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[CPU] oneDNN3.6 cherry-pick backports #28843

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merged 1 commit into from
Feb 11, 2025
Merged

[CPU] oneDNN3.6 cherry-pick backports #28843

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1 change: 1 addition & 0 deletions src/plugins/intel_cpu/src/nodes/kernels/x64/brgemm_kernel.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -273,6 +273,7 @@ void BrgemmKernel::init_brgemm_copy_a(
: DnnlExtensionUtils::sizeOfDataType(static_cast<dnnl::memory::data_type>(dt_in0));
brgCopyKernelConf.transposed_A = transpose;
brgCopyKernelConf.isa = is_avx_f16_only ? avx512_core_fp16 : avx512_core_amx;
brgCopyKernelConf.orig_wei_dt = static_cast<dnnl_data_type_t>(DnnlExtensionUtils::ElementTypeToDataType(inType));

create_brgemm_matmul_copy_a(brgCopyKernel, &brgCopyKernelConf);
}
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6 changes: 0 additions & 6 deletions src/plugins/intel_cpu/tests/functional/shared_tests_instances/skip_tests_config.cpp
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Expand Up @@ -479,18 +479,12 @@ std::vector<std::string> disabledTestPatterns() {
R"(.*smoke_MatMulCompressedWeights_corner_cases_basic/MatmulWeightsDecompression.CompareWithRefs/data_shape=\[\?.\?.\?\]_\(\[1,1,4096\]\)_weights_shape=\[4096,4096\]_group_size=128_weights_precision=nf4_decompression_precision=f16_scale_precision=undefined_transpose_weights=0_decompression_subtract=full_reshape_on_decompression=1_config=\(\).*)");
retVector.emplace_back(R"(.*smoke_RDFT_CPU_1D/RDFTTestCPU.CompareWithRefs/prec=f32_IS0=\[\]_TS0=\(\(126\)\)_constAxes=true_axes=\(\(0\)\)_isInverse=false.*)");
retVector.emplace_back(R"(.*smoke_RDFT_CPU_2D/RDFTTestCPU.CompareWithRefs/prec=f32_IS0=\[\]_TS0=\(\(16.38\)\)_constAxes=true_axes=\(\(0.1\)\)_isInverse=false.*)");
// Issue: MFDNN-12818
retVector.emplace_back(R"(.*smoke_LPT/RecurrentCellTransformation.CompareWithRefImpl/f32_\[1,1,3\]_CPU_f32FQ_X_level=256_.*_FQ_W_level=255.*)");
retVector.emplace_back(R"(.*smoke_static/ConvertFqRnnToQuantizedRnn.CompareWithRefs/Type=GRUSequence.*2.5.10.*2.1.4.*2.1.4.*)");
#endif
if (!ov::with_cpu_x86_avx512_core()) {
// on platforms which do not support bfloat16, we are disabling bf16 tests since there are no bf16 primitives,
// tests are useless on such platforms
retVector.emplace_back(R"(.*(BF|bf)16.*)");
retVector.emplace_back(R"(.*bfloat16.*)");
// Issue: MFDNN-12818
retVector.emplace_back(R"(.*smoke_LPT/RecurrentCellTransformation.CompareWithRefImpl/f32_\[1,1,3\]_CPU_f32FQ_X_level=256_.*_FQ_W_level=255.*)");
retVector.emplace_back(R"(.*smoke_static/ConvertFqRnnToQuantizedRnn.CompareWithRefs/Type=GRUSequence.*2.5.10.*2.1.4.*2.1.4.*)");
}
if (!ov::with_cpu_x86_avx2()) {
// MatMul in Snippets uses BRGEMM that is supported only on AVX2 (and newer) platforms
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2 changes: 1 addition & 1 deletion src/plugins/intel_cpu/thirdparty/onednn
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Submodule onednn updated 23 files
+5 −1 include/oneapi/dnnl/dnnl.hpp
+5 −1 include/oneapi/dnnl/dnnl_types.h
+5 −1 src/common/c_types_map.hpp
+5 −1 src/common/dnnl_debug_autogenerated.cpp
+3 −1 src/common/memory_desc_wrapper.cpp
+5 −1 src/common/tag_traits.hpp
+2 −2 src/cpu/rnn/ref_rnn.cpp
+5 −6 src/cpu/rnn/rnn_reorders.hpp
+9 −7 src/cpu/rnn/rnn_utils.cpp
+11 −6 src/cpu/x64/brgemm/brgemm_utils.cpp
+9 −2 src/cpu/x64/brgemm/jit_brgemm_kernel.cpp
+9 −1 src/cpu/x64/jit_avx512_common_conv_kernel.cpp
+1 −1 src/cpu/x64/matmul/brgemm_matmul.cpp
+56 −16 src/cpu/x64/matmul/brgemm_matmul_copy_utils.cpp
+7 −7 src/cpu/x64/matmul/brgemm_matmul_utils.cpp
+6 −0 src/cpu/x64/matmul/brgemm_matmul_utils.hpp
+3 −4 src/cpu/x64/rnn/brgemm_cell_common_fwd.cpp
+3 −2 src/cpu/x64/rnn/rnn_brgemm_utils.cpp
+2 −2 src/gpu/intel/ocl/ocl_utils.cpp
+4 −0 tests/benchdnn/inputs/matmul/harness_matmul_regression_f32
+5 −5 tests/benchdnn/inputs/matmul/test_matmul_ci
+1 −1 tests/benchdnn/inputs/matmul/test_matmul_int8
+3 −2 tests/benchdnn/rnn/rnn.cpp
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