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failing build of recent TensorFlow easyconfigs on AWS Graviton3 (aarch64/neoverse_v1) #18899

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boegel opened this issue Oct 1, 2023 · 8 comments · Fixed by easybuilders/easybuild-easyblocks#3011
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failing build of recent TensorFlow easyconfigs on AWS Graviton3 (aarch64/neoverse_v1) #18899

boegel opened this issue Oct 1, 2023 · 8 comments · Fixed by easybuilders/easybuild-easyblocks#3011
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boegel commented Oct 1, 2023

TensorFlow-2.13.0-foss-2022b.eb fails with:

  /mnt/shared/home/boegel/easybuild/graviton2/software/GCCcore/12.2.0/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-sections -fdata-sections -MD -MF bazel-out/aarch64-opt/bin/external/XNNPACK/_objs/asm_microkernels/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.pic.d -fPIC '-DBAZEL_CURRENT_REPOSITORY="XNNPACK"' -iquote external/XNNPACK -iquote bazel-out/aarch64-opt/bin/external/XNNPACK -isystem external/XNNPACK/include -isystem bazel-out/aarch64-opt/bin/external/XNNPACK/include -isystem external/XNNPACK/src -isystem bazel-out/aarch64-opt/bin/external/XNNPACK/src -Wno-all -Wno-extra -Wno-deprecated -Wno-deprecated-declarations -Wno-ignored-attributes -Wno-array-bounds -Wunused-result '-Werror=unused-result' -Wswitch '-Werror=switch' '-Wno-error=unused-but-set-variable' -DAUTOLOAD_DYNAMIC_KERNELS -O2 -ftree-vectorize '-mcpu=native' -fno-math-errno -fPIC -fPIC -Iinclude -Isrc '-march=armv8.2-a+fp16+dotprod' -O2 -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__="redacted"' '-D__TIMESTAMP__="redacted"' '-D__TIME__="redacted"' -c external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S -o bazel-out/aarch64-opt/bin/external/XNNPACK/_objs/asm_microkernels/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.pic.o)
# Configuration: 0d4560b813c94b91f71e59ae9e182e4612b959f02afc2e8b9422054dd2bd321f
# Execution platform: @local_execution_config_platform//:platform
cc1: warning: switch '-mcpu=zeus+crypto+sha3+sm4+nodotprod+noprofile+nopauth' conflicts with '-march=armv8.2-a+fp16+dotprod' switch
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S: Assembler messages:
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S:54: Error: selected processor does not support `sdot v28.4s,v16.16b,v0.4b[0]'
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S:56: Error: selected processor does not support `sdot v29.4s,v17.16b,v0.4b[0]'
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S:58: Error: selected processor does not support `sdot v30.4s,v18.16b,v0.4b[0]'
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S:60: Error: selected processor does not support `sdot v31.4s,v19.16b,v0.4b[0]'
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S:62: Error: selected processor does not support `sdot v28.4s,v4.16b,v0.4b[1]'
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S:64: Error: selected processor does not support `sdot v29.4s,v5.16b,v0.4b[1]'
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S:66: Error: selected processor does not support `sdot v30.4s,v6.16b,v0.4b[1]'
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S:68: Error: selected processor does not support `sdot v31.4s,v7.16b,v0.4b[1]'
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S:125: Error: selected processor does not support `sdot v28.4s,v16.16b,v0.4b[0]'
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S:127: Error: selected processor does not support `sdot v29.4s,v17.16b,v0.4b[0]'
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S:129: Error: selected processor does not support `sdot v30.4s,v18.16b,v0.4b[0]'
external/XNNPACK/src/qc8-gemm/gen/qc8-gemm-1x16c4-minmax-fp32-asm-aarch64-neondot-ld64.S:131: Error: selected processor does not support `sdot v31.4s,v19.16b,v0.4b[0]'
SUBCOMMAND: # @XNNPACK//:asm_microkernels [action 'Compiling src/qc8-gemm/gen/qc8-gemm-4x16-minmax-fp32-asm-aarch64-neon-mlal-lane-cortex-a53.S', configuration: 0d4560b813c94b91f71e59ae9e182e4612b959f02afc2e8b9422054dd2bd321f, execution platform: @local_execution_config_platform//:platform]

Similar problems were observed in EESSI build environment for:
@boegel boegel added this to the 4.x milestone Oct 1, 2023
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boegel commented Oct 1, 2023

This warning:

cc1: warning: switch '-mcpu=zeus+crypto+sha3+sm4+nodotprod+noprofile+nopauth' conflicts with '-march=armv8.2-a+fp16+dotprod' switch

suggests that the -mcpu=native that is used by EasyBuild conflicts with the -march=armv8.2-a+fp16+dotprod that the TensorFlow build systems somehow injects, in particular the nodotprod vs dotprod part...

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boegel commented Oct 1, 2023 •
edited

Seems like tensorflow/tensorflow#53449 fixes this, which is included in TensorFlow v2.14.0, I'll give that a try with TensorFlow 2.13.0 and older...

@boegel boegel mentioned this issue Oct 1, 2023
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boegel commented Oct 1, 2023

It looks like tensorflow/tensorflow#53449 is specific to building TensorFlow Lite with CMake, so it doesn't help at all when building TensorFlow with Bazel like we're doing.

