upfirdn2d_plugin Problem

Describe the bug
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!

Please stop closing people's issues without a confirmed fix for this problem. #2 (comment) does not work and there is no confirmed fix on that issue that was closed without a confirmed fix.

Please be serious about it and let's work together for a fix instead of ignoring the problem and referring people to a close topic that does not offer any solution to their problem.

We tried everything proposed we also tried both Cuda 11.0 and 11.1, with different version of PyTorch just in case.
We are a team of 5 people and we all had the same problem in both Windows and Linux machine and even in google Collab which tells me that this is more than just a configuration problem.

and no %pip install ninja did not solve the problem in any of the machines we have in our lab.
also, using verbosity = 'full' does not seem to include any additional helpful information.

Desktop (please complete the following information):

Those are the two machines I used

Machine 1

• ubuntu 20.04.1,
• pytorch 1.7.1
• CUDA 11.1,
• RTX 3090

Machine 2

• Windows 10
• pytorch 1.7.1
• CUDA 11.1, also tried with - CUDA 11.0
• CUDA toolkit version (e.g., CUDA 11.0)
• NVIDIA driver version 461.40
• RTX 3090

[nurpax]

Ok, thanks for filing a separate bug. I’ll keep this one open. There are multiple different problems filed into separate bugs with comments about separate issues added into the same bug. So it gets messy.

Trying to use class AugmentPipe in my project.
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!

Can you give a bit of detail about your project structure? How are you making use of AugmentPipe in your project?

Is unmodified stylegan2-ada-pytorch project working for you?

Hi @nurpax disregard the first line, It was written for something else, I updated my issue with more details.

[nurpax]

Just double checking: your version of stylegan2-ada-pytorch is unmodified and it still does not work?

If you run it in Docker, does it work then? Most users have no issue when running in Docker so you should check if that works and report here. (I understand some people don’t like using Docker but it’s good debug info to check if it works or not.)

Clearly one of the key problems with these custom extensions is that when something goes wrong in their build or first use, the error message throws away too much information about what exactly went wrong.

Yes Correct, I haven't made any changes to it. I just this morning cleaned my driver and made a fresh install, created a new anaconda env and downloaded a fresh copy from this repo but the same problem happens. I don't know why.

[nurpax]

I think you've done this step but I'm adding it here for completeness, even if it may sound like I'm just repeating the same thing over and over.

The simplest form of getting this error:

"Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!"

is when there's no ninja installed with pip install ninja or conda install ninja. The error message unfortunately doesn't give any indication that ninja is missing.

I'm mentioning this here as %pip install ninja from the bug description seems to refer to Colab.

Also: can you please confirm that it works for you in Docker?

I actually tried both pip install ninja and conda install ninja with similar outcome.
for Docker, no I haven't tried it.

[SofianeBenkara]

@nurpax

I have been dealing with the same problem.

when I try to generate it works fine but it is slow and this is my output

Loading networks from "../../Data/ffhq.pkl"...
Generating image for seed 8201 (0/1) ...
Setting up PyTorch plugin "bias_act_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!

then at the end it does generate an image successfully,

for training and projecting,
I get this

Setting up augmentation...
Distributing across 1 GPUs...
Setting up training phases...
Exporting sample images...
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
..
..
...
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Evaluating metrics...

It then get stuck at Evaluating metrics... then the kernel dies

when I try to project I get this

Loading networks from "..\..\Data\ffhq.pkl"...
Computing W midpoint and stddev using 10000 samples...
Setting up PyTorch plugin "bias_act_plugin"... Failed!
Downloading https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt ... done
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
..
..
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!

this continue for few minutes then the kernel dies also.

I hope this help. I have tried all the solution proposed in the other issues opened and was not able to get this working. I have read that other people are having the same problem on reddit and no one is sure what's the problem.

[nurpax]

What seems to be happening is that either the extension build somehow fails or the built extension is not able to run somehow. The pytorch code then will try to fallback to a reference implementation that is slower. It looks like this fallback mechanism is not working all too well, as it's trying to build on every invocation. This probably explains why it's so super slow.

I'd prefer if we'd find a real fix for this, of course, but here's one thing you could try. You could force the custom ops to always use the slower reference path. This will be slower but it should work.

I haven't tried this in a while, but I think you can force the reference implementation by editing the below function (and all the other similar _init functions in that folder):

https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/torch_utils/ops/bias_act.py#L41

def _init():
    global _inited, _plugin
    if not _inited:
        _inited = True
        sources = ['bias_act.cpp', 'bias_act.cu']
        sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
        try:
            _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
        except:
            warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + str(sys.exc_info()[1]))
    return _plugin is not None

to just:

def _init():
    return False

@SBenkara is your repro on Docker or native installation of PyTorch and CUDA? What about the folks on Reddit?

