Repository: pytorch
Issue: Error "attn_bias is not correctly aligned" When Offloading torch.nn.functional.scaled_dot_product_attention Using torch.autograd.graph.save_on_cpu()
Labels: triaged,module: sdpa

### 🐛 Describe the bug

I'm encountering an issue while training a Llama2 model when I use torch.autograd.graph.save_on_cpu() to offload activation tensors to CPU memory. The problem arises specifically when I attempt to offload the attention layer. The implementation of self_attn is based on the LlamaSdpaAttention class from the transformers library.

I have pinpointed the issue to the torch.nn.functional.scaled_dot_product_attention operator. If I offload this operation as follows:
```python
 with torch.autograd.graph.save_on_cpu():
            attn_output = torch.nn.functional.scaled_dot_product_attention(
                query_states,
                key_states,
                value_states,
                attn_mask=causal_mask,
                dropout_p=self.attention_dropout if self.training else 0.0,
                is_causal=is_causal,
            )
```
I get the following error:
```
  File "/home/zbtrs/miniconda3/envs/sglang/lib/python3.10/site-packages/torch/_tensor.py", line 525, in backward
    torch.autograd.backward(
  File "/home/zbtrs/miniconda3/envs/sglang/lib/python3.10/site-packages/torch/autograd/__init__.py", line 267, in backward
    _engine_run_backward(
  File "/home/zbtrs/miniconda3/envs/sglang/lib/python3.10/site-packages/torch/autograd/graph.py", line 744, in _engine_run_backward
    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: attn_bias is not correctly aligned (strideH) .attn_bias.stride(1) = 361, and should be a multiple of 4.
```
However, if I offload other parts of the LlamaSdpaAttention class without offloading this specific operation, the issue does not occur.

What could be causing this misalignment error when offloading the attention layer, specifically with torch.nn.functional.scaled_dot_product_attention, and how can I resolve it?



### Versions

Collecting environment information...
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 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: version 3.30.1
Libc version: glibc-2.31

Python version: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-187-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A100-SXM4-80GB
GPU 1: NVIDIA A100-SXM4-80GB
GPU 2: NVIDIA A100-SXM4-80GB
GPU 3: NVIDIA A100-SXM4-80GB
GPU 4: NVIDIA A100-SXM4-80GB
GPU 5: NVIDIA A100-SXM4-80GB
GPU 6: NVIDIA A100-SXM4-80GB
GPU 7: NVIDIA A100-SXM4-80GB

Nvidia driver version: 535.183.01
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
Byte Order:                         Little Endian
Address sizes:                      46 bits physical, 57 bits virtual
CPU(s):                             128
On-line CPU(s) list:                0-127
Thread(s) per core:                 2
Core(s) per socket:                 32
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              106
Model name:                         Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz
Stepping:                           6
Frequency boost:                    enabled
CPU MHz:                            900.000
CPU max MHz:                        3400.0000
CPU min MHz:                        800.0000
BogoMIPS:                           5200.00
Virtualization:                     VT-x
L1d cache:                          3 MiB
L1i cache:                          2 MiB
L2 cache:                           80 MiB
L3 cache:                           96 MiB
NUMA node0 CPU(s):                  0-31,64-95
NUMA node1 CPU(s):                  32-63,96-127
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             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 IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI Vulnerable, KVM SW loop
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 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 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 invpcid_single 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 rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] flashinfer==0.1.1+cu121torch2.3
[pip3] numpy==1.26.4
[pip3] torch==2.3.0
[pip3] triton==2.3.0
[conda] flashinfer                0.1.1+cu121torch2.3          pypi_0    pypi
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] torch                     2.3.0                    pypi_0    pypi
[conda] triton                    2.3.0                    pypi_0    pypi


cc @jbschlosser @bhosmer @cpuhrsch @erichan1 @drisspg @mikaylagawarecki