diff options
author | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-08-20 21:39:25 +0100 |
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committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-08-25 16:23:15 +0000 |
commit | 7891a73ef36f4ad7b71069b3c57694f85bb79454 (patch) | |
tree | 5b08692989e28ce63de2937d8d92ea5176589dbe /src/cpu/kernels/elementwise/sve | |
parent | a46c9c98c2b1d70acc7c6eee00e2cdc2a1e209a6 (diff) | |
download | ComputeLibrary-7891a73ef36f4ad7b71069b3c57694f85bb79454.tar.gz |
Move CPU/GPU files from Core/Runtime to the respective backend folders
Legacy structure contained two libraries core/runtime with two backends
in each.
We reduce the core/runtime libraries to a single library thus merging
the backend files
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I69545765fe7a730368105cdbd067d3135ec7a174
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6155
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/cpu/kernels/elementwise/sve')
-rw-r--r-- | src/cpu/kernels/elementwise/sve/elementwise.cpp | 311 | ||||
-rw-r--r-- | src/cpu/kernels/elementwise/sve/elementwise_list.h | 171 | ||||
-rw-r--r-- | src/cpu/kernels/elementwise/sve/elementwise_quantized_list.h | 366 | ||||
-rw-r--r-- | src/cpu/kernels/elementwise/sve/elementwise_unary.cpp | 113 | ||||
-rw-r--r-- | src/cpu/kernels/elementwise/sve/elementwise_unary_list.h | 39 |
5 files changed, 1000 insertions, 0 deletions
diff --git a/src/cpu/kernels/elementwise/sve/elementwise.cpp b/src/cpu/kernels/elementwise/sve/elementwise.cpp new file mode 100644 index 0000000000..2f9a7998df --- /dev/null +++ b/src/cpu/kernels/elementwise/sve/elementwise.cpp @@ -0,0 +1,311 @@ +/* + * Copyright (c) 2021 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_SVE) +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/Types.h" +#include "src/cpu/kernels/elementwise/sve/elementwise_list.h" +#include <arm_sve.h> + +namespace arm_compute +{ +namespace cpu +{ +using namespace arm_compute::wrapper; + +template <typename InputScalarType, typename OutputScalarType, typename OperatorType> +struct LoopArguments +{ + OperatorType op; + const InputScalarType *input1_ptr; + const InputScalarType *input2_ptr; + OutputScalarType *output_ptr; +}; + +template <typename InputScalarType, typename OutputScalarType, typename OperatorType> +struct BroadcastLoopArguments +{ + OperatorType op; + const InputScalarType *input1_ptr; + InputScalarType broadcast_value; + OutputScalarType *output_ptr; + bool reorder; +}; + +template <typename InputScalarType, typename OutputScalarType> +void arithmetic_op_loop(svbool_t pg, const LoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args) +{ + const auto in1 = svld1(pg, args.input1_ptr); + const auto in2 = svld1(pg, args.input2_ptr); + const auto res = elementwise_arithmetic_op<typename sve_vector<InputScalarType>::type>(pg, in1, in2, args.op); + svst1(pg, args.output_ptr, res); +} + +template <typename InputScalarType, typename OutputScalarType> +void arithmetic_op_broadcast_loop(svbool_t pg, const BroadcastLoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args) +{ + const auto non_broadcast_vector = svld1(pg, args.input1_ptr); + const auto broadcast_vector = svdup_n(args.broadcast_value); + const auto in1 = args.reorder ? broadcast_vector : non_broadcast_vector; + const auto in2 = args.reorder ? non_broadcast_vector : broadcast_vector; + const auto res = elementwise_arithmetic_op<typename sve_vector<InputScalarType>::type>(pg, in1, in2, args.op); + svst1(pg, args.output_ptr, res); +} + +template <typename InputScalarType, typename OutputScalarType> +void comparison_op_loop(svbool_t pg, const LoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args) +{ + const auto in1 = svld1(pg, args.input1_ptr); + const auto in2 = svld1(pg, args.input2_ptr); + const auto res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, in1, in2, args.op); + const svbool_t output_pg = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg); + svst1(output_pg, args.output_ptr, res); +} + +template <typename InputScalarType, typename OutputScalarType> +void comparison_op_broadcast_loop(svbool_t pg, const BroadcastLoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args) +{ + const auto