From 568aab689c6f0d293a99ea554786a22c76be18b4 Mon Sep 17 00:00:00 2001 From: Pablo Marquez Tello Date: Mon, 20 Nov 2023 14:20:01 +0000 Subject: CpuMul changes to enable fp16 in armv8a multi_isa builds * Moved fp16 and fp32 to their corresponding files src/cpu/kernels/mul/generic/neon/fp16.cpp and src/cpu/kernels/mul/generic/neon/fp32.cpp * Changes in filelist.json: added a new fp16.cpp file for the float16_t kernels * Partially resolves MLCE-1102 Change-Id: I88f24cf034c11b55ff84644b182ba76c7cb94296 Signed-off-by: Pablo Marquez Tello Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10778 Benchmark: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Viet-Hoa Do --- Android.bp | 2 + filelist.json | 6 +- src/BUILD.bazel | 2 + src/CMakeLists.txt | 2 + src/cpu/kernels/CpuMulKernel.cpp | 221 +----------------------------- src/cpu/kernels/mul/generic/neon/fp16.cpp | 145 ++++++++++++++++++++ src/cpu/kernels/mul/generic/neon/fp32.cpp | 138 +++++++++++++++++++ src/cpu/kernels/mul/generic/neon/list.h | 38 +++++ 8 files changed, 337 insertions(+), 217 deletions(-) create mode 100644 src/cpu/kernels/mul/generic/neon/fp16.cpp create mode 100644 src/cpu/kernels/mul/generic/neon/fp32.cpp create mode 100644 src/cpu/kernels/mul/generic/neon/list.h diff --git a/Android.bp b/Android.bp index c4bf740e1f..31ec9b2716 100644 --- a/Android.bp +++ b/Android.bp @@ -543,6 +543,8 @@ cc_library_static { "src/cpu/kernels/meanstddevnorm/generic/neon/fp32.cpp", "src/cpu/kernels/meanstddevnorm/generic/neon/impl.cpp", "src/cpu/kernels/meanstddevnorm/generic/neon/qasymm8.cpp", + "src/cpu/kernels/mul/generic/neon/fp16.cpp", + "src/cpu/kernels/mul/generic/neon/fp32.cpp", "src/cpu/kernels/norm_layer/generic/neon/fp16.cpp", "src/cpu/kernels/norm_layer/generic/neon/fp32.cpp", "src/cpu/kernels/pool2d/neon/fp16.cpp", diff --git a/filelist.json b/filelist.json index ca8b18c0a5..a84db7188d 100644 --- a/filelist.json +++ b/filelist.json @@ -1904,7 +1904,11 @@ "src/cpu/operators/CpuMul.cpp", "src/cpu/kernels/CpuMulKernel.cpp", "src/runtime/NEON/functions/NEPixelWiseMultiplication.cpp" - ] + ], + "neon":{ + "fp16":["src/cpu/kernels/mul/generic/neon/fp16.cpp"], + "fp32":["src/cpu/kernels/mul/generic/neon/fp32.cpp"] + } } }, "Normalize": { diff --git a/src/BUILD.bazel b/src/BUILD.bazel index 6ffc2ebe02..42841fe28d 100644 --- a/src/BUILD.bazel +++ b/src/BUILD.bazel @@ -794,6 +794,8 @@ filegroup( "cpu/kernels/meanstddevnorm/generic/neon/fp32.cpp", "cpu/kernels/meanstddevnorm/generic/neon/impl.cpp", "cpu/kernels/meanstddevnorm/generic/neon/qasymm8.cpp", + "cpu/kernels/mul/generic/neon/fp16.cpp", + "cpu/kernels/mul/generic/neon/fp32.cpp", "cpu/kernels/norm_layer/generic/neon/fp16.cpp", "cpu/kernels/norm_layer/generic/neon/fp32.cpp", "cpu/kernels/pool2d/neon/fp16.cpp", diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 55169b6818..1de9e63737 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -785,6 +785,8 @@ target_sources( cpu/kernels/meanstddevnorm/generic/neon/fp32.cpp cpu/kernels/meanstddevnorm/generic/neon/impl.cpp cpu/kernels/meanstddevnorm/generic/neon/qasymm8.cpp + cpu/kernels/mul/generic/neon/fp16.cpp + cpu/kernels/mul/generic/neon/fp32.cpp cpu/kernels/norm_layer/generic/neon/fp16.cpp cpu/kernels/norm_layer/generic/neon/fp32.cpp cpu/kernels/pool2d/neon/fp16.cpp diff --git a/src/cpu/kernels/CpuMulKernel.cpp b/src/cpu/kernels/CpuMulKernel.cpp index ba086e3ac6..8001482154 100644 --- a/src/cpu/kernels/CpuMulKernel.cpp +++ b/src/cpu/kernels/CpuMulKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016-2022 Arm Limited. + * Copyright (c) 2016-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -26,12 +26,14 @@ #include "arm_compute/core/ITensor.h" #include "arm_compute/core/TensorInfo.h" +#include "src/core/common/Registrars.h" #include "src/core/CPP/Validate.