diff options
Diffstat (limited to 'src/cpu/kernels/elementwise_binary/generic/sve/impl.cpp')
| -rw-r--r-- | src/cpu/kernels/elementwise_binary/generic/sve/impl.cpp | 297 |
1 files changed, 297 insertions, 0 deletions
diff --git a/src/cpu/kernels/elementwise_binary/generic/sve/impl.cpp b/src/cpu/kernels/elementwise_binary/generic/sve/impl.cpp new file mode 100644 index 0000000000..fa48407e9b --- /dev/null +++ b/src/cpu/kernels/elementwise_binary/generic/sve/impl.cpp @@ -0,0 +1,297 @@ +/* + * Copyright (c) 2021-2022 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 "src/cpu/kernels/elementwise_binary/generic/sve/impl.h" + +#include "src/core/NEON/SVEMath.h" + +#include <arm_sve.h> + +namespace arm_compute +{ +namespace cpu +{ +using namespace arm_compute::wrapper; + +template <typename ScalarType> +void elementwise_arithmetic_op( + const ITensor *in1, const ITensor *in2, ITensor *out, ArithmeticOperation op, const Window &window) +{ + using VectorType = typename sve_vector<ScalarType>::type; + + const auto all_true_pg = svptrue<ScalarType>(); + + // Create input windows + Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()); + Window input2_win = window.broadcast_if_dimension_le_one(in2->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)); + + const auto window_start_x = static_cast<int>(window.x().start()); + const auto window_end_x = static_cast<int>(window.x().end()); + const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->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 ? in2 : in1; + const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1; + + // 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 output(out, win); + + execute_window_loop( + win, + [&](const Coordinates &) + { + auto output_ptr = reinterpret_cast<ScalarType *>(output.ptr()); + const auto non_broadcast_input_ptr = reinterpret_cast<const ScalarType *>(non_broadcast_input.ptr()); + const ScalarType broadcast_value = *reinterpret_cast<const ScalarType *>(broadcast_input.ptr()); + const auto broadcast_vector = svdup_n(broadcast_value); + + int x = window_start_x; + + svbool_t pg = svwhilelt<ScalarType>(x, window_end_x); + do + { + const auto non_broadcast_vector = svld1(pg, non_broadcast_input_ptr + x); + VectorType res{}; + + if (is_broadcast_input_2) + { + res = elementwise_arithmetic_op<typename sve_vector<ScalarType>::type>(pg, non_broadcast_vector, + broadcast_vector, op); + } + else + { + res = elementwise_arithmetic_op<typename sve_vector<ScalarType>::type>( + pg, broadcast_vector, non_broadcast_vector, op); + } + svst1(pg, output_ptr + x, res); + + x += svcnt<ScalarType>(); + pg = svwhilelt<ScalarType>(x, window_end_x); + } while (svptest_any(all_true_pg, pg)); + }, + broadcast_input, non_broadcast_input, output); + } + 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(in1, input1_win); + Iterator input2(in2, input2_win); + Iterator output(out, win); + + execute_window_loop( + win, + [&](const Coordinates &) + { + auto output_ptr = reinterpret_cast<ScalarType *>(output.ptr()); + const auto input1_ptr = reinterpret_cast<const ScalarType *>(input1.ptr()); + const auto input2_ptr = reinterpret_cast<const ScalarType *>(input2.ptr()); + + int x = window_start_x; + + svbool_t pg = svwhilelt<ScalarType>(x, window_end_x); + do + { + const auto in1 = svld1(pg, input1_ptr + x); + const auto in2 = svld1(pg, input2_ptr + x); + const auto res = elementwise_arithmetic_op<typename sve_vector<ScalarType>::type>(pg, in1, in2, op); + svst1(pg, output_ptr + x, res); + + x += svcnt<ScalarType>(); + pg = svwhilelt<ScalarType>(x, window_end_x); + } while (svptest_any(all_true_pg, pg)); + }, + input1, input2, output); + } +} +template void elementwise_arithmetic_op<float32_t>( + const ITensor *in1, const ITensor *in2, ITensor *out, const ArithmeticOperation op, const Window &window); +template void elementwise_arithmetic_op<float16_t>( + const ITensor *in1, const ITensor *in2, ITensor *out, const ArithmeticOperation op, const Window &window); +template void elementwise_arithmetic_op<int16_t>( + const ITensor *in1, const ITensor *in2, ITensor *out, const ArithmeticOperation op, const Window &window); +template void elementwise_arithmetic_op<int32_t>( + const ITensor *in1, const ITensor *in2, ITensor *out, const ArithmeticOperation op, const Window &window); + +template <typename InputScalarType, typename OutputScalarType> +void elementwise_comparison_op( + const ITensor *in1, const ITensor *in2, ITensor *out, ComparisonOperation op, const Window &window) +{ + static_assert(sizeof(InputScalarType) >= sizeof(OutputScalarType), + "input data