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Diffstat (limited to 'src/core/NEON/kernels/NESoftmaxLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NESoftmaxLayerKernel.cpp | 648 |
1 files changed, 0 insertions, 648 deletions
diff --git a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp b/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp deleted file mode 100644 index 41bf03ad1d..0000000000 --- a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp +++ /dev/null @@ -1,648 +0,0 @@ -/* - * Copyright (c) 2017-2020 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/NESoftmaxLayerKernel.h" - -#include "arm_compute/core/AccessWindowStatic.h" -#include "arm_compute/core/CPP/Validate.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/NEON/NEFixedPoint.h" -#include "arm_compute/core/NEON/NEMath.h" -#include "arm_compute/core/NEON/wrapper/wrapper.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_compute/core/utils/misc/SaturateCast.h" - -#include <algorithm> -#include <arm_neon.h> -#include <cfloat> -#include <functional> - -namespace arm_compute -{ -template <typename float_vec_type, typename int_vec_type> -int_vec_type convert_float_to_int(const float_vec_type &in); - -template <typename float_vec_type, typename int_vec_type> -float_vec_type convert_int_to_float(const int_vec_type &in); - -template <> -uint8x16_t convert_float_to_int<float32x4x4_t, uint8x16_t>(const float32x4x4_t &in) -{ - uint8x16_t out; - convert_float32x4x4_to_uint8x16(in, out); - return out; -} - -template <> -int8x16_t convert_float_to_int<float32x4x4_t, int8x16_t>(const float32x4x4_t &in) -{ - int8x16_t out; - convert_float32x4x4_to_int8x16(in, out); - return out; -} - -template <> -float32x4x4_t convert_int_to_float<float32x4x4_t, uint8x16_t>(const uint8x16_t &in) -{ - return convert_uint8x16_to_float32x4x4(in); -} - -template <> -float32x4x4_t convert_int_to_float<float32x4x4_t, int8x16_t>(const int8x16_t &in) -{ - return convert_int8x16_to_float32x4x4(in); -} - -namespace -{ -Status validate_arguments_logits_1d_max(const ITensorInfo &input, const ITensorInfo &output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - - // Validate in case of configured output - if(output.total_size() != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input, &output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output.tensor_shape(), TensorShape(input.tensor_shape()).set(0, 1)); - } - - return Status{}; -} - -template <typename T> -void logits_1d_max(const ITensor &in, ITensor &out, const Window &window) -{ - /** NEON vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>; - - constexpr int window_step_x = 16 / sizeof(T); - 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); - - const int sum_stages = log2(window_step_x / 2); - execute_window_loop(win, [&](const Coordinates &) - { - // Get pointers - const auto in_ptr = reinterpret_cast<const T *>(input.ptr()); - const auto out_ptr = reinterpret_cast<T *>(output.ptr()); - - // Init max value - auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{}); - int x = window_start_x; - - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto current_value = wrapper::vloadq(in_ptr + x); - vec_max = wrapper::vmax(vec_max, current_value); - } - auto carry_max = wrapper::vpmax(wrapper::vgethigh(vec_max), wrapper::vgetlow(vec_max)); - - for(int i = 0; i < sum_stages; ++i) - { - carry_max = wrapper::vpmax(carry_max, carry_max); - } - T max_val = wrapper::vgetlane(carry_max, 0); - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - max_val = *(in_ptr + x) > max_val ? *(in_ptr + x) : max_val; - } - - *out_ptr = max_val; - }, - input, output); -} -} // namespace - -NELogits1DMaxKernel::NELogits1DMaxKernel() - : _func(nullptr), _border_size() -{ -} - -BorderSize NELogits1DMaxKernel::border_size() const -{ - return _border_size; -} - -void NELogits1DMaxKernel::configure(const ITensor *input, ITensor *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_ON_NULLPTR(input->info(), output->info()); - // Perform validation step - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_logits_1d_max(*input->info(), *output->info())); - // Configure kernel window - - // Softmax across the x dimension - const TensorShape output_shape = TensorShape(input->info()->tensor_shape()).set(0, 1); - // Output auto initialization if not yet initialized - auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->quantization_info()); - - Window win = calculate_max_window(*input->info(), Steps()); - Coordinates coord; - coord.set_num_dimensions(output->info()->num_dimensions()); - output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); - - switch(input->info()->data_type()) - { - case DataType::QASYMM8: - _func = &logits_1d_max<qasymm8_t>; - break; - case DataType::QASYMM8_SIGNED: - _func = &logits_1d_max<qasymm8_signed_t>; - break; -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - _func = &logits_1d_max<float16_t>; - break; -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - case DataType::F32: - _func = &logits_1d_max<float>; - break; - default: - ARM_COMPUTE_ERROR("Unsupported data type."); - } - - _input = input; - _output = output; - - const int input_width = input->info()->valid_region().shape.x(); - const int num_elems_processed_per_iteration = 16U / data_size_from_type(input->info()->data_type()); - const int num_elems_read_per_iteration = ceil_to_multiple(input_width, num_elems_processed_per_iteration); - - _border_size = BorderSize(0, num_elems_read_per_iteration - input_width, 0, 0); - - INEKernel::configure(win); -} - -Status NELogits1DMaxKernel::validate(const ITensorInfo *input, const ITensorInfo *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_logits_1d_max(*input, *output)); - - return Status{}; -} - -void NELogits1DMaxKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - ARM_COMPUTE_ERROR_ON(_func == nullptr); - - (*_func)(*_input, *_output, window); -} - -namespace -{ -Status validate_arguments_logits_softmax(const ITensorInfo &input, const ITensorInfo &max, - const ITensorInfo &output, const float beta, const ITensorInfo &tmp, bool is_log) -{ - ARM_COMPUTE_UNUSED(beta); - // Check input - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - - const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(input.data_type()); - - // Check max - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &max); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(TensorShape(input.tensor_shape()).set(0, 1), max.tensor_shape()); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input, &max); - - // Check output if configured - if(output.total_size() != 0) - { - const QuantizationInfo output_quantization = is_quantized_asymmetric ? arm_compute::get_softmax_output_quantization_info(input.data_type(), is_log) : output.quantization_info(); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input, &output); - ARM_COMPUTE_RETURN_ERROR_ON(output.quantization_info() != output_quantization); - } - - // Check tmp if configured - if(tmp.total_size() != 0) - { - const DataType tmp_data_type = is_quantized_asymmetric ? DataType::F32 : input.data_type(); - ARM_COMPUTE_RETURN_ERROR_ON(tmp.data_type() != tmp_data_type); - // We could potentially reduce tmp memory if we could predict or make an assumption - // on the maximum number of threads that will run in parallel. - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input, &tmp); - } - - return Status{}; -} -template <typename T, bool is_log> -void logits_1d_softmax_qasymm8(const ITensor &in, const ITensor &max, void *const tmp, ITensor &out, const float beta, const Window &window) -{ - static_assert(std::is_same<T, qasymm8_t>::value - || std::is_same<T, qasymm8_signed_t>::value, - "quantized type should be either qasymm8_t or qasymm8_signed_t."); - - const int start_x = in.info()->valid_region().anchor.x(); - const