Mohamad11Dab opened a new issue, #17998:
URL: https://github.com/apache/tvm/issues/17998
### Expected behavior
TVM and ONNXRuntime should give the same output
### Actual behavior
`––––– MISMATCH DETECTED –––––
Not equal to tolerance rtol=0.01, atol=0.001
Mismatched elements: 2 / 4096 (0.0488%)
Max absolute difference: 0.00342751
Max relative difference: 0.28056583
x: array([[[ 0.046975, 0.060682, -0.14202 , ..., -0.08296 , -0.002197,
-0.10205 ],
[ 0.134745, -0.247786, 0.098007, ..., 0.245494, 0.23562 ,...
y: array([[[ 0.046958, 0.060696, -0.14199 , ..., -0.08295 , -0.002193,
-0.102041],
[ 0.134729, -0.247741, 0.098011, ..., 0.245464, 0.235588,...`
### Environment
TVM:0.17.0
ONNXRuntime:1.16.3
### Steps to reproduce
```python
import random
import sys, os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
'..')))
import torch
import torch.nn as nn
import torch.nn.functional as F
import tempfile
import onnx
import onnxruntime as ort
from numpy.testing import assert_allclose
import tvm
from tvm import relay
from tvm.contrib import graph_executor
import numpy as np
import nas_model_2
class SimpleBugModel(nn.Module):
def __init__(self):
super().__init__()
self.input_conv = torch.nn.modules.conv.Conv2d(in_channels=3,
out_channels=16, kernel_size=1)
self.block0 = torch.nn.modules.pooling.MaxPool2d(ceil_mode=True,
kernel_size=2, stride=2)
self.block1 =
torch.nn.modules.instancenorm.InstanceNorm2d(num_features=16)
self.block2 = nas_model_2.ReciprocalWrapper()
self.block3 = torch.nn.modules.conv.Conv2d(kernel_size=5, padding=2,
in_channels=16,
out_channels=16)
self.block4 = torch.nn.modules.conv.Conv2d(kernel_size=3, padding=1,
in_channels=16,
out_channels=16)
self.block5 = torch.nn.modules.normalization.GroupNorm(num_groups=4,
num_channels=16)
self.block6 = torch.nn.modules.flatten.Flatten(start_dim=2)
self.block7 = torch.nn.modules.conv.Conv1d(kernel_size=3, padding=1,
in_channels=16,
out_channels=16)
self.block8 = torch.nn.modules.batchnorm.BatchNorm1d(num_features=16)
self.block9 = torch.nn.modules.linear.Identity()
def forward(self, x):
__input_conv = self.input_conv(x)
__blocks__0 = self.block0(__input_conv)
__blocks__1 = self.block1(__blocks__0)
__blocks__2 = self.block2(__blocks__1)
__blocks__3 = self.block3(__blocks__2)
__blocks__4 = self.block4(__blocks__3)
__blocks__5 = self.block5(__blocks__4)
__blocks__6 = self.block6(__blocks__5)
__blocks__7 = self.block7(__blocks__6)
__blocks__8 = self.block8(__blocks__7)
__blocks__9 = self.block9(__blocks__8)
return __blocks__9
def main():
seed=295007241
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
model = SimpleBugModel()
model.eval()
dummy = torch.randn(1, 3, 32, 32, dtype=torch.float32)
with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmp:
onnx_path = tmp.name
torch.onnx.export(model, dummy, onnx_path, opset_version=19,
input_names=['input'], output_names=['output'])
ort_sess = ort.InferenceSession(onnx_path,
providers=['CPUExecutionProvider'])
ort_out = ort_sess.run(None, {'input': dummy.numpy()})[0]
onnx_model = onnx.load(onnx_path)
shape_dict = {'input': dummy.numpy().shape}
mod, params = relay.frontend.from_onnx(onnx_model, shape_dict,
freeze_params=True)
with tvm.transform.PassContext(opt_level=4):
lib = relay.build(mod, target='llvm', params=params)
m = graph_executor.GraphModule(lib['default'](tvm.cpu()))
m.set_input('input', tvm.nd.array(dummy.numpy()))
m.run()
tvm_out = m.get_output(0)
tvm_out = tvm_out.numpy()
try:
assert_allclose(ort_out, tvm_out, rtol=1e-2, atol=1e-3,
equal_nan=True)
except AssertionError as e:
print('––––– MISMATCH DETECTED –––––')
print(e)
except Exception as e:
print('––––– UNEXPECTED ERROR DURING COMPARISON –––––')
print(f'{type(e).__name__}: {e}')
if __name__ == '__main__':
main()
```
```python
## nas_model_2
@basic_unit
class ReciprocalWrapper(nni_nn.Module):
def forward(self, x):
return torch.reciprocal(x)
```
### Triage
* needs-triage
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