Repository: pytorch
Issue: torch.onnx.export - `repeat_interleave` produces invalid model
Labels: needs reproduction,module: onnx,triaged

### 🐛 Describe the bug

While adding ONNX export support for Cohere models to Optimum ([PR](https://github.com/huggingface/optimum/pull/1905)), I ran into an issue due to [a single problematic](https://github.com/huggingface/transformers/blob/254b25abd9a04bfff69408b9fdbaf2d270d1caaa/src/transformers/models/cohere/modeling_cohere.py#L119) `repeat_interleave` operation within `CohereRotaryEmbedding`.

After further analysis, this does appear to be an issue with pytorch/onnx exporting. There is a chance this is related to ONNXRuntime, but in their issue page they link here for pytorch conversion errors.

Minimal reproduction (adapted from the HF transformers repo for Cohere models):

```py
import torch
import torch.nn as nn

class CohereRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
        super().__init__()
        self.scaling_factor = scaling_factor
        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

    @torch.no_grad()
    def forward(self, x, position_ids):
        # x: [bs, num_attention_heads, seq_len, head_size]
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
        position_ids_expanded = position_ids[:, None, :].float()

        # Force float32 since bfloat16 loses precision on long contexts
        # See https://github.com/huggingface/transformers/pull/29285
        device_type = x.device.type
        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.repeat_interleave(freqs, 2, dim=-1)
            cos = emb.cos()
            sin = emb.sin()
        return cos, sin

# Example usage in pytorch
torch_model = CohereRotaryEmbedding(dim=16)

input_ids = torch.ones((2, 16))
position_ids = torch.arange(16).expand(2, -1)
cos, sin = torch_model(input_ids, position_ids)

# Export the model (works)
torch.onnx.export(torch_model,
                  (input_ids, position_ids),
                  "model.onnx",
                  export_params=True,
                  opset_version=14,
                  do_constant_folding=True,
                  input_names = ['position_ids'],
                  output_names = ['cos', 'sin'],
                  dynamic_axes={
                      'position_ids' : {0 : 'batch_size', 1: 'sequence_length'},
                      'cos' : {0 : 'batch_size', 1: 'sequence_length'},
                      'sin' : {0 : 'batch_size', 1: 'sequence_length'},
                  })

# Run model with ONNXRuntime and validate
import onnxruntime
import numpy as np

ort_session = onnxruntime.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])

def to_numpy(tensor):
    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()

# compute ONNX Runtime output prediction
ort_inputs = dict(position_ids=position_ids.numpy())
ort_outs = ort_session.run(None, ort_inputs)

# compare ONNX Runtime and PyTorch results
np.testing.assert_allclose(to_numpy(cos), ort_outs[0], rtol=1e-03, atol=1e-05)
np.testing.assert_allclose(to_numpy(sin), ort_outs[1], rtol=1e-03, atol=1e-05)

print("Exported model has been tested with ONNXRuntime, and the result looks good!")
```

This produces the following error:
```
InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Non-zero status code returned while running Expand node. Name:'/Expand' Status Message: invalid expand shape
Fail: [ONNXRuntimeError] : 1 : FAIL : Node (/Expand) Op (Expand) [ShapeInferenceError] Incompatible dimensions
```

---

however, if we replace the `repeat_interleave` with an equivalent op:
```diff
with torch.autocast(device_type=device_type, enabled=False):
    freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-   emb = torch.repeat_interleave(freqs, 2, dim=-1)
+   emb = freqs[..., None].expand(*freqs.shape, 2).reshape(*freqs.shape[:-1], -1)
    cos = emb.cos()
    sin = emb.sin()
```

the model exports and runs correctly:
```
Exported model has been tested with ONNXRuntime, and the result looks good!
```

### Versions

PyTorch version: 2.3.0+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-6.1.85+-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 Reg file data sampling: Not affected
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; BHI: Vulnerable (Syscall hardening enabled)
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.25.2
[pip3] onnx==1.15.0
[pip3] onnxconverter-common==1.14.0
[pip3] onnxruntime==1.17.1
[pip3] torch==2.3.0+cu121
[pip3] torchaudio==2.3.0+cu121
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.18.0
[pip3] torchvision==0.18.0+cu121
[pip3] triton==2.3.0
[conda] Could not collect