[SofianeBenkara]

@nurpax
I haven't tried Linux nor Docker. I am using a Windows 10 with an RTX 3090 GPU and a native installation of PyTorch and Cuda 11.1 and followed all the step on the read me.

I will try if i could find the reddit post and linked it but most people there were using Windows/Linux and I don't remember seeing Docker related issue.

any idea why the extension build is failing? is there any logs i can get that would help?

I will try to make the changes you suggested for now until we fix this issue.

[SofianeBenkara]

I have some updates hopefully then can help in pinpointing the problem.

I forgot to mention that I was using Jupyter notebook. I am not sure what difference it makes but I didn't have any of those issues when I tried using a command line or PyCharm, I just did a pip install and everything started working flawlessly.

The problem might be related to either the Jupyter notebook or Anaconda. I made sure to create more environments to make sure that was not a problem with my anaconda env, but they all failed.

so I made the changes you suggested, it printed less line of Setting up PyTorch plugin "upfirdn2d_plugin"... Failed! without being able to do a projection or training as the kernel continued to stop was still getting the same error from other parts of the code, from \upfirdn2d.py mainly.

No module named 'upfirdn2d_plugin'
  warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + str(sys.exc_info()[1]))
C:\Users\admin\Google Drive\PyTorch\stylegan2-ada-pytorch\torch_utils\ops\upfirdn2d.py:34: UserWarning: Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:

Edit:
after working just fine from my command line for few minute, it's now back at throwing the same error message without me making any changes

UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use S
etuptools' objects directly or at least import Setuptools fir



No module named 'upfirdn2d_plugin'
  warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + str(sys.exc_info()[1]))
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
C:\Users\admin\Google Drive\stylegan2-ada-pytorch-main\stylegan2-ada-pytorch-main\torch_utils\ops\upfirdn2d.py:34: UserWarning: Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:

[nurpax]

@SBenkara @DarXT3mpla4 can you try patching your stylegan2-ada-pytorch code as follows:

diff --git a/torch_utils/ops/bias_act.py b/torch_utils/ops/bias_act.py
index b092c7f..b6190f8 100755
--- a/torch_utils/ops/bias_act.py
+++ b/torch_utils/ops/bias_act.py
@@ -44,10 +44,7 @@ def _init():
         _inited = True
         sources = ['bias_act.cpp', 'bias_act.cu']
         sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
-        try:
-            _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
-        except:
-            warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + str(sys.exc_info()[1]))
+        _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
     return _plugin is not None
 
 #----------------------------------------------------------------------------
diff --git a/torch_utils/ops/upfirdn2d.py b/torch_utils/ops/upfirdn2d.py
index f768b2c..76ac2d6 100755
--- a/torch_utils/ops/upfirdn2d.py
+++ b/torch_utils/ops/upfirdn2d.py
@@ -28,10 +28,7 @@ def _init():
     if not _inited:
         sources = ['upfirdn2d.cpp', 'upfirdn2d.cu']
         sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
-        try:
-            _plugin = custom_ops.get_plugin('upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
-        except:
-            warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + str(sys.exc_info()[1]))
+        _plugin = custom_ops.get_plugin('upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
     return _plugin is not None
 
 def _parse_scaling(scaling):

I.e., remove try/excepts from around the custom_ops.get_plugin() call.

It looks like some exception info is getting lost with the way try/except is written. For example, if I rename my ninja executable in my anaconda3 dirs and rerun with this change, I get a more informative stacktrace. With some luck, maybe this will reveal some new information about the error you are seeing.

Generating image for seed 85 (0/4) ...
Setting up PyTorch plugin "bias_act_plugin"... Failed!
/home/janne/dev/stylegan2-ada-pytorch/torch_utils/ops/bias_act.py:50: UserWarning: Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:

