YOLOv5 with PyTorch 2.0

[SkalskiP]

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Question

Did any of you tried to run YOLOv5 on PyTorch 2.0? Is it faster like they promised?

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Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

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[glenn-jocher]

@SkalskiP not yet. I'll try it by adding an extra line to our notebook, i.e. in Setup cell:

!git clone https://github.com/ultralytics/yolov5  # clone
%cd yolov5
%pip install -qr requirements.txt  # install
%pip install numpy --pre torch[dynamo] --force-reinstall --extra-index-url https://download.pytorch.org/whl/nightly/cu117

import torch
import utils
display = utils.notebook_init()  # checks

[glenn-jocher]

@SkalskiP strange, I get torchvision incompatibility error when running the above code in Colab. Minimum reproducibel example:

!git clone https://github.com/ultralytics/yolov5  # clone
%cd yolov5
%pip install -qr requirements.txt  # install
%pip install numpy --pre torch[dynamo] --force-reinstall --extra-index-url https://download.pytorch.org/whl/nightly/cu117

!python detect.py

EDIT: Also strange on install shows as 1.14 instead of 2.0:

YOLOv5 🚀 v7.0-21-ga1b6e79 Python-3.8.15 torch-1.14.0.dev20221203+cu117 CUDA:0 (Tesla T4, 15110MiB)
Setup complete ✅ (2 CPUs, 12.7 GB RAM, 27.8/78.2 GB disk)

EDIT2: If I use a single pip command install works better, but inference fails:

!git clone https://github.com/ultralytics/yolov5  # clone
%cd yolov5
%pip install -r requirements.txt --pre torch[dynamo] torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/nightly/cu117

# (restart runtime)

%cd yolov5

import torch
import utils
display = utils.notebook_init()  # checks

!python detect.py

Error:

detect: weights=yolov5s.pt, source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
YOLOv5 🚀 v7.0-21-ga1b6e79 Python-3.8.15 torch-1.14.0.dev20221203+cu117 CUDA:0 (Tesla T4, 15110MiB)

Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...
100% 14.1M/14.1M [00:01<00:00, 10.0MB/s]

Fusing layers... 
YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients
Traceback (most recent call last):
  File "detect.py", line 259, in <module>
    main(opt)
  File "detect.py", line 254, in main
    run(**vars(opt))
  File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 34, in decorate_context
    return func(*args, **kwargs)
  File "detect.py", line 130, in run
    pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
  File "/content/yolov5/utils/general.py", line 981, in non_max_suppression
    i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
  File "/usr/local/lib/python3.8/dist-packages/torchvision/ops/boxes.py", line 41, in nms
    return torch.ops.torchvision.nms(boxes, scores, iou_threshold)
  File "/usr/local/lib/python3.8/dist-packages/torch/_ops.py", line 500, in __call__
    return self._op(*args, **kwargs or {})
NotImplementedError: Could not run 'torchvision::nms' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'torchvision::nms' is only available for these backends: [CPU, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PythonDispatcher].

[glenn-jocher]

@AyushExel @Laughing-q the most interesting part seems to be this claim of 80% speedup with AMP on torch 2.0:
https://pytorch.org/get-started/pytorch-2.0/

[SkalskiP]

The whole thing got me super interested, I’ll be taking a look at it today.

[AyushExel]

@glenn-jocher there is torch.compile(model) that claims to speed up training by 30% or more without any code change.

[SkalskiP]

Hi, @glenn-jocher and @AyushExel 👋🏻 ! I spend ~3/4 h trying to run YOLOv5 on PyTorch 2.0 on different machines, on cpu and cuda. Failed every time.

I managed to setup the environment with latest PyTorch but didn't noticed any speedups without torch.compile(model) and when I try to do it I fail with:

