From afd38f0c617d6f89b2b4532c6c44f116617e2b6f Mon Sep 17 00:00:00 2001 From: Felix Thomasmathibalan Date: Wed, 27 Sep 2023 17:46:17 +0100 Subject: Apply clang-format on repository Code is formatted as per a revised clang format configuration file(not part of this delivery). Version 14.0.6 is used. Exclusion List: - files with .cl extension - files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...) And the following directories - compute_kernel_writer/validation/ - tests/ - include/ - src/core/NEON/kernels/convolution/ - src/core/NEON/kernels/arm_gemm/ - src/core/NEON/kernels/arm_conv/ - data/ There will be a follow up for formatting of .cl files and the files under tests/ and compute_kernel_writer/validation/. Signed-off-by: Felix Thomasmathibalan Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391 Benchmark: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Gunes Bayir --- .../kernels/batchnormalization/impl/SVE/fp32.cpp | 115 ++++++++++++--------- 1 file changed, 64 insertions(+), 51 deletions(-) (limited to 'src/core/NEON/kernels/batchnormalization/impl/SVE/fp32.cpp') diff --git a/src/core/NEON/kernels/batchnormalization/impl/SVE/fp32.cpp b/src/core/NEON/kernels/batchnormalization/impl/SVE/fp32.cpp index 952ab320bf..df4fbfe607 100644 --- a/src/core/NEON/kernels/batchnormalization/impl/SVE/fp32.cpp +++ b/src/core/NEON/kernels/batchnormalization/impl/SVE/fp32.cpp @@ -25,6 +25,7 @@ #include "arm_compute/core/ITensorPack.h" #include "arm_compute/core/Window.h" #include "arm_compute/function_info/ActivationLayerInfo.h" + #include "src/core/NEON/SVEMath.h" #include @@ -37,8 +38,15 @@ namespace arm_compute { namespace cpu { -void fp32_sve_batch_normalization(ITensor *src, ITensor *dst, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, - float epsilon, ActivationLayerInfo &act_info, const Window &window) +void fp32_sve_batch_normalization(ITensor *src, + ITensor *dst, + const ITensor *mean, + const ITensor *var, + const ITensor *beta, + const ITensor *gamma, + float epsilon, + ActivationLayerInfo &act_info, + const Window &window) { const auto window_start_x = static_cast(window.x().start()); const auto window_end_x = static_cast(window.x().end()); @@ -49,69 +57,74 @@ void fp32_sve_batch_normalization(ITensor *src, ITensor *dst, const ITensor *mea Iterator input(src, win_collapsed); Iterator output(dst, win_collapsed); - const auto input_mean = reinterpret_cast(mean->ptr_to_element(Coordinates(0, 0))); - const auto input_var = reinterpret_cast(var->ptr_to_element(Coordinates(0, 0))); - const auto input_gamma = (gamma != nullptr) ? reinterpret_cast(gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; - const auto input_beta = (beta != nullptr) ? reinterpret_cast(beta->ptr_to_element(Coordinates(0, 0))) : nullptr; + const auto input_mean = reinterpret_cast(mean->ptr_to_element(Coordinates(0, 0))); + const auto input_var = reinterpret_cast(var->ptr_to_element(Coordinates(0, 0))); + const auto input_gamma = + (gamma != nullptr) ? reinterpret_cast(gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; + const auto input_beta = + (beta != nullptr) ? reinterpret_cast(beta->ptr_to_element(Coordinates(0, 0))) : nullptr; const auto epsilon_vec = svdup_n_f32(epsilon); const auto const_1 = svdup_n_f32(1.f); const auto const_0 = svdup_n_f32(0.f); const auto va = svdup_n_f32(act_info.a()); const auto vb = svdup_n_f32(act_info.b()); - execute_window_loop(win_collapsed, [&](const Coordinates &) - { - const auto input_ptr = reinterpret_cast(input.ptr()); - const auto output_ptr = reinterpret_cast(output.ptr()); - - // Compute S elements per iteration - int x = window_start_x; - svbool_t pg = svwhilelt_b32(x, window_end_x); - do + execute_window_loop( + win_collapsed, + [&](const Coordinates &) { - // Conctruct vectors - const auto mean_vec = svld1_f32(pg, input_mean + x); - const auto var_vec = svld1_f32(pg, input_var + x); - const auto gamma_vec = (input_gamma != nullptr) ? svld1_f32(pg, input_gamma + x) : const_1; - const auto beta_vec = (input_beta != nullptr) ? svld1_f32(pg, input_beta + x) : const_0; + const auto input_ptr = reinterpret_cast(input.ptr()); + const auto output_ptr = reinterpret_cast(output.ptr()); - // Calculate denominator - const auto tmp = svadd_f32_z(pg, var_vec, epsilon_vec); - auto denominator = svrsqrte_f32(tmp); - denominator = svmul_f32_z(pg, svrsqrts_f32(svmul_f32_z(pg, tmp, denominator), denominator), denominator); - denominator = svmul_f32_z(pg, svrsqrts_f32(svmul_f32_z(pg, tmp, denominator), denominator), denominator); + // Compute S elements per iteration + int x = window_start_x; + svbool_t pg = svwhilelt_b32(x, window_end_x); + do + { + // Conctruct vectors + const auto mean_vec = svld1_f32(pg, input_mean + x); + const auto var_vec = svld1_f32(pg, input_var + x); + const auto gamma_vec = (input_gamma != nullptr) ? svld1_f32(pg, input_gamma + x) : const_1; + const auto beta_vec = (input_beta != nullptr) ? svld1_f32(pg, input_beta + x) : const_0; - // Calculate x bar - const auto numerator = svsub_f32_z(pg, svld1_f32(pg, input_ptr + x), mean_vec); - const auto x_bar = svmul_f32_z(pg, numerator, denominator); - auto res = svmla_f32_z(pg, beta_vec, x_bar, gamma_vec); + // Calculate denominator + const auto tmp = svadd_f32_z(pg, var_vec, epsilon_vec); + auto denominator = svrsqrte_f32(tmp); + denominator = + svmul_f32_z(pg, svrsqrts_f32(svmul_f32_z(pg, tmp, denominator), denominator), denominator); + denominator = + svmul_f32_z(pg, svrsqrts_f32(svmul_f32_z(pg, tmp, denominator), denominator), denominator); - // Perform fused activation - if(act_info.enabled()) - { - if(act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU) - { - res = svmax_f32_z(pg, const_0, res); - } - else if(act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU) - { - res = svmin_f32_z(pg, va, svmax_f32_z(pg, const_0, res)); - } - else if(act_info.activation() == ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) + // Calculate x bar + const auto numerator = svsub_f32_z(pg, svld1_f32(pg, input_ptr + x), mean_vec); + const auto x_bar = svmul_f32_z(pg, numerator, denominator); + auto res = svmla_f32_z(pg, beta_vec, x_bar, gamma_vec); + + // Perform fused activation + if (act_info.enabled()) { - res = svmin_f32_z(pg, va, svmax_f32_z(pg, vb, res)); + if (act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU) + { + res = svmax_f32_z(pg, const_0, res); + } + else if (act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU) + { + res = svmin_f32_z(pg, va, svmax_f32_z(pg, const_0, res)); + } + else if (act_info.activation() == ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) + { + res = svmin_f32_z(pg, va, svmax_f32_z(pg, vb, res)); + } } - } - // Store results - svst1_f32(pg, output_ptr + x, res); + // Store results + svst1_f32(pg, output_ptr + x, res); - x += svcntw(); - pg = svwhilelt_b32(x, window_end_x); - } - while(svptest_any(svptrue_b32(), pg)); - }, - input, output); + x += svcntw(); + pg = svwhilelt_b32(x, window_end_x); + } while (svptest_any(svptrue_b32(), pg)); + }, + input, output); } } // namespace cpu } // namespace arm_compute -- cgit v1.2.1