| /* |
| * Copyright (c) 2017-2018 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/NEActivationLayerKernel.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/NEON/NEAsymm.h" |
| #include "arm_compute/core/NEON/NEFixedPoint.h" |
| #include "arm_compute/core/NEON/NEMath.h" |
| #include "arm_compute/core/QAsymm8.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include <arm_neon.h> |
| #include <array> |
| #include <cmath> |
| #include <map> |
| |
| using namespace arm_compute; |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, DataType::F16, DataType::F32); |
| |
| // Checks performed when output is configured |
| if((output != nullptr) && (output->total_size() != 0)) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) |
| { |
| constexpr unsigned int num_elems_processed_per_iteration = 16; |
| Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); |
| bool window_changed = false; |
| |
| if(output != nullptr && (output->total_size() != 0)) |
| { |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); |
| |
| window_changed = update_window_and_padding(win, |
| AccessWindowHorizontal(input, 0, num_elems_processed_per_iteration), |
| output_access); |
| |
| output_access.set_valid_region(win, input->valid_region()); |
| } |
| else |
| { |
| // In-place computation |
| window_changed = update_window_and_padding(win, |
| AccessWindowHorizontal(input, 0, num_elems_processed_per_iteration)); |
| } |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| } // namespace |
| |
| NEActivationLayerKernel::NEActivationLayerKernel() |
| : _input(nullptr), _output(nullptr), _func(nullptr), _act_info(ActivationFunction::LOGISTIC) |
| { |
| } |
| |
| void NEActivationLayerKernel::configure(ITensor *input, ITensor *output, ActivationLayerInfo activation_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input); |
| |
| _input = input; |
| _act_info = activation_info; |
| _output = input; |
| |
| if(output != nullptr) |
| { |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output->info(), *input->info()->clone()); |
| _output = output; |
| } |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (output != nullptr) ? output->info() : nullptr)); |
| |
| ARM_COMPUTE_ERROR_ON_MSG((input->info()->data_type() == DataType::QASYMM8) && (activation_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) |
| && (activation_info.activation() != ActivationLayerInfo::ActivationFunction::RELU), |
| "For QASYMM8 only relu and lower/upper bounded relu are supported"); |
| |
| // Activation functions : FP32 |
| static std::map<ActivationFunction, ActivationFunctionExecutorPtr> act_map_f32 = |
| { |
| { ActivationFunction::ABS, &NEActivationLayerKernel::activation<ActivationFunction::ABS, float> }, |
| { ActivationFunction::LINEAR, &NEActivationLayerKernel::activation<ActivationFunction::LINEAR, float> }, |
| { ActivationFunction::LOGISTIC, &NEActivationLayerKernel::activation<ActivationFunction::LOGISTIC, float> }, |
| { ActivationFunction::RELU, &NEActivationLayerKernel::activation<ActivationFunction::RELU, float> }, |
| { ActivationFunction::BOUNDED_RELU, &NEActivationLayerKernel::activation<ActivationFunction::BOUNDED_RELU, float> }, |