File "/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py", line 690, in <lambda>
    lambda f: self._run_callback(functools.partial(callback, future))
  File "/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py", line 743, in _run_callback
    ret = callback()
  File "/usr/local/lib/python3.8/dist-packages/tornado/gen.py", line 787, in inner
    self.run()
  File "/usr/local/lib/python3.8/dist-packages/tornado/gen.py", line 748, in run
    yielded = self.gen.send(value)
  File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 365, in process_one
    yield gen.maybe_future(dispatch(*args))
  File "/usr/local/lib/python3.8/dist-packages/tornado/gen.py", line 209, in wrapper
    yielded = next(result)
  File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 268, in dispatch_shell
    yield gen.maybe_future(handler(stream, idents, msg))
  File "/usr/local/lib/python3.8/dist-packages/tornado/gen.py", line 209, in wrapper
    yielded = next(result)
  File "/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py", line 543, in execute_request
    self.do_execute(
  File "/usr/local/lib/python3.8/dist-packages/tornado/gen.py", line 209, in wrapper
    yielded = next(result)
  File "/usr/local/lib/python3.8/dist-packages/ipykernel/ipkernel.py", line 306, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/usr/local/lib/python3.8/dist-packages/ipykernel/zmqshell.py", line 536, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2854, in run_cell
    result = self._run_cell(
  File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 2881, in _run_cell
    return runner(coro)
  File "/usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py", line 68, in _pseudo_sync_runner
    coro.send(None)
  File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3057, in run_cell_async
    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
  File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3249, in run_ast_nodes
    if (await self.run_code(code, result,  async_=asy)):
  File "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py", line 3326, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-33-d0001f8fb468>", line 4, in <module>
    model(image)
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1480, in _call_impl
    return forward_call(*args, **kwargs)
  File "/content/yolov5/models/yolo.py", line 209, in forward
    return self._forward_once(x, profile, visualize)  # single-scale inference, train
  File "/content/yolov5/models/yolo.py", line 121, in _forward_once
    x = m(x)  # run
  File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1480, in _call_impl
    return forward_call(*args, **kwargs)
  File "/content/yolov5/models/common.py", line 228, in forward
    def forward(self, x):
  File "/content/yolov5/models/common.py", line 229, in forward
    x = self.cv1(x)
  File "/content/yolov5/models/common.py", line 57, in forward
    return self.act(self.bn(self.conv(x)))

==========
[2022-12-04 01:22:58,785] torch._dynamo.convert_frame: [WARNING] torch._dynamo hit config.cache_size_limit (64)
   function: 'forward_fuse' (/content/yolov5/models/common.py:59)
   reasons:  ['___check_obj_id(self, 140048282396896)']
to diagnose recompilation issues, see https://github.com/pytorch/torchdynamo/blob/main/TROUBLESHOOTING.md.

The link in description doesn't work.

Here is my colab: https://colab.research.google.com/drive/1uUTLumfMEe0x95Qqjankjy0JSwawcigL?usp=sharing

[Zengyf-CVer]

I am more interested in this issue and keep an eye on it. Hope Mr. Glenn resolves this issue soon. @glenn-jocher

[glenn-jocher]

@SkalskiP I came up with a much simpler reproduce example, but for some reason my install command is installing CPU version of torchvision with a CUDA version of torch. In a Colab notebook I did this, which only works correctly if I send the model to CPU first, i.e. mode.cpu():

%pip install gitpython ipython scipy seaborn --pre torch[dynamo] torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/nightly/cu117
# (RESTART COLAB RUNTIME)

import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
results = model('https://ultralytics.com/images/zidane.jpg')
results.print()  # shows speeds

My environment shows torchvision on CPU :(

!wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
!python collect_env.py


Collecting environment information...
PyTorch version: 1.14.0.dev20221204+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: 6.0.0-1ubuntu2 (tags/RELEASE_600/final)
CMake version: version 3.25.0
Libc version: glibc-2.27

Python version: 3.8.15 (default, Oct 12 2022, 19:14:39)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.10.133+-x86_64-with-glibc2.27
Is CUDA available: True
CUDA runtime version: 11.2.152
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 460.32.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.1.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.24.0rc2
[pip3] torch==1.14.0.dev20221204+cu117
[pip3] torchaudio==0.12.1+cu113
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.13.1
[pip3] torchtriton==2.0.0+0d7e753227
[pip3] torchvision==0.15.0.dev20221204+cpu
[conda] Could not collect

[NeuralAIM]

mode use the function with the argument :

model = torch.compile(model, mode="reduce overhead")
Or:
model = torch.compile(model, mode="max-autotune")

OR:

model = torch.compile(model, mode="reduce-overhead", fullgraph=True, backend='Eager') - fastest

[SkalskiP]

@NeuralAIM did you manage to actually run it with YOLOv5 model?

[NeuralAIM]

@NeuralAIM did you manage to actually run it with YOLOv5 model?

Yes 💯

[2catycm]

@NeuralAIM did you manage to actually run it with YOLOv5 model?

Yes 💯

Hi, where should I add the model = torch.compile(model) code?
Before traininig?