[GitHub] [tvm] ekalda commented on a diff in pull request #14483: [TOPI][Target] Add SVE specific convolution

["ekalda (via GitHub)" <gi...@apache.org>]

ekalda commented on code in PR #14483:
URL: https://github.com/apache/tvm/pull/14483#discussion_r1158615483


##########
python/tvm/topi/arm_cpu/conv2d_spatial_pack.py:
##########
@@ -316,12 +317,23 @@ def _tile_size(axis, candidates):
                     return candidate
             return 1
 
-        # Tile size 8 results in efficient vectorization for these schedules.
-        # If the axis is not divisible by 8, try 4
+        # For data tensors with unity height and width we can leave it to the
+        # backend to vectorize the inner loop. This has been observed to be more
+        # performant on SVE targets with a vector width > 128bits.
+        target = Target.current(allow_none=False)
+        if target.features.has_sve and OW == OH and OW == 1:

Review Comment:
   Is there no required minimum length for the output channels for this to be beneficial?



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