torch.onnx.dynamo_export optimizer failed when slice the array with dynamic_shapes

[laan8613]

Problem

Transfer model from pytorch to onnx by torch.onnx.dynamo_export failed.
These failed occured when array slicing inside model and set torch.onnx.ExportOptions(dynamic_shapes=True), it can run successfully when not set dynamic_shapes.
Also, some slicing can run successfully, and some not

I am not sure pytorch or onnxscript cause the problem, so I report both.

Provided a minimum reproduced code below

Code

import torch
from torch import nn
class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc0 = nn.Conv2d(3, 64, 3, bias=True)
    
    def forward(self, x: torch.Tensor):
        x = self.fc0(x)
        return x[:, 2:, :-1] # failed
        # return x[:, :, :, :2] # failed
        # return x[:, 2:] # ok
        # return x[:, :-1] # ok
        # return x[:, :, 2:, 2:] # ok
        # return x[:, :, 2:, :-1] # failed

model = Model()
tensor_x = torch.rand((1, 3, 64,64), dtype=torch.float32)
export_options = torch.onnx.ExportOptions(dynamic_shapes=True)
onnx_program = torch.onnx.dynamo_export(model, tensor_x, export_options=export_options)

Log

/home/user/anaconda3/envs/imw/lib/python3.10/site-packages/onnxscript/converter.py:820: FutureWarning: 'onnxscript.values.Op.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signature' instead.
  param_schemas = callee.param_schemas()
/home/user/anaconda3/envs/imw/lib/python3.10/site-packages/onnxscript/converter.py:820: FutureWarning: 'onnxscript.values.OnnxFunction.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signature' instead.
  param_schemas = callee.param_schemas()
/home/user/anaconda3/envs/imw/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py:137: 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(
/home/user/anaconda3/envs/imw/lib/python3.10/site-packages/torch/onnx/_internal/fx/onnxfunction_dispatcher.py:529: FutureWarning: 'onnxscript.values.TracedOnnxFunction.param_schemas' is deprecated in version 0.1 and will be removed in the future. Please use '.op_signature' instead.
  self.param_schema = self.onnxfunction.param_schemas()
/home/user/anaconda3/envs/imw/lib/python3.10/site-packages/torch/onnx/_internal/exporter.py:1281: UserWarning: ONNXScript optimizer failed. Skipping optimization. 

PLEASE REPORT A BUG AT https://github.com/microsoft/onnxscript/issues 

Detail:
'<' not supported between instances of 'int' and 'SymbolicDim'
  warnings.warn(

Versions

PyTorch version: 2.4.0
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.30.2
Libc version: glibc-2.35

Python version: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-6.1.21.2-microsoft-standard-WSL2+-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
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): 20
On-line CPU(s) list: 0-19
Vendor ID: GenuineIntel
Model name: 12th Gen Intel(R) Core(TM) i7-12700
CPU family: 6
Model: 151
Thread(s) per core: 2
Core(s) per socket: 10
Socket(s): 1
Stepping: 2
BogoMIPS: 4223.99
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 tsc_reliable nonstop_tsc cpuid pni pclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves avx_vnni umip waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize ibt flush_l1d arch_capabilities
Virtualization: VT-x
Hypervisor vendor: Microsoft
Virtualization type: full
L1d cache: 480 KiB (10 instances)
L1i cache: 320 KiB (10 instances)
L2 cache: 12.5 MiB (10 instances)
L3 cache: 25 MiB (1 instance)
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: Mitigation; Enhanced 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; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] numpy==2.0.2
[pip3] onnx==1.17.0
[pip3] onnx-tf==1.10.0
[pip3] onnxruntime==1.19.2
[pip3] onnxscript==0.1.0.dev20241104
[pip3] onnxslim==0.1.36
[pip3] optree==0.13.0
[pip3] pytorch-lightning==1.4.9
[pip3] torch==2.4.0
[pip3] torch-xla==2.5.1
[pip3] torchmetrics==0.6.0
[pip3] torchvision==0.19.0
[pip3] triton==3.0.0
[conda] blas 2.16 mkl conda-forge
[conda] cuda-cudart 12.1.105 0 nvidia
[conda] cuda-cupti 12.1.105 0 nvidia
[conda] cuda-libraries 12.1.0 0 nvidia
[conda] cuda-nvrtc 12.1.105 0 nvidia
[conda] cuda-nvtx 12.1.105 0 nvidia
[conda] cuda-opencl 12.6.77 0 nvidia
[conda] cuda-runtime 12.1.0 0 nvidia
[conda] libblas 3.8.0 16_mkl conda-forge
[conda] libcblas 3.8.0 16_mkl conda-forge
[conda] libcublas 12.1.0.26 0 nvidia
[conda] libcufft 11.0.2.4 0 nvidia
[conda] libcurand 10.3.7.77 0 nvidia
[conda] libcusolver 11.4.4.55 0 nvidia
[conda] libcusparse 12.0.2.55 0 nvidia
[conda] liblapack 3.8.0 16_mkl conda-forge
[conda] liblapacke 3.8.0 16_mkl conda-forge
[conda] libnvjitlink 12.1.105 0 nvidia
[conda] mkl 2020.2 256
[conda] numpy 1.23.5 pypi_0 pypi
[conda] pytorch 2.4.0 py3.10_cuda12.1_cudnn9.1.0_0 pytorch
[conda] pytorch-cuda 12.1 ha16c6d3_6 pytorch
[conda] pytorch-lightning 1.4.9 pypi_0 pypi
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] torch-xla 2.5.1 pypi_0 pypi
[conda] torchmetrics 0.6.0 pypi_0 pypi
[conda] torchtriton 3.0.0 py310 pytorch
[conda] torchvision 0.19.0 pypi_0 pypi

[justinchuby]

Cc @gramalingam