cxx122 opened a new issue, #12375:
URL: https://github.com/apache/tvm/issues/12375
```
tensor_0 = te.placeholder([2,2,2], dtype="float16", name="TENSOR_0")
tensor_1 = te.placeholder([2,2], dtype="bool", name="TENSOR_1")
tensor_2 = te.compute([2,2], lambda
icq,wcd:tensor_0[icq,icq,wcd]*tensor_1[wcd,wcd], name ="TENSOR_2")
```
This te program will produce tir program like `cast(bool,
TENSOR_1[(wcd*3)])`, which will cause a core dump.
### Expected behavior
`passed`
### Actual behavior
```
@main = 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_0: Buffer(TENSOR_0_2: Pointer(float16), float16, [8],
[]),
TENSOR_1: Buffer(TENSOR_1_2: Pointer(int8), int8, [4], []),
TENSOR_2: Buffer(TENSOR_2_2: Pointer(float16), float16, [4],
[])}
buffer_map = {TENSOR_0_1: TENSOR_0, TENSOR_1_1: TENSOR_1, TENSOR_2_1:
TENSOR_2}
preflattened_buffer_map = {TENSOR_0_1: TENSOR_0_3: Buffer(TENSOR_0_2,
float16, [2, 2, 2], []), TENSOR_1_1: TENSOR_1_3: Buffer(TENSOR_1_2, bool, [2,
2], []), TENSOR_2_1: TENSOR_2_3: Buffer(TENSOR_2_2, float16, [2, 2], [])} {
for (icq: int32, 0, 2) {
for (wcd: int32, 0, 2) {
TENSOR_2[((icq*2) + wcd)] = (TENSOR_0[((icq*6) + wcd)]*cast(float16,
cast(bool, TENSOR_1[(wcd*3)])))
}
}
}
Segmentation fault (core dumped)
```
### Environment
Operating System: Ubuntu 18.04, TVM version: tag0.9.0 [d361585]
### Steps to reproduce
```
import os
import numpy as np
import tvm
from tvm import te, auto_scheduler, topi
import tvm.testing
tensor_0 = te.placeholder([2,2,2], dtype="float16", name="TENSOR_0")
tensor_1 = te.placeholder([2,2], dtype="bool", name="TENSOR_1")
tensor_2 = te.compute([2,2], lambda
icq,wcd:tensor_0[icq,icq,wcd]*tensor_1[wcd,wcd], name ="TENSOR_2")
s = te.create_schedule(tensor_2.op)
tensor_list = [tensor_0, tensor_1, tensor_2]
dev = tvm.cpu(0)
pre_list = []
for tensor in tensor_list:
shape = [x.value if 'value' in dir(x) and isinstance(x.value, int) else
1 for x in tensor.shape]
params = (5*np.random.uniform(size=shape)+1).astype(tensor.dtype)
pre_list.append(tvm.nd.array(params.copy(), dev))
print(tvm.lower(s, tensor_list, simple_mode=True))
f = tvm.build(s, tensor_list, "llvm")
f(*pre_list)
print("passed")
```
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