non_broadcast_vector = svld1(pg, args.input1_ptr); + const auto broadcast_vector = svdup_n(args.broadcast_value); + const auto in1 = args.reorder ? broadcast_vector : non_broadcast_vector; + const auto in2 = args.reorder ? non_broadcast_vector : broadcast_vector; + const auto res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, in1, in2, args.op); + const svbool_t output_pg = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg); + svst1(output_pg, args.output_ptr, res); +} + +template <typename InputScalarType, typename OutputScalarType, typename OperatorType> +using LoopFuncType = void (*)(svbool_t, const LoopArguments<InputScalarType, OutputScalarType, OperatorType> &); + +template <typename InputScalarType, typename OutputScalarType, typename OperatorType> +using BroadcastLoopFuncType = void (*)(svbool_t, const BroadcastLoopArguments<InputScalarType, OutputScalarType, OperatorType> &); + +template <typename InputVectorType, typename OutputVectorType, typename OperatorType, + typename InputScalarType = typename sve_scalar<InputVectorType>::type, + typename OutputScalarType = typename sve_scalar<OutputVectorType>::type> +void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, + OperatorType op, + LoopFuncType<InputScalarType, OutputScalarType, OperatorType> func, + BroadcastLoopFuncType<InputScalarType, OutputScalarType, OperatorType> broadcast_func) +{ + 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()); + + int x = window_start_x; + + svbool_t pg = svwhilelt<InputScalarType>(x, window_end_x); + do + { + broadcast_func(pg, + { + op, + non_broadcast_input_ptr + x, + broadcast_value, + output_ptr + x, + !is_broadcast_input_2 + }); + 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 + { + func(pg, + { + op, + input1_ptr + x, + input2_ptr + x, + output_ptr + x + }); + x += svcnt<InputScalarType>(); + pg = svwhilelt<InputScalarType>(x, window_end_x); + } + while(svptest_any(all_true_pg, pg)); + }, + input1, input2, output); + } +} + +template <ArithmeticOperation op, typename ScalarType> +void elementwise_arithmetic_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) +{ + using VectorType = typename sve_vector<ScalarType>::type; + + elementwise_op<VectorType, VectorType, ArithmeticOperation>(in1, in2, out, window, op, + &arithmetic_op_loop<ScalarType, ScalarType>, + &arithmetic_op_broadcast_loop<ScalarType, ScalarType>); +} + +template <ComparisonOperation op, typename InputScalarType, typename OutputScalarType = uint8_t> +void elementwise_comparison_op(const ITensor *in1, const ITensor *in2, ITensor *out, 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 InputVectorType = typename sve_vector<InputScalarType>::type; + using OutputVectorType = typename sve_vector<OutputScalarType>::type; + + elementwise_op<InputVectorType, OutputVectorType, ComparisonOperation>(in1, in2, out, window, op, + &comparison_op_loop<InputScalarType, OutputScalarType>, + &comparison_op_broadcast_loop<InputScalarType, OutputScalarType>); +} + +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"); +} + +template void elementwise_arithmetic_op<ArithmeticOperation::MAX, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::MAX, float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::MAX, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::MAX, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); + +template void elementwise_arithmetic_op<ArithmeticOperation::MIN, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::MIN, float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::MIN, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::MIN, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); + +template void elementwise_arithmetic_op<ArithmeticOperation::SQUARED_DIFF, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::SQUARED_DIFF, float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::SQUARED_DIFF, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::SQUARED_DIFF, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); + +template void elementwise_arithmetic_op<ArithmeticOperation::PRELU, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::PRELU, float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::PRELU, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::PRELU, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); + +template