COMPMID-2966 Add support for QASYMM8_SIGNED in NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel
Signed-off-by: Luca Foschiani <luca.foschiani@arm.com>
Change-Id: Ia8692f8fda16fa3b73f343e4b5b1b55e14403225
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2750
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp
new file mode 100644
index 0000000..80ba2af
--- /dev/null
+++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp
@@ -0,0 +1,321 @@
+/*
+ * Copyright (c) 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/NEGEMMLowpQuantizeDownInt32ScaleKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/wrapper/wrapper.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+
+#include <arm_neon.h>
+#include <cstddef>
+#include <cstdint>
+
+namespace arm_compute
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
+
+ ARM_COMPUTE_RETURN_ERROR_ON(output_stage->gemmlowp_max_bound > std::get<1>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type)));
+ ARM_COMPUTE_RETURN_ERROR_ON(output_stage->gemmlowp_min_bound < std::get<0>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))
+ || output_stage->gemmlowp_min_bound > output_stage->gemmlowp_max_bound);
+
+ // Check biases if exist
+ if(bias != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
+ ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
+ }
+
+ if(output->total_size() != 0)
+ {
+ if(output->data_type() != output_stage->output_data_type && (output_stage->output_data_type == DataType::QASYMM8 || output_stage->output_data_type == DataType::QASYMM8_SIGNED))
+ {
+ ARM_COMPUTE_RETURN_ERROR_MSG("Mismatching data types");
+ }
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
+ }
+
+ return Status{};
+}
+
+inline void scale_input(int32x4x4_t &in_s32, int32x4_t result_offset_s32, int32_t result_mult_int)
+{
+ // Add the offset terms to GEMM's result
+ in_s32.val[0] = vaddq_s32(in_s32.val[0], result_offset_s32);
+ in_s32.val[1] = vaddq_s32(in_s32.val[1], result_offset_s32);
+ in_s32.val[2] = vaddq_s32(in_s32.val[2], result_offset_s32);
+ in_s32.val[3] = vaddq_s32(in_s32.val[3], result_offset_s32);
+
+ // Multiply by result_mult_int
+ in_s32.val[0] = vmulq_n_s32(in_s32.val[0], result_mult_int);
+ in_s32.val[1] = vmulq_n_s32(in_s32.val[1], result_mult_int);
+ in_s32.val[2] = vmulq_n_s32(in_s32.val[2], result_mult_int);
+ in_s32.val[3] = vmulq_n_s32(in_s32.val[3], result_mult_int);
+}
+
+template <typename T>
+inline typename std::enable_if<std::is_same<T, uint8_t>::value,
+ typename wrapper::traits::neon_vector<T, 16>::type>::type
+ convert_to_8bit(const int16x8x2_t in_s16)
+{
+ return wrapper::vcombine(wrapper::vqmovun(in_s16.val[0]), wrapper::vqmovun(in_s16.val[1]));
+}
+
+template <typename T>
+inline typename std::enable_if<std::is_same<T, int8_t>::value,
+ typename wrapper::traits::neon_vector<T, 16>::type>::type
+ convert_to_8bit(const int16x8x2_t in_s16)
+{
+ return wrapper::vcombine(wrapper::vqmovn(in_s16.val[0]), wrapper::vqmovn(in_s16.val[1]));
+}
+
+template <typename T>
+inline typename wrapper::traits::neon_vector<T, 16>::type finalize_quantization(int32x4x4_t &in_s32, int32x4_t result_shift_s32, typename wrapper::traits::neon_vector<T, 16>::type min,
+ typename wrapper::traits::neon_vector<T, 16>::type max)
+{
+ // Shift final result (negative value shift right)
+ in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32);
+ in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32);
+ in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32);
+ in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32);
+
+ // Convert S32 to S16
+ const int16x8x2_t in_s16 =
+ {
+ {
+ vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])),
+ vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3]))
+ }
+ };
+
+ // Convert S16 to S8 or U8
+ typename wrapper::traits::neon_vector<T, 16>::type out = convert_to_8bit<T>(in_s16);
+
+ out = wrapper::vmax(out, min);
+ out = wrapper::vmin(out, max);
+
+ return out;
+}
+
+class Coordinates;
+
+template <typename T>
+void NEGEMMLowpQuantizeDownInt32ScaleKernel::run(const Window &window)
+{
+ using VectorType = typename wrapper::traits::neon_vector<T, 16>::type;
+
+ const int32x4_t result_offset_s32 = vdupq_n_s32(_output_stage->gemmlowp_offset);
+ const int32x4_t result_shift_s32 = vdupq_n_s32(-_output_stage->gemmlowp_shift);
+ const int window_step_x = 16;
