cxx122 opened a new issue, #12373:
URL: https://github.com/apache/tvm/issues/12373
```
REDUCE_0 = te.reduce_axis((5, 0), name="REDUCE_0")
REDUCE_1 = te.reduce_axis((7, 0), name="REDUCE_1")
TENSOR_0 = te.placeholder([10,10,10,10], dtype="int8", name="TENSOR_0")
TENSOR_1 = te.compute([10,10], lambda
mcq,uch:te.sum(expr=TENSOR_0[REDUCE_0,mcq,uch,REDUCE_1],
axis=[REDUCE_0,REDUCE_1]), name ="TENSOR_1")
```
The args in the reduce_axis should be like (0,5), the args like (5,0) will
cause a wrong result.
### Expected behavior
An error message to point out the misuse.
### Actual behavior
```
Traceback (most recent call last):
File
"/Scuzer/src/bugs/IncorrectResult__f3f22bd7-feec-4f44-890c-2cd7d1c86814/Incorrect_bug.py",
line 48, 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: 100 / 100 (100%)
Max absolute difference: 92
Max relative difference: 1.
x: array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],...
y: array([[75, 68, 41, 69, 74, 68, 66, 63, 65, 72],
[65, 67, 58, 88, 82, 74, 63, 87, 66, 66],
[71, 69, 80, 69, 52, 71, 75, 51, 69, 72],...
```
### 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
REDUCE_0 = te.reduce_axis((0, 5), name="REDUCE_0")
REDUCE_1 = te.reduce_axis((7, 0), name="REDUCE_1")
TENSOR_0 = te.placeholder([10,10,10,10], dtype="int8", name="TENSOR_0")
TENSOR_1 = te.compute([10,10], lambda
mcq,uch:te.sum(expr=TENSOR_0[REDUCE_0,mcq,uch,REDUCE_1],
axis=[REDUCE_0,REDUCE_1]), name ="TENSOR_1")
s = te.create_schedule(TENSOR_1.op)
tensor_list = [TENSOR_0,TENSOR_1]
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))
pre_mod = tvm.lower(s, tensor_list, simple_mode=True)
with tvm.transform.PassContext(opt_level=4):
f = tvm.build(pre_mod)
f(*pre_list)
# Schedule
TENSOR_1_mcq, TENSOR_1_uch, TENSOR_1_REDUCE_0, TENSOR_1_REDUCE_1 =
tuple(TENSOR_1.op.axis) + tuple(TENSOR_1.op.reduce_axis)
TENSOR_1_REDUCE_0_REDUCE_1_fused = s[TENSOR_1].fuse(TENSOR_1_REDUCE_0,
TENSOR_1_REDUCE_1)
now_mod = tvm.lower(s, tensor_list, simple_mode=True)
with tvm.transform.PassContext(opt_level=4):
f = tvm.build(now_mod)
f(*after_list)
tvm.testing.assert_allclose(pre_list[1].numpy(),
after_list[1].numpy(),rtol=1e-5)
```
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