Hi, Thank you for your response.
I remember from this thread, that Convolutions require an input with Batch size and color channels as 16 and 16 respectively. However, I am currently unsure how to perform said Padding here. If you have some inputs here, it would be greatly appreciated.
Also, May i please know how you tensorized resnet50 with Batch Size as 16? If you can share a code snippet, it would be helpful. Thanks again.
Regards,
Krishna
No,it does not need Batch size and color channels as 16 and 16 respectively. You can find the requirement here [RFC][Tensor Core] Optimization of CNNs on Tensor Core. it says that
4, TensorCore_common: Tensor Core instructions for conv2d and dense, including loading, and storing data between shared memory and register. Supporting wmma (Tensor Core instructions) for three input shapes, 8x16x32, 16x16x16, and 32x16x8.
These 3 so-called input shapes are calculated from the [B,C,H,W]. Please refer to this link Convolutional Layers User's Guide - NVIDIA Docs
My code is the same as the official one. https://github.com/apache/tvm/blob/main/tests/python/integration/test_auto_tensorize.py You can see that a batch size of 16 is okay to be tuned, don’t need a channel padding.
Thank you so much for this, will check!