| { ActivationFunction::LU_BOUNDED_RELU, &NEActivationLayerKernel::activation<ActivationFunction::LU_BOUNDED_RELU, float> }, |
| { ActivationFunction::LEAKY_RELU, &NEActivationLayerKernel::activation<ActivationFunction::LEAKY_RELU, float> }, |
| { ActivationFunction::SOFT_RELU, &NEActivationLayerKernel::activation<ActivationFunction::SOFT_RELU, float> }, |
| { ActivationFunction::SQRT, &NEActivationLayerKernel::activation<ActivationFunction::SQRT, float> }, |
| { ActivationFunction::SQUARE, &NEActivationLayerKernel::activation<ActivationFunction::SQUARE, float> }, |
| { ActivationFunction::TANH, &NEActivationLayerKernel::activation<ActivationFunction::TANH, float> }, |
| }; |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| // Activation functions : FP16 |
| static std::map<ActivationFunction, ActivationFunctionExecutorPtr> act_map_f16 = |
| { |
| { ActivationFunction::ABS, &NEActivationLayerKernel::activation<ActivationFunction::ABS, float16_t> }, |
| { ActivationFunction::LINEAR, &NEActivationLayerKernel::activation<ActivationFunction::LINEAR, float16_t> }, |
| { ActivationFunction::LOGISTIC, &NEActivationLayerKernel::activation<ActivationFunction::LOGISTIC, float16_t> }, |
| { ActivationFunction::RELU, &NEActivationLayerKernel::activation<ActivationFunction::RELU, float16_t> }, |
| { ActivationFunction::BOUNDED_RELU, &NEActivationLayerKernel::activation<ActivationFunction::BOUNDED_RELU, float16_t> }, |
| { ActivationFunction::LU_BOUNDED_RELU, &NEActivationLayerKernel::activation<ActivationFunction::LU_BOUNDED_RELU, float16_t> }, |
| { ActivationFunction::SOFT_RELU, &NEActivationLayerKernel::activation<ActivationFunction::SOFT_RELU, float16_t> }, |
| { ActivationFunction::SQRT, &NEActivationLayerKernel::activation<ActivationFunction::SQRT, float16_t> }, |
| { ActivationFunction::SQUARE, &NEActivationLayerKernel::activation<ActivationFunction::SQUARE, float16_t> }, |
| { ActivationFunction::TANH, &NEActivationLayerKernel::activation<ActivationFunction::TANH, float16_t> }, |
| }; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC*/ |
| |
| // Activation functions : QASYMM8 |
| static std::map<ActivationFunction, ActivationFunctionExecutorPtr> act_map_qasymm8 = |
| { |
| { ActivationFunction::LU_BOUNDED_RELU, &NEActivationLayerKernel::activation<ActivationFunction::LU_BOUNDED_RELU, qasymm8_t> }, |
| { ActivationFunction::RELU, &NEActivationLayerKernel::activation<ActivationFunction::RELU, qasymm8_t> }, |
| }; |
| |
| switch(input->info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| _func = act_map_qasymm8[activation_info.activation()]; |
| break; |
| case DataType::F32: |
| _func = act_map_f32[activation_info.activation()]; |
| break; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| _func = act_map_f16[activation_info.activation()]; |
| break; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| default: |
| ARM_COMPUTE_ERROR("Unsupported data type."); |
| } |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), (output != nullptr) ? output->info() : nullptr); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| ICPPKernel::configure(win_config.second); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| template <ActivationLayerInfo::ActivationFunction F, typename T> |