Ninja is required to load C++ extensions
  warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + str(sys.exc_info()[1]))
Setting up PyTorch plugin "upfirdn2d_plugin"... Failed!
Traceback (most recent call last):
  File "generate.py", line 127, in <module>
    generate_images() # pylint: disable=no-value-for-parameter
  File "/home/janne/anaconda3/lib/python3.8/site-packages/click/core.py", line 829, in __call__
    return self.main(*args, **kwargs)
  File "/home/janne/anaconda3/lib/python3.8/site-packages/click/core.py", line 782, in main
    rv = self.invoke(ctx)
  File "/home/janne/anaconda3/lib/python3.8/site-packages/click/core.py", line 1066, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/home/janne/anaconda3/lib/python3.8/site-packages/click/core.py", line 610, in invoke
    return callback(*args, **kwargs)
  File "/home/janne/anaconda3/lib/python3.8/site-packages/click/decorators.py", line 21, in new_func
    return f(get_current_context(), *args, **kwargs)
  File "generate.py", line 119, in generate_images
    img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
  File "/home/janne/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "<string>", line 491, in forward
  File "/home/janne/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "<string>", line 463, in forward
  File "/home/janne/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "<string>", line 397, in forward
  File "/home/janne/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "<string>", line 291, in forward
  File "/home/janne/dev/stylegan2-ada-pytorch/torch_utils/misc.py", line 101, in decorator
    return fn(*args, **kwargs)
  File "<string>", line 72, in modulated_conv2d
  File "/home/janne/dev/stylegan2-ada-pytorch/torch_utils/misc.py", line 101, in decorator
    return fn(*args, **kwargs)
  File "/home/janne/dev/stylegan2-ada-pytorch/torch_utils/ops/conv2d_resample.py", line 139, in conv2d_resample
    x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter)
  File "/home/janne/dev/stylegan2-ada-pytorch/torch_utils/ops/upfirdn2d.py", line 159, in upfirdn2d
    if impl == 'cuda' and x.device.type == 'cuda' and _init():
  File "/home/janne/dev/stylegan2-ada-pytorch/torch_utils/ops/upfirdn2d.py", line 31, in _init
    _plugin = custom_ops.get_plugin('upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
  File "/home/janne/dev/stylegan2-ada-pytorch/torch_utils/custom_ops.py", line 110, in get_plugin
    torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
  File "/home/janne/anaconda3/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 986, in load
    return _jit_compile(
  File "/home/janne/anaconda3/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1193, in _jit_compile
    _write_ninja_file_and_build_library(
  File "/home/janne/anaconda3/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1268, in _write_ninja_file_and_build_library
    verify_ninja_availability()
  File "/home/janne/anaconda3/lib/python3.8/site-packages/torch/utils/cpp_extension.py", line 1323, in verify_ninja_availability
    raise RuntimeError("Ninja is required to load C++ extensions")
RuntimeError: Ninja is required to load C++ extensions

[SofianeBenkara]

this is what I am getting now, also it just crash without any output

C:\Users\admin\Google Drive\PyTorch\stylegan2-ada-pytorch\torch_utils\ops\upfirdn2d.py:34: UserWarning: Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:

Error building extension 'upfirdn2d_plugin': [1/2] C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\bin\nvcc -Xcudafe --diag_suppress=dll_interface_conflict_dllexport_assumed -Xcudafe --diag_suppress=dll_interface_conflict_none_assumed -Xcudafe --diag_suppress=field_without_dll_interface -Xcudafe --diag_suppress=base_class_has_different_dll_interface -Xcompiler /EHsc -Xcompiler /wd4190 -Xcompiler /wd4018 -Xcompiler /wd4275 -Xcompiler /wd4267 -Xcompiler /wd4244 -Xcompiler /wd4251 -Xcompiler /wd4819 -Xcompiler /MD -DTORCH_EXTENSION_NAME=upfirdn2d_plugin -DTORCH_API_INCLUDE_EXTENSION_H -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include\torch\csrc\api\include -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include\TH -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include\THC "-IC:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\include" -IC:\Users\admin\anaconda3\envs\ptx\Include -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_86,code=sm_86 --use_fast_math -c "C:\Users\admin\Google Drive\PyTorch\stylegan2-ada-pytorch\torch_utils\ops\upfirdn2d.cu" -o upfirdn2d.cuda.o 
FAILED: upfirdn2d.cuda.o 
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\bin\nvcc -Xcudafe --diag_suppress=dll_interface_conflict_dllexport_assumed -Xcudafe --diag_suppress=dll_interface_conflict_none_assumed -Xcudafe --diag_suppress=field_without_dll_interface -Xcudafe --diag_suppress=base_class_has_different_dll_interface -Xcompiler /EHsc -Xcompiler /wd4190 -Xcompiler /wd4018 -Xcompiler /wd4275 -Xcompiler /wd4267 -Xcompiler /wd4244 -Xcompiler /wd4251 -Xcompiler /wd4819 -Xcompiler /MD -DTORCH_EXTENSION_NAME=upfirdn2d_plugin -DTORCH_API_INCLUDE_EXTENSION_H -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include\torch\csrc\api\include -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include\TH -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include\THC "-IC:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\include" -IC:\Users\admin\anaconda3\envs\ptx\Include -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_86,code=sm_86 --use_fast_math -c "C:\Users\admin\Google Drive\PyTorch\stylegan2-ada-pytorch\torch_utils\ops\upfirdn2d.cu" -o upfirdn2d.cuda.o 
nvcc fatal   : Unsupported gpu architecture 'compute_86'
ninja: build stopped: subcommand failed.

  warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + str(sys.exc_info()[1]))
C:\Users\admin\Google Drive\PyTorch\stylegan2-ada-pytorch\torch_utils\ops\upfirdn2d.py:34: UserWarning: Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:

No module named 'upfirdn2d_plugin'
  warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + str(sys.exc_info()[1]))

[nurpax]

@SBenkara I guess you left the warnings.warn line there? My patch above had that taken out too.

