Flash Attention 2 + Dynamo + FSDP accelerate plugin + torch.compile error #158186
Description
🐛 Describe the bug
When using the above-mentioned combination of packages (torch==2.7.1, flash_attn==2.8.1, accelerate==1.8.1) and using accelerate (accelerate launch ...) with FSDP and TorchDynamoPlugin plugins, as well as Flash Attention 2 enabled in one of the modules, I get the following error message:
lib/python3.13/site-packages/torch/_dynamo/variables/functions.py:1263: UserWarning: Dynamo does not know how to trace the builtin
flash_attn_2_cuda.PyCapsule.varlen_fwd.This function is either a Python builtin (e.g. _warnings.warn) or a third-party C/C++ Python extension (perhaps created with pybind).
If it is a Python builtin, please file an issue on GitHub so the PyTorch team can add support for it and see the next case for a workaround.
If it is a third-party C/C++ Python extension, please either wrap it into a PyTorch-understood custom operator (see https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html for more details) or, if it is traceable, usetorch.compiler.allow_in_graph.
torch._dynamo.utils.warn_once(explanation + "\n" + "\n".join(hints))
Versions
Collecting environment information...
PyTorch version: 2.7.1+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: version 3.28.0
Libc version: glibc-2.31
Python version: 3.13.2 | packaged by Anaconda, Inc. | (main, Feb 6 2025, 18:56:02) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-1089-azure-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100 80GB PCIe
GPU 1: NVIDIA A100 80GB PCIe
GPU 2: NVIDIA A100 80GB PCIe
GPU 3: NVIDIA A100 80GB PCIe
Nvidia driver version: 570.133.07
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.5.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.5.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.5.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.5.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.5.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.5.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.5.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.5.1
Is XPU available: False
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: 48 bits physical, 48 bits virtual
CPU(s): 96
On-line CPU(s) list: 0-95
Thread(s) per core: 1
Core(s) per socket: 48
Socket(s): 2
NUMA node(s): 4
Vendor ID: AuthenticAMD
CPU family: 25
Model: 1
Model name: AMD EPYC 7V13 64-Core Processor
Stepping: 1
CPU MHz: 2445.440
BogoMIPS: 4890.88
Hypervisor vendor: Microsoft
Virtualization type: full
L1d cache: 3 MiB
L1i cache: 3 MiB
L2 cache: 48 MiB
L3 cache: 384 MiB
NUMA node0 CPU(s): 0-23
NUMA node1 CPU(s): 24-47
NUMA node2 CPU(s): 48-71
NUMA node3 CPU(s): 72-95
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 Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; safe RET, no microcode
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
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 mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm
Versions of relevant libraries:
[pip3] lion-pytorch==0.2.3
[pip3] numpy==2.2.5
[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==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pytorch-lightning==2.5.1.post0
[pip3] pytorch-metric-learning==2.8.1
[pip3] pytorch-ranger==0.1.1
[pip3] torch==2.7.1
[pip3] torch-audiomentations==0.12.0
[pip3] torch-optimizer==0.3.0
[pip3] torch_pitch_shift==1.2.5
[pip3] torchaudio==2.7.1
[pip3] torchcrepe==0.0.24
[pip3] torchmetrics==1.7.1
[pip3] torchvision==0.22.1
[pip3] triton==3.3.1
[conda] blas 1.0 mkl
[conda] cuda-cudart 12.4.127 0 nvidia
[conda] cuda-cudart-dev 12.4.127 0 nvidia
[conda] cuda-cudart-static 12.4.127 0 nvidia
[conda] cuda-cupti 12.4.127 0 nvidia
[conda] cuda-cupti-static 12.4.127 0 nvidia
[conda] cuda-libraries 12.4.1 h06a4308_1
[conda] cuda-libraries-dev 12.4.1 h06a4308_1
[conda] cuda-libraries-static 12.4.1 0 nvidia
[conda] cuda-nvrtc 12.4.127 0 nvidia
[conda] cuda-nvrtc-dev 12.4.127 0 nvidia
[conda] cuda-nvrtc-static 12.4.127 0 nvidia
[conda] cuda-nvtx 12.4.127 0 nvidia
[conda] cuda-opencl 12.4.127 0 nvidia
[conda] cuda-opencl-dev 12.4.127 0 nvidia
[conda] cudatoolkit 11.1.1 hb139c0e_13 conda-forge
[conda] intel-openmp 2023.1.0 hdb19cb5_46306
[conda] libcublas 12.4.5.8 0 nvidia
[conda] libcublas-dev 12.4.5.8 0 nvidia
[conda] libcublas-static 12.4.5.8 0 nvidia
[conda] libcufft 11.2.1.3 0 nvidia
[conda] libcufft-dev 11.2.1.3 0 nvidia
[conda] libcufft-static 11.2.1.3 0 nvidia
[conda] libcurand 10.3.5.147 0 nvidia
[conda] libcurand-dev 10.3.5.147 0 nvidia
[conda] libcurand-static 10.3.5.147 0 nvidia
[conda] libcusolver 11.6.1.9 0 nvidia
[conda] libcusolver-dev 11.6.1.9 0 nvidia
[conda] libcusolver-static 11.6.1.9 0 nvidia
[conda] libcusparse 12.3.1.170 0 nvidia
[conda] libcusparse-dev 12.3.1.170 0 nvidia
[conda] libcusparse-static 12.3.1.170 0 nvidia
[conda] libnvjitlink 12.4.127 0 nvidia
[conda] libnvjitlink-dev 12.4.127 0 nvidia
[conda] mkl 2023.1.0 h213fc3f_46344
[conda] mkl-service 2.4.0 py38h5eee18b_1
[conda] mkl_fft 1.3.8 py38h5eee18b_0
[conda] mkl_random 1.2.4 py38hdb19cb5_0
[conda] numpy 1.24.4 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.1.3.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.0.2.54 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.2.106 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.1.0.106 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi
[conda] pytorch-lightning 2.4.0 pypi_0 pypi
[conda] pytorch-metric-learning 2.7.0 pypi_0 pypi
[conda] tbb 2021.8.0 hdb19cb5_0
[conda] torch 2.4.1 pypi_0 pypi
[conda] torch-audiomentations 0.11.1 pypi_0 pypi
[conda] torch-pitch-shift 1.2.5 pypi_0 pypi
[conda] torchaudio 0.8.0 py38 pytorch
[conda] torchmetrics 1.5.1 pypi_0 pypi
[conda] torchvision 0.15.2 cpu_py38h83e0c9b_0
[conda] triton 3.0.0 pypi_0 pypi