void elementwise_arithmetic_op<ArithmeticOperation::DIV, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::DIV, float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::DIV, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::DIV, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); + +template void elementwise_arithmetic_op<ArithmeticOperation::POWER, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::POWER, float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::POWER, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_arithmetic_op<ArithmeticOperation::POWER, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); + +template void elementwise_comparison_op<ComparisonOperation::Equal, float>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::Equal, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::Equal, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::Equal, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::Equal, uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); + +template void elementwise_comparison_op<ComparisonOperation::NotEqual, float>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::NotEqual, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::NotEqual, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::NotEqual, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::NotEqual, uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); + +template void elementwise_comparison_op<ComparisonOperation::Greater, float>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::Greater, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::Greater, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::Greater, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::Greater, uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); + +template void elementwise_comparison_op<ComparisonOperation::GreaterEqual, float>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::GreaterEqual, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::GreaterEqual, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::GreaterEqual, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::GreaterEqual, uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); + +template void elementwise_comparison_op<ComparisonOperation::Less, float>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::Less, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::Less, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::Less, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::Less, uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); + +template void elementwise_comparison_op<ComparisonOperation::LessEqual, float>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::LessEqual, int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::LessEqual, float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::LessEqual, int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +template void elementwise_comparison_op<ComparisonOperation::LessEqual, uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +} // namespace cpu +} // namespace arm_compute +#endif /* defined(__ARM_FEATURE_SVE) */
\ No newline at end of file diff --git a/src/cpu/kernels/elementwise/sve/elementwise_list.h b/src/cpu/kernels/elementwise/sve/elementwise_list.h new file mode 100644 index 0000000000..f762587ce7 --- /dev/null +++ b/src/cpu/kernels/elementwise/sve/elementwise_list.h @@ -0,0 +1,171 @@ +/* + * Copyright (c) 2021 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 SRC_CORE_SVE_KERNELS_ELEMENTWISE_LIST_H +#define SRC_CORE_SVE_KERNELS_ELEMENTWISE_LIST_H +#if defined(ARM_COMPUTE_ENABLE_SVE) +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Window.h" +#include "arm_compute/core/utils/misc/Traits.h" +#include "src/core/NEON/SVEMath.h" +#include "src/core/NEON/wrapper/intrinsics/intrinsics.h" +#include "src/core/NEON/wrapper/svtraits.h" +#include "src/cpu/kernels/elementwise/sve/elementwise_list.h" +#include <arm_sve.h> + +namespace arm_compute +{ +namespace cpu +{ +using namespace arm_compute::wrapper; + +template <typename VectorType> +VectorType elementwise_pow(svbool_t &pg, const VectorType &a, const VectorType &b) +{ + return svpow_z(pg, a, b); +} + +template <typename VectorType> +VectorType elementwise_div(svbool_t &pg, const VectorType &a, const VectorType &b) +{ + return svdiv_z(pg, a, b); +} + +template <uint32_t bytewidth> +svbool_t narrow_to_byte_predicate(svbool_t pg) +{ + const auto all_false = svpfalse(); + + switch(bytewidth) + { + case 8: + pg = svuzp1_b32(pg, all_false); + /* fall through */ + case 4: + pg = svuzp1_b16(pg, all_false); + /* fall through */ + case 2: + pg = svuzp1_b8(pg, all_false); + /* fall through */ + default: + break; + } + return pg; +} + +template <typename VectorType> +VectorType elementwise_arithmetic_op(svbool_t &pg, const VectorType &a, const VectorType &b, ArithmeticOperation op) +{ + using ScalarType = typename wrapper::sve_scalar<VectorType>::type; + VectorType res{}; + + switch(op) + { + case ArithmeticOperation::MAX: + res = svmax_z(pg, a, b); + break; + case ArithmeticOperation::MIN: + res = svmin_z(pg, a, b); + break; + case ArithmeticOperation::SQUARED_DIFF: + { + const auto tmp = svsub_z(pg, a, b); + res = svmul_z(pg, tmp, tmp); + break; + } + case ArithmeticOperation::PRELU: + { + const auto zero = svdup_n(ScalarType(0)); + const auto tmp = svmul_z(pg, a, b); + const auto gt = svcmpgt(pg, a, zero); + res = svsel(gt, a, tmp); + break; + } + case ArithmeticOperation::DIV: + { + res = elementwise_div(pg, a, b); + break; + } + case ArithmeticOperation::POWER: + { + res = elementwise_pow(pg, a, b); + break; + } + default: + ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); + } + + return res; +} + +template <typename InputVectorType, typename OutputVectorType> +OutputVectorType elementwise_comparison_op(svbool_t &pg, const InputVectorType &a, const InputVectorType &b, ComparisonOperation op) +{ + svbool_t selection_vector{}; + + switch(op) + { + case ComparisonOperation::Equal: + selection_vector = svcmpeq(pg, a, b); + break; + case ComparisonOperation::NotEqual: + selection_vector = svcmpne(pg, a, b); + break; + case ComparisonOperation::Greater: + selection_vector = svcmpgt(pg, a, b); + break; + case ComparisonOperation::GreaterEqual: + selection_vector = svcmpge(pg, a, b); + break; + case ComparisonOperation::Less: + selection_vector = svcmplt(pg, a, b); + break; + case ComparisonOperation::LessEqual: + selection_vector = svcmple(pg, a, b); + break; + default: + ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); + } + + using InputScalarType = typename wrapper::sve_scalar<InputVectorType>::type; + selection_vector = narrow_to_byte_predicate<sizeof(InputScalarType)>(selection_vector); + + using OutputScalarType = typename wrapper::sve_scalar<OutputVectorType>::type; + const auto false_vector = svdup_n(static_cast<OutputScalarType>((uint32_t)0)); + const auto true_vector = svdup_n(static_cast<OutputScalarType>(~(uint32_t)0)); + auto ret = svsel(selection_vector, true_vector, false_vector); + + return ret; +} + +template <ArithmeticOperation op, typename ScalarType> +void elementwise_arithmetic_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); + +template <ComparisonOperation op, typename ScalarType, typename OutputScalarType = uint8_t> +void elementwise_comparison_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window); +} // namespace cpu +} // namespace arm_compute +#endif // defined(ARM_COMPUTE_ENABLE_SVE) +#endif /* SRC_CORE_SVE_KERNELS_ELEMENTWISE_LIST_H */ diff --git a/src/cpu/kernels/elementwise/sve/elementwise_quantized_list.h b/src/cpu/kernels/elementwise/sve/elementwise_quantized_list.h new file mode 100644 index 0000000000..a5d17a86a7 --- /dev/null +++ b/src/cpu/kernels/elementwise/sve/elementwise_quantized_list.h @@ -0,0 +1,366 @@ +/* + * Copyright (c) 2021 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 SRC_CORE_SVE_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H +#define SRC_CORE_SVE_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H + +#if defined(ARM_COMPUTE_ENABLE_SVE2) + +#include "src/core/NEON/wrapper/svtraits.h" +#include "src/cpu/kernels/elementwise/sve/elementwise_list.h" + +namespace arm_compute +{ +namespace cpu +{ +using namespace arm_compute::wrapper; + +template <typename InputScalarType, typename OutputScalarType, typename OperatorType> +struct QuantizedLoopArguments +{ + OperatorType op; + const InputScalarType *input1_ptr; + const InputScalarType *input2_ptr; + OutputScalarType *output_ptr; + + const svint32_t &in1_offset; + const svint32_t &in2_offset; + const svint32_t &out_offset; + const svfloat32_t &in1_scale; + const svfloat32_t &in2_scale; + const svfloat32_t &out_scale; +}; + +template <typename InputScalarType, typename OutputScalarType, typename OperatorType> +struct BroadcastQuantizedLoopArguments +{ + OperatorType op; + const InputScalarType *input1_ptr; + float broadcast_value; + OutputScalarType *output_ptr; + bool reorder; + + const svint32_t &in1_offset; + const svint32_t &out_offset; + const svfloat32_t &in1_scale; + const svfloat32_t &out_scale; +}; + +svfloat32x4_t load_quantized(const int8_t *ptr, svbool_t pg, const svint32_t &offset, const svfloat32_t &scale) +{ + auto x = svld1(pg, ptr); + + const auto widened = svcreate4( + svmovlb(svmovlb(x)), + svmovlt(svmovlb(x)), + svmovlb(svmovlt(x)), + svmovlt(svmovlt(x))); + + pg = svptrue_b8(); + + return svcreate4( + svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svget4(widened, 0), offset)), scale), + svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svget4(widened, 1), offset)), scale), + svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svget4(widened, 2), offset)), scale), + svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svget4(widened, 3), offset)), scale)); +} + +svfloat32x4_t load_quantized(const uint8_t *ptr, svbool_t pg, const svint32_t &offset, const svfloat32_t &scale) +{ + auto x = svld1(pg, ptr); + + //vprint(x); + + const auto widened = svcreate4( + svmovlb(svmovlb(x)), + svmovlt(svmovlb(x)), + svmovlb(svmovlt(x)), + svmovlt(svmovlt(x))); + + pg = svptrue_b8(); + + return svcreate4( + svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svreinterpret_s32(svget4(widened, 0)), offset)), scale), + svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svreinterpret_s32(svget4(widened, 1)), offset)), scale), + svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svreinterpret_s32(svget4(widened, 2)), offset)), scale), + svmul_z(pg, svcvt_f32_z(pg, svsub_z(pg, svreinterpret_s32(svget4(widened, 3)), offset)), scale)); +} + +void store_quantized(uint8_t *ptr, svbool_t pg, svfloat32x4_t data, const svint32_t &offset, const svfloat32_t &inv_scale) +{ + const auto quantized = svcreate4( + svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 0), inv_scale))), offset), + svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 1), inv_scale))), offset), + svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 2), inv_scale))), offset), + svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 3), inv_scale))), offset)); + + const auto narrowed_bottom = svqxtunt(svqxtunb(svget4(quantized, 0)), svget4(quantized, 1)); + const auto narrowed_top = svqxtunt(svqxtunb(svget4(quantized, 2)), svget4(quantized, 3)); + const auto narrowed = svqxtnt(svqxtnb(narrowed_bottom), narrowed_top); + svst1(pg, ptr, narrowed); +} + +void store_quantized(int8_t *ptr, svbool_t pg, svfloat32x4_t data, const svint32_t &offset, const svfloat32_t &inv_scale) +{ + const auto quantized = svcreate4( + svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 0), inv_scale))), offset), + svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 1), inv_scale))), offset), + svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 2), inv_scale))), offset), + svadd_z(pg, svcvt_s32_z(pg, svrinta_z(pg, svmul_z(pg, svget4(data, 3), inv_scale))), offset)); + + const auto narrowed_bottom = svqxtnt(svqxtnb(svget4(quantized, 0)), svget4(quantized, 1)); + const auto narrowed_top = svqxtnt(svqxtnb(svget4(quantized, 2)), svget4(quantized, 3)); + const auto narrowed = svqxtnt(svqxtnb(narrowed_bottom), narrowed_top); + + svst1(pg, ptr, narrowed); +} + +template <typename InputScalarType, typename OutputScalarType> +inline void arithmetic_op_quantized_loop(svbool_t pg, const QuantizedLoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args) +{ + const auto in1 = load_quantized(args.input1_ptr, pg, args.in1_offset, args.in1_scale); + const auto in2 = load_quantized(args.input2_ptr, pg, args.in2_offset, args.in2_scale); + + const auto result = svcreate4( + elementwise_arithmetic_op<svfloat32_t>(pg, svget4(in1, 