AttributeError: module 'xformers.ops' has no attribute 'AttentionOpDispatch'

[LarsDoorenbos]

🐛 Bug

When trying to call xformers.ops.AttentionOpDispatch, an AttributeError is thrown.

Command

To Reproduce

Steps to reproduce the behavior:

1. Create a Conda environment with Python 3.11
2. Install PyTorch conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 -c pytorch
3. Install xformers conda install xformers -c xformers
4. Import xformers.ops and call xformers.ops.AttentionOpDispatch (as used in https://github.com/deeplearning-wisc/dream-ood/blob/main/ldm/modules/attention.py)

iPython 3.11.10 (main, Oct  3 2024, 07:29:13) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import xformers.ops
>>> xformers.ops.AttentionOpDispatch
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: module 'xformers.ops' has no attribute 'AttentionOpDispatch'

The same error happens when using the 0.0.13 version used in the repository I'm using (deeplearning-wisc/dream-ood#7), akin to deeplearning-wisc/dream-ood#18.

Expected behavior

The AttributeError is not thrown.

Environment

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: Rocky Linux 9.3 (Blue Onyx) (x86_64)
GCC version: (GCC) 11.4.1 20230605 (Red Hat 11.4.1-2)
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.34

Python version: 3.11.10 (main, Oct  3 2024, 07:29:13) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.14.0-362.24.1.el9_3.0.1.x86_64-x86_64-with-glibc2.34
Is CUDA available: False
CUDA runtime version: 12.2.91
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
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:                      43 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 7742 64-Core Processor
CPU family:                         23
Model:                              49
Thread(s) per core:                 2
Core(s) per socket:                 64
Socket(s):                          1
Stepping:                           0
Frequency boost:                    enabled
CPU max MHz:                        2250.0000
CPU min MHz:                        1500.0000
BogoMIPS:                           4500.22
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 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 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 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 avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es
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 (16 instances)
NUMA node(s):                       4
NUMA node0 CPU(s):                  0-15,64-79
NUMA node1 CPU(s):                  16-31,80-95
NUMA node2 CPU(s):                  32-47,96-111
NUMA node3 CPU(s):                  48-63,112-127
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:             Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec rstack overflow: Mitigation; Safe RET
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, 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==2.0.1
[pip3] pytorch-ignite==0.5.0.post2
[pip3] pytorch-lightning==2.4.0
[pip3] torch==2.4.1
[pip3] torchaudio==2.3.0
[pip3] torchmetrics==1.4.3
[pip3] torchvision==0.19.1
[pip3] triton==3.0.0
[conda] blas                      1.0                         mkl
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46344
[conda] mkl-service               2.4.0           py311h5eee18b_1
[conda] mkl_fft                   1.3.10          py311h5eee18b_0
[conda] mkl_random                1.2.7           py311ha02d727_0
[conda] numpy                     2.0.1           py311h08b1b3b_1
[conda] numpy-base                2.0.1           py311hf175353_1
[conda] pytorch                   2.4.1           py3.11_cuda12.4_cudnn9.1.0_0    pytorch
[conda] pytorch-cuda              12.4                 hc786d27_7    pytorch
[conda] pytorch-lightning         2.4.0                    pypi_0    pypi
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                2.4.1               py311_cu124    pytorch
[conda] torchmetrics              1.4.3                    pypi_0    pypi
[conda] torchtriton               3.0.0                     py311    pytorch
[conda] torchvision               0.19.1              py311_cu124    pytorch

Additional context

The repo I'm copying does have a line of code stating NotImplementedError(f"Please install xformers with the flash attention / cutlass components.\n{err}"), perhaps that is what I am missing even though it throws an AttributeError instead of a NotImplementedError? How would I go about that?

Or, should I use a different attention.py file, such as e.g. described in AUTOMATIC1111/stable-diffusion-webui#576?

[LarsDoorenbos]

Solved by replacing MemoryEfficientCrossAttention with the one in https://github.com/Stability-AI/stablediffusion/blob/main/ldm/modules/attention.py.