nn.ConvTranspose2d with metal any way

username: MrNeither
cooked: <p>Hi! I’m trying to run my model on ios mobile device(iphone X) with gpu support (metal). But i get some errors, so i hope for your help. This small example</p>
<pre><code class="lang-auto">import torch
import torch.nn as nn
from torch.utils.mobile_optimizer import optimize_for_mobile

class TEST(nn.Module):
    def __init__(self):
        super().__init__()
        self.quant = torch.quantization.QuantStub()
        self.dequant = torch.quantization.DeQuantStub()
        self.convt = torch.nn.ConvTranspose2d(3, 3, 3)

    def forward(self, x):
        x = self.quant(x)
        x = self.convt(x)
        x = self.dequant(x)
        return x

rand_input = torch.randn(1, 3, 256, 256)
with torch.no_grad():
    model = TEST().eval()
    backend = "qnnpack" #"fbgemm" # this var also error
    torch.backends.quantized.engine = backend
    model.qconfig = torch.quantization.get_default_qat_qconfig(backend)
    torch.quantization.prepare(model, inplace=True)
    torch.quantization.convert(model, inplace=True)
    script_module = torch.jit.script(model)
    optimized_model = optimize_for_mobile(script_module, backend='metal')
    print(torch.jit.export_opnames(optimized_model))
    optimized_model._save_for_lite_interpreter("quant_test.ptl")
</code></pre>
<p>output</p>
<pre><code class="lang-auto">['aten::dequantize.self', 'aten::len.t', 'aten::ne.int', 'aten::quantize_per_tensor', 'aten::size', 'prim::RaiseException', 'quantized::conv_transpose2d']
</code></pre>
<p>Then if i run this model</p>
<pre><code class="lang-auto">at::Tensor rand_input = torch::randn({1, 3, 256, 256}).metal();
auto outputTensor = _impl.forward({ rand_input }).toTensor().cpu();
</code></pre>
<p>I have output</p>
<pre><code class="lang-auto">2022-02-03 10:47:15.805418+0300 Proj-iOS[25878:25338470] Metal API Validation Enabled
2022-02-03 10:47:15.859409+0300 Proj-iOS[25878:25338470] Could not run 'aten::quantize_per_tensor' with arguments from the 'Metal' 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. 'aten::quantize_per_tensor' is only available for these backends: [CPU, BackendSelect, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradLazy, AutogradXPU, AutogradMLC, AutogradHPU, Functionalize].

CPU: registered at /Users/distiller/project/build_ios/aten/src/ATen/RegisterCPU.cpp:20943 [kernel]
BackendSelect: fallthrough registered at /Users/distiller/project/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
ADInplaceOrView: fallthrough registered at /Users/distiller/project/aten/src/ATen/core/VariableFallbackKernel.cpp:64 [backend fallback]
AutogradOther: fallthrough registered at /Users/distiller/project/aten/src/ATen/core/VariableFallbackKernel.cpp:35 [backend fallback]
AutogradCPU: fallthrough registered at /Users/distiller/project/aten/src/ATen/core/VariableFallbackKernel.cpp:39 [backend fallback]
AutogradCUDA: fallthrough registered at /Users/distiller/project/aten/src/ATen/core/VariableFallbackKernel.cpp:47 [backend fallback]
AutogradXLA: fallthrough registered at /Users/distiller/project/aten/src/ATen/core/VariableFallbackKernel.cpp:51 [backend fallback]
AutogradLazy: fallthrough registered at /Users/distiller/project/aten/src/ATen/core/VariableFallbackKernel.cpp:55 [backend fallback]
AutogradXPU: fallthrough registered at /Users/distiller/project/aten/src/ATen/core/VariableFallbackKernel.cpp:43 [backend fallback]
AutogradMLC: fallthrough registered at /Users/distiller/project/aten/src/ATen/core/VariableFallbackKernel.cpp:59 [backend fallback]
AutogradHPU: fallthrough registered at /Users/distiller/project/aten/src/ATen/core/VariableFallbackKernel.cpp:68 [backend fallback]
Functionalize: registered at /Users/distiller/project/aten/src/ATen/FunctionalizeFallbackKernel.cpp:52 [backend fallback]

  
  Debug info for handle(s): debug_handles:{-1}, was not found.
  
