extract the sharded checkpoint from the .nemo tarbal.

NOTE: The following cloning method uses ssh, and assume you have registered the ssh-key in Hugging Face. If you are want to clone with https, then git clone https://huggingface.co/nvidia/Mistral-NeMo-12B-Base with an access token.

git lfs install
git clone git@hf.co:nvidia/Mistral-NeMo-12B-Base
cd Mistral-NeMo-12B-Base
tar -xvf Mistral-NeMo-12B-Base.nemo
cd ..

Then log in to huggingface so that you can access to model

NOTE: You need a token generated from huggingface.co/settings/tokens and access to mistralai/Mistral-Nemo-Base-2407 on huggingface

pip install -U "huggingface_hub[cli]"
huggingface-cli login

Now launch the PTQ + TensorRT-LLM checkpoint export script,

bash examples/inference/quantization/ptq_trtllm_mistral_12b.sh ./Mistral-NeMo-12B-Base None

Then build TensorRT engine and run text generation example using the newly built TensorRT engine

export trtllm_options=" \
    --checkpoint_dir /tmp/trtllm_ckpt \
    --output_dir /tmp/trtllm_engine \
    --max_input_len 2048 \
    --max_output_len 512 \
    --max_batch_size 8 "

trtllm-build ${trtllm_options}

python examples/inference/quantization/trtllm_text_generation.py --tokenizer mistralai/Mistral-Nemo-Base-2407

llama2-text-7b INT8 SmoothQuant and TensorRT-LLM Deployment

NOTE: Due to the LICENSE issue, we do not provide a MCore checkpoint to download. Users can follow the instruction in docs/llama2.md to convert the checkpoint to megatron legacy GPTModel format and use --export-legacy-megatron flag which will remap the checkpoint to the MCore GPTModel spec that we support.

bash examples/inference/quantization/ptq_trtllm_llama_7b.sh ${CHECKPOINT_DIR}

The script expect ${CHECKPOINT_DIR} to have the following structure:

├── hf
│   ├── tokenizer.config
│   ├── tokenizer.model
│   ...
│
├── iter_0000001
│   ├── mp_rank_00
│   ...
│
├── latest_checkpointed_iteration.txt

In short, other than the converted llama megatron checkpoint, also put the Hugging Face checkpoint inside as the source of the tokenizer.

llama3-8b / llama3.1-8b INT8 SmoothQuant and TensorRT-LLM Deployment

NOTE: For llama3.1, the missing rope_scaling parameter will be fixed in modelopt-0.17 and trtllm-0.12.

NOTE: There are two ways to acquire the checkpoint. Users can follow the instruction in docs/llama2.md to convert the checkpoint to megatron legacy GPTModel format and use --export-legacy-megatron flag which will remap the checkpoint to the MCore GPTModel spec that we support. Or Users can download nemo model from NGC and extract the sharded checkpoint from the .nemo tarbal.

If users choose to download the model from NGC, first extract the sharded checkpoint from the .nemo tarbal.

tar -xvf 8b_pre_trained_bf16.nemo

Now launch the PTQ + TensorRT-LLM checkpoint export script for llama-3,

bash examples/inference/quantization/ptq_trtllm_llama3_8b.sh ./llama-3-8b-nemo_v1.0 None

or llama-3.1

bash examples/inference/quantization/ptq_trtllm_llama3_1_8b.sh ./llama-3_1-8b-nemo_v1.0 None

Then build TensorRT engine and run text generation example using the newly built TensorRT engine

export trtllm_options=" \
    --checkpoint_dir /tmp/trtllm_ckpt \
    --output_dir /tmp/trtllm_engine \
    --max_input_len 2048 \
    --max_output_len 512 \
    --max_batch_size 8 "

trtllm-build ${trtllm_options}

python examples/inference/quantization/trtllm_text_generation.py --tokenizer meta-llama/Meta-Llama-3-8B
# For llama-3

python examples/inference/quantization/trtllm_text_generation.py --tokenizer meta-llama/Meta-Llama-3.1-8B
#For llama-3.1