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ const int clamp_min = (_is_bounded_relu) ? _output_stage->gemmlowp_min_bound : std::numeric_limits<T>::lowest();
+ const int clamp_max = (_is_bounded_relu) ? _output_stage->gemmlowp_max_bound : std::numeric_limits<T>::max();
+
+ VectorType min = wrapper::vdup_n(static_cast<T>(clamp_min), wrapper::traits::vector_128_tag{});
+ VectorType max = wrapper::vdup_n(static_cast<T>(clamp_max), wrapper::traits::vector_128_tag{});
+
+ Window win(window);
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator in(_input, win);
+ Iterator out(_output, win);
+
+ if(_bias != nullptr)
+ {
+ Window win_biases;
+ win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
+ win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+ Iterator bias(_bias, win_biases);
+ execute_window_loop(win, [&](const Coordinates &)
+ {
+ // Compute 16 elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ int32x4x4_t in_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
+ }
+ };
+
+ const int32x4x4_t bias_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
+ vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
+ vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 12)
+ }
+ };
+
+ // Add the bias to GEMM's result
+ in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
+ in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
+ in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
+ in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);
+
+ // Add the offset terms to GEMM's result and multiply by result_mult_int
+ scale_input(in_s32, result_offset_s32, _output_stage->gemmlowp_multiplier);
+
+ wrapper::vstore(reinterpret_cast<T *>(out.ptr() + x), finalize_quantization<T>(in_s32, result_shift_s32, min, max));
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ const int bias_value = *(reinterpret_cast<const int *>(bias.ptr()) + x);
+ int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x);
+
+ // Quantize
+ in_value = ((in_value + bias_value + _output_stage->gemmlowp_offset) * _output_stage->gemmlowp_multiplier) >> _output_stage->gemmlowp_shift;
+
+ // Store the result
+ *(out.ptr() + x) = static_cast<T>(utility::clamp<int>(in_value, clamp_min, clamp_max));
+ }
+ },
+ in, bias, out);
+ }
+ else
+ {
+ execute_window_loop(win, [&](const Coordinates &)
+ {
+ // Compute 16 elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ int32x4x4_t in_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
+ }
+ };
+
+ // Add the offset terms to GEMM's result and multiply by result_mult_int
+ scale_input(in_s32, result_offset_s32, _output_stage->gemmlowp_multiplier);
+
+ wrapper::vstore(reinterpret_cast<T *>(out.ptr() + x), finalize_quantization<T>(in_s32, result_shift_s32, min, max));
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x);
+
+ // Quantize
+ in_value = ((in_value + _output_stage->gemmlowp_offset) * _output_stage->gemmlowp_multiplier) >> _output_stage->gemmlowp_shift;
+
+ // Store the result
+ *(out.ptr() + x) = static_cast<T>(utility::clamp<int>(in_value, clamp_min, clamp_max));
+ }
+ },
+ in, out);
+ }
+}
+
+NEGEMMLowpQuantizeDownInt32ScaleKernel::NEGEMMLowpQuantizeDownInt32ScaleKernel()
+ : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _output_stage(nullptr), _is_bounded_relu(false)
+{
+}
+
+void NEGEMMLowpQuantizeDownInt32ScaleKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, const GEMMLowpOutputStageInfo *output_stage)
+{
+ // Perform validate step
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, output_stage);
+
+ // Output auto inizialitation if not yet initialized
+ auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(output_stage->output_data_type));
+
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
+ (bias != nullptr) ? bias->info() : nullptr,
+ output->info(),
+ output_stage));
+
+ _input = input;
+ _bias = bias;
+ _output = output;
+ _output_stage = output_stage;
+
+ // Configure kernel window
+ 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()));
+
+ INEKernel::configure(win);
+
+ // Check if we need to clamp the result using min and max
+ _is_bounded_relu = ((_output_stage->gemmlowp_min_bound != _output_stage->gemmlowp_max_bound)
+ && !(_output_stage->gemmlowp_min_bound == std::get<0>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))
+ && _output_stage->gemmlowp_max_bound == std::get<1>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))));
+ if(_output_stage->output_data_type == DataType::QASYMM8)
+ {
