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Revert "Enable onednn in pytorch for ppc64le architecture (#143743)"

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This reverts commit d4cf0e5af4.

Reverted https://github.com/pytorch/pytorch/pull/143743 on behalf of https://github.com/davidberard98 due to windows build failures look related [GH job link](https://github.com/pytorch/pytorch/actions/runs/13705127978/job/38329845095) [HUD commit link](d4cf0e5af4) ([comment](https://github.com/pytorch/pytorch/pull/143743#issuecomment-2704903253))
This commit is contained in:
PyTorch MergeBot 2025-03-06 20:47:57 +00:00
parent 1add61c242
commit cf9efbdf16
3 changed files with 3 additions and 16 deletions
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7
CMakeLists.txt
View File

@ -180,14 +180,11 @@ endif()
set(CPU_AARCH64 OFF)
set(CPU_INTEL OFF)
set(CPU_POWER OFF)
if(CMAKE_SYSTEM_PROCESSOR MATCHES "(AMD64|x86_64)")
set(CPU_INTEL ON)
elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm64)")
set(CPU_AARCH64 ON)
elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "^(ppc64le)")
set(CPU_POWER ON)
endif()
# For non-supported platforms, turn USE_DISTRIBUTED off by default. It is not
@ -318,8 +315,8 @@ cmake_dependent_option(USE_ITT "Use Intel(R) VTune Profiler ITT functionality"
# Ensure that an MKLDNN build is the default for x86 CPUs but optional for
# AArch64 (dependent on -DUSE_MKLDNN).
cmake_dependent_option(
USE_MKLDNN "Use MKLDNN. Only available on x86, x86_64, AArch64, and ppc64le."
"${CPU_INTEL}" "CPU_INTEL OR CPU_AARCH64 OR CPU_POWER" OFF)
USE_MKLDNN "Use MKLDNN. Only available on x86, x86_64, and AArch64."
"${CPU_INTEL}" "CPU_INTEL OR CPU_AARCH64" OFF)
cmake_dependent_option(
USE_MKLDNN_ACL "Use Compute Library for the Arm architecture." OFF
"USE_MKLDNN AND CPU_AARCH64" OFF)

6
aten/src/ATen/native/CPUBlas.cpp
View File

@ -348,8 +348,6 @@ void gemm(
// MKLDNN also supports ARM for bf16, and the bypass is only
// currently intended for x86/x86_64.
const bool use_bf16_gemv_trans = false;
#elif defined(__powerpc__)
const bool use_bf16_gemv_trans = false;
#else
const bool bf16_gemv_trans_would_be_faster = cpuinfo_initialize() &&
!cpuinfo_has_x86_avx512bf16();
@ -380,12 +378,8 @@ void gemm(
// we should not bother checking for !cpuinfo_has_x86_avx512fp16() here,
// because "onednn (mkldnn) won't use avx512fp16 to compute gemms by default
// because the avx512fp16 fma would incur accuracy loss".
#if defined(__powerpc__)
const bool fp16_gemv_trans_would_be_faster = false;
#else
const bool fp16_gemv_trans_would_be_faster = cpuinfo_initialize() &&
cpuinfo_has_x86_f16c();
#endif
const bool use_fp16_gemv_trans = fp16_gemv_trans_would_be_faster &&
transa == TransposeType::Transpose &&
transb == TransposeType::NoTranspose && n == 1 && alpha == 1.0;

6
aten/src/ATen/native/quantized/cpu/OnednnUtils.h
View File

@ -5,9 +5,7 @@
#include <ATen/Tensor.h>
#include <ATen/native/quantized/PackedParams.h>
#include <ideep.hpp>
#if !defined(__powerpc__)
#include <cpuinfo.h>
#endif
#include <c10/util/CallOnce.h>
@ -433,16 +431,14 @@ inline bool should_use_onednn_quant(
// TODO Support more OSs.
#if !defined(__linux__)
return false;
#elif defined(__powerpc__)
bool vnni_available = true;
#else
bool vnni_available = cpuinfo_has_x86_avx512vnni();
#endif
bool w_sym_quant =
is_weight_symmetric_quant(weight, is_transposed_conv);
bool opad_all_zero =
std::all_of(output_padding.begin(), output_padding.end(), [](int i) { return i==0; });
return vnni_available && (groups <= 100) && w_sym_quant && opad_all_zero;
#endif
}
} // onednn_utils