Repository: pytorch
Issue: NestedTensor doesn't support elementwise operations?
Labels: triaged,module: nestedtensor

### 🐛 Describe the bug

I copied this code from this tutorial: https://pytorch.org/tutorials/prototype/nestedtensor.html and elementwise sum, subtraction, etc. is supposed to be supported yet I get this. Is it only supported for non-jagged layouts? If so, the tutorial/docs should be more clear given that the different layouts are very briefly talked about.

```python
def zipf_sentence_lengths(alpha: float, batch_size: int) -> torch.Tensor:
    # generate fake corpus by unigram Zipf distribution
    # from wikitext-2 corpus, we get rank "." = 3, "!" = 386, "?" = 858
    sentence_lengths = np.empty(batch_size, dtype=int)
    for ibatch in range(batch_size):
        sentence_lengths[ibatch] = 1
        word = np.random.zipf(alpha)
        while word != 3 and word != 386 and word != 858:
            sentence_lengths[ibatch] += 1
            word = np.random.zipf(alpha)
    return torch.tensor(sentence_lengths)

def gen_batch(N, E_q, E_k, E_v, device):
    # generate semi-realistic data using Zipf distribution for sentence lengths
    sentence_lengths = zipf_sentence_lengths(alpha=1.2, batch_size=N)

    # Note: the torch.jagged layout is a nested tensor layout that supports a single ragged
    # dimension and works with torch.compile. The batch items each have shape (B, S*, D)
    # where B = batch size, S* = ragged sequence length, and D = embedding dimension.
    query = torch.nested.nested_tensor([
        torch.randn(l.item(), E_q, device=device)
        for l in sentence_lengths
    ], layout=torch.jagged)

    key = torch.nested.nested_tensor([
        torch.randn(s.item(), E_k, device=device)
        for s in sentence_lengths
    ], layout=torch.jagged)

    value = torch.nested.nested_tensor([
        torch.randn(s.item(), E_v, device=device)
        for s in sentence_lengths
    ], layout=torch.jagged)

    return query, key, value, sentence_lengths

device = "cuda"
N = 512
E_q, E_k, E_v, E_total = 512, 512, 512, 512
E_out = E_q
nheads = 8
dropout_p = 0.0

query, key, value, sentence_lengths = gen_batch(N, E_q, E_k, E_v, device)
print(f"{query.size()=}, {key.size()=}, {value.size()=}")
query + key
```

```
query.size()=torch.Size([512, j1, 512]), key.size()=torch.Size([512, j2, 512]), value.size()=torch.Size([512, j3, 512])

RuntimeError: cannot call binary pointwise function add.Tensor with inputs of shapes (512, j1, 512) and (512, j2, 512)
```

### Versions

```
Collecting environment information...
PyTorch version: 2.4.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.12.5 (main, Aug 14 2024, 05:08:31) [Clang 18.1.8 ] (64-bit runtime)
Python platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA RTX A5000
GPU 1: NVIDIA RTX A5000
GPU 2: NVIDIA RTX A5000
GPU 3: NVIDIA RTX A5000

Nvidia driver version: 535.161.07
cuDNN version: Could not collect
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:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             128
On-line CPU(s) list:                0-127
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7543 32-Core Processor
CPU family:                         25
Model:                              1
Thread(s) per core:                 2
Core(s) per socket:                 32
Socket(s):                          2
Stepping:                           1
Frequency boost:                    enabled
CPU max MHz:                        3737.8899
CPU min MHz:                        1500.0000
BogoMIPS:                           5599.86
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 pcid sse4_1 sse4_2 x2apic 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 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 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 amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca
Virtualization:                     AMD-V
L1d cache:                          2 MiB (64 instances)
L1i cache:                          2 MiB (64 instances)
L2 cache:                           32 MiB (64 instances)
L3 cache:                           512 MiB (16 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-31,64-95
NUMA node1 CPU(s):                  32-63,96-127
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: Mitigation; safe RET
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

Versions of relevant libraries:
[pip3] numpy==2.1.1
[pip3] pytorch-lightning==2.4.0
[pip3] torch==2.4.1
[pip3] torch-tb-profiler==0.4.3
[pip3] torchmetrics==1.4.3
[pip3] triton==3.0.0
[conda] Could not collect
```

cc @cpuhrsch @jbschlosser @bhosmer @drisspg @soulitzer @davidberard98 @YuqingJ