ntcmp2u commented on issue #15907:
URL: https://github.com/apache/tvm/issues/15907#issuecomment-1793374902
Hi @CookedMelon , I generated the model with a custom fuzzer. Bellow I
provide a code snippet which does not depend on external model files and
illustrate how the model is like. I think it will help developers locate which
operator they optimize in a wrong way. Are you a developer of TVM? Will this
bug be fixed?
```python
import onnxruntime as ort
import onnx
import numpy as np
import pickle
from numpy import testing
import tvm
from tvm import relay
import torch
class Model0(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, *args):
_args = args
getitem = _args[0]
tril = getitem.tril(0)
div = torch.div(tril, tril)
to = div.to(dtype = torch.int64)
getitem_1 = to[(slice(-13, -12, 1), slice(None, None, None))]
expand = getitem_1.expand(1, 25)
return (expand,)
model_0 = Model0()
output_names_0 = ['v4_0']
input_dict_0 = pickle.load(open('./0.pickle', 'rb'))
inputs_0 = tuple(torch.from_numpy(v).to('cpu') for _, v in
input_dict_0.items())
torch.onnx.export(model_0, inputs_0, '0.onnx', verbose=False,
input_names=['v5_0'], output_names=output_names_0, opset_version=14,
do_constant_folding=False)
onnx_model_0 = onnx.load('0.onnx')
onnx_model_outputs_0 = [node.name for node in onnx_model_0.graph.output]
shape_dict_0 = {key: val.shape for key, val in input_dict_0.items()}
mod_0, params_0 = relay.frontend.from_onnx(onnx_model_0, shape_dict_0,
freeze_params=True)
def func():
with tvm.transform.PassContext(opt_level=4):
executor_0 = relay.build_module.create_executor("graph", mod_0,
tvm.cpu(), tvm.target.Target("llvm"), params_0).evaluate()
executor_res_0 = [executor_0(**input_dict_0).numpy()]
output_0 = dict(zip(onnx_model_outputs_0, executor_res_0))
return output_0
output_0 = func()
output_1 = func()
print('=========================')
try:
for tensor_name in output_names_0:
testing.assert_allclose(output_0[tensor_name], output_1[tensor_name])
print("no problem")
except AssertionError as e:
print("assertion failure for inconsistency")
print(e)
print('=========================')
```
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