+ _func = &NEGEMMLowpQuantizeDownInt32ScaleKernel::run<uint8_t>;
+ }
+ else if(_output_stage->output_data_type == DataType::QASYMM8_SIGNED)
+ {
+ _func = &NEGEMMLowpQuantizeDownInt32ScaleKernel::run<int8_t>;
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR("Data type not supported");
+ }
+}
+
+Status NEGEMMLowpQuantizeDownInt32ScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, output_stage));
+
+ return Status{};
+}
+
+void NEGEMMLowpQuantizeDownInt32ScaleKernel::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);
+
+ (this->*_func)(window);
+}
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp
deleted file mode 100644
index a68e4e7..0000000
--- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp
+++ /dev/null
@@ -1,349 +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/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h"
-
-#include "arm_compute/core/AccessWindowStatic.h"
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/ITensor.h"
-#include "arm_compute/core/Types.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/Window.h"
-
-#include <arm_neon.h>
-#include <cstddef>
-#include <cstdint>
-
-using namespace arm_compute;
-
-namespace
-{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
- ARM_COMPUTE_RETURN_ERROR_ON(min > max);
-
- // Check biases if exist
- if(bias != nullptr)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
- ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
- ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
- }
-
- if(output->total_size() != 0)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
- }
-
- return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output)
-{
- // Note: This kernel performs 16 elements per iteration.
- // However, since we use a left-over for loop, we cannot have any read or write out of memory
- // For this reason num_elems_processed_per_iteration is set to 1
- constexpr unsigned int num_elems_processed_per_iteration = 1;
-
- // Configure kernel window
- Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
-
- AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
-
- bool window_changed = update_window_and_padding(win,
- input_access);
-
- if(output->total_size() != 0)
- {
- AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration);
- window_changed = window_changed || update_window_and_padding(win, output_result_access);
-
- output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
- }
-
- if(bias != nullptr)
- {
- AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1));
- window_changed = window_changed || update_window_and_padding(win, bias_access);
- }
-
- Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_pair(err, win);
-}
-
-inline void scale_input(int32x4x4_t &in_s32, int32x4_t result_offset_s32, int32_t result_mult_int)
-{
- // Add the offset terms to GEMM's result
- in_s32.val[0] = vaddq_s32(in_s32.val[0], result_offset_s32);
- in_s32.val[1] = vaddq_s32(in_s32.val[1], result_offset_s32);
- in_s32.val[2] = vaddq_s32(in_s32.val[2], result_offset_s32);
- in_s32.val[3] = vaddq_s32(in_s32.val[3], result_offset_s32);
-
- // Multiply by result_mult_int
- in_s32.val[0] = vmulq_n_s32(in_s32.val[0], result_mult_int);
- in_s32.val[1] = vmulq_n_s32(in_s32.val[1], result_mult_int);
- in_s32.val[2] = vmulq_n_s32(in_s32.val[2], result_mult_int);
- in_s32.val[3] = vmulq_n_s32(in_s32.val[3], result_mult_int);
-}
-
-template <bool is_bounded_relu>
-inline uint8x16_t finalize_quantization(int32x4x4_t &in_s32, int32x4_t result_shift_s32, uint8x16_t min_u8, uint8x16_t max_u8)
-{
- const static int32x4_t zero_s32 = vdupq_n_s32(0);
-
- // Shift final result (negative value shift right)
- in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32);
- in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32);
- in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32);
- in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32);
-
- // Saturate negative values
- in_s32.val[0] = vmaxq_s32(in_s32.val[0], zero_s32);
- in_s32.val[1] = vmaxq_s32(in_s32.val[1], zero_s32);
- in_s32.val[2] = vmaxq_s32(in_s32.val[2], zero_s32);
- in_s32.val[3] = vmaxq_s32(in_s32.val[3], zero_s32);
-
- // Convert S32 to S16
- const int16x8x2_t in_s16 =
- {
- {
- vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])),
- vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3]))
- }
- };
-
- // Convert S16 to U8
- uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_s16.val[0]), vqmovun_s16(in_s16.val[1]));
