Faster Vision Transformer(ViT)

The Faster Transformer contains the Vision Transformer model which was presented in An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. The abstract of the paper is the following:

While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.

Our implementation is aligned with OpenSource PyTorch implementation ViT Github.

Computation Flow

Fig. 1 Flowchart of FP16/FP32 ViT.

Fig. 2 Flowchart of INT8 ViT (with fused MHA and quant_mode='ft2').

Demo

In this demo, you can run Faster ViT as a C++ program.

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 1.10.0, >= 1.5.0 should work.

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

Setup

Start the docker container, ensure mounting the project directory into it. For example:

docker run \
    -it \
    --rm \
    --gpus=all \
    "--cap-add=SYS_ADMIN" \
    --shm-size=16g \
    --ulimit memlock=-1 \
    --ulimit stack=67108864 \
    -v {YOUR_FASTER_TRANSFORMER_PROJECT_DIR_ON_HOST}:/workspace/FasterTransformer \
    --workdir /workspace/FasterTransformer \
    nvcr.io/nvidia/pytorch:22.09-py3 bash
export WORKSPACE = /workspace/FasterTransformer

Here, we use nvcr.io/nvidia/pytorch:22.09-py3, you can also switch it to another CUDA-enabled PyTorch containers, but need to comply with the previous requirements.

Install additional dependencies (not included by container)

cd $WORKSPACE
pip install -r examples/pytorch/vit/requirement.txt

Build the FasterTransformer with C++:

cd $WORKSPACE
git submodule update --init
mkdir -p build
cd build
cmake -DSM=xx -DCMAKE_BUILD_TYPE=Release -DBUILD_PYT=ON -DBUILD_TRT=ON ..
make -j12

Note: xx is the compute capability of your GPU. For example, 60 (P40) or 61 (P4) or 70 (V100) or 75(T4) or 80 (A100).

Run

Run ViT on C++

Firstly we use ./bin/vit_gemm as the tool to search the best GEMM configuration. And then run ./bin/vit_example or ./bin/vit_int8_example.

# is_fp16=0 indicates FP32, is_fp16=1 indicates FP16
# with_cls_token=1 indicates concatenated class token in feature embedding, with_cls_token=0 indicates no class token in feature embedding.
./bin/vit_gemm <batch_size> <img_size> <patch_size> <embed_dim> <head_number> <with_cls_token> <is_fp16> <int8_mode> 
./bin/vit_example <batch_size> <img_size> <patch_size> <embed_dim> <head_number> <layer_num> <with_cls_token> <is_fp16>
./bin/vit_int8_example <batch_size> <img_size> <patch_size> <embed_dim> <head_number> <layer_num> <with_cls_token> <is_fp16> <int8_mode>

Take ViT-B_16 with batch=32 and w=h=384 as an example:

# Run ViT-B_16 under FP32 on C++:
./bin/vit_gemm 32 384 16 768 12 1 0 0
./bin/vit_example 32 384 16 768 12 12 1 0

# Run ViT-B_16 under FP16 on C++
./bin/vit_gemm 32 384 16 768 12 1 1 0
./bin/vit_example 32 384 16 768 12 12 1 1

# Run ViT-B_16 under INT8 on C++
./bin/vit_gemm 32 384 16 768 12 1 1 2
./bin/vit_int8_example 32 384 16 768 12 12 1 1 2

Run with PyTorch op

Download Pre-trained model (Google's Official Checkpoint)

cd $WORKSPACE/examples/pytorch/vit/ViT-quantization
# MODEL_NAME={ViT-B_16-224, ViT-B_16, ViT-B_32, ViT-L_16-224, ViT-L_16, ViT-L_32}
# wget https://storage.googleapis.com/vit_models/imagenet21k+imagenet2012/{MODEL_NAME}.npz
wget https://storage.googleapis.com/vit_models/imagenet21k+imagenet2012/ViT-B_16.npz

Run FP16/FP32 pytorch op

cd $WORKSPACE/examples/pytorch/vit
pip install ml_collections

##profile of FP16/FP32 model
python infer_visiontransformer_op.py \
  --model_type=ViT-B_16  \
  --img_size=384 \
  --pretrained_dir=./ViT-quantization/ViT-B_16.npz \
  --batch-size=32 \
  --th-path=$WORKSPACE/build/lib/libpyt_vit.so

Run INT8 pytorch op

Get calibrated checkpoint

Refer to Guide of ViT Quantization Toolkit for details on setting datasets, PTQ and QAT.

cd $WORKSPACE/examples/pytorch/vit/ViT-quantization
export DATA_DIR=Path to the dataset
python -m torch.distributed.launch --nproc_per_node 1 \
    --master_port 12345 main.py \
    --calib \
    --name vit \
    --pretrained_dir ViT-B_16.npz \
    --data-path $DATA_DIR \
    --model_type ViT-B_16 \
    --img_size 384 \
    --num-calib-batch 20 \
    --calib-batchsz 8 \
    --quant-mode ft2 \
    --calibrator percentile \
    --percentile 99.99 \
    --calib-output-path .

