[onnx] UnsupportedOperatorError: Exporting the operator 'aten::l1_loss' to ONNX opset version 17 is not supported
Labels: module: onnx, triaged, oncall: pt2

### 🐛 Describe the bug

My repro:

```
python benchmarks/dynamo/torchbench.py --only Super_SloMo --backend onnxrt -dcuda --performance --inference
```

with this line `opset_version=17,` added for [line](https://github.com/pytorch/pytorch/blob/b0a372e1faef97adf46bab08510c7e0abdff2611/torch/_dynamo/backends/onnxrt.py#L77).

And with the following error:
```
cuda eval  Super_SloMo                         ========= Diagnostic Run torch.onnx.export version 2.0.0a0+gitc263bd4 ==========
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 1 ERROR ========================
ERROR: missing-standard-symbolic-function
=========================================
Exporting the operator 'aten::l1_loss' to ONNX opset version 17 is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues.
None
<Set verbose=True to see more details>


ERROR:common:Backend dynamo failed in warmup()
Traceback (most recent call last):
  File "/home/yj/pytorch/benchmarks/dynamo/common.py", line 1485, in warmup
    fn(model, example_inputs)
  File "/home/yj/pytorch/torch/_dynamo/eval_frame.py", line 280, in _fn
    return fn(*args, **kwargs)
  File "/home/yj/pytorch/torch/_dynamo/eval_frame.py", line 433, in catch_errors
    return callback(frame, cache_size, hooks, frame_state)
  File "/home/yj/pytorch/torch/_dynamo/convert_frame.py", line 519, in _convert_frame
    result = inner_convert(frame, cache_size, hooks, frame_state)
  File "/home/yj/pytorch/torch/_dynamo/convert_frame.py", line 122, in _fn
    return fn(*args, **kwargs)
  File "/home/yj/pytorch/torch/_dynamo/convert_frame.py", line 355, in _convert_frame_assert
    return _compile(
  File "/home/yj/pytorch/torch/_dynamo/utils.py", line 177, in time_wrapper
    r = func(*args, **kwargs)
  File "/home/yj/pytorch/torch/_dynamo/convert_frame.py", line 425, in _compile
    out_code = transform_code_object(code, transform)
  File "/home/yj/pytorch/torch/_dynamo/bytecode_transformation.py", line 1000, in transform_code_object
    transformations(instructions, code_options)
  File "/home/yj/pytorch/torch/_dynamo/convert_frame.py", line 410, in transform
    tracer.run()
  File "/home/yj/pytorch/torch/_dynamo/symbolic_convert.py", line 2010, in run
    super().run()
  File "/home/yj/pytorch/torch/_dynamo/symbolic_convert.py", line 703, in run
    and self.step()
  File "/home/yj/pytorch/torch/_dynamo/symbolic_convert.py", line 663, in step
    getattr(self, inst.opname)(inst)
  File "/home/yj/pytorch/torch/_dynamo/symbolic_convert.py", line 2098, in RETURN_VALUE
    self.output.compile_subgraph(
  File "/home/yj/pytorch/torch/_dynamo/output_graph.py", line 723, in compile_subgraph
    self.compile_and_call_fx_graph(tx, pass2.graph_output_vars(), root)
  File "/home/yj/anaconda3/envs/dynamite/lib/python3.8/contextlib.py", line 75, in inner
    return func(*args, **kwds)
  File "/home/yj/pytorch/torch/_dynamo/output_graph.py", line 800, in compile_and_call_fx_graph
    compiled_fn = self.call_user_compiler(gm)
  File "/home/yj/pytorch/torch/_dynamo/utils.py", line 177, in time_wrapper
    r = func(*args, **kwargs)
  File "/home/yj/pytorch/torch/_dynamo/output_graph.py", line 859, in call_user_compiler
    raise BackendCompilerFailed(self.compiler_fn, e).with_traceback(
  File "/home/yj/pytorch/torch/_dynamo/output_graph.py", line 855, in call_user_compiler
    compiled_fn = compiler_fn(gm, self.example_inputs())
  File "/home/yj/pytorch/torch/_dynamo/repro/after_dynamo.py", line 108, in debug_wrapper
    compiled_gm = compiler_fn(gm, example_inputs)
  File "/home/yj/pytorch/torch/_dynamo/backends/common.py", line 125, in wrapper
    return fn(model, inputs, **kwargs)
  File "/home/yj/pytorch/torch/_dynamo/backends/onnxrt.py", line 55, in onnxrt
    return onnxrt(gm, example_inputs, filename=tmp.name)
  File "/home/yj/pytorch/torch/_dynamo/backends/common.py", line 125, in wrapper
    return fn(model, inputs, **kwargs)
  File "/home/yj/pytorch/torch/_dynamo/backends/onnxrt.py", line 72, in onnxrt
    torch.onnx.export(
  File "/home/yj/pytorch/torch/onnx/utils.py", line 507, in export
    _export(
  File "/home/yj/pytorch/torch/onnx/utils.py", line 1567, in _export
    graph, params_dict, torch_out = _model_to_graph(
  File "/home/yj/pytorch/torch/onnx/utils.py", line 1128, in _model_to_graph
    graph = _optimize_graph(
  File "/home/yj/pytorch/torch/onnx/utils.py", line 666, in _optimize_graph
    graph = _C._jit_pass_onnx(graph, operator_export_type)
  File "/home/yj/pytorch/torch/onnx/utils.py", line 1918, in _run_symbolic_function
    raise errors.UnsupportedOperatorError(
torch._dynamo.exc.BackendCompilerFailed: backend='onnxrt' raised:
UnsupportedOperatorError: Exporting the operator 'aten::l1_loss' to ONNX opset version 17 is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues.


You can suppress this exception and fall back to eager by setting:
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True
```

