yzh119 commented on issue #14906:
URL: https://github.com/apache/tvm/issues/14906#issuecomment-1565940775
Hi @uslumt , The reason you cannot parallelize the loop is because the
`compute block` is not a reduction block.
A reduction block should have an init block indicating how to initialize the
output buffer, in your case, to make `compute` a reduction block, you need to
write a `T.init` inside the block body:
```python
for b, o, h, w, kc, kh, kw in T.grid(10, 2, 124, 124, 3, 3, 3):
with T.block("compute"):
vb, vo, vh, vw, vkc, vkh, vkw = T.axis.remap("SSSSRRR", [b,
o, h, w, kc, kh, kw])
with T.init():
result_compute[vb, vo, vh, vw] = T.float32(0)
result_compute[vb, vo, vh, vw] = result_compute[vb, vo, vh,
vw] + input[vb, vkc, vh+vkh, vw+vkw] * kernel[vo, vkc, vkh, vkw]
```
Another issue with your program is you seem annotate all block axes as
"reduction", which is wrong because in `compute` block, `b, o, h, w` should be
"spatial" axes. If you don't understand how to differentiate spatial/reduce
axis, you could go through this tutorial first:
https://mlc.ai/chapter_tensor_program/case_study.html.
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