-
- if(is_bounded_relu)
- {
- out_u8 = vmaxq_u8(out_u8, min_u8);
- out_u8 = vminq_u8(out_u8, max_u8);
- }
-
- return out_u8;
-}
-} // namespace
-
-namespace arm_compute
-{
-class Coordinates;
-} // namespace arm_compute
-
-template <bool is_bounded_relu>
-void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window)
-{
- const int32x4_t result_offset_s32 = vdupq_n_s32(_result_offset);
- const int32x4_t result_shift_s32 = vdupq_n_s32(-_result_shift);
- const uint8x16_t min_u8 = vdupq_n_u8(static_cast<uint8_t>(_min));
- const uint8x16_t max_u8 = vdupq_n_u8(static_cast<uint8_t>(_max));
-
- ARM_COMPUTE_UNUSED(min_u8);
- ARM_COMPUTE_UNUSED(max_u8);
-
- const int window_step_x = 16;
- 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 in(_input, win);
- Iterator out(_output, win);
-
- if(_bias != nullptr)
- {
- Window win_biases;
- win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
- win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
-
- Iterator bias(_bias, win_biases);
- execute_window_loop(win, [&](const Coordinates &)
- {
- // Compute 16 elements per iteration
- int x = window_start_x;
- for(; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- int32x4x4_t in_s32 =
- {
- {
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
- }
- };
-
- const int32x4x4_t bias_s32 =
- {
- {
- vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0),
- vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4),
- vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8),
- vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 12)
- }
- };
-
- // Add the bias to GEMM's result
- in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
- in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
- in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]);
- in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]);
-
- // Add the offset terms to GEMM's result and multiply by result_mult_int
- scale_input(in_s32, result_offset_s32, _result_mult_int);
-
- vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
- }
-
- // Compute left-over elements
- for(; x < window_end_x; ++x)
- {
- const int bias_value = *(reinterpret_cast<const int *>(bias.ptr()) + x);
- int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x);
-
- // Quantize
- in_value = ((in_value + bias_value + _result_offset) * _result_mult_int) >> _result_shift;
-
- // Finalize and store the result
- if(is_bounded_relu)
- {
- *(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value)));
- }
- else
- {
- *(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
- }
- }
- },
- in, bias, out);
- }
- else
- {
- execute_window_loop(win, [&](const Coordinates &)
- {
- // Compute 16 elements per iteration
- int x = window_start_x;
- for(; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- int32x4x4_t in_s32 =
- {
- {
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4),
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8),
- vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12)
- }
- };
-
- // Add the offset terms to GEMM's result and multiply by result_mult_int
- scale_input(in_s32, result_offset_s32, _result_mult_int);
-
- vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
- }
-
- // Compute left-over elements
- for(; x < window_end_x; ++x)
- {
- int in_value = *(reinterpret_cast<const int *>(in.ptr()) + x);
-
- // Quantize
- in_value = ((in_value + _result_offset) * _result_mult_int) >> _result_shift;
-
- // Finalize and store the result
- if(is_bounded_relu)
- {
- *(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value)));
- }
- else
- {
- *(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
- }
- }
- },
- in, out);
- }
-}
-
-NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel()
- : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_offset(0), _result_mult_int(0), _result_shift(0), _min(0), _max(0)
-{
-}
-
-void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max)
-{
- // Perform validate step
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-
- // Output auto inizialitation if not yet initialized
- auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8));
-
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
- (bias != nullptr) ? bias->info() : nullptr,
- output->info(),
- min,
- max));
-
- _input = input;
- _bias = bias;
- _output = output;
- _result_offset = result_offset;
- _result_mult_int = result_mult_int;
- _result_shift = result_shift;
