Azyka opened a new issue, #16054:
URL: https://github.com/apache/tvm/issues/16054
### Expected behavior
When model contains an obvious div-by-zero, tvm should recognize it the stop
the execution. And normally 2 same model should output the same for the same
inputs.
### Actual behavior
The outputs of 2 same graphs turn out to be different and have strange large
numbers after tvm_opt_4.
```
=========================
tvm_opt_4 triggers assertion
Not equal to tolerance rtol=1e-07, atol=0
Mismatched elements: 21 / 25 (84%)
Max absolute difference: 4503603923386370
Max relative difference: 1.
x: array([[ 1, 0, 0, 0,
0, 0, 0, 0,
0, 0, 0, 0,...
y: array([[ 1, 4294967296, 0,
94244568770664, 137438953472, 94244568246608,
94244569409280, 20, 94244568246608,...
=========================
=========================
tvm_opt_0 does not trigger assertion
=========================
```
### Environment
- OS: Ubuntu 22.04.3 LTS (x86_64)
- TVM version: 0.14.dev189
- Execution provider: cpu
- ONNX opset version: 14
### Steps to reproduce
Input data file:
[input_data.zip](https://github.com/apache/tvm/files/13246324/input_data.zip)
Sample code:
```
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))]
return (getitem_1,)
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)
class Model1(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))]
return (getitem_1,)
model_1 = Model1()
output_names_1 = ['v5_0']
input_dict_1 = pickle.load(open('./1.pickle', 'rb'))
inputs_1 = tuple(torch.from_numpy(v).to('cpu') for _, v in
input_dict_1.items())
torch.onnx.export(model_1, inputs_1, '1.onnx', verbose=False,
input_names=['v0_0'], output_names=output_names_1, 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)
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))
onnx_model_1 = onnx.load('1.onnx')
onnx_model_outputs_1 = [node.name for node in onnx_model_1.graph.output]
shape_dict_1 = {key: val.shape for key, val in input_dict_1.items()}
mod_1, params_1 = relay.frontend.from_onnx(onnx_model_1, shape_dict_1,
freeze_params=True)
with tvm.transform.PassContext(opt_level=4):
executor_1 = relay.build_module.create_executor("graph", mod_1,
tvm.cpu(), tvm.target.Target("llvm"), params_1).evaluate()
executor_res_1 = [executor_1(**input_dict_1).numpy()]
output_1 = dict(zip(onnx_model_outputs_1, executor_res_1))
output_name_dict = {'v4_0': 'v5_0'}
print('=========================')
try:
for tensor_name_0, tensor_name_1 in output_name_dict.items():
testing.assert_allclose(output_0[tensor_name_0],
output_1[tensor_name_1])
print("tvm_opt_4 does not trigger assertion")
except AssertionError as e:
print("tvm_opt_4 triggers assertion")
print(e)
print('=========================')
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)
with tvm.transform.PassContext(opt_level=0):
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))
shape_dict_1 = {key: val.shape for key, val in input_dict_1.items()}
mod_1, params_1 = relay.frontend.from_onnx(onnx_model_1, shape_dict_1,
freeze_params=True)
with tvm.transform.PassContext(opt_level=0):
executor_1 = relay.build_module.create_executor("graph", mod_1,
tvm.cpu(), tvm.target.Target("llvm"), params_1).evaluate()
executor_res_1 = [executor_1(**input_dict_1).numpy()]
output_1 = dict(zip(onnx_model_outputs_1, executor_res_1))
print('=========================')
try:
for tensor_name_0, tensor_name_1 in output_name_dict.items():
testing.assert_allclose(output_0[tensor_name_0],
output_1[tensor_name_1])
print("tvm_opt_0 does not trigger assertion")
except AssertionError as e:
print("tvm_opt_0 triggers assertion")
print(e)
print('=========================')
```
1. Download the data file and put data and code file in same directory.
2. Execute the code.
### Triage
* needs-triage
* frontend:onnx
* flow:relay
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