Repository: pytorch
Issue: Pad operator produces non-nan value with nan input
Labels: module: onnx,triaged

### 🐛 Describe the bug

```python
import os
import keras
import numpy as np
import tensorflow as tf
import tf2onnx
from onnx2torch import convert


def _init_input(input_shape):
    input_shape = list(input_shape)
    input_shape[0] = 10
    input_shape = tuple(input_shape)
    input = np.random.rand(*input_shape)
    return input


class CustomCastLayer(keras.layers.Layer):
    def __init__(self, target_dtype, **kwargs):
        self.target_dtype = target_dtype
        super().__init__(**kwargs)

    def build(self, input_shape):
        """
        Do Nothing
        """
        super().build(input_shape)

    def call(self, inputs, **kwargs):
        output = tf.cast(inputs, self.target_dtype)
        return output

    def get_config(self):
        config = super().get_config()
        config.update({"target_dtype": self.target_dtype})
        return config

    def compute_output_shape(self, input_shape):
        """
        Do Nothing
        """
        return input_shape


class CustomPadLayer(keras.layers.Layer):
    def __init__(self, padding=[[0, 0]], constant_values=0, **kwargs):
        self.padding = padding  # add [0,0] to padding so we will not change the shape of batch dimension
        self.constant_values = constant_values
        super().__init__(**kwargs)

    def build(self, input_shape):
        super().build(input_shape)

    def call(self, inputs, **kwargs):
        output = tf.pad(inputs, paddings=tf.constant([[0, 0]] + self.padding), mode="CONSTANT",
                        constant_values=self.constant_values)
        return output

    def get_config(self):
        config = super().get_config()
        config.update({"padding": self.padding, "constant_values": self.constant_values})
        return config

    def compute_output_shape(self, input_shape):
        """
        Formula to calculate the output shape
        Suppose the input_shape is (None, N, W, C), the `paddings` is [[a, b], [c, d]].
        The output_shape is (None, N+a+b, W+c+d, C).
        """
        input_shape_list = list(input_shape)
        padding = [[0, 0]] + self.padding
        assert len(input_shape_list) == len(padding)  # Two dimensions should match.
        output_shape = [None]
        for i, pad in zip(input_shape_list[1:], padding[1:]):
            output_shape.append(i + pad[0] + pad[1])
        return tuple(output_shape)


class CustomCropLayer(keras.layers.Layer):
    def __init__(self, cropping, **kwargs):
        self.cropping = cropping
        super().__init__(**kwargs)

    def build(self, input_shape):
        super().build(input_shape)

    def call(self, inputs, **kwargs):
        input_shape = inputs.shape.as_list()
        indices = [slice(None)]
        cropping = [[0, 0]] + self.cropping  # add [0,0] to padding so we will not change the shape of batch dimension
        for shape, crop in zip(input_shape[1:], cropping[1:]):
            indices.append(slice(0 + crop[0], shape - crop[1]))
        return inputs[indices]

    def get_config(self):
        config = super().get_config()
        config.update({"cropping": self.cropping})
        return config

    def compute_output_shape(self, input_shape):
        """
        Formula to calculate the output shape
        Suppose the input_shape is (None, N, W, C), the `cropping` is [[a, b], [c, d]].
        The output_shape is (None, N-a-b, W-c-d, C).
        """
        input_shape_list = list(input_shape)
        cropping = [[0, 0]] + self.cropping
        assert len(input_shape_list) == len(cropping)  # Two dimensions should match.
        output_shape = [None]
        for i, crop in zip(input_shape[1:], cropping[1:]):
            output_shape.append(i - crop[0] - crop[1])
        return tuple(output_shape)


class CustomExpandLayer(keras.layers.Layer):
    def __init__(self, axis=1, **kwargs):
        self.axis = axis
        super().__init__(**kwargs)

    def build(self, input_shape):
        super().build(input_shape)

    def call(self, inputs, **kwargs):
        return tf.expand_dims(inputs, self.axis)

    def get_config(self):
        config = super().get_config()
        config.update({"axis": self.axis})
        return config

    def compute_output_shape(self, input_shape):
        """
        Formula to calculate the output shape
        Suppose the input_shape is [None, N, W, C]:
        axis=0:
            output_shape: [1, None, N, W, C]
        axis=1 (default):
            output_shape: [None, 1, N, W, C]
        axis=2:
            output_shape: [None, N, 1, W, C]
        axis=3:
            output_shape: [None, N, W, 1, C]
        axis=4:
            output_shape: [None, N, W, C, 1]
        axis=5:
            raise Exception
        """
        input_shape_list = list(input_shape)
        if self.axis > len(input_shape_list):
            raise ValueError(f"axis {self.axis} should be smaller than input_shape + 1: {len(input_shape_list) + 1}")
        output_shape = input_shape_list[0:self.axis] + [1] + input_shape_list[self.axis:]
        return tuple(output_shape)  # we should use tuple!!! not list !!!


class CustomDropDimLayer(keras.layers.Layer):
    def __init__(self, axis=1, **kwargs):
        self.axis = axis
        super().__init__(**kwargs)

    def build(self, input_shape):
        super().build(input_shape)

    def call(self, inputs, **kwargs):
        """
        Something magic to automatically generate indices for array slicing.
        To determine a specific axis, we can use slice(None) to replace `:`
        """
        dim = len(inputs.shape)
        if self.axis > dim - 1 or self.axis < 1:
            raise ValueError(f"axis: {self.axis} should be within the range: [1, {dim - 1}] for {dim}D tensor")
        indices = [slice(None) for i in range(dim)]
        indices[self.axis] = 0
        return inputs[indices]

    def get_config(self):
        config = super().get_config()
        config.update({"axis": self.axis})
        return config

    def compute_output_shape(self, input_shape):
        """
        Formula to calculate the output shape
        Suppose the input_shape is [None, N, W, C]:
        axis=0:  # Although it is feasible, we don't allow this to happen
            Raise Exception
        axis=1 (default):
            output_shape: [None, W, C]
        axis=2:
            output_shape: [None, N, C]
        axis=3:
            output_shape: [None, N, W]
        axis=4:
            Raise Exception
        """
        input_shape_list = list(input_shape)
        output_shape = input_shape_list[0:self.axis] + input_shape_list[self.axis + 1:]
        return tuple(output_shape)


def custom_objects(mode="custom"):
    def no_activation(x):
        return x

    def leakyrelu(x):
        import keras.backend as K
        return K.relu(x, alpha=0.01)

    # objects = {}
    objects = {'no_activation': no_activation, 'leakyrelu': leakyrelu}
    if mode == "custom":
        objects['CustomPadLayer'] = CustomPadLayer
        objects['CustomCropLayer'] = CustomCropLayer
        objects['CustomDropDimLayer'] = CustomDropDimLayer
        objects['CustomExpandLayer'] = CustomExpandLayer
        objects['CustomCastLayer'] = CustomCastLayer

    return objects


filename = "lenet5-fashion-mnist_origin-SpecialI5-MDims8-Edge17-NLAll46-NLAll52.h5"
model = keras.models.load_model(filename, custom_objects=custom_objects())
model_name = os.path.basename(filename)[:-3]
model_proto, _ = tf2onnx.convert.from_keras(model, opset=13, output_path="onnx/" + model_name + ".onnx")
onnx_model_path = "onnx/" + model_name + ".onnx"
print("onnx_model_path", onnx_model_path)
inpu = _init_input(model.input_shape)
input_data = inpu.astype(np.float32)
torch_model = convert(onnx_model_path)
```