int input_width = in.info()->valid_region().shape.x(); - - const float scale_beta = -beta * in.info()->quantization_info().uniform().scale; - const auto scale_beta_vec = vdupq_n_f32(scale_beta); - - Iterator in_it(&in, window); - Iterator max_it(&max, window); - Iterator out_it(&out, window); - constexpr int vec_size = 16; - - execute_window_loop(window, [&](const Coordinates &) - { - /* Get pointers */ - const auto in_ptr = reinterpret_cast<const T *>(in_it.ptr()) + start_x; - const auto out_ptr = reinterpret_cast<T *>(out_it.ptr()) + start_x; - const auto tmp_ptr = reinterpret_cast<float *>(tmp); - - float sum{}; - float sum_inversed{}; - - /* Compute exponentials and sum */ - { - /* Get max value */ - const auto max_val = *reinterpret_cast<const T *>(max_it.ptr()); - const auto vec_max = wrapper::vdup_n(max_val, wrapper::traits::vector_128_tag{}); - - /* Init sum to zero */ - float32x4x4_t vec_sum = - { - vdupq_n_f32(0.f), - vdupq_n_f32(0.f), - vdupq_n_f32(0.f), - vdupq_n_f32(0.f), - }; - - /* Loop over row and compute exponentials and sum */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - auto vec_elements = wrapper::vloadq(in_ptr + x); - vec_elements = wrapper::vqsub(vec_max, vec_elements); - auto vec_elements_flt = convert_int_to_float<float32x4x4_t>(vec_elements); - - if(is_log) - { - vec_elements_flt.val[0] = vmulq_f32(vec_elements_flt.val[0], scale_beta_vec); - vec_elements_flt.val[1] = vmulq_f32(vec_elements_flt.val[1], scale_beta_vec); - vec_elements_flt.val[2] = vmulq_f32(vec_elements_flt.val[2], scale_beta_vec); - vec_elements_flt.val[3] = vmulq_f32(vec_elements_flt.val[3], scale_beta_vec); - vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vexpq_f32(vec_elements_flt.val[0])); - vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vexpq_f32(vec_elements_flt.val[1])); - vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vexpq_f32(vec_elements_flt.val[2])); - vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vexpq_f32(vec_elements_flt.val[3])); - } - else - { - vec_elements_flt.val[0] = vexpq_f32(vmulq_f32(vec_elements_flt.val[0], scale_beta_vec)); - vec_elements_flt.val[1] = vexpq_f32(vmulq_f32(vec_elements_flt.val[1], scale_beta_vec)); - vec_elements_flt.val[2] = vexpq_f32(vmulq_f32(vec_elements_flt.val[2], scale_beta_vec)); - vec_elements_flt.val[3] = vexpq_f32(vmulq_f32(vec_elements_flt.val[3], scale_beta_vec)); - vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vec_elements_flt.val[0]); - vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vec_elements_flt.val[1]); - vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vec_elements_flt.val[2]); - vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vec_elements_flt.val[3]); - } - - vst4q_f32(tmp_ptr + x, vec_elements_flt); - } - - /* Reduce sum */ - const auto sum_16_byte = vaddq_f32(vaddq_f32(vec_sum.val[0], vec_sum.val[1]), vaddq_f32(vec_sum.val[2], vec_sum.val[3])); - auto sum_res = vpadd_f32(vget_high_f32(sum_16_byte), vget_low_f32(sum_16_byte)); - sum_res = vpadd_f32(sum_res, sum_res); - sum = wrapper::vgetlane(sum_res, 0); - - /* Run remaining elements */ - for(; x < input_width; ++x) - { - float element{}; - if(is_log) - { - element = (max_val - in_ptr[x]) * scale_beta; - sum += std::exp(element); - } - else - { - element = std::exp((max_val - in_ptr[x]) * scale_beta); - sum += element; - } - - tmp_ptr[x] = element; - } - - if(!is_log) - { - sum_inversed = 256.f / sum; - } - } - - /* Normalize exponentials */ - { - constexpr bool is_qasymm8_signed = std::is_same<T, qasymm8_signed_t>::value; - /* Loop over row and compute softmax */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - using int_vec_type = wrapper::traits::neon_vector_t<T, 16>; - float32x4x4_t vec_in = vld4q_f32(tmp_ptr + x); - int_vec_type normalized_value{}; - if(is_log) - { - const float32x4x4_t sub = - { - vsubq_f32(vec_in.val[0], vdupq_n_f32(sum)), - vsubq_f32(vec_in.val[1], vdupq_n_f32(sum)), - vsubq_f32(vec_in.val[2], vdupq_n_f32(sum)), - vsubq_f32(vec_in.val[3], vdupq_n_f32(sum)), - }; - normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(sub); - } - else - { - float32x4x4_t mul = - { - vmulq_f32(vec_in.val[0], vdupq_n_f32(sum_inversed)), - vmulq_f32(vec_in.val[1], vdupq_n_f32(sum_inversed)), - vmulq_f32(vec_in.val[2], vdupq_n_f32(sum_inversed)), - vmulq_f32(vec_in.val[3], vdupq_n_f32(sum_inversed)), - }; - - if(is_qasymm8_signed) - { - const auto offset_vec = wrapper::vdup_n(128.f, wrapper::traits::vector_128_tag{}); - mul.val[0] = wrapper::vsub(mul.val[0], offset_vec); - mul.val[1] = wrapper::vsub(mul.val[1], offset_vec); - mul.val[2] = wrapper::vsub(mul.val[2], offset_vec); - mul.val[3] = wrapper::vsub(mul.val[3], offset_vec); - } - - normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(mul); - } - wrapper::vstore(out_ptr + x, normalized_value); - } - /* Run remaining elements */ - for(; x < input_width; ++x) - { - if(is_log) - { - out_ptr[x] = utils::cast::saturate_cast<T>(tmp_ptr[x] - sum); - } - else - { - out_ptr[x] = utils::cast::saturate_cast<T>((tmp_ptr[x] * sum_inversed) - (is_qasymm8_signed ? 128.f : 0)); - } - } - } - }, - in_it, max_it, out_it); -} - -template <typename T, bool is_log = false> -void logits_1d_softmax_float(const ITensor &in, const ITensor &max, void *const tmp, - ITensor &out, const float beta, const Window &window) -{ - const int start_x = in.info()->valid_region().anchor.x(); - const int input_width = in.info()->valid_region().shape.x(); - - Iterator in_it(&in, window); - Iterator max_it(&max, window); - Iterator out_it(&out, window); - - /** NEON vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>; - - constexpr int vec_size = 16 / sizeof(T); - const int sum_stages = log2(vec_size / 2); - - execute_window_loop(window, [&](const Coordinates &) - { - /* Get pointers */ - const auto in_ptr = reinterpret_cast<const T *>(in_it.ptr()) + start_x; - const auto out_ptr = reinterpret_cast<T *>(out_it.ptr()) + start_x; - const auto tmp_ptr = reinterpret_cast<T *>(tmp); - - T sum{}; - T sum_inversed{}; - - /* Compute exponentials and sum */ - { - /* Get max value */ - const auto max_val = *reinterpret_cast<const T *>(max_it.ptr()); - const auto vec_max = wrapper::vdup_n(max_val, ExactTagType{}); - - /* Init sum to zero */ - auto vec_sum = wrapper::vdup_n(static_cast<T>(0), ExactTagType{}); - - /* Loop over row and compute exponentials and sum */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - auto vec_elements = wrapper::vloadq(in_ptr + x); - vec_elements = wrapper::vsub(vec_elements, vec_max); - if(is_log) - { - vec_elements = wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{})); - vec_sum = wrapper::vadd(vec_sum, wrapper::vexpq(vec_elements)); - } - else - { - vec_elements = wrapper::vexpq(wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{}))); - vec_sum = wrapper::vadd(vec_sum, vec_elements); - } - wrapper::vstore(tmp_ptr + x, vec_elements); - } - - /* Reduce sum */ - auto sum_res = wrapper::vpadd(wrapper::vgethigh(vec_sum), wrapper::vgetlow(vec_sum)); - for(int i = 0; i < sum_stages; ++i) - { - sum_res = wrapper::vpadd(sum_res, sum_res); - } - sum = wrapper::vgetlane(sum_res, 0); - - /* Run remaining elements */ - for(; x < input_width; ++x) - { - T element{}; - - if(is_log) - { - element = (in_ptr[x] - max_val) * beta; - sum += std::exp(element); - } - else - { - element = std::exp((in_ptr[x] - max_val) * beta); - sum += element; - } - tmp_ptr[x] = element; - } - - if(!is_log) - { - sum_inversed = T(1) / sum; - } - } - - /* Normalize exponentials */ - { - /* Loop over row and compute softmax */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - auto vec_in = wrapper::vloadq(tmp_ptr + x); - auto normalized_value = wrapper::vdup_n(static_cast<T>(0), ExactTagType{}); - if(is_log) - { - normalized_value = wrapper::vsub(vec_in, wrapper::vdup_n(static_cast<T>(sum), ExactTagType{})); - } - else - { - normalized_value = wrapper::vmul(vec_in, wrapper::vdup_n(static_cast<T>(sum_inversed), ExactTagType{})); - } - wrapper::vstore(out_ptr + x, normalized_value); - } - /* Run remaining elements */ - for(; x < input_width; ++x) - { - if(is_log) - { - out_ptr[x] = tmp_ptr[x] - sum; - } - else - { - out_ptr[x] = tmp_ptr[x] * sum_inversed; - } - } - } - }, - in_it, max_it, out_it); -} -} // namespace - -template <bool IS_LOG> -NELogits1DSoftmaxKernel<IS_LOG>::NELogits1DSoftmaxKernel() - : _func(nullptr), _input(nullptr), _max(nullptr), _output(nullptr), _beta(1.0f), _tmp(nullptr) -{ -} - -template <bool IS_LOG> -void NELogits1DSoftmaxKernel<IS_LOG>::configure(const ITensor *input, const ITensor *max, ITensor *output, const float beta, ITensor *tmp) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, max, output, tmp); - ARM_COMPUTE_ERROR_ON_NULLPTR(input->info(), max->info(), output->info(), tmp->info()); - // Perform validation step - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_logits_softmax(*input->info(), *max->info(), *output->info(), beta, *tmp->info(), IS_LOG)); - - // Configure kernel window - const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(input->info()->data_type()); - - // Output auto initialization if not yet initialized - const QuantizationInfo output_quantization = is_quantized_asymmetric ? arm_compute::get_softmax_output_quantization_info(input->info()->data_type(), IS_LOG) : output->info()->quantization_info(); - auto_init_if_empty(*output->info(), TensorInfo(*input->info()).set_quantization_info(output_quantization).reset_padding()); - - // Tmp auto initialization if not yet initialized - const DataType tmp_data_type = is_quantized_asymmetric ? DataType::F32 : input->info()->data_type(); - auto_init_if_empty(*tmp->info(), TensorInfo(*input->info()).set_data_type(tmp_data_type).reset_padding()); - - // Configure kernel window - Window win = calculate_max_window(*max->info(), Steps()); - Coordinates coord; - coord.set_num_dimensions(output->info()->num_dimensions()); - output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); - - switch(input->info()->data_type()) - { - case DataType::QASYMM8: - _func = &logits_1d_softmax_qasymm8<qasymm8_t, IS_LOG>; - break; - case DataType::QASYMM8_SIGNED: - _func = &logits_1d_softmax_qasymm8<qasymm8_signed_t, IS_LOG>; - break; -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - _func = &logits_1d_softmax_float<float16_t, IS_LOG>; - break; -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - case DataType::F32: - _func = &logits_1d_softmax_float<float, IS_LOG>; - break; - default: - ARM_COMPUTE_ERROR("Unsupported data type."); - break; - } - - _input = input; - _max = max; - _output = output; - _beta = beta; - _tmp = tmp; - - INEKernel::configure(win); -} - -template <bool IS_LOG> -Status NELogits1DSoftmaxKernel<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *max, - const ITensorInfo *output, const float beta, const ITensorInfo *tmp) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, max, output, tmp); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_logits_softmax(*input, *max, *output, beta, *tmp, IS_LOG)); - - return Status{}; -} - -template <bool IS_LOG> -void NELogits1DSoftmaxKernel<IS_LOG>::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - - const unsigned int num_elems_processed_per_iteration = _input->info()->valid_region().shape.x(); - const unsigned int tmp_size_for_thread = _tmp->info()->element_size() * num_elems_processed_per_iteration; - - ARM_COMPUTE_ERROR_ON(_tmp->info()->total_size() < (info.num_threads * tmp_size_for_thread)); - - void *tmp_for_thread = _tmp->buffer() + (info.thread_id * tmp_size_for_thread); - - (*_func)(*_input, *_max, tmp_for_thread, *_output, _beta, window); -} - -template class NELogits1DSoftmaxKernel<true>; -template class NELogits1DSoftmaxKernel<false>; - -} // namespace arm_compute |