Inconsistent Behavior of `ConvTranspose2d` on CPU and CUDA
Labels: needs reproduction, module: nn, triaged

### 🐛 Describe the bug

```python
import torch
import torch.nn as nn

# ConvTranspose spec
in_channels = 192
out_channels = 192
kernel_size = 5
stride = 2
padding = 0
input_height = 8
input_width = 12

cuda = torch.device("cuda:0")
cpu = torch.device("cpu")

conv2d_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding=0)

# Initialization
torch.nn.init.normal_(conv2d_transpose.weight)
torch.nn.init.normal_(conv2d_transpose.bias)
conv2d_transpose.to(cuda)

x = torch.rand((1, 192, 19, 27))
x = x.to(cuda)

# Check
conv2d_transpose.train(False)
cuda_out = torch.abs(conv2d_transpose(x) - conv2d_transpose(x)).sum()
print(cuda_out)

conv2d_transpose.to(cpu)
x = x.to(cpu)
cpu_out = torch.abs(conv2d_transpose(x) - conv2d_transpose(x)).sum()
print(cpu_out)
```
The code initializes a ConvTranspose2d layer, sets its weights and biases, and computes the output on both CPU and CUDA devices. However, the cpu_out and cuda_out results are inconsistent, which suggests a potential issue with the ConvTranspose2d implementation.

Expected Behavior:
I expect the results of conv2d_transpose(x) to be the same on both CPU and CUDA devices, so cuda_out and cpu_out should be 0.0.

Actual Behavior:
In the actual behavior, cuda_out is not 0.0, indicating that different values are produced during the convolution operation. This suggests that unexpected floting errors may accumulate during the computation.
Environment:
    PyTorch version: 2.0.1
    CUDA version: 11.8
    Operating system: ubuntu 22.04

### Versions

Collecting environment information...
PyTorch version: 2.0.1+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.25.0
Libc version: glibc-2.35

Python version: 3.11.5 | packaged by conda-forge | (main, Aug 27 2023, 03:34:09) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.2.0-32-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA TITAN Xp
GPU 1: NVIDIA TITAN Xp
GPU 2: NVIDIA TITAN Xp
GPU 3: NVIDIA TITAN Xp

Nvidia driver version: 535.104.05
cuDNN version: Could not collect
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
Address sizes:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             16
On-line CPU(s) list:                0-15
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 8
Socket(s):                          1
Stepping:                           4
CPU max MHz:                        4500.0000
CPU min MHz:                        1200.0000
BogoMIPS:                           7200.00
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 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 pti ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm 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 hwp hwp_act_window hwp_epp hwp_pkg_req md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          256 KiB (8 instances)
L1i cache:                          256 KiB (8 instances)
L2 cache:                           8 MiB (8 instances)
L3 cache:                           11 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-15
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:                  Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:             Mitigation; PTI
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; IBRS
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; Clear CPU buffers; SMT vulnerable

Versions of relevant libraries:
[pip3] numpy==1.24.4
[pip3] pytorch-msssim==1.0.0
[pip3] torch==2.0.1+cu118
[pip3] torchaudio==2.0.2+cu118
[pip3] torchvision==0.15.2+cu118
[pip3] triton==2.0.0
[conda] cudatoolkit               11.8.0              h4ba93d1_12    conda-forge
[conda] numpy                     1.24.4          py311h64a7726_0    conda-forge
[conda] pytorch-msssim            1.0.0                    pypi_0    pypi
[conda] torch                     2.0.1+cu118              pypi_0    pypi
[conda] torchaudio                2.0.2+cu118              pypi_0    pypi
[conda] torchvision               0.15.2+cu118             pypi_0    pypi
[conda] triton                    2.0.0                    pypi_0    pypi


cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki