about torch.compile

[Shiroha-Key]

System Info / 系統信息

PyTorch version: 2.4.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OneFlow version: path: ['/root/miniforge3/envs/py310-CogVideo/lib/python3.10/site-packages/oneflow'], version: 0.9.1.dev20240923+cu121, git_commit: d23c061, cmake_build_type: Release, rdma: True, mlir: True, enterprise: False
Nexfort version: 0.1.dev271
OneDiff version: 1.2.0
OneDiffX version: 1.2.0

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: 3.10.15 | packaged by conda-forge | (main, Sep 20 2024, 16:37:05) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-5.4.0-26-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.66
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090
GPU 4: NVIDIA GeForce RTX 3090
GPU 5: NVIDIA GeForce RTX 3090
GPU 6: NVIDIA GeForce RTX 3090
GPU 7: NVIDIA GeForce RTX 3090

Nvidia driver version: 525.60.13
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):                          96
On-line CPU(s) list:             0-95
Thread(s) per core:              2
Core(s) per socket:              24
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           106
Model name:                      Intel(R) Xeon(R) Gold 6342 CPU @ 2.80GHz
Stepping:                        6
Frequency boost:                 enabled
CPU MHz:                         3373.456
CPU max MHz:                     3500.0000
CPU min MHz:                     800.0000
BogoMIPS:                        5600.00
Virtualization:                  VT-x
L1d cache:                       2.3 MiB
L1i cache:                       1.5 MiB
L2 cache:                        60 MiB
L3 cache:                        72 MiB
NUMA node0 CPU(s):               0-23,48-71
NUMA node1 CPU(s):               24-47,72-95
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          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
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 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 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] diffusers==0.30.3
[pip3] numpy==1.26.0
[pip3] torch==2.4.1
[pip3] torchvision==0.19.1
[pip3] transformers==4.44.2
[pip3] triton==3.0.0
[conda] numpy                     1.26.0                   pypi_0    pypi
[conda] torch                     2.4.1                    pypi_0    pypi
[conda] torchvision               0.19.1                   pypi_0    pypi
[conda] triton                    3.0.0                    pypi_0    pypi

Information / 问题信息

• [X] The official example scripts / 官方的示例脚本
• [X] My own modified scripts / 我自己修改的脚本和任务

Reproduction / 复现过程

on defalut cli_demo.py

pipe.enable_sequential_cpu_offload()

    pipe.vae.enable_slicing()
    pipe.vae.enable_tiling()
添加：
    pipe.transformer = torch.compile(
        pipe.transformer, mode="max-autotune", fullgraph=True
    )

运行 之后会在torch._dynamo.exc.py的第221行得到报错

hasattr ConstDictVariable to

from user code:
   File "/root/miniforge3/envs/py310-CogVideo/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward
    args, kwargs = module._hf_hook.pre_forward(module, *args, **kwargs)
  File "/root/miniforge3/envs/py310-CogVideo/lib/python3.10/site-packages/accelerate/hooks.py", line 364, in pre_forward
    return send_to_device(args, self.execution_device), send_to_device(
  File "/root/miniforge3/envs/py310-CogVideo/lib/python3.10/site-packages/accelerate/utils/operations.py", line 149, in send_to_device
    if is_torch_tensor(tensor) or hasattr(tensor, "to"):

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information

是我的torch.compile用法有问题吗，我想知道你们是怎么使用torch.compile优化性能的。 谢谢🙏

Expected behavior / 期待表现

use torch.compie to speed up cogvideo

[zRzRzRzRzRzRzR]

如果你compile，你需要移除

pipe.enable_sequential_cpu_offload()

[Shiroha-Key]

嗯嗯 我还想问一下关于int8

1. int8量化需不需要源码安装 torch、torchao、diffusers 和 accelerate Python 包。
2. 理论上来说量化会降低显存并提升推理速度，为什么手册写的是推理速度大幅降低。https://github.com/THUDM/CogVideo/blob/main/README_zh.md
3. 我的GPU只有24gb vram 现在开启enable_sequential_cpu_offload()才能将最大显存使用控制在22GB左右，也就是不能开启torch.compile以及torchao的优化，但我i2v一个6s的视频需要12min 请问compile不能用的情况下 还有什么方案可以优化一些速度吗，int8真的不会帮忙加速吗

如果你能回答就太好了！！ @zRzRzRzRzRzRzR