leeexyz commented on pull request #7879:
URL: https://github.com/apache/tvm/pull/7879#issuecomment-823752535
@llehtahw @comaniac The modification does not consider this reusing case. :(
Substitute LHS (aka the provide) should solve this reusing case. The reason is
after Normalize step, IterVar i is rebased, but only RHS (aka the intrin) has
been updated.
```c++
// src/te/operation/tensorize.cc:330
PrimExpr lhs = ana.Simplify(Substitute(body[i], value_map));
```
I think it is okay to reuse compute since this is a way to reuse the compute
concepts to describe the behavior of HW intrinsic. Actually, we also do some
reusings, but we don't use the same compute directly, what we do like follows.
```
a, b, c = get_compute_args()
s = tvm.te.create_schedule([c.op])
# just like create a new op
_, _, intrin_c = get_compute_args()
# get the intrinsic
intrin_c = get_intrin(intrin_c)
# do tensorize
s[c].tensorize(c.op.axis[0], intrin_c)
tvm.lower(s, (a, b, c))
```
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