Repository: pytorch
Issue: `torch.onnx.export` (dynamo=False) fails with uninformative error when exporting `apply_rotary_pos_emb`/`repeat_interleave`
Labels: module: onnx,triaged,OSS contribution wanted

### 🐛 Describe the bug

When attempting to export a HF transformers model that performs `apply_rotary_pos_emb` (definition below), an uninformative error is thrown:

<details>

<summary>See reproduction code</summary>


```py
import torch


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., 0::2]
    x2 = x[..., 1::2]
    return torch.stack((-x2, x1), dim=-1).flatten(-2)


def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`, *optional*):
            Deprecated and unused.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)

    # Interleave them instead of usual shape
    cos = cos[..., : cos.shape[-1] // 2].repeat_interleave(2, dim=-1)
    sin = sin[..., : sin.shape[-1] // 2].repeat_interleave(2, dim=-1)

    # Keep half or full tensor for later concatenation
    rotary_dim = cos.shape[-1]
    q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
    k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]

    # Apply rotary embeddings on the first half or full tensor
    q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
    k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)

    # Concatenate back to full shape
    q_embed = torch.cat([q_embed, q_pass], dim=-1)
    k_embed = torch.cat([k_embed, k_pass], dim=-1)
    return q_embed, k_embed


class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
    def forward(self, q, k, cos, sin):
        return apply_rotary_pos_emb(q, k, cos, sin)

model = Model()

torch.manual_seed(0)
q = torch.randn(2, 2, 40, 32)
k = torch.randn(2, 2, 40, 32)
cos = torch.randn(1, 40, 28)
sin = torch.randn(1, 40, 28)
output = model(q, k, cos, sin)
assert output[0].shape == q.shape
assert output[1].shape == k.shape

assert abs(output[0].mean().item() - -0.022467557340860367) < 1e-8
assert abs(output[1].mean().item() - 0.0071802930906414986) < 1e-8


torch.onnx.export(
    model,
    (q, k, cos, sin),
    "model.onnx",
    input_names=["q", "k", "cos", "sin"],
    output_names=["q_embed", "k_embed"],
    opset_version=18,
    dynamo=False, # True works, False is broken
)
```
</details>

produces:
```
Traceback (most recent call last):
  File "/workspaces/optimum/o/rot.py", line 74, in <module>
    torch.onnx.export(
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/torch/onnx/__init__.py", line 375, in export
    export(
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/torch/onnx/utils.py", line 502, in export
    _export(
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/torch/onnx/utils.py", line 1564, in _export
    graph, params_dict, torch_out = _model_to_graph(
                                    ^^^^^^^^^^^^^^^^
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/torch/onnx/utils.py", line 1117, in _model_to_graph
    graph = _optimize_graph(
            ^^^^^^^^^^^^^^^^
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/torch/onnx/utils.py", line 639, in _optimize_graph
    graph = _C._jit_pass_onnx(graph, operator_export_type)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/torch/onnx/utils.py", line 1836, in _run_symbolic_function
    return symbolic_fn(graph_context, *inputs, **attrs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/torch/onnx/symbolic_helper.py", line 369, in wrapper
    return fn(g, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/torch/onnx/symbolic_opset11.py", line 519, in cat
    return opset9.cat(g, tensor_list, dim)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/torch/onnx/symbolic_helper.py", line 281, in wrapper
    return fn(g, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/python/3.12.1/lib/python3.12/site-packages/torch/onnx/symbolic_opset9.py", line 563, in cat
    assert all(
AssertionError
```

The issue seems to be caused by the `repeat_interleave` call. However, the error message does not describe this.

NOTE: The model exports correctly with dynamo=True, and I understand that the previous exporter is not being worked on, so feel free to close this issue if needed. I raise it for these reasons:
- [Optimum](http://github.com/huggingface/optimum) still uses the previous exporter, and so this issue is present.
- I haven't been able to find this issue reported, so I'm adding it so I can link to it in my workaround (rewriting the `repeat_interleave` function).
- To possibly get a more informative error (since the current one doesn't indicate what the problem is)

### Versions

Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: 10.0.0-4ubuntu1 
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.12.1 (main, Sep 30 2024, 17:05:21) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-1025-azure-x86_64-with-glibc2.31
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
Byte Order:                         Little Endian
Address sizes:                      48 bits physical, 48 bits virtual
CPU(s):                             2
On-line CPU(s) list:                0,1
Thread(s) per core:                 2
Core(s) per socket:                 1
Socket(s):                          1
NUMA node(s):                       1
Vendor ID:                          AuthenticAMD
CPU family:                         25
Model:                              1
Model name:                         AMD EPYC 7763 64-Core Processor
Stepping:                           1
CPU MHz:                            3243.502
BogoMIPS:                           4890.85
Virtualization:                     AMD-V
Hypervisor vendor:                  Microsoft
Virtualization type:                full
L1d cache:                          32 KiB
L1i cache:                          32 KiB
L2 cache:                           512 KiB
L3 cache:                           32 MiB
NUMA node0 CPU(s):                  0,1
Vulnerability Gather data sampling: Not affected
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 rstack overflow: Vulnerable: Safe RET, no microcode
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI 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 tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload umip vaes vpclmulqdq rdpid fsrm

Versions of relevant libraries:
[pip3] numpy==2.0.2
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] onnx==1.17.0
[pip3] onnx-graphsurgeon==0.5.2
[pip3] onnxconverter-common==1.14.0
[pip3] onnxruntime==1.20.1
[pip3] onnxscript==0.1.0.dev20241219
[pip3] onnxslim==0.1.42
[pip3] torch==2.5.1
[pip3] torchvision==0.20.1
[pip3] triton==3.1.0
[conda] No relevant packages