| typename std::enable_if<std::is_same<T, float16_t>::value, void>::type NEActivationLayerKernel::activation(const Window &window) |
| { |
| Iterator input(_input, window); |
| Iterator output(_output, window); |
| |
| static const float16x8_t CONST_0 = vdupq_n_f16(0.f); |
| static const float16x8_t CONST_1 = vdupq_n_f16(1.f); |
| |
| const float16x8_t a = vdupq_n_f16(_act_info.a()); |
| const float16x8_t b = vdupq_n_f16(_act_info.b()); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| const auto input_ptr = reinterpret_cast<const float16_t *>(input.ptr()); |
| const auto output_ptr = reinterpret_cast<float16_t *>(output.ptr()); |
| |
| const float16x8x2_t in = vld2q_f16(input_ptr); |
| float16x8x2_t tmp = { {} }; |
| |
| switch(F) |
| { |
| case ActivationFunction::ABS: |
| tmp = |
| { |
| { |
| vabsq_f16(in.val[0]), |
| vabsq_f16(in.val[1]), |
| } |
| }; |
| break; |
| case ActivationFunction::BOUNDED_RELU: |
| tmp = |
| { |
| { |
| vminq_f16(a, vmaxq_f16(CONST_0, in.val[0])), |
| vminq_f16(a, vmaxq_f16(CONST_0, in.val[1])) |
| } |
| }; |
| break; |
| case ActivationFunction::LU_BOUNDED_RELU: |
| tmp = |
| { |
| { |
| vminq_f16(a, vmaxq_f16(b, in.val[0])), |
| vminq_f16(a, vmaxq_f16(b, in.val[1])) |
| } |
| }; |
| break; |
| case ActivationFunction::LINEAR: |
| tmp = |
| { |
| { |
| vaddq_f16(b, vmulq_f16(a, in.val[0])), |
| vaddq_f16(b, vmulq_f16(a, in.val[1])) |
| } |
| }; |
| break; |
| case ActivationFunction::LOGISTIC: |
| tmp = |
| { |
| { |
| vinvq_f16(vaddq_f16(CONST_1, vexpq_f16(vnegq_f16(in.val[0])))), |
| vinvq_f16(vaddq_f16(CONST_1, vexpq_f16(vnegq_f16(in.val[1])))), |
| } |
| }; |
| break; |
| case ActivationFunction::RELU: |
| tmp = |
| { |
| { |
| vmaxq_f16(CONST_0, in.val[0]), |
| vmaxq_f16(CONST_0, in.val[1]) |
| } |
| }; |
| break; |
| case ActivationFunction::LEAKY_RELU: |
| tmp = |
| { |
| { |
| vbslq_f16(vcgtq_f16(in.val[0], CONST_0), in.val[0], vmulq_f16(a, in.val[0])), |
| vbslq_f16(vcgtq_f16(in.val[1], CONST_0), in.val[1], vmulq_f16(a, in.val[1])) |
| } |
| }; |
| break; |
| case ActivationFunction::SOFT_RELU: |
| tmp = |
| { |
| { |
| vlogq_f16(vaddq_f16(CONST_1, vexpq_f16(in.val[0]))), |
| vlogq_f16(vaddq_f16(CONST_1, vexpq_f16(in.val[1]))), |
| } |
| }; |
| break; |
| case ActivationFunction::SQRT: |
| tmp = |
| { |
| { |
| vinvq_f16(vinvsqrtq_f16(in.val[0])), |
| vinvq_f16(vinvsqrtq_f16(in.val[1])), |
| } |
| }; |
| break; |
| case ActivationFunction::SQUARE: |
| tmp = |
| { |
| { |
| vmulq_f16(in.val[0], in.val[0]), |
| vmulq_f16(in.val[1], in.val[1]) |
| } |
| }; |
| break; |
| case ActivationFunction::TANH: |
| tmp = |
| { |
| { |
| vmulq_f16(a, vtanhq_f16(vmulq_f16(b, in.val[0]))), |
| vmulq_f16(a, vtanhq_f16(vmulq_f16(b, in.val[1]))), |
| } |
| }; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not implemented"); |
| break; |
| } |
| |
| vst2q_f16(output_ptr, tmp); |
| }, |
| input, output); |
| } |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| template <ActivationLayerInfo::ActivationFunction F, typename T> |
