Repository: pytorch
Issue: Cannot compile a block that contains Flex attention without graph breaks
Labels: triaged,oncall: pt2,module: higher order operators,module: pt2-dispatcher,module: flex attention

### 🐛 Describe the bug

Flex attention needs to be compiled independently to the rest of the block which creates graphbreaks.
To make it work it wrap the  flex attention as follow:
```python
flex_attention_compiled = torch.compile(
    flex_attention,
    dynamic=False,
)


@torch.compiler.disable(recursive=False)
def compile_friendly_flex_attention(q, k, v, block_mask=None):
    return flex_attention_compiled(q, k, v, block_mask=block_mask)
```
Then when i compile my transformer block i get a graph break.

I tried to not compile flex attention independently and only compile it with the full block, but then i ran out of memory

### Error logs

_No response_

### Versions

PyTorch version: 2.6.0.dev20241212+cu126
Is debug build: False
CUDA used to build PyTorch: 12.6
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: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31

Python version: 3.10.16 (main, Dec 11 2024, 16:24:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-125-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA RTX A6000
Nvidia driver version: 555.42.02
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture :                          x86_64
Mode(s) opératoire(s) des processeurs : 32-bit, 64-bit
Boutisme :                              Little Endian
Address sizes:                          46 bits physical, 48 bits virtual
Processeur(s) :                         64
Liste de processeur(s) en ligne :       0-63
Thread(s) par cœur :                    2
Cœur(s) par socket :                    16
Socket(s) :                             2
Nœud(s) NUMA :                          2
Identifiant constructeur :              GenuineIntel
Famille de processeur :                 6
Modèle :                                85
Nom de modèle :                         Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz
Révision :                              7
Vitesse du processeur en MHz :          2900.000
Vitesse maximale du processeur en MHz : 3900,0000
Vitesse minimale du processeur en MHz : 1200,0000
BogoMIPS :                              5800.00
Virtualisation :                        VT-x
Cache L1d :                             1 MiB
Cache L1i :                             1 MiB
Cache L2 :                              32 MiB
Cache L3 :                              44 MiB
Nœud NUMA 0 de processeur(s) :          0-15,32-47
Nœud NUMA 1 de processeur(s) :          16-31,48-63
Vulnerability Gather data sampling:     Mitigation; Microcode
Vulnerability Itlb multihit:            KVM: Mitigation: VMX disabled
Vulnerability L1tf:                     Not affected
Vulnerability Mds:                      Not affected
Vulnerability Meltdown:                 Not affected
Vulnerability Mmio stale data:          Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Reg file data sampling:   Not affected
Vulnerability Retbleed:                 Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow:     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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds:                    Not affected
Vulnerability Tsx async abort:          Mitigation; TSX disabled
Drapaux :                               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] lovely-numpy==0.2.13
[pip3] numpy==2.2.0
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pytorch-lightning==2.4.0
[pip3] pytorch-triton==3.2.0+git35c6c7c6
[pip3] torch==2.6.0.dev20241212+cu126
[pip3] torch-fidelity==0.3.0
[pip3] torch-tb-profiler==0.4.3
[pip3] torchmetrics==1.6.0
[pip3] torchvision==0.22.0.dev20241212+cu126
[pip3] triton==3.1.0
[conda] lovely-numpy              0.2.13                   pypi_0    pypi
[conda] numpy                     2.2.0                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.6.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.6.80                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.6.77                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.6.77                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.5.1.17                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.0.4                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.7.77                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.1.2                 pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.4.2                 pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.6.3                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.21.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.6.85                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.6.77                  pypi_0    pypi
[conda] pytorch-lightning         2.4.0                    pypi_0    pypi
[conda] pytorch-triton            3.2.0+git35c6c7c6          pypi_0    pypi
[conda] torch                     2.6.0.dev20241212+cu126          pypi_0    pypi
[conda] torch-fidelity            0.3.0                    pypi_0    pypi
[conda] torch-tb-profiler         0.4.3                    pypi_0    pypi
[conda] torchmetrics              1.6.0                    pypi_0    pypi
[conda] torchvision               0.22.0.dev20241212+cu126          pypi_0    pypi
[conda] triton                    3.1.0                    pypi_0    pypi

cc @chauhang @penguinwu @zou3519 @ydwu4 @bdhirsh @yf225 @Chillee @drisspg @yanboliang @BoyuanFeng