NOTE: Difference between --quant-mode ft1 and --quant-mode ft2:

--quant-mode 1 indicates that all GEMMs are quantized to be INT8-in-INT32-out, while --quant-mode 2 means quantizating all GEMMs to be INT8-in-INT8-out. This is a speed-versus-accuracy trade-off: ft2 is faster in CUDA implementation but its accuracy is lower.

name	resolution	Original Accuracy	PTQ(mode=1)	PTQ(mode=2)
ViT-B_16	384x384	83.97%	82.57%(-1.40%)	81.82%(-2.15%)

In order to reduce the accuracy loss for ft2, QAT is a reasonable choice.

Run test

cd $WORKSPACE/examples/pytorch/vit
python infer_visiontransformer_int8_op.py \
    --model_type=ViT-B_16  \
    --img_size 384 \
    --calibrated_dir ViT-B_16_calib.pth \
    --batch-size=32 \
    --th-path=$WORKSPACE/build/lib/libpyt_vit.so \
    --quant-mode ft2

Run with TensorRT plugin

FP16/FP32 TensorRT plugin

cd $WORKSPACE/examples/tensorrt/vit
#FP16 engine build & infer
python infer_visiontransformer_plugin.py \
  --model_type=ViT-B_16 \
  --img_size=384 \
  --pretrained_dir=$WORKSPACE/examples/pytorch/vit/ViT-quantization/ViT-B_16.npz \
  --plugin_path=../../../build/lib/libvit_plugin.so \
  --batch-size=32 \
  --fp16

#FP32 engine build & infer
python infer_visiontransformer_plugin.py \
  --model_type=ViT-B_16 \
  --img_size=384 \
  --pretrained_dir=$WORKSPACE/examples/pytorch/vit/ViT-quantization/ViT-B_16.npz \
  --plugin_path=../../../build/lib/libvit_plugin.so \
  --batch-size=32

INT8 TensorRT plugin

cd $WORKSPACE/examples/tensorrt/vit
#INT8 engine build & infer
python infer_visiontransformer_int8_plugin.py \
  --model_type=ViT-B_16 \
  --img_size=384 \
  --pretrained_dir=$WORKSPACE/examples/pytorch/vit/ViT-quantization/ViT-B_16_calib.pth \
  --plugin_path=../../../build/lib/libvit_plugin.so \
  --batch-size=32

Performance

Hardware settings:

T4 (with mclk 5000MHz, pclk 1590MHz) with Intel(R) Xeon(R) Gold 6132 CPU @ 2.60GHz
A100 (with mclk 1215, pclk 1410MHz) with Intel(R) Xeon(R) Gold 6132 CPU @ 2.60GHz

Software settings:

CUDA 11.4

We here compared the performance between Vision Transformer and FT Vision Transformer on T4 & A100. Here we used ViT-B_16 as an example, and the hyper-parameters of the model are:

img_size = 384
patches = 16
head_num = 12
embed_dim = 768
num_of_layers = 12
with_cls_token = 1

ViT Performance on T4

FP32

Batch_size	torch latency(ms)	cpp latency(ms)	speedup	trt plugin latency(ms)	speedup	torch op latency(ms)	speedup
1	36.79	33.92	1.08	35.94	1.02	34.94	1.05
8	295.55	259.98	1.13	276.95	1.06	264.08	1.11
16	571.62	533.05	1.07	526.4	1.08	525.36	1.08
32	1212.99	1123.58	1.07	1140.77	1.06	1116.38	1.08

FP16

Batch_size	torch latency(ms)	cpp latency(ms)	speedup	trt plugin latency(ms)	speedup	torch op latency(ms)	speedup
1	18.48	9.01	2.05	9.25	1.99	9.19	2.01
8	161.72	65.59	2.46	66.51	2.43	68.23	2.37
16	330.52	131.48	2.51	134.87	2.45	137.9	2.39
32	684.27	263.17	2.60	263.54	2.59	294.23	2.32