| typename std::enable_if<std::is_same<T, float>::value, void>::type NEActivationLayerKernel::activation(const Window &window) |
| { |
| Iterator input(_input, window); |
| Iterator output(_output, window); |
| |
| static const float32x4_t CONST_1 = vdupq_n_f32(1.f); |
| static const float32x4_t CONST_0 = vdupq_n_f32(0.f); |
| const float32x4_t a = vdupq_n_f32(_act_info.a()); |
| const float32x4_t b = vdupq_n_f32(_act_info.b()); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const auto input_ptr = reinterpret_cast<const float *>(input.ptr()); |
| const auto output_ptr = reinterpret_cast<float *>(output.ptr()); |
| |
| const float32x4x4_t in = |
| { |
| { |
| vld1q_f32(input_ptr), |
| vld1q_f32(input_ptr + 4), |
| vld1q_f32(input_ptr + 8), |
| vld1q_f32(input_ptr + 12) |
| } |
| }; |
| float32x4x4_t tmp = { {} }; |
| |
| switch(F) |
| { |
| case ActivationFunction::ABS: |
| tmp = |
| { |
| { |
| vabsq_f32(in.val[0]), |
| vabsq_f32(in.val[1]), |
| vabsq_f32(in.val[2]), |
| vabsq_f32(in.val[3]), |
| } |
| }; |
| break; |
| case ActivationFunction::LINEAR: |
| tmp = |
| { |
| { |
| vmlaq_f32(b, a, in.val[0]), |
| vmlaq_f32(b, a, in.val[1]), |
| vmlaq_f32(b, a, in.val[2]), |
| vmlaq_f32(b, a, in.val[3]), |
| } |
| }; |
| break; |
| case ActivationFunction::LOGISTIC: |
| tmp = |
| { |
| { |
| vinvq_f32(vaddq_f32(CONST_1, vexpq_f32(vnegq_f32(in.val[0])))), |
| vinvq_f32(vaddq_f32(CONST_1, vexpq_f32(vnegq_f32(in.val[1])))), |
| vinvq_f32(vaddq_f32(CONST_1, vexpq_f32(vnegq_f32(in.val[2])))), |
| vinvq_f32(vaddq_f32(CONST_1, vexpq_f32(vnegq_f32(in.val[3])))), |
| } |
| }; |
| break; |
| case ActivationFunction::RELU: |
| tmp = |
| { |
| { |
| vmaxq_f32(CONST_0, in.val[0]), |
| vmaxq_f32(CONST_0, in.val[1]), |
| vmaxq_f32(CONST_0, in.val[2]), |
| vmaxq_f32(CONST_0, in.val[3]), |
| } |
| }; |
| break; |
| case ActivationFunction::BOUNDED_RELU: |
| tmp = |
| { |
| { |
| vminq_f32(a, vmaxq_f32(CONST_0, in.val[0])), |
| vminq_f32(a, vmaxq_f32(CONST_0, in.val[1])), |
| vminq_f32(a, vmaxq_f32(CONST_0, in.val[2])), |
| vminq_f32(a, vmaxq_f32(CONST_0, in.val[3])), |
| } |
| }; |
| break; |
| case ActivationFunction::LU_BOUNDED_RELU: |
| tmp = |
| { |
| { |
| vminq_f32(a, vmaxq_f32(b, in.val[0])), |
| vminq_f32(a, vmaxq_f32(b, in.val[1])), |
| vminq_f32(a, vmaxq_f32(b, in.val[2])), |
| vminq_f32(a, vmaxq_f32(b, in.val[3])), |
| } |
| }; |
| break; |
| case ActivationFunction::LEAKY_RELU: |
| tmp = |
| { |
| { |
| vbslq_f32(vcgtq_f32(in.val[0], CONST_0), in.val[0], vmulq_f32(a, in.val[0])), |
| vbslq_f32(vcgtq_f32(in.val[1], CONST_0), in.val[1], vmulq_f32(a, in.val[1])), |
| vbslq_f32(vcgtq_f32(in.val[2], CONST_0), in.val[2], vmulq_f32(a, in.val[2])), |
| vbslq_f32(vcgtq_f32(in.val[3], CONST_0), in.val[3], vmulq_f32(a, in.val[3])), |
| } |
| }; |
| break; |
| case ActivationFunction::SOFT_RELU: |
| tmp = |
| { |
| { |
| vlogq_f32(vaddq_f32(CONST_1, vexpq_f32(in.val[0]))), |
| vlogq_f32(vaddq_f32(CONST_1, vexpq_f32(in.val[1]))), |
| vlogq_f32(vaddq_f32(CONST_1, vexpq_f32(in.val[2]))), |
| vlogq_f32(vaddq_f32(CONST_1, vexpq_f32(in.val[3]))), |
| } |
| }; |
| break; |
| case ActivationFunction::SQRT: |
| tmp = |
| { |
| { |
