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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 | 274 |
1 files changed, 274 insertions, 0 deletions
diff --git a/compute/ARMComputeEx/src/core/NEON/kernels/NEPReLUKernel.cpp b/compute/ARMComputeEx/src/core/NEON/kernels/NEPReLUKernel.cpp new file mode 100644 index 000000000..ad1bb9051 --- /dev/null +++ b/compute/ARMComputeEx/src/core/NEON/kernels/NEPReLUKernel.cpp @@ -0,0 +1,274 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved + * 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 qinfo.quantize(elementwise_conditional_op_scalar<op>(a, b), 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{}; +} |