### Versions

PyTorch version: 2.0.0a0+gitc263bd4
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.25.0
Libc version: glibc-2.31

Python version: 3.8.15 (default, Nov 24 2022, 15:19:38)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-69-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3070
Nvidia driver version: 520.61.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.6.0
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
Address sizes:                   48 bits physical, 48 bits virtual
CPU(s):                          24
On-line CPU(s) list:             0-23
Thread(s) per core:              2
Core(s) per socket:              12
Socket(s):                       1
NUMA node(s):                    1
Vendor ID:                       AuthenticAMD
CPU family:                      25
Model:                           33
Model name:                      AMD Ryzen 9 5900X 12-Core Processor
Stepping:                        0
Frequency boost:                 enabled
CPU MHz:                         2786.283
CPU max MHz:                     3700.0000
CPU min MHz:                     2200.0000
BogoMIPS:                        7399.70
Virtualization:                  AMD-V
L1d cache:                       384 KiB
L1i cache:                       384 KiB
L2 cache:                        6 MiB
L3 cache:                        64 MiB
NUMA node0 CPU(s):               0-23
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Not affected
Vulnerability Retbleed:          Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm

Versions of relevant libraries:
[pip3] bert-pytorch==0.0.1a4
[pip3] clip-anytorch==2.5.2
[pip3] CoCa-pytorch==0.0.7
[pip3] dalle2-pytorch==1.12.4
[pip3] ema-pytorch==0.1.4
[pip3] functorch==1.14.0a0+408bcf1
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.23.5
[pip3] open-clip-torch==2.16.0
[pip3] pytorch-transformers==1.2.0
[pip3] pytorch-triton==2.1.0+46672772b4
[pip3] pytorch-warmup==0.1.1
[pip3] rotary-embedding-torch==0.2.1
[pip3] torch==2.1.0a0+git2f95380
[pip3] torch-fidelity==0.3.0
[pip3] torch-scatter==2.1.1+pt20cpu
[pip3] torch-sparse==0.6.17+pt20cpu
[pip3] torch-struct==0.5
[pip3] torchaudio==2.1.0a0+d5b2996
[pip3] torchdata==0.7.0a0+f083d52
[pip3] torchmetrics==0.11.0
[pip3] torchrec-nightly==2023.1.25
[pip3] torchtext==0.16.0a0+79100a6
[pip3] torchvision==0.16.0a0+0d75d9e
[pip3] torchx==0.4.0
[pip3] vector-quantize-pytorch==0.10.15
[conda] bert-pytorch              0.0.1a4                   dev_0    <develop>
[conda] clip-anytorch             2.5.2                    pypi_0    pypi
[conda] coca-pytorch              0.0.7                    pypi_0    pypi
[conda] dalle2-pytorch            1.12.4                   pypi_0    pypi
[conda] ema-pytorch               0.1.4                    pypi_0    pypi
[conda] functorch                 1.14.0a0+408bcf1          pypi_0    pypi
[conda] magma-cuda118             2.6.1                         1    pytorch
[conda] mkl                       2023.1.0         h6d00ec8_46342  
[conda] mkl-include               2023.1.0         h06a4308_46342  
[conda] numpy                     1.23.5                   pypi_0    pypi
[conda] open-clip-torch           2.16.0                   pypi_0    pypi
[conda] pytorch-transformers      1.2.0                    pypi_0    pypi
[conda] pytorch-triton            2.1.0+46672772b4          pypi_0    pypi
[conda] pytorch-warmup            0.1.1                    pypi_0    pypi
[conda] rotary-embedding-torch    0.2.1                    pypi_0    pypi
[conda] torch                     2.1.0a0+git2f95380           dev_0    <develop>
[conda] torch-fidelity            0.3.0                    pypi_0    pypi
[conda] torch-scatter             2.1.1+pt20cpu            pypi_0    pypi
[conda] torch-sparse              0.6.17+pt20cpu           pypi_0    pypi
[conda] torch-struct              0.5                      pypi_0    pypi
[conda] torchaudio                2.1.0a0+d5b2996           dev_0    <develop>
[conda] torchdata                 0.7.0a0+f083d52          pypi_0    pypi
[conda] torchmetrics              0.11.0                   pypi_0    pypi
[conda] torchrec-nightly          2023.1.25                pypi_0    pypi
[conda] torchtext                 0.16.0a0+79100a6           dev_0    <develop>
[conda] torchvision               0.15.0a0+85983a5          pypi_0    pypi
[conda] torchx                    0.4.0                    pypi_0    pypi
[conda] vector-quantize-pytorch   0.10.15                  pypi_0    pypi

cc @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh @anijain2305