![image](https://github.com/pytorch/pytorch/assets/77827720/5ac8fb2f-5b20-45fc-ae73-9c088683e2f6)
![image](https://github.com/pytorch/pytorch/assets/77827720/22270c34-adb4-48d0-bb21-291353768cb8)
As shown in the figure, the layer called "PadV2" will produce a non-nan value with nan input. You can just reproduce the model with the codes given above. The model checkpoint is uploaded in the attachment.
[lenet5-fashion-mnist_origin-SpecialI5-MDims8-Edge17-NLAll46-NLAll52.zip](https://github.com/pytorch/pytorch/files/14557152/lenet5-fashion-mnist_origin-SpecialI5-MDims8-Edge17-NLAll46-NLAll52.zip)

### Versions

PyTorch version: 1.13.1+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~18.04) 9.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.17

Python version: 3.7.16 (default, Jan 17 2023, 22:20:44)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-156-generic-x86_64-with-debian-buster-sid
Is CUDA available: True
CUDA runtime version: 11.3.109
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090
GPU 4: NVIDIA GeForce RTX 3090
GPU 5: NVIDIA GeForce RTX 3090
GPU 6: NVIDIA GeForce RTX 3090
GPU 7: NVIDIA GeForce RTX 3090

Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              64
On-line CPU(s) list: 0-63
Thread(s) per core:  2
Core(s) per socket:  16
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
CPU family:          6
Model:               85
Model name:          Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz
Stepping:            7
CPU MHz:             3616.329
CPU max MHz:         3900.0000
CPU min MHz:         1200.0000
BogoMIPS:            5800.00
Virtualization:      VT-x
L1d cache:           32K
L1i cache:           32K
L2 cache:            1024K
L3 cache:            22528K
NUMA node0 CPU(s):   0-15,32-47
NUMA node1 CPU(s):   16-31,48-63
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.21.6
[pip3] onnx==1.14.1
[pip3] onnx2torch==1.5.13
[pip3] onnxruntime==1.14.1
[pip3] tf2onnx==1.16.0
[pip3] torch==1.13.1
[pip3] torchvision==0.14.1
[conda] numpy                     1.21.6                   pypi_0    pypi
[conda] onnx2torch                1.5.13                   pypi_0    pypi
[conda] torch                     1.13.1                   pypi_0    pypi
[conda] torchvision               0.14.1                   pypi_0    pypi