[COLLAGE][BYOC][ADRENO] Integrate byoc preprocess in collage and benchmark #17512
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Integrate implicit call of BYOC preprocessing module into collage tunning module and enable benchmark script for adreno targets. Benchmark results: **Networks | OpenCL texture | OpenCLML | Collage** resnet-18-float32 | 0.010584622 | 0.00720695 | 0.007289728 resnet-18-float16 | 0.007052029 | 0.0045642 | 0.004857585 resnet-34-float32 | 0.016259185 | 0.01242092 | 0.013071063 resnet-34-float16 | 0.011350326 | 0.0073473 | 0.00796802 resnet-50-float32 | 0.019188419 | 0.02085548 | 0.018910226 resnet-50-float16 | 0.01338978 | 0.01199576 | 0.011089206 densenet-121-float32 | 0.025430062 | 0.01798478 | 0.013212844 densenet-121-float16 | 0.012384599 | 0.01101491 | 0.008722716 inception_v3-float32 | 0.040408253 | 0.02229727 | 0.022636675 inception_v3-float16 | 0.029910533 | 0.01368941 | 0.014519823 mobilenet-float32 | 0.004093148 | 0.00367917 | 0.003189258 mobilenet-float16 | 0.00280268 | 0.00244494 | 0.002101514 </body> </html> Co-authored-by: krishnaraj36 <[email protected]>
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echuraev
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Jan 8, 2025
| @@ -48,8 +48,7 @@ | |||
| from tvm.runtime.vm import VirtualMachine | |||
| import json | |||
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| NDK_CROSS_COMPILER = os.getenv("TVM_NDK_CC", "aarch64-linux-android-g++") | |||
| from tvm.relay.collage.collage import * | |||
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I would prefer explicitly import modules
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Integrate implicit call of BYOC preprocessing module into collage tunning module and enable benchmark script for adreno targets.
Benchmark results:
Networks | OpenCL texture | OpenCLML | Collage
resnet-18-float32 | 10.58 | 7.21 | 7.29
resnet-18-float16 | 7.05 | 4.56 | 4.86
resnet-34-float32 | 16.26 | 12.42 | 13.07
resnet-34-float16 | 11.35 | 7.35 | 7.97
resnet-50-float32 | 19.19 | 20.86 | 18.91
resnet-50-float16 | 13.39 | 12 | 11.09 (8%)
densenet-121-float32 | 25.43 | 17.98 | 13.21 (36%)
densenet-121-float16 | 12.38 | 11.01 | 8.72 (26%)
inception_v3-float32 | 40.41 | 22.3 | 22.64
inception_v3-float16 | 29.91 | 13.69 | 14.52
mobilenet-float32 | 4.09 | 3.68 | 3.19 (15%)
mobilenet-float16 | 2.8 | 2.44 | 2.1 (16%)