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "src/core/NEON/NEAsymm.h" #include "src/core/NEON/NESymm.h" #include "src/core/NEON/wrapper/wrapper.h" +#include "src/cpu/kernels/mul/generic/neon/list.h" #include @@ -1170,108 +1172,6 @@ void mul_S32_S32_S32(const ITensor *src1, const ITensor *src2, ITensor *out, con } } -void mul_F32_F32_F32(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale) -{ - // Create input windows - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - constexpr int window_step_x = 16 / sizeof(float); - const auto window_start_x = static_cast(window.x().start()); - const auto window_end_x = static_cast(window.x().end()); - const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x(); - - using ExactTagType = typename wrapper::traits::neon_vector::tag_type; - - if (is_broadcast_across_x) - { - const bool is_broadcast_input_2 = input2_win.x().step() == 0; - Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; - Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; - const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1; - const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1; - - // Clear X Dimension on execution window as we handle manually - non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator broadcast_input(broadcast_tensor, broadcast_win); - Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); - Iterator dst(out, win); - - execute_window_loop( - win, - [&](const Coordinates &) - { - const auto non_broadcast_input_ptr = reinterpret_cast(non_broadcast_input.ptr()); - const auto output_ptr = reinterpret_cast(dst.ptr()); - - const float broadcast_value = *reinterpret_cast(broadcast_input.ptr()); - const auto broadcast_value_vec = wrapper::vdup_n(broadcast_value, ExactTagType{}); - const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{}); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for (; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto non_broadcast_v = wrapper::vloadq(non_broadcast_input_ptr + x); - auto res = wrapper::vmul(wrapper::vmul(broadcast_value_vec, non_broadcast_v), scale_vec); - wrapper::vstore(output_ptr + x, res); - } - - // Compute left-over elements - for (; x < window_end_x; ++x) - { - const auto non_broadcast_v = *(non_broadcast_input_ptr + x); - *(output_ptr + x) = broadcast_value * non_broadcast_v * scale; - } - }, - broadcast_input, non_broadcast_input, dst); - } - else - { - // Clear X Dimension on execution window as we handle manually - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - - execute_window_loop( - win, - [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast(input1.ptr()); - const auto input2_ptr = reinterpret_cast(input2.ptr()); - const auto output_ptr = reinterpret_cast(dst.ptr()); - - // Compute window_step_x elements per iteration - int x = window_start_x; - for (; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto ta1 = wrapper::vloadq(input1_ptr + x); - const auto ta2 = wrapper::vloadq(input2_ptr + x); - const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{}); - const auto res = wrapper::vmul(wrapper::vmul(ta1, ta2), scale_vec); - wrapper::vstore(output_ptr + x, res); - } - - // Compute left-over elements - for (; x < window_end_x; ++x) - { - const auto ta1 = *(input1_ptr + x); - const auto ta2 = *(input2_ptr + x); - *(output_ptr + x) = ta1 * ta2 * scale; - } - }, - input1, input2, dst); - } -} - void c_mul_F32_F32_F32_n(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window) { // Create input windows @@ -1409,115 +1309,6 @@ void c_mul_F32_F32_F32_n(const ITensor *src1, const ITensor *src2, ITensor *out, } } -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -void mul_F16_F16_F16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale) -{ - // Create input windows - Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); - Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); - - // Clear X Dimension on execution window as we handle manually - Window win = window; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - constexpr int window_step_x = 16; - const auto window_start_x = static_cast(window.x().start()); - const auto window_end_x = static_cast(window.x().end()); - const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x(); - if (is_broadcast_across_x) - { - const bool is_broadcast_input_2 = input2_win.x().step() == 0; - Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; - Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; - const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1; - const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1; - // Clear X Dimension on execution window as we handle manually - non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - Iterator