GLM-130B/docs/inference-with-fastertransformer.md

Inference with FasterTransformer

FasterTransformer provides a script and recipe to run the highly optimized transformer-based encoder and decoder component, and it is tested and maintained by NVIDIA.

We adapted the GLM-130B based on Fastertransformer for fast inference, with details in benchmark section.

Setup

Requirements

• CMake >= 3.13 for PyTorch
• CUDA 11.0 or newer version
• NCCL 2.10 or newer version
• Python 3 is recommended because some features are not supported in python 2
• PyTorch: Verify on 1.10.1, >= 1.8.0 should work.

Setup Using Docker

We recommend use nvcr image like nvcr.io/nvidia/pytorch:21.09-py3 with NVIDIA Container Toolkit.

docker run -it --rm --gpus all nvcr.io/nvidia/pytorch:21.09-py3 /bin/bash
conda install -y pybind11

Setup Using Conda

As another way, all the packages can be installed using conda.

Some of our current structure requires that g++ and libtorch produce the same results, so a pre-compiled libtorch may only work with g++-7 or g++-9. And although GLM-130B itself does not rely on openmpi, FasterTransformer requires it during the build process. We are working on these issues.

conda install -y cmake pybind11
conda install -y -c conda-forge cudatoolkit-dev cudnn
cp -r $CONDA_PREFIX/lib/libcudnn* /usr/local/cuda/lib64/
cp -r $CONDA_PREFIX/include/cudnn*.h /usr/local/cuda/include/

If it's hard to install cudatoolkit-dev and cudnn by conda, just install them from NVIDIA Developer, and make sure cmake is able to find cudnn.

cp cudnn/include/cudnn*.h /usr/local/cuda/include
cp cudnn/lib/libcudnn* /usr/local/cuda/lib64
chmod a+r /usr/local/cuda/include/cudnn*.h 
chmod a+r /usr/local/cuda/lib64/libcudnn*

GLM-130B is trained with FP16 precision, a total of 260G of GPU memory is required to store model weights. The model is tested with 8 * 40G A100s.

Build

Get the code and install all dependencies:

git clone https://github.com/THUDM/FasterTransformer.git
mkdir -p FasterTransformer/build
cd FasterTransformer/build
pip3 install icetk transformers

Note: the xx of -DSM=xx in following scripts means the compute capability of your GPU. For example, 60 (P40) or 61 (P4) or 70 (V100) or 75(T4) or 80 (A100) or 86(RTX 3090). Default setting is including 70, 75, 80 and 86.

cmake -DSM=80 -DCMAKE_BUILD_TYPE=Release -DBUILD_PYT=ON -DBUILD_MULTI_GPU=ON ..
make -j

Download the Model

See Get Model.

Run GLM-130B

Generate the gemm_config.in file.

# ./bin/gpt_gemm <batch_size> <beam_width> <max_input_len> <head_number> <size_per_head> <inter_size> <vocab_size> <data_type> <tensor_para_size>
./bin/gpt_gemm 1 1 128 96 128 49152 150528 1 8

Running GLM_130B in Pytorch.

bash ../examples/pytorch/glm/benchmark-generation.sh

You need to check and edit this file to set arguments such as CHECKPOINT_PATH.

Optimization methods

Optimization in GLM_130B are similar to optimization in GPT and GPT-J, describing in the FasterTransformer/gpt_guide.md. Meanwhile, some of the operators are differ from GPT, such as the implementation of RotaryEmbedding, and the use of GeGLU, so we add them additionally into FasterTransformer.

Benchmark

• Hardware: DGX-A100(8 * 40G)

Encode

Sequence Len	512	1024	2048
Megatron	145 ms	250 ms	453 ms
FasterTransformer	120 ms	220 ms	OOM

Decode

Sequence Len	512	1024	2048
Megatron	45.21 s	89.00 s	179.22 s
FasterTransformer	18.77 s	39.81 s	89.88 s