0), svget4(in2, 0), args.op), + elementwise_arithmetic_op<svfloat32_t>(pg, svget4(in1, 1), svget4(in2, 1), args.op), + elementwise_arithmetic_op<svfloat32_t>(pg, svget4(in1, 2), svget4(in2, 2), args.op), + elementwise_arithmetic_op<svfloat32_t>(pg, svget4(in1, 3), svget4(in2, 3), args.op)); + + store_quantized(args.output_ptr, pg, result, args.out_offset, args.out_scale); +} + +template <typename InputScalarType, typename OutputScalarType> +inline void arithmetic_op_broadcast_quantized_loop(svbool_t pg, const BroadcastQuantizedLoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args) +{ + const auto in1 = load_quantized(args.input1_ptr, pg, args.in1_offset, args.in1_scale); + const auto in2 = svcreate4( + svdup_n(args.broadcast_value), svdup_n(args.broadcast_value), svdup_n(args.broadcast_value), svdup_n(args.broadcast_value)); + + const auto &af = args.reorder ? in2 : in1; + const auto &bf = args.reorder ? in1 : in2; + + const auto result = svcreate4( + elementwise_arithmetic_op<svfloat32_t>(pg, svget4(af, 0), svget4(bf, 0), args.op), + elementwise_arithmetic_op<svfloat32_t>(pg, svget4(af, 1), svget4(bf, 1), args.op), + elementwise_arithmetic_op<svfloat32_t>(pg, svget4(af, 2), svget4(bf, 2), args.op), + elementwise_arithmetic_op<svfloat32_t>(pg, svget4(af, 3), svget4(bf, 3), args.op)); + + store_quantized(args.output_ptr, pg, result, args.out_offset, args.out_scale); +} + +template <typename InputScalarType, typename OutputScalarType> +inline void comparison_op_quantized_loop(svbool_t pg, const QuantizedLoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args) +{ + const auto in1 = load_quantized(args.input1_ptr, pg, args.in1_offset, args.in1_scale); + const auto in2 = load_quantized(args.input2_ptr, pg, args.in2_offset, args.in2_scale); + + using OutputVectorType = typename wrapper::traits::sve_vector<OutputScalarType>::type; + + const auto result = svcreate4( + elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(in1, 0), svget4(in2, 0), args.op), + elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(in1, 1), svget4(in2, 1), args.op), + elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(in1, 2), svget4(in2, 2), args.op), + elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(in1, 3), svget4(in2, 3), args.op)); + + const auto zipped_bottom = svzip1(svget4(result, 0), svget4(result, 1)); + const auto zipped_top = svzip1(svget4(result, 2), svget4(result, 3)); + const auto zipped = svzip1(zipped_bottom, zipped_top); + svst1(pg, args.output_ptr, zipped); +} + +template <typename InputScalarType, typename OutputScalarType> +inline void comparison_op_broadcast_quantized_loop(svbool_t pg, const BroadcastQuantizedLoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args) +{ + const auto in1 = load_quantized(args.input1_ptr, pg, args.in1_offset, args.in1_scale); + const auto in2 = svcreate4( + svdup_n(args.broadcast_value), svdup_n(args.broadcast_value), svdup_n(args.broadcast_value), svdup_n(args.broadcast_value)); + + const auto &af = args.reorder ? in2 : in1; + const auto &bf = args.reorder ? in1 : in2; + + using OutputVectorType = typename wrapper::traits::sve_vector<OutputScalarType>::type; + + const auto result = svcreate4( + elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(af, 0), svget4(bf, 0), args.op), + elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(af, 1), svget4(bf, 1), args.op), + elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(af, 2), svget4(bf, 2), args.op), + elementwise_comparison_op<svfloat32_t, OutputVectorType>(pg, svget4(af, 3), svget4(bf, 3), args.op)); + + const auto zipped_bottom = svzip1(svget4(result, 0), svget4(result, 1)); + const auto zipped_top = svzip1(svget4(result, 2), svget4(result, 3)); + const auto zipped = svzip1(zipped_bottom, zipped_top); + svst1(pg, args.output_ptr, zipped); +} + +template <typename InputScalarType, typename OutputScalarType, typename OperatorType> +using LoopQuantizedFuncType = void (*)(svbool_t, const QuantizedLoopArguments<InputScalarType, OutputScalarType, OperatorType> &); + +template <typename InputScalarType, typename OutputScalarType, typename OperatorType> +using BroadcastQuantizedLoopFuncType = void (*)(svbool_t, const