Repository: pytorch
Issue: Slow performance when running torch.jit traced model with Flash Attention using libtorch on Windows
Labels: module: windows,triaged,module: sdpa

### 🐛 Describe the bug

I have encountered a performance problem when executing a model that utilizes Flash Attention using torch.jit trace with C++ libtorch on Windows. The inference speed on Windows is 2 to 3 times slower than on Linux, leading me to question whether Flash Attention is genuinely being utilized during the operation.

While there are no warnings in the stable version(2.0.1) of PyTorch, when I use the nightly version(2.2.0.dev20230920+cu118) I receive the following warning:
```bash
[W sdp_utils.cpp:234] Warning: Torch was not compiled with flash attention. (function use_flash_attention)
```

I'll provide more detailed steps to describe the bug:

1. My model has a module that employs Flash Attention, as shown in the code below:
```python
# Use flash attention
self.backend_map = {
    "enable_math": False,
    "enable_flash": True,
    "enable_mem_efficient": False,
}

# Run scaled dot product attention
with sdp_kernel(**self.backend_map):
    a = F.scaled_dot_product_attention(q.half(), k.half(), v.half()).transpose(2, 3)
```

2. I then convert it to a torch.jit traced model, as illustrated below:
```python
# Trace model
with torch.no_grad():
    traced_script_module = torch.jit.trace(
        model,
        input,
        strict=True
    )

# Save model
traced_script_module.save("traced_model.pt")
```

3. I download libtorch for Windows.
```
https://download.pytorch.org/libtorch/cu118/libtorch-win-shared-with-deps-2.0.1%2Bcu118.zip
```

4. I compile and then execute using libtorch
```cpp
The C compiler identification is MSVC 19.37.32822.0
 
torch::jit::script::Module module;
module = torch::jit::load(model_path);
at::Tensor outputTensor= module(inputs).toTensor().squeeze(0);
```

5. While it runs without any errors or warnings, the speed is more than twice as slow on Windows compared to its performance on Linux.

6. Upon switching to the nightly version (2.2.0.dev20230920+cu118) of libtorch and recompiling, the execution time remains consistent with the previous result. However, I now see the warning message mentioned above.

Any assistance on this matter would be greatly appreciated!

### Versions

#### The machine used to torch.jit.trace the model (Ubuntu)

Collecting environment information...
PyTorch version: 2.1.0a0+b5021ba
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: Could not collect
CMake version: version 3.26.4
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-144-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA RTX A6000
GPU 1: NVIDIA RTX A6000
GPU 2: NVIDIA RTX A6000
GPU 3: NVIDIA RTX A6000
GPU 4: NVIDIA RTX A6000
GPU 5: NVIDIA RTX A6000
GPU 6: NVIDIA RTX A6000
GPU 7: NVIDIA RTX A6000

Nvidia driver version: 525.125.06
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.3
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.3
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.3
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.3
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.3
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.3
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.3
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 7513 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:                     2600.0000
CPU min MHz:                     1500.0000
BogoMIPS:                        5190.25
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 pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 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 wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif 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:                        256 MiB (8 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-31,64-95
NUMA node1 CPU(s):               32-63,96-127
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 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==1.22.2
[pip3] pytorch-quantization==2.1.2
[pip3] torch==2.1.0a0+b5021ba
[pip3] torch-tensorrt==1.5.0.dev0
[pip3] torchdata==0.7.0a0
[pip3] torchtext==0.16.0a0
[pip3] torchvision==0.16.0a0
[pip3] triton==2.1.0
[conda] Could not collect


#### The machine used to run traced model with libtorch (Windows)

Collecting environment information...
PyTorch version: N/A
Is debug build: N/A
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: N/A

OS: Microsoft Windows 10 Pro
GCC version: Could not collect
Clang version: Could not collect
CMake version: version 3.27.4
Libc version: N/A

Python version: 3.11.5 (tags/v3.11.5:cce6ba9, Aug 24 2023, 14:38:34) [MSC v.1936 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.19044-SP0
Is CUDA available: N/A
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080
Nvidia driver version: 537.13
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: N/A

CPU:
Architecture=9
CurrentClockSpeed=3801
DeviceID=CPU0
Family=107
L2CacheSize=4096
L2CacheSpeed=
Manufacturer=AuthenticAMD
MaxClockSpeed=3801
Name=AMD Ryzen 7 5800X 8-Core Processor
ProcessorType=3
Revision=8448

Versions of relevant libraries:
[pip3] No relevant packages
[conda] Could not collect

cc @peterjc123 @mszhanyi @skyline75489 @nbcsm @iremyux @Blackhex @jbschlosser @bhosmer @cpuhrsch @erichan1 @drisspg @mikaylagawarecki @vladimir-aubrecht @cristianPanaite