torch.onnx.dynamo_export: Unsupported FX nodes: {'call_function': ['aten.pixel_shuffle.default']}

[Fannovel16]

🐛 Describe the bug

torch.onnx.dynamo_export doesn't work on nn.PixelShuffle

a = nn.Sequential(nn.PixelShuffle(2))
onnx_program = torch.onnx.dynamo_export(a, torch.rand(1, 8, 256, 256))

The code above gives the following exception:

/usr/local/lib/python3.10/dist-packages/torch/onnx/_internal/exporter.py:130: UserWarning: torch.onnx.dynamo_export only implements opset version 18 for now. If you need to use a different opset version, please register them with register_custom_op.
  warnings.warn(
---------------------------------------------------------------------------
RuntimeErrorWithDiagnostic                Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/torch/onnx/_internal/exporter.py](https://localhost:8080/#) in dynamo_export(model, export_options, *model_args, **model_kwargs)
   1194             model_kwargs=model_kwargs,
-> 1195         ).export()
   1196     except Exception as e:

6 frames
[/usr/local/lib/python3.10/dist-packages/torch/onnx/_internal/exporter.py](https://localhost:8080/#) in export(self)
    949             # TODO: Design the passes API
--> 950             graph_module = pre_export_passes(
    951                 self.options, self.model, graph_module, updated_model_args

[/usr/local/lib/python3.10/dist-packages/torch/onnx/_internal/exporter.py](https://localhost:8080/#) in pre_export_passes(options, original_model, fx_module, fx_module_args)
   1251         diagnostic_context, module, options.onnxfunction_dispatcher
-> 1252     ).analyze(infra.levels.ERROR)
   1253 

[/usr/local/lib/python3.10/dist-packages/torch/onnx/_internal/fx/analysis/unsupported_nodes.py](https://localhost:8080/#) in analyze(self, diagnostic_level)
     73         analysis_result = UnsupportedFxNodesAnalysisResult(op_to_target_mapping)
---> 74         self._lint(analysis_result, diagnostic_level)
     75         return analysis_result

[/usr/local/lib/python3.10/dist-packages/torch/onnx/_internal/fx/analysis/unsupported_nodes.py](https://localhost:8080/#) in _lint(self, analysis_result, diagnostic_level)
     37         )
---> 38         self.diagnostic_context.log_and_raise_if_error(diagnostic)
     39 

[/usr/local/lib/python3.10/dist-packages/torch/onnx/_internal/diagnostics/infra/context.py](https://localhost:8080/#) in log_and_raise_if_error(self, diagnostic)
    366                 raise diagnostic.source_exception
--> 367             raise RuntimeErrorWithDiagnostic(diagnostic)
    368 

RuntimeErrorWithDiagnostic: Unsupported FX nodes: {'call_function': ['aten.pixel_shuffle.default']}. 

The above exception was the direct cause of the following exception:

OnnxExporterError                         Traceback (most recent call last)
[<ipython-input-13-6e845f5e265a>](https://localhost:8080/#) in <cell line: 2>()
      1 a = nn.Sequential(nn.PixelShuffle(2))
----> 2 onnx_program = torch.onnx.dynamo_export(a, torch.rand(1, 8, 256, 256))

[/usr/local/lib/python3.10/dist-packages/torch/onnx/_internal/exporter.py](https://localhost:8080/#) in dynamo_export(model, export_options, *model_args, **model_kwargs)
   1204             f"Please report a bug on PyTorch Github: {_PYTORCH_GITHUB_ISSUES_URL}"
   1205         )
-> 1206         raise OnnxExporterError(
   1207             ExportOutput._from_failure(e, resolved_export_options.diagnostic_context),
   1208             message,

OnnxExporterError: Failed to export the model to ONNX. Generating SARIF report at 'report_dynamo_export.sarif'. SARIF is a standard format for the output of static analysis tools. SARIF logs can be loaded in VS Code SARIF viewer extension, or SARIF web viewer (https://microsoft.github.io/sarif-web-component/). Please report a bug on PyTorch Github: https://github.com/pytorch/pytorch/issues

Serif report: report_dynamo_export.sarif

Versions

Collecting environment information...
PyTorch version: 2.1.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.27.9
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.58+-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
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):                             2
On-line CPU(s) list:                0,1
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family:                         6
Model:                              79
Thread(s) per core:                 2
Core(s) per socket:                 1
Socket(s):                          1
Stepping:                           0
BogoMIPS:                           4399.99
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          32 KiB (1 instance)
L1i cache:                          32 KiB (1 instance)
L2 cache:                           256 KiB (1 instance)
L3 cache:                           55 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0,1
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Mitigation; PTE Inversion
Vulnerability Mds:                  Vulnerable; SMT Host state unknown
Vulnerability Meltdown:             Vulnerable
Vulnerability Mmio stale data:      Vulnerable
Vulnerability Retbleed:             Vulnerable
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:           Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.25.2
[pip3] onnx==1.15.0
[pip3] onnxscript==0.1.0.dev20240216
[pip3] torch==2.1.0+cu121
[pip3] torchaudio==2.1.0+cu121
[pip3] torchdata==0.7.0
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.16.0
[pip3] torchvision==0.16.0+cu121
[pip3] triton==2.1.0
[conda] Could not collect

[justinchuby]

We may be able to implement this with DepthToSpace @thiagocrepaldi @titaiwangms

[SandishKumarHN]

@justinchuby can you give me more details on how to implement?. I will be able to put some time working on it.

[justinchuby]

This is a duplicate of #115637. @SandishKumarHN you may take a look at pixel_unshuffle if you are interested. The scaffolding is at https://github.com/microsoft/onnxscript/blob/84170f7cf202cb68cf9a00ae80b9c6e3070e8f1a/onnxscript/function_libs/torch_lib/ops/core.py#L6412-L6415. You may refer to this guide: https://github.com/microsoft/onnxscript/wiki/TorchLib-function-authoring-guide