yzh119 edited a comment on pull request #10207:
URL: https://github.com/apache/tvm/pull/10207#issuecomment-1034575574
There are some issues to be solved:
If in the following case:
```python
@T.prim_func
def reduce(a: T.handle, b: T.handle, n: T.int32) -> None:
A = T.match_buffer(a, [1024, 4, 8])
B = T.match_buffer(b, [1024, 4])
for i, j, k in T.grid(1024, 4, 8):
with T.block("reduce"):
vi, vj, vk = T.axis.remap("SSR", [i, j, k])
with T.init():
B[vi, vj] = 0.
B[vi, vj] = B[vi, vj] + A[vi, vj, vk]
```
we bind j to `threadIdx.y` and k to `threadIdx.x`, different `j`'s might be
mapped to the same warp, we need different masks for different `j` to
distinguish them.
Another thing worth noting is, we can only allow cross warp reduction by
shuffle-down, thus warp size might be a multiple of `blockDim.x` when
`blockDim.y * blockDim.z != 1`.
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