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Diffstat (limited to 'compute/ARMComputeEx/src/core/NEON/kernels/NEPReLUKernel.cpp')
| -rw-r--r-- | compute/ARMComputeEx/src/core/NEON/kernels/NEPReLUKernel.cpp | 291 |
1 files changed, 0 insertions, 291 deletions
diff --git a/compute/ARMComputeEx/src/core/NEON/kernels/NEPReLUKernel.cpp b/compute/ARMComputeEx/src/core/NEON/kernels/NEPReLUKernel.cpp deleted file mode 100644 index 641641b5a..000000000 --- a/compute/ARMComputeEx/src/core/NEON/kernels/NEPReLUKernel.cpp +++ /dev/null @@ -1,291 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. 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. - */ - -/* - * Copyright (c) 2017-2019 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/NEON/kernels/NEPReLUKernel.h" - -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/NEON/NEAsymm.h" -#include "arm_compute/core/NEON/NEElementwiseOperationFuncs.h" -#include "arm_compute/core/NEON/wrapper/wrapper.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Window.h" - -#include <arm_neon.h> - -using namespace arm_compute; -namespace -{ - -/** Conditional element-wise operations */ -enum class ConditionalOperation -{ - PRELU, /**< (x * y) for x < 0, x for x >= 0 */ -}; - -template <ConditionalOperation op, typename ScalarType> -inline ScalarType elementwise_conditional_op_scalar(const ScalarType &a, const ScalarType &b) -{ - auto res = ScalarType(0); - - switch (op) - { - case ConditionalOperation::PRELU: - res = a < 0 ? a * b : a; - break; - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } - return res; -} - -template <ConditionalOperation op> -inline uint8_t elementwise_conditional_op_quantized_scalar(const float &a, const float &b, - QuantizationInfo qinfo) -{ - return quantize_qasymm8(elementwise_conditional_op_scalar<op>(a, b), qinfo, - RoundingPolicy::TO_NEAREST_UP); -} - -template <ConditionalOperation op, typename VectorType> -inline VectorType elementwise_conditional_op(const VectorType &a, const VectorType &b) -{ - VectorType res = {0, 0, 0, 0}; - VectorType const_0 = {0, 0, 0, 0}; - - switch (op) - { - case ConditionalOperation::PRELU: - res = wrapper::vbsl(wrapper::vcgt(a, const_0), a, wrapper::vmul(a, b)); - ; - break; - default: - ARM_COMPUTE_ERROR("NOT_SUPPORTED!"); - } - return res; -} - -template <ConditionalOperation op> -inline float32x4x4_t elementwise_conditional_op(const float32x4x4_t &a, const float32x4x4_t &b) -{ - float32x4x4_t out = {{ - elementwise_conditional_op<op>(a.val[0], b.val[0]), - elementwise_conditional_op<op>(a.val[1], b.val[1]), - elementwise_conditional_op<op>(a.val[2], b.val[2]), - elementwise_conditional_op<op>(a.val[3], b.val[3]), - }}; - return out; -} - -template <ConditionalOperation op, typename ScalarType, typename VectorType> -inline VectorType elementwise_conditional_op_broadcast(const VectorType &a, - const ScalarType &broadcast_value, - const bool reorder) -{ - VectorType broadcast_vector = wrapper::vdup_n(broadcast_value, wrapper::traits::vector_128_tag()); - return elementwise_conditional_op<op>(reorder ? broadcast_vector : a, - reorder ? a : broadcast_vector); -} - -template <ConditionalOperation op, typename ScalarType, typename VectorType> -inline int elementwise_conditional_op_loop(int window_start_x, int window_end_x, int window_step_x, - const ScalarType *input1_ptr, - const ScalarType *input2_ptr, ScalarType *output_ptr) -{ - int x = window_start_x; - for (; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto a = wrapper::vloadq(input1_ptr + x); - const auto b = wrapper::vloadq(input2_ptr + x); - wrapper::vstore(output_ptr + x, elementwise_conditional_op<op>(a, b)); - } - return x; -} - -template <ConditionalOperation op> -inline int elementwise_conditional_op_quantized_loop(int window_start_x, int window_end_x, - int window_step_x, const uint8_t *input1_ptr, - const uint8_t *input2_ptr, uint8_t *output_ptr, - int32x4_t voffset1, int32x4_t voffset2, - float32x4_t vscale1, float32x4_t vscale2, - float32x4_t voffseto, float32x4_t invvscaleo) -{ - int x = window_start_x; - for (; x <= (window_end_x - window_step_x); x += window_step_x) - { - // Get inputs and compute output - const float32x4x4_t af = load_quantized(input1_ptr + x, voffset1, vscale1); - const float32x4x4_t bf = load_quantized(input2_ptr + x, voffset2, vscale2); - const float32x4x4_t rf = elementwise_conditional_op<op>(af, bf); - store_quantized(output_ptr + x, rf, voffseto, invvscaleo); - } - return x; -} - -template <ConditionalOperation op, typename ScalarType, typename VectorType> -inline int elementwise_conditional_op_broadcast_loop(int window_start_x, int window_end_x, - int window_step_x, - const ScalarType *non_broadcast_input_ptr, - const ScalarType &broadcast_value, - ScalarType *output_ptr, const bool reorder) -{ - int x = window_start_x; - for (; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto a = wrapper::vloadq((non_broadcast_input_ptr + x)); - wrapper::vstore(output_ptr + x, - elementwise_conditional_op_broadcast<op>(a, broadcast_value, reorder)); - } - return x; -} - -template <ConditionalOperation op> -inline int elementwise_conditional_op_quantized_broadcast_loop( - int window_start_x, int window_end_x, int window_step_x, const uint8_t *non_broadcast_input_ptr, - float32x4x4_t broadcast_vector, uint8_t *output_ptr, int32x4_t voffset_non_broadcast, - float32x4_t vscale_non_broadcast, float32x4_t voffseto, float32x4_t invvscaleo, bool reorder) -{ - int x = window_start_x; - for (; x <= (window_end_x - window_step_x); x += window_step_x) - { - const float32x4x4_t af = - load_quantized(non_broadcast_input_ptr + x, voffset_non_broadcast, vscale_non_broadcast); - const float32x4x4_t rf = elementwise_conditional_op<op>(reorder ? broadcast_vector : af, - reorder ? af : broadcast_vector); - store_quantized(output_ptr + x, rf, voffseto, invvscaleo); - } - return x; -} - -template <ConditionalOperation op, typename ScalarType, typename VectorType> -void elementwise_conditional_op(const ITensor *in1, const ITensor *in2, ITensor *out, - const Window &window) -{ - elementwise_op(in1, in2, out, window, &elementwise_conditional_op_scalar<op, ScalarType>, - &elementwise_conditional_op_broadcast_loop<op, ScalarType, VectorType>, - &elementwise_conditional_op_loop<op, ScalarType, VectorType>); -} - -template <ConditionalOperation op> -void elementwise_conditional_op_quantized(const ITensor *in1, const ITensor *in2, ITensor *out, - const Window &window) -{ - elementwise_op_quantized(in1, in2, out, window, &elementwise_conditional_op_quantized_scalar<op>, - &elementwise_conditional_op_quantized_broadcast_loop<op>, - &elementwise_conditional_op_quantized_loop<op>); -} -} // namespace - -NEPReLUKernel::NEPReLUKernel() : _input(nullptr), _alpha(nullptr), _output(nullptr) {} - -void NEPReLUKernel::configure(const ITensor *input, const ITensor *alpha, ITensor *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, alpha, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input->info(), *alpha->info(), *output->info())); - - // Configure kernel window - const std::pair<TensorShape, ValidRegion> broadcast_pair = - ITensorInfo::broadcast_shape_and_valid_region(*input->info(), *alpha->info()); - const TensorShape &out_shape = broadcast_pair.first; - const ValidRegion &valid_region = broadcast_pair.second; - - // Auto initialize output if not initialized - auto_init_if_empty(*output->info(), out_shape, 1, input->info()->data_type()); - - Window win = calculate_max_window(valid_region); - - _input = input; - _alpha = alpha; - _output = output; - INEKernel::configure(win); -} - -void NEPReLUKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); - - if (_input->info()->data_type() == DataType::F32) - { - elementwise_conditional_op<ConditionalOperation::PRELU, float, float32x4_t>(_input, _alpha, - _output, window); - } - else if (_input->info()->data_type() == DataType::QASYMM8) - { - elementwise_conditional_op_quantized<ConditionalOperation::PRELU>(_input, _alpha, _output, - window); - } - else - { - ARM_COMPUTE_ERROR("Wrong Type"); - } -} - -Status NEPReLUKernel::validate_arguments(const ITensorInfo &input, const ITensorInfo &alpha, - const ITensorInfo &output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &alpha, &output); - - const TensorShape out_shape = - TensorShape::broadcast_shape(input.tensor_shape(), alpha.tensor_shape()); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, - "Inputs are not broadcast compatible"); - - // Checks performed when output is configured - if (output.total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG( - detail::have_different_dimensions(out_shape, output.tensor_shape(), 0), - "Wrong shape for output"); - } - - return Status{}; -} - -Status NEPReLUKernel::validate(const ITensorInfo *input, const ITensorInfo *alpha, - const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, alpha, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input, *alpha, *output)); - - return Status{}; -} |