cxx122 commented on issue #12569:
URL: https://github.com/apache/tvm/issues/12569#issuecomment-1235054211
> If that's the case, that seems we need to check the generated code.
Because normally if the generated code remains the semantic of div by zero, it
should raise a floating-point exception.
Here is the generated code I got from this program, seems to be a problem
with T_subtract().
```
primfn(TENSOR_0_1: handle, TENSOR_1_1: handle, TENSOR_2_1: handle) -> ()
attr = {"from_legacy_te_schedule": True, "global_symbol": "main",
"tir.noalias": True}
buffers = {TENSOR_2: Buffer(TENSOR_2_2: Pointer(int8), int8, [5], []),
TENSOR_0: Buffer(TENSOR_0_2: Pointer(int8), int8, [], []),
TENSOR_1: Buffer(TENSOR_1_2: Pointer(int8), int8, [], [])}
buffer_map = {TENSOR_0_1: TENSOR_0, TENSOR_1_1: TENSOR_1, TENSOR_2_1:
TENSOR_2} {
allocate(T_subtract: Pointer(global int8), int8, [1]), storage_scope =
global {
T_subtract[0] = ((int8*)TENSOR_0_2[0] - (int8*)TENSOR_0_2[0])
for (fcc: int32, 0, 5) {
TENSOR_2_2[fcc] = floordiv((int8*)TENSOR_1_2[0], (int8*)T_subtract[0])
}
}
}
```
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