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[onert-micro] Reduce duplicate code in binary kernels
This commit reduces duplicate code in binary kernels.
- Introduce PALBinaryOpCommon.h that has common functions for binary kernels.
- Introduce binary function objects.
- Introduce `BinaryOp()` that unifies binary kernels without broadcast.
- Introduce `BroadcastBinaryOp4DSlow()` that unifies binary kernels with broadcast.
- Apply common functions for binary kernels.
ONE-DCO-1.0-Signed-off-by: ragmani <[email protected]>- Loading branch information
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onert-micro/luci-interpreter/pal/common/PALBinaryOpCommon.h
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| /* | ||
| * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved | ||
| * Copyright 2017 The TensorFlow Authors. All Rights Reserved. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| #ifndef LUCI_INTERPRETER_PAL_BINARYOPCOMMON_H | ||
| #define LUCI_INTERPRETER_PAL_BINARYOPCOMMON_H | ||
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| #include "Params.h" | ||
| #include "PALUtils.h" | ||
| #include "ProcessBroadcastShapes.h" | ||
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| namespace luci_interpreter_pal | ||
| { | ||
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| template <typename T, std::enable_if_t<std::is_floating_point<T>::value, bool> = true> | ||
| struct FloorDivFn | ||
| { | ||
| T operator()(T lhs, T rhs) | ||
| { | ||
| return std::floor(static_cast<double>(lhs) / static_cast<double>(rhs)); | ||
| } | ||
| }; | ||
| template <typename T, std::enable_if_t<std::is_floating_point<T>::value, bool> = true> | ||
| struct FloorModFn | ||
| { | ||
| T operator()(T lhs, T rhs) | ||
| { | ||
| T trunc_mod = std::fmod(lhs, rhs); | ||
| return (trunc_mod != 0) && ((rhs < 0) != (trunc_mod < 0)) ? (trunc_mod + rhs) : trunc_mod; | ||
| } | ||
| }; | ||
| template <typename T> struct MaximumFn | ||
| { | ||
| T operator()(T lhs, T rhs) { return std::max(lhs, rhs); } | ||
| }; | ||
| template <typename T> struct MinimumFn | ||
| { | ||
| T operator()(T lhs, T rhs) { return std::min(lhs, rhs); } | ||
| }; | ||
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| // TODO: check if there real activation value | ||
| template <typename T, typename Fn> | ||
| inline void BinaryOp(const int flat_size, const T *input1_data, const T *input2_data, | ||
| T *output_data) | ||
| { | ||
| Fn func; | ||
| for (int i = 0; i < flat_size; ++i) | ||
| { | ||
| output_data[i] = func(input1_data[i], input2_data[i]); | ||
| } | ||
| } | ||
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| template <typename T, typename Fn> | ||
| inline void BroadcastBinaryOp4DSlow(const luci_interpreter::RuntimeShape &input1_shape, | ||
| const float *input1_data, | ||
| const luci_interpreter::RuntimeShape &input2_shape, | ||
| const float *input2_data, | ||
| const luci_interpreter::RuntimeShape &output_shape, | ||
| float *output_data) | ||
| { | ||
| NdArrayDesc<4> desc1; | ||
| NdArrayDesc<4> desc2; | ||
| NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, &desc2); | ||
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| const luci_interpreter::RuntimeShape extended_output_shape = | ||
| luci_interpreter::RuntimeShape::extendedShape(4, output_shape); | ||
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| // In Tensorflow, the dimensions are canonically named (batch_number, row, | ||
| // col, channel), with extents (batches, height, width, depth), with the | ||
| // trailing dimension changing most rapidly (channels has the smallest stride, | ||
| // typically 1 element). | ||
| // | ||
| // In generated C code, we store arrays with the dimensions reversed. The | ||
| // first dimension has smallest stride. | ||
| // | ||
| // We name our variables by their Tensorflow convention, but generate C code | ||
| // nesting loops such that the innermost loop has the smallest stride for the | ||
| // best cache behavior. | ||
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| Fn func; | ||
| for (int b = 0; b < extended_output_shape.dims(0); ++b) | ||
| { | ||
| for (int y = 0; y < extended_output_shape.dims(1); ++y) | ||
| { | ||
| for (int x = 0; x < extended_output_shape.dims(2); ++x) | ||
| { | ||
| for (int c = 0; c < extended_output_shape.dims(3); ++c) | ||
| { | ||
| const int output_data_offset = | ||
| ((b * extended_output_shape.dims(1) + y) * extended_output_shape.dims(2) + x) * | ||
| extended_output_shape.dims(3) + | ||
| c; | ||
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| output_data[output_data_offset] = func(input1_data[subscriptToIndex(desc1, b, y, x, c)], | ||
| input2_data[subscriptToIndex(desc2, b, y, x, c)]); | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| } // namespace luci_interpreter_pal | ||
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| #endif // LUCI_INTERPRETER_PAL_BINARYOPCOMMON_H |
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