Nevertheless, the error is a little more apparent now (emphasis mine):

Error building extension 'upfirdn2d_plugin': [1/2] C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\ v11.0 \bin
nvcc fatal : Unsupported gpu architecture 'compute_86'

CUDA 11.0 does not support compiling for compute_86 arch, to build for compute_86, you need CUDA 11.1. You can see from above that it's building with CUDA 11.0 nvcc.

Another way to verify what compiler versions and flags are actually used, you can check the build.ninja files under ~/.cache/torch_extensions/ (e.g., bias_act_plugin/build.ninja). I'm not sure where exactly this file resides on Windows. Please attach or copy&paste the full contents of one of these files here.

[SofianeBenkara]

ninja_required_version = 1.3
cxx = cl
nvcc = C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\bin\nvcc

cflags = -DTORCH_EXTENSION_NAME=bias_act_plugin -DTORCH_API_INCLUDE_EXTENSION_H -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include\torch\csrc\api\include -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include\TH -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include\THC "-IC:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\include" -IC:\Users\admin\anaconda3\envs\ptx\Include -D_GLIBCXX_USE_CXX11_ABI=0 /MD /wd4819 /wd4251 /wd4244 /wd4267 /wd4275 /wd4018 /wd4190 /EHsc
post_cflags = 
cuda_cflags = -Xcudafe --diag_suppress=dll_interface_conflict_dllexport_assumed -Xcudafe --diag_suppress=dll_interface_conflict_none_assumed -Xcudafe --diag_suppress=field_without_dll_interface -Xcudafe --diag_suppress=base_class_has_different_dll_interface -Xcompiler /EHsc -Xcompiler /wd4190 -Xcompiler /wd4018 -Xcompiler /wd4275 -Xcompiler /wd4267 -Xcompiler /wd4244 -Xcompiler /wd4251 -Xcompiler /wd4819 -Xcompiler /MD -DTORCH_EXTENSION_NAME=bias_act_plugin -DTORCH_API_INCLUDE_EXTENSION_H -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include\torch\csrc\api\include -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include\TH -IC:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\include\THC "-IC:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\include" -IC:\Users\admin\anaconda3\envs\ptx\Include -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_86,code=sm_86 --use_fast_math
cuda_post_cflags = 
ldflags = /DLL c10.lib c10_cuda.lib torch_cpu.lib torch_cuda.lib -INCLUDE:?warp_size@cuda@at@@YAHXZ torch.lib torch_python.lib /LIBPATH:C:\Users\admin\anaconda3\envs\ptx\libs /LIBPATH:C:\Users\admin\anaconda3\envs\ptx\lib\site-packages\torch\lib "/LIBPATH:C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\lib/x64" cudart.lib

rule compile
  command = cl /showIncludes $cflags -c $in /Fo$out $post_cflags
  deps = msvc

rule cuda_compile
  command = $nvcc $cuda_cflags -c $in -o $out $cuda_post_cflags

rule link
  command = "C$:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.28.29333\bin\Hostx64\x64/link.exe" $in /nologo $ldflags /out:$out

build bias_act.o: compile C$:\Users\admin\Google$ Drive\stylegan2-ada-pytorch-main\torch_utils\ops\bias_act.cpp
build bias_act.cuda.o: cuda_compile C$:\Users\admin\Google$ Drive\stylegan2-ada-pytorch-main\torch_utils\ops\bias_act.cu

build bias_act_plugin.pyd: link bias_act.o bias_act.cuda.o

default bias_act_plugin.pyd

[nurpax]

Yes, definitely confirms that 11.0 is being used instead of 11.1.

What you will need is to install CUDA 11.1 toolkit from NVIDIA and make sure that you set it up so that 11.1 version comes up first in PATH. E.g., try running "nvcc --version" and check that it's the right version. On my computer this reports something like this:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Mon_Oct_12_20:09:46_PDT_2020
Cuda compilation tools, release 11.1, V11.1.105
Build cuda_11.1.TC455_06.29190527_0