INT8

Batch_size	cpp latency(ms)	speedup(vs FP16)	torch op latency(ms)	speedup(vs FP16)
1	4.93	1.73	5.50	1.67
8	36.40	1.68	41.56	1.64
16	74.94	1.65	87.17	1.58
32	150.31	1.68	173.32	1.70

INT8 vs. FP16 speedup on ViT :

Batch_size	B_16 (FP16) latency(ms)	B_16 (INT8) latency(ms)	Speedup	B_16-224 (FP16) latency(ms)	B_16-224 (INT8) latency(ms)	Speedup	L_16 (FP16) latency(ms)	L_16 (INT8) latency(ms)	Speedup	L_16-224 (FP16) latency(ms)	L_16-224 (INT8) latency(ms)	Speedup
1	8.53	4.93	1.73	3.43	2.16	1.59	23.45	13.10	1.79	9.69	5.04	1.92
8	61.07	36.40	1.68	14.46	8.37	1.73	177.54	103.66	1.71	47.55	24.38	1.95
16	123.55	74.94	1.65	28.77	16.67	1.73	358.70	211.89	1.69	90.30	47.29	1.91
32	253.20	150.31	1.68	57.22	35.43	1.62	748.81	425.36	1.76	171.89	101.25	1.70

ViT Performance on A100

FP32

User can use export NVIDIA_TF32_OVERRIDE=0 to enforce the program run under FP32 on Ampere GPU.

Batch_size	torch latency(ms)	cpp latency(ms)	speedup	trt plugin latency(ms)	speedup	torch op latency(ms)	speedup
1	12.38	10.38	1.19	10.93	1.13	11.1	1.12
8	74.31	72.03	1.03	72.19	1.03	72.28	1.03
16	147.48	135.5	1.09	137.69	1.07	138.51	1.06
32	290.7	266.29	1.09	270.74	1.07	270.9	1.07

FP16

Batch_size	torch latency(ms)	cpp latency(ms)	speedup	trt plugin latency(ms)	speedup	torch op latency(ms)	speedup
1	9.58	2.13	4.50	2.33	4.11	3.51	2.73
8	30.41	11.23	2.71	11.59	2.62	11.63	2.61
16	60.39	21.56	2.80	22	2.75	22.3	2.71
32	121.17	43.91	2.76	44.65	2.71	45.43	2.67

INT8

Batch_size	cpp latency(ms)	speedup(vs FP16)	torch op latency(ms)	speedup(vs FP16)
1	2.26	0.99	2.38	1.47
8	7.93	1.41	8.45	1.38
16	14.66	1.47	15.91	1.40
32	29.07	1.51	31.78	1.43

INT8 vs. FP16 speedup on ViT:

Batch_size	B_16 (FP16) latency(ms)	B_16 (INT8) latency(ms)	Speedup	B_16-224 (FP16) latency(ms)	B_16-224 (INT8) latency(ms)	Speedup	L_16 (FP16) latency(ms)	L_16 (INT8) latency(ms)	Speedup	L_16-224 (FP16) latency(ms)	L_16-224 (INT8) latency(ms)	Speedup
1	2.24	2.26	0.99	1.53	1.52	1.01	5.36	4.77	1.12	2.97	2.91	1.02
8	11.14	7.93	1.41	3.03	2.38	1.27	30.95	20.25	1.53	8.09	5.44	1.49
16	21.50	14.66	1.47	5.30	3.74	1.42	60.99	39.43	1.57	15.03	9.23	1.63
32	43.81	29.07	1.51	10.04	6.43	1.56	124.85	80.05	1.56	29.66	17.28	1.72

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vit_guide.md

vit_guide.md

Faster Vision Transformer(ViT)

Table of Contents

Computation Flow

Demo

Requirements

Setup

Run

Run ViT on C++

Run with PyTorch op

Run with TensorRT plugin

Performance

ViT Performance on T4

FP32

FP16

INT8

ViT Performance on A100

FP32

FP16

INT8

INT8 vs. FP16 speedup on ViT:

Files

vit_guide.md

Latest commit

History

vit_guide.md

File metadata and controls

Faster Vision Transformer(ViT)

Table of Contents

Computation Flow

Demo

Requirements

Setup

Run

Run ViT on C++

Run with PyTorch op

Run with TensorRT plugin

Performance

ViT Performance on T4

FP32

FP16

INT8

ViT Performance on A100

FP32

FP16

INT8

INT8 vs. FP16 speedup on ViT: