[Bug] Inconcistency after meta schedule for the conv2d_hwcn operator

### Actual behavior

```
An Inconsistency bug detected.
Mismatched elements: 4093 / 294912 (1.39%)
Max absolute difference among violations: 0.00019836
Max relative difference among violations: 0.25589287
ACTUAL: array([[[[ 0.653972, 4.73613 , 47.223606, ..., 1.366684,
-10.288207, 2.612887],
[ 8.710161, -28.032656, 5.167299, ..., -5.342846,...
DESIRED: array([[[[ 0.653983, 4.736097, 47.22358 , ..., 1.366686,
-10.288215, 2.612871],
[ 8.710163, -28.032671, 5.167283, ..., -5.342826,...

```

### Environment

tvm-v0.21.dev0

### Steps to reproduce

```
import tvm
from tvm import te, topi, tir
from tvm import meta_schedule as ms
import numpy as np

def compile_mod(mod, np_input_list, output_shape, output_type, opt_level=3):
with tvm.transform.PassContext(opt_level):
ref_mod = tvm.build(mod, target='llvm')
mod_output = tvm.nd.empty(output_shape, dtype=output_type, device=tvm.cpu(0))

tvm_inputs = [tvm.nd.array(x) for x in np_input_list]
ref_mod(*tvm_inputs, mod_output)
return mod_output

Input = te.placeholder([7, 7, 512, 32], dtype='float32', name='Input')
Filter = te.placeholder([3, 3, 512, 1024], dtype='float32', name='Filter')
op_config = {'Input': Input, 'Filter': Filter, 'stride': [1, 1], 'padding': [1, 1], 'dilation': [3, 3], }
op_output = topi.nn.conv2d_hwcn(**op_config)
np_inputs = [np.random.uniform(-1, 1, size=[7, 7, 512, 32]).astype('float32'),np.random.uniform(-1, 1, size=[3, 3, 512, 1024]).astype('float32')]
sch = tir.Schedule(te.create_prim_func([Input, Filter, op_output]).with_attr('target', tvm.target.Target('llvm')))
ref_output = compile_mod(sch.mod, np_inputs, op_output.shape, op_output.dtype, opt_level=0)

database = ms.tir_integration.tune_tir(mod=sch.mod, target='llvm --num-cores=16', work_dir='./tune_tmp', max_trials_global=1, num_trials_per_iter=1)
sch = ms.tir_integration.compile_tir(database, sch.mod, 'llvm --num-cores=16')

opt_mod_output = compile_mod(sch.mod, np_inputs, op_output.shape, op_output.dtype, opt_level=4)

np.testing.assert_allclose(
ref_output.numpy(), opt_mod_output.numpy(), rtol=1e-5, atol=1e-5, err_msg=f"An Inconsistency detected."
)

```

### Triage

* needs-triage

Start by running the provided Python reproduction with TVM v0.21.dev0 and compare the unoptimized LLVM output with the meta-schedule-tuned output. Trace the generated schedule for topi.nn.conv2d_hwcn and investigate the numerical mismatch; done means the tuned result satisfies the stated allclose tolerance without regressing the reference result.

Written by the indexing model from the issue text.