YuanchengJiang opened a new issue, #20465:
URL: https://github.com/apache/tvm/issues/20465
**Repo:** apache/tvm · **Commit:** `5b4b753` (main, 2026-09-21) · LLVM
18.1.8, `RelWithDebInfo` without `NDEBUG`
## What happens
`tvm.compile(..., target="nvptx")` fails TVM's own LLVM module verification,
because the
generated IR mixes integer widths in binary operators:
```
tvm.error.InternalError: LLVM module verification failed with the following
errors:
Both operands to a binary operator are not of the same type!
%5 = mul nsw i32 %0, i64 1024
Both operands to a binary operator are not of the same type!
%7 = add nsw i64 %6, i32 %5
...
(src/target/llvm/codegen_llvm.cc:372, CodeGenLLVM::Verify)
```
The same module compiles cleanly for `llvm` and for `cuda`; only the NVPTX
path mixes the
i32 thread index with the i64 symbolic extents.
## Reproducer
`nvptx_reshape.py`:
```python
import tvm
from tvm.script import ir as I
from tvm.script import tirx as T
from tvm.script import relax as R
@I.ir_module(s_tir=True)
class Module:
@R.function
def main(A: R.Tensor([16], "float16")):
return R.call_tir(Module.reshape, A, out_ty=R.Tensor([2, 8],
"float16"))
@T.prim_func(s_tir=True)
def reshape(A: T.Buffer(16, "float16"), B_handle: T.handle):
M = T.int64()
N = T.int64()
B = T.match_buffer(B_handle, [M, N], dtype="float16")
for i, j in T.grid(M, N):
with T.sblock("compute"):
vi, vj = T.axis.remap("SS", [i, j])
B[vi, vj] = A[vi * N + vj]
# Compiles for "llvm" and for "cuda"; fails LLVM module verification for
nvptx.
lib = tvm.compile(Module, target="nvptx")
print("compiled for nvptx")
```
```bash
python3 nvptx_reshape.py
```
No GPU and no scheduling primitives are involved; a `USE_LLVM=ON` build is
enough.
## Expected
Valid IR: the index computation should happen in one integer width. The
trigger is a
`call_tir` whose output buffer is `T.match_buffer` with `T.int64()` symbolic
extents.
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