Cookiee235 opened a new issue, #17244:
URL: https://github.com/apache/tvm/issues/17244
### Actual behavior
```
Traceback (most recent call last):
File "/share_container/optfuzz/res/bugs/simple/wrong_res.py", line 59, in
<module>
np.testing.assert_allclose(outputs1.numpy(), outputs2.numpy(),
rtol=1e-3, atol=1e-3)
File
"/root/miniconda3/lib/python3.12/site-packages/numpy/testing/_private/utils.py",
line 1504, in assert_allclose
assert_array_compare(compare, actual, desired, err_msg=str(err_msg),
File "/root/miniconda3/lib/python3.12/contextlib.py", line 81, in inner
return func(*args, **kwds)
^^^^^^^^^^^^^^^^^^^
File
"/root/miniconda3/lib/python3.12/site-packages/numpy/testing/_private/utils.py",
line 718, in assert_array_compare
flagged = func_assert_same_pos(x, y, func=isnan, hasval='nan')
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/root/miniconda3/lib/python3.12/site-packages/numpy/testing/_private/utils.py",
line 688, in func_assert_same_pos
raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=0.001, atol=0.001
x and y nan location mismatch:
x: array([ nan, nan, 3.084220e-37, 0.000000e+00,
nan, nan, 3.085111e-37, 0.000000e+00,
nan, nan], dtype=float32)
y: array([1.401298e-44, 0.000000e+00, 0.000000e+00, 0.000000e+00,
0.000000e+00, 0.000000e+00, 2.879961e-37, 0.000000e+00,
0.000000e+00, 0.000000e+00], dtype=float32)
```
### Steps to reproduce
```
import tvm
from tvm import relax
import numpy as np
from tvm.script import ir as I
from tvm.script import tir as T
from tvm.script import relax as R
@I.ir_module
class Module:
@T.prim_func
def add_8(rxplaceholder: T.Buffer((T.int64(8),), "float32"),
rxplaceholder_1: T.Buffer((), "float32"), T_add: T.Buffer((T.int64(8),),
"float32")):
T.evaluate(0)
@T.prim_func
def exp(rxplaceholder: T.Buffer((T.int64(2), T.int64(4)), "float32"),
compute: T.Buffer((T.int64(2), T.int64(4)), "float32")):
T.evaluate(0)
@T.prim_func
def log(rxplaceholder: T.Buffer((T.int64(10),), "float32"), compute:
T.Buffer((T.int64(10),), "float32")):
T.evaluate(0)
@T.prim_func
def pad(rxplaceholder: T.Buffer((T.int64(8),), "float32"), PadInput:
T.Buffer((T.int64(10),), "float32")):
T.evaluate(0)
@T.prim_func
def relu(rxplaceholder: T.Buffer((T.int64(8),), "float32"), compute:
T.Buffer((T.int64(8),), "float32")):
T.evaluate(0)
@T.prim_func
def reshape_1(rxplaceholder: T.Buffer((T.int64(2), T.int64(4)),
"float32"), T_reshape: T.Buffer((T.int64(8),), "float32")):
T.evaluate(0)
@R.function
def main(x: R.Tensor((2, 4), dtype="float32")) -> R.Tensor((10,),
dtype="float32"):
R.func_attr({"relax.force_pure": 1})
storage_1: R.Object = R.memory.alloc_storage(R.shape([40]),
R.prim_value(0), R.str("global"), R.dtype("uint8"))
alloc4: R.Tensor((10,), dtype="float32") =
R.memory.alloc_tensor(storage_1, R.prim_value(0), R.shape([10]),
R.dtype("float32"))
gv: R.Tensor((10,), dtype="float32") = alloc4
return gv
mod = Module
mod = tvm.relax.transform.DeadCodeElimination()(mod)
mod.show()
mod = tvm.relax.transform.LegalizeOps()(mod)
def compile_mod(mod, target_func, *inputs):
mod = relax.transform.FuseTIR()(mod)
mod = relax.transform.LambdaLift()(mod)
ex = relax.build(mod, target='llvm')
vm = relax.VirtualMachine(ex, tvm.cpu())
mod_outputs = vm[f'{target_func}'](*inputs)
mod_outputs = mod_outputs
return mod_outputs
input_0 = tvm.nd.array(10 * np.random.random([2, 4]).astype('float32'))
outputs1 = compile_mod(mod, 'main', input_0)
outputs2 = compile_mod(mod, 'main', input_0)
np.testing.assert_allclose(outputs1.numpy(), outputs2.numpy(), rtol=1e-3,
atol=1e-3)
```
cc @Lunderberg @junrushao
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