[GitHub] [incubator-mxnet] bartekkuncer commented on issue #17884: [MKL-DNN] Integrate Conv3d and Pool3d/1d

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bartekkuncer commented on issue #17884:
URL: https://github.com/apache/incubator-mxnet/pull/17884#issuecomment-617739799


   **Some more performance numbers for Conv3d:**
   | Shape | Kernel shape  | w/o mkldnn | w/mkldnn |
   | ------------- | ------------- | ------- | --- |
   | (3, 3, 12, 128, 64)  | (2, 2, 2) | 0.56526 sec | 0.00332 sec |
   | (3, 3, 12, 128, 128)  | (2, 2, 2) | 1.06378 sec | 0.00453 sec |
   | (3, 3, 128, 128, 128)  | (2, 2, 2) | 10.53919 sec | 0.04117 sec |
   | (5, 5, 12, 128, 128)  | (2, 2, 2) | 2.76165 sec | 0.00688 sec |
   | (5, 5, 128, 128, 128)  | (2, 2, 2) | 29.06880 sec | 0.07062 sec |
   | (3, 3, 12, 128, 64)  | (3, 3, 3) | 1.48476 sec | 0.00172 sec |
   | (3, 3, 12, 128, 128)  | (3, 3, 3) | 2.87235 sec | 0.00424 sec |
   | (3, 3, 128, 128, 128)  | (3, 3, 3) | 34.26913 sec | 0.04124 sec |
   | (5, 5, 12, 128, 128)  | (3, 3, 3)  | 7.77486 sec | 0.00697 sec |
   | (5, 5, 128, 128, 128)  | (3, 3, 3)  | 141.04758 sec | 0.07259 sec |
   
   **Test I used:**
   ```
   import mxnet as mx
   import time
   
   def perf_conv3d():
       shapes = [(3, 3, 12, 128, 64), (3, 3, 12, 128, 128), (3, 3, 128, 128, 128), (5, 5, 16, 128, 128), (5, 5, 128, 128, 128)]
       kernel_shapes = [(2, 2, 2), (3, 3, 3)]
       num_filter = 32
   
       warmup = 10
       runs = 40
   
       for shape in shapes:
           for kernel_shape in kernel_shapes:
               a = mx.random.uniform(shape=shape)
               w = mx.random.uniform(shape=(num_filter, shape[1], kernel_shape[0], kernel_shape[1], kernel_shape[2]))
   
               tic = 0
               for i in range(runs + warmup):
                   if i == warmup:
                       tic = time.time()
                   _ = mx.nd.Convolution(data=a, weight=w, kernel=kernel_shape, num_filter=num_filter, stride=(1,1,1), pad=(0,0,0), no_bias=True, cudnn_off=True)
                   mx.nd.waitall()
   
               toc = time.time()
               print('conv3d benchmark, shape={}, kernel_shape={} time={} ms/iter'.format(shape, kernel_shape, (toc-tic)*1000/(runs-warmup)))
   
   
   if __name__ == '__main__':
       perf_conv3d()
   ```
   
   Sorry for the delay.


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