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ResNet-50

In this example, we will demo how to use AITemplate for inference on the ResNet-50 model from PyTorch Image Models (TIMM).

We will demo two usages:

  • Using AIT to accelerate PyTorch inference
  • Using AIT standalone without PyTorch

Code structure

modeling
    resnet.py              # ResNet definition using AIT's frontend API
weight_utils.py            # Utils to convert TIMM R-50 weights to AIT
infer_with_torch.py        # Example to accelerate PyTorch, and seamlessly use with other PyTorch code
infer_with_numpy.py        # Dump TIMM weights to Numpy and use AIT & Numpy without 3rdparties
benchmark_pt.py            # Benchmark code for PyTorch
benchmark_ait.py           # Benchmark code for AIT

Multi-GPU profiling

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

For example, CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 benchmark_ait.py.

Benchmark is fast once the profilers are built.

Reference Speed vs PyTorch Eager

A100-40GB / CUDA 11.6.2

PT = PyTorch 1.12 Eager

Batch size PT Latency (ms) PT QPS (im/s) AIT Latency (ms) AIT QPS (im/s)
1 7.68 130.29 0.58 1730.17
2 7.16 279.36 0.62 3250.74
4 7.17 557.68 0.69 5773.20
8 7.02 1138.83 0.88 9104.44
16 7.01 2280.97 1.33 12012.81
32 7.53 4251.30 2.40 13350.58
64 13.98 4578.09 4.53 14140.83
128 26.57 4816.71 8.57 14935.82
256 50.93 5026.40 16.58 15444.57

MI-250 / ROCm 5.2.3 / HIPCC-10736

PT = PyTorch 1.12 Eager

1 GCD

Batch size PT Latency (ms) PT QPS (im/s) AIT Latency (ms) AIT QPS (im/s)
1 3.94 254.06 2.28 438.60
2 3.89 514.48 2.25 888.89
4 3.82 1047.11 2.38 1680.67
8 4.40 1819.27 2.62 3053.44
16 6.48 2468.65 3.41 4692.08
32 10.40 3076.97 4.86 6584.36
64 18.35 3488.12 8.26 7748.18
128 34.36 3724.76 15.38 8322.50
256 65.35 3917.29 29.62 8642.81

2 GCDs

Batch size PT Latency (ms) PT QPS (im/s) AIT Latency (ms) AIT QPS (im/s)
1
2 3.94 507.54 2.36 848.15
4 3.89 1028.60 2.34 1710.94
8 3.88 2059.41 2.70 2960.46
16 4.56 3507.48 2.83 5663.52
32 6.72 4762.89 3.87 8275.98
64 10.82 5917.63 5.26 12173.67
128 18.79 6812.09 8.98 14247.09
256 35.99 7112.59 16.69 15338.58

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 reproduce and for reference only. It should not be used for other purposes.