@Flamefire Any suggestions here? How can I avoid that the -mcpu=native that EasyBuild includes in $CXXFLAGS is passed down to the XNNPACK component of TensorFlow (while keeping it for everything else)?

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boegel commented Oct 1, 2023

Hmm, I was going to try and add mcpu=native to nocopts in build_defs.bzl in XNNPACK, but it seems like the support for nocopts was removed a long time ago in Bazel even though the docs still mention it 🤦, see bazelbuild/bazel#8706

@boegel boegel mentioned this issue Oct 2, 2023
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boegel commented Oct 2, 2023

Also asked about this upstream at XNNPACK: google/XNNPACK#5566

This was referenced Oct 2, 2023
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boegel commented Oct 2, 2023

A workaround here could be to replace the use of -mcpu=native with -march=native -mtune=native by configuring EasyBuild with --optarch="march=native -mtune=native" when installing TensorFlow on aarch64/* (except aarch64/generic), but I'm not sure that's wise, see also https://community.arm.com/arm-community-blogs/b/tools-software-ides-blog/posts/compiler-flags-across-architectures-march-mtune-and-mcpu

@boegel boegel mentioned this issue Oct 3, 2023
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boegel commented Oct 4, 2023

The XNNPACK issue is fixed with the changes in easybuilders/easybuild-easyblocks#3011, but there's more trouble, in particular this:

  /mnt/shared/home/boegel/easybuild/neoverse_v1/software/GCCcore/11.3.0/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-sections -fdata-sections '-std=c++0x' -MD -MF bazel-out/aarch64-opt/bin/tensorflow/compiler/xla/service/cpu/_objs/runtime_single_threaded_conv2d/runtime_single_threaded_conv2d.pic.d '-frandom-seed=bazel-out/aarch64-opt/bin/tensorflow/compiler/xla/service/cpu/_objs/runtime_single_threaded_conv2d/runtime_single_threaded_conv2d.pic.o' -fPIC -DEIGEN_MPL2_ONLY '-DEIGEN_MAX_ALIGN_BYTES=64' -DGEMM_KERNEL_H '-DEIGEN_ALTIVEC_USE_CUSTOM_PACK=0' '-DEIGEN_NEON_GEBP_NR=4' -iquote . -iquote bazel-out/aarch64-opt/bin -iquote external/com_google_absl -iquote bazel-out/aarch64-opt/bin/external/com_google_absl -iquote external/eigen_archive -iquote bazel-out/aarch64-opt/bin/external/eigen_archive -isystem external/eigen_archive -isystem bazel-out/aarch64-opt/bin/external/eigen_archive -Wno-all -Wno-extra -Wno-deprecated -Wno-deprecated-declarations -Wno-ignored-attributes -Wno-unknown-warning -Wno-array-parameter -Wno-stringop-overflow -Wno-array-bounds -Wunused-result '-Werror=unused-result' -DAUTOLOAD_DYNAMIC_KERNELS -O2 -ftree-vectorize -fno-math-errno -fPIC -fPIC '-std=c++17' -DEIGEN_AVOID_STL_ARRAY '-mcpu=native' -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__="redacted"' '-D__TIMESTAMP__="redacted"' '-D__TIME__="redacted"' -c tensorflow/compiler/xla/service/cpu/runtime_single_threaded_conv2d.cc -o bazel-out/aarch64-opt/bin/tensorflow/compiler/xla/service/cpu/_objs/runtime_single_threaded_conv2d/runtime_single_threaded_conv2d.pic.o)
# Configuration: 86547f283a1d059053877fee5e6069b279735e8af740087f6f2b9dde52ab09ac
# Execution platform: @local_execution_config_platform//:platform
/tmp/eb-z0316qi3/ccd9Mksr.s: Assembler messages:
/tmp/eb-z0316qi3/ccd9Mksr.s:254: Error: register number out of range 0 to 15 at operand 3 -- `fmla v20.8h,v23.8h,v22.h[0]'
/tmp/eb-z0316qi3/ccd9Mksr.s:258: Error: register number out of range 0 to 15 at operand 3 -- `fmla v17.8h,v23.8h,v22.h[1]'

It seems like this requires an update of Eigen, or a backport of https://gitlab.com/libeigen/eigen/-/merge_requests/1104

@boegel boegel changed the title failing installation of recent TensorFlow easyconfigs on AWS Graviton3 (aarch64/neoverse_v1) failing build of recent TensorFlow easyconfigs on AWS Graviton3 (aarch64/neoverse_v1) Oct 11, 2023
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Yes it seems a patch to (the downloaded) Eigen will solve this. See e.g. TensorFlow-2.11.0_fix-eigen-gemm-on-PPC.patch how to do that. Basically:

  • find the Eigen commit used by TF by looking at the tf_http_archive( name = "eigen_archive", in TF (might even be in a variable EIGEN_COMMIT)
  • checkout that eigen version
  • apply/backport the fix
  • save the diff as a patch to tensorflow/third_party/eigen3
  • add the patch to the patch_file list argument of tf_gttp_archive

I just checked TF 2.13.0 and it uses Eigen b0f877f8e01e90a5b0f3a79d46ea234899f8b499 where that code fixed by your linked MR is not present anymore, i.e. it already contains that MR.

So sorry, I have no idea where/why that is an issue with TF 2.13.0

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