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Getting Started with AIT for the Inference of Detectron2 Based Models

This document describes the usage of AIT for detectron2 vision models such as mask RCNN and faster RCNN.

For an end-to-end example with the API, see prepare_and_run_rcnn.sh which covers how to prepare and run inference with mask_rcnn_R_50_FPN.

Create the AIT Model from a Config File

  1. Pick a model and its config file from configs, for example, mask_rcnn_R_50_FPN.yaml.

  2. Build the AIT Model by running compile_model.py with the config file, for example,

cfg=examples/02_detectron2/configs/mask_rcnn_R_50_FPN.yaml
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 examples/02_detectron2/compile_model.py   \
  --config $cfg \
  --batch 1

All parameters in the built AIT model are not initialized, and therefore are filled with random values. We will initialize these parameters in the following step (i.e., exporting the weights of the pre-trained model to the AIT model). Check tmp/mask_rcnn_R_50_FPN/params.json for the list of parameters in the AIT model and their shapes.

Download the Detectron2 Pre-trained Model, and Export the Weights to the AIT Model

  1. For example, download Detectron2 mask_rcnn_R_50_FPN pre-trained model and save it to tmp/pt_mask_rcnn_R_50_FPN.pkl:
wget https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl -O tmp/pt_mask_rcnn_R_50_FPN.pkl
  1. Export the weights from the pre-trained model to AIT model by running tools/convert_pt2ait.py:
python3 examples/02_detectron2/tools/convert_pt2ait.py  \
  --d2-weight tmp/pt_mask_rcnn_R_50_FPN.pkl \
  --ait-weight tmp/ait_mask_rcnn_R_50_FPN.pt \
  --model-name mask_rcnn_R_50_FPN

The weights are exported to AIT and saved as tmp/ait_mask_rcnn_R_50_FPN.pt for inference run.

Download Inference DataSet and Run AIT Model

  1. For example, download the COCO 2017 Dataset:
mkdir -p ~/.torch/datasets/coco

wget https://dl.fbaipublicfiles.com/detectron2/annotations/coco/val2017_100.tgz -O ~/.torch/datasets/coco/val2017_100.tgz
tar xzf ~/.torch/datasets/coco/val2017_100.tgz -C ~/.torch/datasets/coco && rm -f ~/.torch/datasets/coco/val2017_100.tgz
  1. Run inference of the AIT model on the inputs with demo.py:
python3 examples/02_detectron2/demo.py \
  --weight tmp/ait_mask_rcnn_R_50_FPN.pt \
  --config examples/02_detectron2/configs/mask_rcnn_R_50_FPN.yaml \
  --batch 1 --input "~/.torch/datasets/coco/val2017/*.jpg" \
  --confidence-threshold 0.5 \
  --display \
  --cudagraph

Multi-GPU profiling

AIT requires to do profiling to decide best algorithms for CUTLASS and CK. To enable multiple GPUs profiling, set the environment variable CUDA_VISIBLE_DEVICES on NVIDIA platform and HIP_VISIBLE_DEVICES on AMD platform with available GPU ids.

Results

PT = PyTorch 1.12 Eager

A100-40GB / CUDA 11.6

  • Input size: 448x608
Batch size PT Latency (ms) PT FPS AIT Latency (ms) AIT FPS
1 21.70 46.09 4.40 227.27
2 29.71 67.32 6.68 299.40
4 35.67 112.13 11.12 359.71
8 59.71 133.98 22.24 359.71
16 112.91 141.70 36.64 436.68
32 224.24 142.70 71.04 450.45
64 448.84 142.59 140.16 456.62
  • Input size: 800x1344
Batch size PT Latency (ms) PT FPS AIT Latency (ms) AIT FPS
1 22.99 43.50 8.50 117.65
2 34.48 58.01 13.42 149.03
4 65.00 61.54 22.88 174.83
8 125.25 63.87 41.44 193.05
16 246.49 64.91 78.56 203.67
32 503.21 63.59 154.56 207.04
64 OOM OOM 304.64 210.08

MI-250 / ROCm 5.2.3 / HIPCC-10736

PT = PyTorch 1.12 Eager

1 GCDs

  • Input size: 448x608
Batch size PT Latency (ms) PT FPS AIT Latency (ms) AIT FPS
1 24.75 40.41 10.63 94.07
2 29.28 68.30 15.96 125.31
4 42.45 94.24 26.24 152.44
8 79.73 100.34 51.04 156.74
16 141.84 112.81 89.12 179.53
32 284.39 112.52 161.92 197.63
64 600.84 106.52 Error Error
  • Input size: 800x1344
Batch size PT Latency (ms) PT FPS AIT Latency (ms) AIT FPS
1 26.80 37.31 19.23 52.00
2 43.61 45.86 30.28 66.05
4 98.88 40.45 51.56 77.58
8 189.45 42.23 98.80 80.97
16 389.94 41.03 177.28 90.25
32 807.22 39.64 333.44 95.97
64 1768.66 36.19 Error Error

2 GCDs

  • Input size: 448x608
Batch size AIT Latency (ms) AIT FPS
1
2 12.78 156.49
4 20.66 193.61
8 32.16 248.76
16 61.52 260.08
32 106.08 301.66
64 194.24 329.49
  • Input size: 800x1344
Batch size AIT Latency (ms) AIT FPS
1
2 22 90.91
4 34 117.65
8 55.52 144.09
16 104.48 153.14
32 190.24 168.21
64 362.88 176.37

Sample outputs

sample

sample

sample

Note for Performance Results

  • For NVIDIA A100, our test cluster doesn't allow to lock frequency. We make warm up longer to collect more stable results, but it is expected to have small variance to the results with locked frequency.
  • To benchmark MI-250, the first step is to run python3 benchmark_ait.py to generate all necessary model dynamic library files with single GCD. Then run ./benchmark_mi250.sh {batch_size} to simulate data parallel execution on 2 GCDs, each GCD is processing half of the batch.
  • To benchmark MI-250 1 GCD, we lock the frequency with command rocm-smi -d x --setperfdeterminism 1700, where x is the GPU id.
  • To benchmark MI-250 2 GCDs, we observed performance regression with rocm perf-determ mode. The 2 GCDs number is running without perf-determ mode set with command rocm-smi -d x --resetperfdeterminism, where x is the GPU id.
  • Performance results are what we can reproduced. It should not be used for other purposes.