type's width should be equal to or greater than output data type's width"); + + using OutputVectorType = typename sve_vector<OutputScalarType>::type; + const auto all_true_pg = svptrue<InputScalarType>(); + + // Create input windows + Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()); + Window input2_win = window.broadcast_if_dimension_le_one(in2->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)); + + const auto window_start_x = static_cast<int>(window.x().start()); + const auto window_end_x = static_cast<int>(window.x().end()); + const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->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 ? in2 : in1; + const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1; + + // 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 output(out, win); + + execute_window_loop( + win, + [&](const Coordinates &) + { + auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr()); + const auto non_broadcast_input_ptr = + reinterpret_cast<const InputScalarType *>(non_broadcast_input.ptr()); + const InputScalarType broadcast_value = + *reinterpret_cast<const InputScalarType *>(broadcast_input.ptr()); + const auto broadcast_vector = svdup_n(broadcast_value); + + int x = window_start_x; + + svbool_t pg = svwhilelt<InputScalarType>(x, window_end_x); + do + { + const auto non_broadcast_vector = svld1(pg, non_broadcast_input_ptr + x); + const svbool_t output_pg = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg); + OutputVectorType res{}; + if (is_broadcast_input_2) + { + res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, + typename sve_vector<OutputScalarType>::type>( + pg, non_broadcast_vector, broadcast_vector, op); + } + else + { + res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, + typename sve_vector<OutputScalarType>::type>( + pg, broadcast_vector, non_broadcast_vector, op); + } + svst1(output_pg, output_ptr + x, res); + + x += svcnt<InputScalarType>(); + pg = svwhilelt<InputScalarType>(x, window_end_x); + } while (svptest_any(all_true_pg, pg)); + }, + broadcast_input, non_broadcast_input, output); + } + 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(in1, input1_win); + Iterator input2(in2, input2_win); + Iterator output(out, win); + + execute_window_loop( + win, + [&](const Coordinates &) + { + auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr()); + const auto input1_ptr = reinterpret_cast<const InputScalarType *>(input1.ptr()); + const auto input2_ptr = reinterpret_cast<const InputScalarType *>(input2.ptr()); + + int x = window_start_x; + + svbool_t pg = svwhilelt<InputScalarType>(x, window_end_x); + do + { + const auto in1 = svld1(pg, input1_ptr + x); + const auto in2 = svld1(pg, input2_ptr + x); + const auto res = + elementwise_comparison_op<typename sve_vector<InputScalarType>::type, + typename sve_vector<OutputScalarType>::type>(pg, in1, in2, op); + const svbool_t output_pg = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg); + svst1(output_pg, output_ptr + x, res); + + x += svcnt<InputScalarType>(); + pg = svwhilelt<InputScalarType>(x, window_end_x); + } while (svptest_any(all_true_pg, pg)); + }, + input1, input2, output); + } +} + +template void elementwise_comparison_op<float32_t>( + const ITensor *in1, const ITensor *in2, ITensor *out, const ComparisonOperation op, const Window &window); +template void elementwise_comparison_op<float16_t>( + const ITensor *in1, const ITensor *in2, ITensor *out, const ComparisonOperation op, const Window &window); +template void elementwise_comparison_op<uint8_t>( + const ITensor *in1, const ITensor *in2, ITensor *out, const ComparisonOperation op, const Window &window); +template void elementwise_comparison_op<int16_t>( + const ITensor *in1, const ITensor *in2, ITensor *out, const ComparisonOperation op, const Window &window); +template void elementwise_comparison_op<int32_t>( + const ITensor *in1, const ITensor *in2, ITensor *out, const ComparisonOperation op, const Window &window); + +template <> +svint32_t elementwise_pow<svint32_t>(svbool_t &pg, const svint32_t &a, const svint32_t &b) +{ + return svcvt_s32_z(pg, svpow_z(pg, svcvt_f32_z(pg, a), svcvt_f32_z(pg, b))); +} + +template <> +svint32_t elementwise_div<svint32_t>(svbool_t &pg, const svint32_t &a, const svint32_t &b) +{ + return svcvt_s32_z(pg, svdiv_z(pg, svcvt_f32_z(pg, a), svcvt_f32_z(pg, b))); +} + +template <> +svint16_t elementwise_div<svint16_t>(svbool_t &pg, const svint16_t &a, const svint16_t &b) +{ + ARM_COMPUTE_UNUSED(pg, a, b); + ARM_COMPUTE_ERROR("Not supported"); +} + +} // namespace cpu +} // namespace arm_compute |