broadcast_input(broadcast_tensor, broadcast_win); - Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); - Iterator dst(out, win); - execute_window_loop( - win, - [&](const Coordinates &) - { - const auto non_broadcast_input_ptr = reinterpret_cast(non_broadcast_input.ptr()); - const auto output_ptr = reinterpret_cast(dst.ptr()); - const auto broadcast_value = *reinterpret_cast(broadcast_input.ptr()); - const float16x8x2_t broadcast_value_vec = {{ - vdupq_n_f16(broadcast_value), - vdupq_n_f16(broadcast_value), - }}; - const auto scale_vec = vdupq_n_f16(scale); - // Compute window_step_x elements per iteration - int x = window_start_x; - for (; x <= (window_end_x - window_step_x); x += window_step_x) - { - const float16x8x2_t non_broadcast_v = {{ - vld1q_f16(non_broadcast_input_ptr + x), - vld1q_f16(non_broadcast_input_ptr + x + 8), - }}; - const float16x8x2_t result = {{ - vmulq_f16(vmulq_f16(broadcast_value_vec.val[0], non_broadcast_v.val[0]), scale_vec), - vmulq_f16(vmulq_f16(broadcast_value_vec.val[1], non_broadcast_v.val[1]), scale_vec), - }}; - vst1q_f16(output_ptr + x, result.val[0]); - vst1q_f16(output_ptr + x + 8, result.val[1]); - } - // Compute left-over elements - for (; x < window_end_x; ++x) - { - const auto non_broadcast_v = *(non_broadcast_input_ptr + x); - *(output_ptr + x) = broadcast_value * non_broadcast_v * scale; - } - }, - broadcast_input, non_broadcast_input, dst); - } - else - { - input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - Iterator input1(src1, input1_win); - Iterator input2(src2, input2_win); - Iterator dst(out, win); - execute_window_loop( - win, - [&](const Coordinates &) - { - const auto input1_ptr = reinterpret_cast(input1.ptr()); - const auto input2_ptr = reinterpret_cast(input2.ptr()); - const auto output_ptr = reinterpret_cast(dst.ptr()); - // Compute window_step_x elements per iteration - int x = window_start_x; - for (; x <= (window_end_x - window_step_x); x += window_step_x) - { - const float16x8x2_t ta1 = {{ - vld1q_f16(input1_ptr + x), - vld1q_f16(input1_ptr + x + 8), - }}; - const float16x8x2_t ta2 = {{ - vld1q_f16(input2_ptr + x), - vld1q_f16(input2_ptr + x + 8), - }}; - const float16x8_t scale_vec = vdupq_n_f16(scale); - const float16x8x2_t result = {{ - vmulq_f16(vmulq_f16(ta1.val[0], ta2.val[0]), scale_vec), - vmulq_f16(vmulq_f16(ta1.val[1], ta2.val[1]), scale_vec), - }}; - vst1q_f16(output_ptr + x, result.val[0]); - vst1q_f16(output_ptr + x + 8, result.val[1]); - } - // Compute left-over elements - for (; x < window_end_x; ++x) - { - const auto ta1 = *(input1_ptr + x); - const auto ta2 = *(input2_ptr + x); - *(output_ptr + x) = ta1 * ta2 * scale; - } - }, - input1, input2, dst); - } -} -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - template void mul_U8_U8_S16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, int n) { @@ -1857,13 +1648,11 @@ void CpuMulKernel::configure(ITensorInfo *src1, } } break; -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: - _func_float = &mul_F16_F16_F16; + _func_float = REGISTER_FP16_NEON(cpu::mul_F16_F16_F16); break; -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ case DataType::F32: - _func_float = &mul_F32_F32_F32; + _func_float = REGISTER_FP32_NEON(cpu::mul_F32_F32_F32); break; default: ARM_COMPUTE_ERROR("You called with the wrong img formats"); diff --git a/src/cpu/kernels/mul/generic/neon/fp16.cpp b/src/cpu/kernels/mul/generic/neon/fp16.cpp new file mode 100644 index 0000000000..920f298527 --- /dev/null +++ b/src/cpu/kernels/mul/generic/neon/fp16.cpp @@ -0,0 +1,145 @@ +/* + * Copyright (c) 2023 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) + +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/TensorInfo.h" + +#include "src/core/CPP/Validate.h" +#include "src/core/NEON/wrapper/wrapper.h" +#include "src/cpu/CpuTypes.h" + +namespace arm_compute +{ +namespace cpu +{ +void mul_F16_F16_F16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale) +{ + // Create input windows + Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); + Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); + + // Clear X Dimension on execution window as we handle manually + Window win = window; + win.set(Window::DimX, Window::Dimension(0, 1, 1)); + constexpr int