Hzfengsy commented on pull request #10420:
URL: https://github.com/apache/tvm/pull/10420#issuecomment-1056217935
Thanks, @yzh119 for pointing this out. On the other side, can we somehow
determine `outer` is a complete block?
By this approach, we may meet another case that we should take care of.
```python
for i, j in T.grid(16, 16):
with T.block("outer_1"):
vi, vj = T.axis.remap("SS", [i, j])
for k, l in T.grid(16, 16):
with T.block("inner_1"):
vk, vl = T.axis.remap("SR", [k, l])
with T.init():
b[vi, vj, vk] = 0.0
b[vi, vj, vk] = b[vi, vj, vk] + a[vi, vj, vk, vl]
with T.block("outer_2"):
vi, vj = T.axis.remap("SS", [i, j])
for k, l in T.grid(16, 16):
with T.block("inner_2"):
vk, vl = T.axis.remap("SR", [k, l])
with T.init():
b[vi, vj, vk] = 0.0
b[vi, vj, vk] = b[vi, vj, vk] + a[vi, vj, vk, vl]
```
`outer_1` and `outer_2` are not `complete block` or `reduction block`, while
`inner_1` and `inner_2` are both reduction blocks of its scope. However, in
this case, we cannot simply bind the thread index.
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