Exception raised from reportError at /Users/distiller/project/aten/src/ATen/core/dispatch/OperatorEntry.cpp:434 (most recent call first):
frame #0: _ZNK3c104impl13OperatorEntry11reportErrorENS_11DispatchKeyE + 464 (0x10328d438 in Proj-iOS)
frame #1: _ZNK3c1010Dispatcher9callBoxedERKNS_14OperatorHandleEPNSt3__16vectorINS_6IValueENS4_9allocatorIS6_EEEE + 244 (0x10370bf74 in Proj-iOS)
frame #2: _ZN5torch3jit6mobile16InterpreterState3runERNSt3__16vectorIN3c106IValueENS3_9allocatorIS6_EEEE + 4580 (0x103717464 in Proj-iOS)
frame #3: _ZN5torch3jit6mobile8Function3runERNSt3__16vectorIN3c106IValueENS3_9allocatorIS6_EEEE + 108 (0x10370a3ac in Proj-iOS)
frame #4: _ZNK5torch3jit6mobile6Method3runERNSt3__16vectorIN3c106IValueENS3_9allocatorIS6_EEEE + 560 (0x103719d08 in Proj-iOS)
frame #5: _ZNK5torch3jit6mobile6MethodclENSt3__16vectorIN3c106IValueENS3_9allocatorIS6_EEEE + 24 (0x10371a8b4 in Proj-iOS)
frame #6: _ZN5torch3jit6mobile6Module7forwardENSt3__16vectorIN3c106IValueENS3_9allocatorIS6_EEEE + 148 (0x1038b8ef8 in Proj-iOS)
frame #7: -[InferenceModule + + (0x1038b840c in Proj-iOS)
frame #8: -[ViewController + + (0x103a350e4 in Proj-iOS)
frame #9: -[CvVideoCamera + + (0x103fddaf4 in Proj-iOS)
frame #10: CF54A5DB-6EE5-3E73-B7E1-D2D0AB7598C8 + 147436 (0x1c7081fec in AVFCapture)
frame #11: CF54A5DB-6EE5-3E73-B7E1-D2D0AB7598C8 + 146764 (0x1c7081d4c in AVFCapture)
frame #12: 2F509455-C380-3E7C-AFB5-CB0461F08A60 + 143420 (0x1c713f03c in CMCapture)
frame #13: 2F509455-C380-3E7C-AFB5-CB0461F08A60 + 2651332 (0x1c73a34c4 in CMCapture)
frame #14: 03AD11F9-67AE-3219-ACA3-DF0A9AF629D4 + 397976 (0x1ae03f298 in libdispatch.dylib)
frame #15: 03AD11F9-67AE-3219-ACA3-DF0A9AF629D4 + 236144 (0x1ae017a70 in libdispatch.dylib)
frame #16: 03AD11F9-67AE-3219-ACA3-DF0A9AF629D4 + 303900 (0x1ae02831c in libdispatch.dylib)
frame #17: 03AD11F9-67AE-3219-ACA3-DF0A9AF629D4 + 250384 (0x1ae01b210 in libdispatch.dylib)
frame #18: 03AD11F9-67AE-3219-ACA3-DF0A9AF629D4 + 253484 (0x1ae01be2c in libdispatch.dylib)
frame #19: 03AD11F9-67AE-3219-ACA3-DF0A9AF629D4 + 292460 (0x1ae02566c in libdispatch.dylib)
frame #20: _pthread_wqthread + 272 (0x1f68b55bc in libsystem_pthread.dylib)
frame #21: start_wqthread + 8 (0x1f68b886c in libsystem_pthread.dylib)
</code></pre>
<p>does this mean that I can’t run the quantized model on metal at the moment? Maybe I can have in some other way?<br>
Also, as far as I understand, there is no way to run ConvTranspose2d on metal in any way, when I try convert without quantization, an error crashes</p>
<p>convolution_overrideable not implemented. You are likely triggering this with tensor backend other than CPU/CUDA/MKLDNN, if this is intended, please use TORCH_LIBRARY_IMPL to override this function<br>
Thank you very much for any help!</p>
<p>Versions</p>
<pre><code class="lang-auto">Versions
Collecting environment information...
PyTorch version: 1.11.0.dev20220114
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 12.1 (x86_64)
GCC version: Could not collect
Clang version: 13.0.0 (clang-1300.0.29.30)
CMake version: version 3.20.5
Libc version: N/A

Python version: 3.9.9 | packaged by conda-forge | (main, Dec 20 2021, 02:41:37) [Clang 11.1.0 ] (64-bit runtime)
Python platform: macOS-12.1-x86_64-i386-64bit
Is CUDA available: False
CUDA runtime version: No CUDA
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] numpy==1.21.2
[pip3] torch==1.11.0.dev20220114
[pip3] torchvision==0.12.0.dev20220119
[conda] blas 1.0 mkl
[conda] cudatoolkit 9.0 h41a26b3_0
[conda] libblas 3.9.0 12_osx64_mkl conda-forge
[conda] libcblas 3.9.0 12_osx64_mkl conda-forge
[conda] liblapack 3.9.0 12_osx64_mkl conda-forge
[conda] liblapacke 3.9.0 12_osx64_mkl conda-forge
[conda] mkl 2021.4.0 hecd8cb5_637
[conda] mkl-service 2.4.0 py39h9ed2024_0
[conda] mkl_fft 1.3.1 py39h4ab4a9b_0
[conda] mkl_random 1.2.2 py39hb2f4e1b_0
[conda] numpy 1.21.2 py39h4b4dc7a_0
[conda] numpy-base 1.21.2 py39he0bd621_0
[conda] pytorch 1.11.0.dev20220114 py3.9_0 pytorch-nightly
[conda] torchvision 0.12.0.dev20220119 py39_cpu pytorch-nightly

Install LibTorch (demo nets work correctly):
pod 'LibTorch-Lite-Nightly'
</code></pre>

username: axitkhurana
cooked: <p>Can you help here <a class="mention" href="/u/xta0">@xta0</a></p>

username: MrNeither
cooked: <p>Hello! Is there anything known about this issue?</p>

username: kimishpatel
cooked: <aside class="quote no-group" data-username="MrNeither" data-post="1" data-topic="143280">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/mrneither/48/46307_2.png" class="avatar"> MrNeither:</div>
<blockquote>
<p>convolution_overrideable not implemented. You are likely triggering this with tensor backend other than CPU/CUDA/MKLDNN, if this is intended, please use TORCH_LIBRARY_IMPL to override this function</p>
</blockquote>
</aside>
<p>Yes you cannot run quantized models on metal.</p>
<p>What about fp32 model? What error did you get? Can you paste the full repro for that?</p>