window_step_x = 16; + const auto window_start_x = static_cast(window.x().start()); + const auto window_end_x = static_cast(window.x().end()); + const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x(); + if (is_broadcast_across_x) + { + const bool is_broadcast_input_2 = input2_win.x().step() == 0; + Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; + Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; + const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1; + const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1; + // Clear X Dimension on execution window as we handle manually + non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); + Iterator broadcast_input(broadcast_tensor, broadcast_win); + Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); + Iterator dst(out, win); + execute_window_loop( + win, + [&](const Coordinates &) + { + const auto non_broadcast_input_ptr = reinterpret_cast(non_broadcast_input.ptr()); + const auto output_ptr = reinterpret_cast(dst.ptr()); + const auto broadcast_value = *reinterpret_cast(broadcast_input.ptr()); + const float16x8x2_t broadcast_value_vec = {{ + vdupq_n_f16(broadcast_value), + vdupq_n_f16(broadcast_value), + }}; + const auto scale_vec = vdupq_n_f16(scale); + // Compute window_step_x elements per iteration + int x = window_start_x; + for (; x <= (window_end_x - window_step_x); x += window_step_x) + { + const float16x8x2_t non_broadcast_v = {{ + vld1q_f16(non_broadcast_input_ptr + x), + vld1q_f16(non_broadcast_input_ptr + x + 8), + }}; + const float16x8x2_t result = {{ + vmulq_f16(vmulq_f16(broadcast_value_vec.val[0], non_broadcast_v.val[0]), scale_vec), + vmulq_f16(vmulq_f16(broadcast_value_vec.val[1], non_broadcast_v.val[1]), scale_vec), + }}; + vst1q_f16(output_ptr + x, result.val[0]); + vst1q_f16(output_ptr + x + 8, result.val[1]); + } + // Compute left-over elements + for (; x < window_end_x; ++x) + { + const auto non_broadcast_v = *(non_broadcast_input_ptr + x); + *(output_ptr + x) = broadcast_value * non_broadcast_v * scale; + } + }, + broadcast_input, non_broadcast_input, dst); + } + else + { + input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); + input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); + Iterator input1(src1, input1_win); + Iterator input2(src2, input2_win); + Iterator dst(out, win); + execute_window_loop( + win, + [&](const Coordinates &) + { + const auto input1_ptr = reinterpret_cast(input1.ptr()); + const auto input2_ptr = reinterpret_cast(input2.ptr()); + const auto output_ptr = reinterpret_cast(dst.ptr()); + // Compute window_step_x elements per iteration + int x = window_start_x; + for (; x <= (window_end_x - window_step_x); x += window_step_x) + { + const float16x8x2_t ta1 = {{ + vld1q_f16(input1_ptr + x), + vld1q_f16(input1_ptr + x + 8), + }}; + const float16x8x2_t ta2 = {{ + vld1q_f16(input2_ptr + x), + vld1q_f16(input2_ptr + x + 8), + }}; + const float16x8_t scale_vec = vdupq_n_f16(scale); + const float16x8x2_t result = {{ + vmulq_f16(vmulq_f16(ta1.val[0], ta2.val[0]), scale_vec), + vmulq_f16(vmulq_f16(ta1.val[1], ta2.val[1]), scale_vec), + }}; + vst1q_f16(output_ptr + x, result.val[0]); + vst1q_f16(output_ptr + x + 8, result.val[1]); + } + // Compute left-over elements + for (; x < window_end_x; ++x) + { + const auto ta1 = *(input1_ptr + x); + const auto ta2 = *(input2_ptr + x); + *(output_ptr + x) = ta1 * ta2 * scale; + } + }, + input1, input2, dst); + } +} +} // namespace cpu +} // namespace arm_compute +#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */ diff --git a/src/cpu/kernels/mul/generic/neon/fp32.cpp b/src/cpu/kernels/mul/generic/neon/fp32.cpp new file mode 100644 index 0000000000..3001eb5110 --- /dev/null +++ b/src/cpu/kernels/mul/generic/neon/fp32.cpp @@ -0,0 +1,138 @@ +/* + * Copyright (c) 2023 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ + +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/TensorInfo.h" + +#include "src/core/CPP/Validate.h" +#include "src/core/NEON/wrapper/wrapper.h" +#include "src/cpu/CpuTypes.h" + +namespace arm_compute +{ +namespace cpu +{ +void mul_F32_F32_F32(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale) +{ + // Create input windows + Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); + Window