NNPACK unsupported hardware despite AVX2 and 3-level cache hierarchy available (on Docker)

username: abaschkim
cooked: <p>I am facing the following issue when training a CNN pytorch model within a Docker container:</p>
<blockquote>
<p>[W NNPACK.cpp:64] Could not initialize NNPACK! Reason: Unsupported hardware.</p>
</blockquote>
<ul>
<li>pytorch version: 2.0</li>
</ul>
<p>I am aware that this is only a warning, but it slows down my training tremendously, when compared to running the same code outside of Docker.</p>
<p>I’ve checked the platform requirements of NNPack, but looks like both requirements of having AVX2 and 3-level cache hierarchy available are met:</p>
<pre><code class="lang-auto">- lscpu:
Architecture:            x86_64
  CPU op-mode(s):        32-bit, 64-bit
  Address sizes:         48 bits physical, 48 bits virtual
  Byte Order:            Little Endian
CPU(s):                  48
Vendor ID:               AuthenticAMD
  Model name:            AMD EPYC 7502P 32-Core Processor
    Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx mmxe xt fxsr_opt pdpe1gb rdtscp lm rep_good nopl cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_leg acy abm sse4a misalignsse 3dnowprefetch osvw perfctr_core ssbd ibrs ibpb stibp vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr wbnoinvd ar at npt nrip_save umip rdpid arch_capabilities
Virtualization features: 
  Virtualization:        AMD-V
  Hypervisor vendor:     KVM
  Virtualization type:   full
Caches (sum of all):     
  L1d:                   3 MiB (48 instances)
  L1i:                   3 MiB (48 instances)
[details="Summary"]
This text will be hidden
[/details]

  L2:                    24 MiB (48 instances)
  L3:                    768 MiB (48 instances)
NUMA:                    
  NUMA node(s):          1
  NUMA node0 CPU(s):     0-47
</code></pre>
<p>From within the Docker container at runtime, I can verify the availability of AVX2:</p>
<ul>
<li>torch.<strong>config</strong>.show()</li>
</ul>
<pre><code class="lang-auto">Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
OpenMP 201511 (a.k.a. OpenMP 4.5)
NNPACK is enabled
CPU capability usage: AVX2
Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
</code></pre>
<p>My Docker file is defined as follows:</p>
<pre><code class="lang-auto">FROM ubuntu:22.04
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update &amp;&amp; apt-get install -y software-properties-common
RUN add-apt-repository ppa:deadsnakes/ppa
RUN apt-get update &amp;&amp; apt-get install -y python3.8 python3.8-dev python3.8-distutils python3.8-venv
RUN apt-get install -y curl net-tools
RUN curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
RUN python3.8 get-pip.py
RUN apt-get update &amp;&amp; apt-get install -y gcc cmake git
WORKDIR /fedstellar
COPY requirements.txt
RUN python3.8 -m pip install -r requirements.txt
</code></pre>
<p>Outside of Docker, everything works fine and the NNPack warning is not thrown at training time.<br>
Could this be an issue with the pip installation such that the hardware specifications are ignored?<br>
Thanks in advance!</p>
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username: KirilloCirillo
cooked: <p>Hi <a class="mention" href="/u/abaschkim">@abaschkim</a>, I am also facing the same issue, have you found a solution to this problem?</p>