BroadcastQuantizedLoopArguments<InputScalarType, OutputScalarType, OperatorType> &); + +template <typename InputVectorType, typename OutputVectorType, typename OperatorType, + typename InputScalarType = typename wrapper::sve_scalar<InputVectorType>::type, + typename OutputScalarType = typename wrapper::sve_scalar<OutputVectorType>::type> +void elementwise_quantized_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, + OperatorType op, + LoopQuantizedFuncType<InputScalarType, OutputScalarType, OperatorType> func, + BroadcastQuantizedLoopFuncType<InputScalarType, OutputScalarType, OperatorType> broadcast_func) +{ + const auto all_true_pg = wrapper::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(); + + const auto output_voffset = svdup_n(out->info()->quantization_info().uniform().offset); + const auto output_vscale = svdup_n(1.f / out->info()->quantization_info().uniform().scale); + + 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; + + const auto non_broadcast_qinfo = is_broadcast_input_2 ? in1->info()->quantization_info() : in2->info()->quantization_info(); + const auto broadcast_qinfo = is_broadcast_input_2 ? in2->info()->quantization_info() : in1->info()->quantization_info(); + + const auto non_broadcast_voffset = svdup_n(non_broadcast_qinfo.uniform().offset); + const auto non_broadcast_vscale = svdup_n(non_broadcast_qinfo.uniform().scale); + + // 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()); + + int x = window_start_x; + + svbool_t pg = wrapper::svwhilelt<InputScalarType>(x, window_end_x); + do + { + const auto args = BroadcastQuantizedLoopArguments<InputScalarType, OutputScalarType, OperatorType> + { + op, + non_broadcast_input_ptr + x, + Qasymm8QuantizationHelper<InputScalarType>::dequantize(broadcast_value, broadcast_qinfo), + output_ptr + x, + !is_broadcast_input_2, + non_broadcast_voffset, output_voffset, + non_broadcast_vscale, output_vscale + }; + broadcast_func(pg, args); + x += wrapper::svcnt<InputScalarType>(); + pg = wrapper::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); + + const auto in1_voffset = svdup_n(in1->info()->quantization_info().uniform().offset); + const auto in1_vscale = svdup_n(in1->info()->quantization_info().uniform().scale); + + const auto in2_voffset = svdup_n(in2->info()->quantization_info().uniform().offset); + const auto in2_vscale = svdup_n(in2->info()->quantization_info().uniform().scale); + + 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 = wrapper::svwhilelt<InputScalarType>(x, window_end_x); + do + { + const auto args = QuantizedLoopArguments<InputScalarType, OutputScalarType, OperatorType> + { + op, + input1_ptr + x, + input2_ptr + x, + output_ptr + x, + in1_voffset, in2_voffset, output_voffset, + in1_vscale, in2_vscale, output_vscale + }; + func(pg, args); + x += wrapper::svcnt<InputScalarType>(); + pg = wrapper::svwhilelt<InputScalarType>(x, window_end_x); + } + while(svptest_any(all_true_pg, pg)); + }, + input1, input2, output); + } +} + +template <ArithmeticOperation op, typename ScalarType> +void elementwise_arithmetic_quantized_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) +{ + using VectorType = typename wrapper::traits::sve_vector<ScalarType>::type; + elementwise_quantized_op<VectorType, VectorType, ArithmeticOperation>(in1, in2, out, window, op, + &arithmetic_op_quantized_loop<ScalarType, ScalarType>, + &arithmetic_op_broadcast_quantized_loop<ScalarType, ScalarType>); +} + +template <ComparisonOperation op, typename InputScalarType, typename OutputScalarType = uint8_t> +void elementwise_comparison_quantized_op(const ITensor *in1, const ITensor *in2, ITensor *out, 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 InputVectorType = typename wrapper::traits::sve_vector<InputScalarType>::type; + using OutputVectorType = typename wrapper::traits::sve_vector<OutputScalarType>::type; + elementwise_quantized_op<InputVectorType, OutputVectorType, ComparisonOperation>(in1, in2, out, window, op, + &comparison_op_quantized_loop<InputScalarType, OutputScalarType>, + &comparison_op_broadcast_quantized_loop<InputScalarType, OutputScalarType>); +} +} // namespace cpu +} // namespace arm_compute + +#endif /* defined(ARM_COMPUTE_ENABLE_SVE2) */ +#endif /* SRC_CORE_SVE_KERNELS_ELEMENTWISE_QUANTIZED_LIST_H */