input2_win = window.broadcast_if_dimension_le_one(src2->info()->tensor_shape()); + + // Clear X Dimension on execution window as we handle manually + Window win = window; + win.set(Window::DimX, Window::Dimension(0, 1, 1)); + + constexpr int window_step_x = 16 / sizeof(float); + const auto window_start_x = static_cast(window.x().start()); + const auto window_end_x = static_cast(window.x().end()); + const bool is_broadcast_across_x = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x(); + + using ExactTagType = typename wrapper::traits::neon_vector::tag_type; + + if (is_broadcast_across_x) + { + const bool is_broadcast_input_2 = input2_win.x().step() == 0; + Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; + Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; + const ITensor *broadcast_tensor = is_broadcast_input_2 ? src2 : src1; + const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1; + + // Clear X Dimension on execution window as we handle manually + non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator broadcast_input(broadcast_tensor, broadcast_win); + Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); + Iterator dst(out, win); + + execute_window_loop( + win, + [&](const Coordinates &) + { + const auto non_broadcast_input_ptr = reinterpret_cast(non_broadcast_input.ptr()); + const auto output_ptr = reinterpret_cast(dst.ptr()); + + const float broadcast_value = *reinterpret_cast(broadcast_input.ptr()); + const auto broadcast_value_vec = wrapper::vdup_n(broadcast_value, ExactTagType{}); + const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{}); + + // Compute window_step_x elements per iteration + int x = window_start_x; + for (; x <= (window_end_x - window_step_x); x += window_step_x) + { + const auto non_broadcast_v = wrapper::vloadq(non_broadcast_input_ptr + x); + auto res = wrapper::vmul(wrapper::vmul(broadcast_value_vec, non_broadcast_v), scale_vec); + wrapper::vstore(output_ptr + x, res); + } + + // Compute left-over elements + for (; x < window_end_x; ++x) + { + const auto non_broadcast_v = *(non_broadcast_input_ptr + x); + *(output_ptr + x) = broadcast_value * non_broadcast_v * scale; + } + }, + broadcast_input, non_broadcast_input, dst); + } + else + { + // Clear X Dimension on execution window as we handle manually + input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); + input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator input1(src1, input1_win); + Iterator input2(src2, input2_win); + Iterator dst(out, win); + + execute_window_loop( + win, + [&](const Coordinates &) + { + const auto input1_ptr = reinterpret_cast(input1.ptr()); + const auto input2_ptr = reinterpret_cast(input2.ptr()); + const auto output_ptr = reinterpret_cast(dst.ptr()); + + // Compute window_step_x elements per iteration + int x = window_start_x; + for (; x <= (window_end_x - window_step_x); x += window_step_x) + { + const auto ta1 = wrapper::vloadq(input1_ptr + x); + const auto ta2 = wrapper::vloadq(input2_ptr + x); + const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{}); + const auto res = wrapper::vmul(wrapper::vmul(ta1, ta2), scale_vec); + wrapper::vstore(output_ptr + x, res); + } + + // Compute left-over elements + for (; x < window_end_x; ++x) + { + const auto ta1 = *(input1_ptr + x); + const auto ta2 = *(input2_ptr + x); + *(output_ptr + x) = ta1 * ta2 * scale; + } + }, + input1, input2, dst); + } +} +} // namespace cpu +} // namespace arm_compute diff --git a/src/cpu/kernels/mul/generic/neon/list.h b/src/cpu/kernels/mul/generic/neon/list.h new file mode 100644 index 0000000000..710cb68b72 --- /dev/null +++ b/src/cpu/kernels/mul/generic/neon/list.h @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2023 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef ACL_SRC_CPU_KERNELS_MUL_GENERIC_NEON_LIST_H +#define ACL_SRC_CPU_KERNELS_MUL_GENERIC_NEON_LIST_H +namespace arm_compute +{ +namespace cpu +{ +#define DECLARE_MUL_KERNEL(func_name) \ + void func_name(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale) + +DECLARE_MUL_KERNEL(mul_F32_F32_F32); +DECLARE_MUL_KERNEL(mul_F16_F16_F16); +#undef DECLARE_MUL_KERNEL +} // namespace cpu +} // namespace arm_compute +#endif // ACL_SRC_CPU_KERNELS_MUL_GENERIC_NEON_LIST_H -- cgit v1.2.1