cxx122 opened a new issue, #12372:
URL: https://github.com/apache/tvm/issues/12372
```
tensor_1 = te.placeholder((2,2,), dtype="int16", name="A")
tensor_2 = te.compute((2,3,), lambda dj,z,: tensor_1[z][dj], name ="IR")
```
### Expected behavior
An error message or a definite thing like 'nan' when out of range.
### Actual behavior
```
Traceback (most recent call last):
File "/Scuzer/src/bugs/bug1_patch/bug1/bug1.py", line 35, in <module>
tvm.testing.assert_allclose(pre_list[1].numpy(),
after_list[1].numpy(),rtol=1e-5)
File "/Scuzer/tvm_cov_patch/tvm/python/tvm/testing/utils.py", line 114, in
assert_allclose
np.testing.assert_allclose(actual, desired, rtol=rtol, atol=atol,
verbose=True)
File
"/root/miniconda3/envs/py38/lib/python3.8/site-packages/numpy/testing/_private/utils.py",
line 1527, in assert_allclose
assert_array_compare(compare, actual, desired, err_msg=str(err_msg),
File
"/root/miniconda3/envs/py38/lib/python3.8/site-packages/numpy/testing/_private/utils.py",
line 840, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=1e-05, atol=1e-07
Mismatched elements: 2 / 6 (33.3%)
Max absolute difference: 29748
Max relative difference: 1.
x: array([[ 2, 3, 49],
[ 4, 2, 0]], dtype=int16)
y: array([[ 2, 3, 29797],
[ 4, 2, 28520]], dtype=int16)
```
### Environment
Operating System: Ubuntu 18.04, TVM version: tag0.9.0 [d361585]
### Steps to reproduce
```
import numpy as np
import tvm
from tvm import te, auto_scheduler
import tvm.testing
tensor_1 = te.placeholder((2,2,), dtype="int16", name="A")
tensor_2 = te.compute((2,3,), lambda dj,z,: tensor_1[z][dj], name ="IR")
s = te.create_schedule(tensor_2.op)
tensor_list = [tensor_1, tensor_2]
dev = tvm.cpu(0)
pre_list = []
after_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)).astype(tensor.dtype)
pre_list.append(tvm.nd.array(params.copy(), dev))
after_list.append(tvm.nd.array(params.copy(), dev))
print(tvm.lower(s,tensor_list, simple_mode=True))
with tvm.transform.PassContext(opt_level=4):
f = tvm.build(s, tensor_list, "llvm")
f(*pre_list)
print(tvm.lower(s,tensor_list, simple_mode=True))
with tvm.transform.PassContext(opt_level=4):
f = tvm.build(s, tensor_list, "llvm")
f(*after_list)
tvm.testing.assert_allclose(pre_list[1].numpy(),
after_list[1].numpy(),rtol=1e-5)
```
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