\ No newline at end of file diff --git a/src/cpu/kernels/elementwise/sve/elementwise_unary.cpp b/src/cpu/kernels/elementwise/sve/elementwise_unary.cpp new file mode 100644 index 0000000000..ddf1febd66 --- /dev/null +++ b/src/cpu/kernels/elementwise/sve/elementwise_unary.cpp @@ -0,0 +1,113 @@ +/* + * Copyright (c) 2021 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_SVE) +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Window.h" +#include "arm_compute/core/utils/misc/Traits.h" +#include "src/core/NEON/SVEMath.h" +#include "src/core/NEON/wrapper/intrinsics/intrinsics.h" +#include <arm_sve.h> + +namespace arm_compute +{ +namespace cpu +{ +template <typename ScalarType, typename VectorType> +inline typename std::enable_if<utils::traits::is_floating_point<ScalarType>::value, VectorType>::type elementwise_op_sve_imp(svbool_t pg, ElementWiseUnary op, const VectorType &a) +{ + switch(op) + { + case ElementWiseUnary::RSQRT: + return svinvsqrt(pg, a); + case ElementWiseUnary::EXP: + return wrapper::svexp_z(pg, a); + case ElementWiseUnary::NEG: + return svneg_z(pg, a); + case ElementWiseUnary::LOG: + return wrapper::svlog_z(pg, a); + case ElementWiseUnary::ABS: + return svabs_z(pg, a); + case ElementWiseUnary::ROUND: + return svrintn_z(pg, a); + case ElementWiseUnary::SIN: + return wrapper::svsin_z(pg, a); + default: + ARM_COMPUTE_ERROR("NOT_SUPPORTED"); + } +} + +template <typename ScalarType, typename VectorType> +inline typename std::enable_if<std::is_integral<ScalarType>::value, VectorType>::type elementwise_op_sve_imp(svbool_t pg, ElementWiseUnary op, const VectorType &a) +{ + switch(op) + { + case ElementWiseUnary::NEG: + return svneg_z(pg, a); + case ElementWiseUnary::ABS: + return svabs_z(pg, a); + default: + ARM_COMPUTE_ERROR("NOT_SUPPORTED"); + } +} + +template <typename ScalarType> +void elementwise_sve_op(const ITensor *in, ITensor *out, const Window &window, ElementWiseUnary op) +{ + const auto all_true_pg = wrapper::svptrue<ScalarType>(); + const auto window_start_x = static_cast<int>(window.x().start()); + const auto window_end_x = static_cast<int>(window.x().end()); + + Window win = window; + win.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator input(in, win); + Iterator output(out, win); + + execute_window_loop(win, [&](const Coordinates &) + { + auto output_ptr = reinterpret_cast<ScalarType *>(output.ptr()); + const auto input_ptr = reinterpret_cast<const ScalarType *>(input.ptr()); + int x = window_start_x; + + svbool_t pg = wrapper::svwhilelt<ScalarType>(x, window_end_x); + do + { + const auto vin = svld1(pg, input_ptr + x); + svst1(pg, output_ptr + x, elementwise_op_sve_imp<ScalarType, decltype(vin)>(pg, op, vin)); + x += wrapper::svcnt<ScalarType>(); + pg = wrapper::svwhilelt<ScalarType>(x, window_end_x); + } + while(svptest_any(all_true_pg, pg)); + }, + input, output); +} + +template void elementwise_sve_op<float16_t>(const ITensor *in, ITensor *out, const Window &window, ElementWiseUnary op); +template void elementwise_sve_op<float32_t>(const ITensor *in, ITensor *out, const Window &window, ElementWiseUnary op); +template void elementwise_sve_op<int32_t>(const ITensor *in, ITensor *out, const Window &window, ElementWiseUnary op); +} // namespace cpu +} // namespace arm_compute +#endif /* defined(__ARM_FEATURE_SVE) */
\ No newline at end of file diff --git a/src/cpu/kernels/elementwise/sve/elementwise_unary_list.h b/src/cpu/kernels/elementwise/sve/elementwise_unary_list.h new file mode 100644 index 0000000000..c2b495f27c --- /dev/null +++ b/src/cpu/kernels/elementwise/sve/elementwise_unary_list.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2021 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 SRC_CORE_SVE_KERNELS_ELEMENTWISE_UNARY_LIST_H +#define SRC_CORE_SVE_KERNELS_ELEMENTWISE_UNARY_LIST_H + +#include "arm_compute/core/Types.h" +#if defined(ARM_COMPUTE_ENABLE_SVE) + +namespace arm_compute +{ +namespace cpu +{ +template <typename ScalarType> +void elementwise_sve_op(const ITensor *in, ITensor *out, const Window &window, ElementWiseUnary op); +} // namespace cpu +} // namespace arm_compute +#endif // defined(ARM_COMPUTE_ENABLE_SVE) +#endif // SRC_CORE_NEON_KERNELS_ELEMENTWISE_UNARY_LIST_H
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