| vinvq_f32(vinvsqrtq_f32(in.val[0])), |
| vinvq_f32(vinvsqrtq_f32(in.val[1])), |
| vinvq_f32(vinvsqrtq_f32(in.val[2])), |
| vinvq_f32(vinvsqrtq_f32(in.val[3])), |
| } |
| }; |
| break; |
| case ActivationFunction::SQUARE: |
| tmp = |
| { |
| { |
| vmulq_f32(in.val[0], in.val[0]), |
| vmulq_f32(in.val[1], in.val[1]), |
| vmulq_f32(in.val[2], in.val[2]), |
| vmulq_f32(in.val[3], in.val[3]), |
| } |
| }; |
| break; |
| case ActivationFunction::TANH: |
| tmp = |
| { |
| { |
| vmulq_f32(a, vtanhq_f32(vmulq_f32(b, in.val[0]))), |
| vmulq_f32(a, vtanhq_f32(vmulq_f32(b, in.val[1]))), |
| vmulq_f32(a, vtanhq_f32(vmulq_f32(b, in.val[2]))), |
| vmulq_f32(a, vtanhq_f32(vmulq_f32(b, in.val[3]))), |
| } |
| }; |
| break; |
| default: |
| break; |
| } |
| |
| vst1q_f32(output_ptr, tmp.val[0]); |
| vst1q_f32(output_ptr + 4, tmp.val[1]); |
| vst1q_f32(output_ptr + 8, tmp.val[2]); |
| vst1q_f32(output_ptr + 12, tmp.val[3]); |
| }, |
| input, output); |
| } |
| |
| template <ActivationLayerInfo::ActivationFunction F, typename T> |
| typename std::enable_if<std::is_same<T, qasymm8_t>::value, void>::type NEActivationLayerKernel::activation(const Window &window) |
| { |
| Iterator input(_input, window); |
| Iterator output(_output, window); |
| const QuantizationInfo qi_in = _input->info()->quantization_info(); |
| const QuantizationInfo qi_out = _output->info()->quantization_info(); |
| const qasymm8x16_t a = vdupq_n_u8(sqcvt_qasymm8_f32(_act_info.a(), qi_in.scale, qi_in.offset)); |
| const qasymm8x16_t b = vdupq_n_u8(sqcvt_qasymm8_f32(_act_info.b(), qi_in.scale, qi_in.offset)); |
| const qasymm8x16_t CONST_0 = vdupq_n_u8(sqcvt_qasymm8_f32(0.f, qi_in.scale, qi_in.offset)); |
| |
| // Initialise scale/offset for re-quantization |
| float s = qi_in.scale / qi_out.scale; |
| float o = -qi_in.offset * s + qi_out.offset; |
| float32x4_t vs = vdupq_n_f32(s); |
| float32x4_t vo = vdupq_n_f32(o); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const auto input_ptr = reinterpret_cast<const qasymm8_t *>(input.ptr()); |
| const auto output_ptr = reinterpret_cast<qasymm8_t *>(output.ptr()); |
| |
| const qasymm8x16_t in = vld1q_u8(input_ptr); |
| qasymm8x16_t tmp = {}; |
| |
| switch(F) |
| { |
| case ActivationFunction::LU_BOUNDED_RELU: |
| // Perform activation |
| tmp = vminq_u8(a, vmaxq_u8(b, in)); |
| // Re-quantize to new output space |
| tmp = vmlaq_qasymm8(tmp, vs, vo); |
| break; |
| case ActivationFunction::RELU: |
| // Perform activation |
| tmp = vmaxq_u8(CONST_0, in); |
| // Re-quantize to new output space |
| tmp = vmlaq_qasymm8(tmp, vs, vo); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Function not implemented"); |
| break; |
| } |
| |
| vst1q_u8(output_ptr, tmp); |
| }, |
| input, output); |
| } |
| |
| Status NEActivationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_UNUSED(act_info); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (output != nullptr) ? output->clone().get() : nullptr).first); |
| |
| return Status{}; |
| } |
| |
| void NEActivationLayerKernel::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); |
| ARM_COMPUTE_ERROR_ON(_func == nullptr); |
| |
| (this->*_func)(window); |
| } |