Skip to content

Latest commit

 

History

History
109 lines (79 loc) · 4.98 KB

bart_guide.md

File metadata and controls

109 lines (79 loc) · 4.98 KB
Raw

FasterTransformer BART

The FasterTransformer BART implements the huggingface BART model (https://huggingface.co/docs/transformers/model_doc/bart).

Table Of Contents

Introduction

This document describes what FasterTransformer provides for the BART model, explaining the workflow and optimization. We also provide a guide to help users to run the BART model on FasterTransformer. Finally, we provide benchmark to demonstrate the speed of FasterTransformer on BART.

Supported features

  • Checkpoint loading
    • Huggingface
  • Data type
    • FP32
    • FP16
    • BF16
  • Feature
    • Multi-GPU multi-node inference
    • Dynamic random seed
    • Stop tokens
    • Beam search and sampling are both supported
  • Frameworks
    • PyTorch

Optimization

  1. Kernel optimization: First, since the sequence length of query in SelfAttention and CrossAttention is always 1, we use customed fused multi-head attention kernel to optimize. Second, we fuse many small operations into one kernel. For example, AddBiasResidualLayerNorm combines the adding bias, adding residual of previous block and the computation of layer normalization into 1 kernel. Third, we optimize top k operation and sampling to accelerate the beam search and sampling. Finally, to prevent from recomputing the previous keys and values, we allocate a buffer to store them at each step. Although it takes some additional memory usage, we can save the cost of recomputing, allocating buffer at each step, and the cost of concatenation.

Setup

The following section lists the requirements to use FasterTransformer.

Requirements

  • CMake >= 3.13 for PyTorch
  • CUDA 11.0 or newer version
  • NCCL 2.10 or newer version
  • Python: Only verify on Python 3.
  • PyTorch: Verify on 1.10.0, >= 1.5.0 should work.

Recommend use nvcr image like nvcr.io/nvidia/pytorch:22.09-py3.

Ensure you have the following components:

For more information about how to get started with NGC containers, see the following sections from the NVIDIA GPU Cloud Documentation and the Deep Learning Documentation:

For those unable to use the NGC container, to set up the required environment or create your own container, see the versioned NVIDIA Container Support Matrix.

Build FasterTransformer

Prepare

You can choose the pytorch version and python version you want. Here, we suggest image nvcr.io/nvidia/pytorch:22.09-py3, which contains the PyTorch 1.13.0 and python 3.8.

```bash
nvidia-docker run -ti --shm-size 5g --rm nvcr.io/nvidia/pytorch:22.09-py3 bash
git clone https://github.com/NVIDIA/FasterTransformer.git
mkdir -p FasterTransformer/build
cd FasterTransformer/build
git submodule init && git submodule update
```

Build the project

  • Note: the xx of -DSM=xx in following scripts means the compute capability of your GPU. The following table shows the compute capability of common GPUs.
GPU compute capacity
P40 60
P4 61
V100 70
T4 75
A100 80
A30 80
A10 86

By default, -DSM is set by 70, 75, 80 and 86. When users set more kinds of -DSM, it requires longer time to compile. So, we suggest setting the -DSM for the device you use only. Here, we use xx as an example due to convenience.

  1. build with PyTorch

    cmake -DSM=xx -DCMAKE_BUILD_TYPE=Release -DBUILD_PYT=ON -DBUILD_MULTI_GPU=ON ..
make -j12

    This will build the TorchScript custom class. Please make sure that the PyTorch >= 1.5.0.

How to use

Please refer to BART Jupyter notebook for demo of FT BART usage. Meanwhile, task specific examples are under development.