- _min = min;
- _max = max;
-
- // Configure kernel window
- auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info());
- ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
- INEKernel::configure(win_config.second);
-
- // Check if we need to clamp the result using min and max
- const bool is_bounded_relu = !(min <= 0 && max >= 255);
- _func = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run<false>;
-}
-
-Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(),
- (bias != nullptr) ? bias->clone().get() : nullptr,
- output->clone().get())
- .first);
-
- return Status{};
-}
-
-void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::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);
-
- (this->*_func)(window);
-}
diff --git a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
index fbd1820..2114d39 100644
--- a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
@@ -156,22 +156,9 @@
}
case GEMMLowpOutputStageType::QUANTIZE_DOWN:
{
- switch(info.output_data_type)
- {
- case DataType::QASYMM8:
- case DataType::QASYMM8_SIGNED:
- {
- auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ScaleKernel>();
- k->configure(input, bias, output, &info);
- _kernel = std::move(k);
- break;
- }
- default:
- {
- ARM_COMPUTE_ERROR("Unsupported output data type.");
- break;
- }
- }
+ auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ScaleKernel>();
+ k->configure(input, bias, output, &info);
+ _kernel = std::move(k);
break;
}
case GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT:
@@ -206,22 +193,9 @@
}
}
case GEMMLowpOutputStageType::QUANTIZE_DOWN:
- {
- switch(output->data_type())
- {
- case DataType::QASYMM8:
- case DataType::QASYMM8_SIGNED:
- {
- return CLGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
- }
- default:
- return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
- }
- }
+ return CLGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
case GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT:
- {
return CLGEMMLowpQuantizeDownInt32ScaleByFloatKernel::validate(input, bias, output, &info);
- }
default:
return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported GEMMLowpOutputStage type.");
}
diff --git a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
index 42d2ffc..43ca7b3 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
@@ -24,10 +24,10 @@
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h"
-#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h"
#include "arm_compute/core/Validate.h"
#include "support/MemorySupport.h"
@@ -35,14 +35,25 @@
{
void NEGEMMLowpQuantizeDownInt32ToUint8Scale::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max)
{
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel>();
- k->configure(input, bias, output, result_offset, result_mult_int, result_shift, min, max);
+ GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
+ info.gemmlowp_offset = result_offset;
+ info.gemmlowp_multiplier = result_mult_int;
+ info.gemmlowp_shift = result_shift;
+ info.gemmlowp_min_bound = min;
+ info.gemmlowp_max_bound = max;
+
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ScaleKernel>();
+ k->configure(input, bias, output, &info);
_kernel = std::move(k);
}
Status NEGEMMLowpQuantizeDownInt32ToUint8Scale::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
{
- return NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(input, bias, output, min, max);
+ GEMMLowpOutputStageInfo info = GEMMLowpOutputStageInfo();
+ info.gemmlowp_min_bound = min;
+ info.gemmlowp_max_bound = max;
+
+ return NEGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
}
void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
@@ -89,53 +100,63 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_ERROR_THROW_ON(NEGEMMLowpOutputStage::validate(input->info(), bias != nullptr ? bias->info() : nullptr, output->info(), info));
- if(info.type == GEMMLowpOutputStageType::QUANTIZE_DOWN)
+ switch(info.type)
{
- switch(output->info()->data_type())
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT:
{
- case DataType::QASYMM8:
+ switch(info.output_data_type)
{
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel>();
- k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- _kernel = std::move(k);
- break;
+ case DataType::QASYMM8:
+ {
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>();
+ k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+ _kernel = std::move(k);
+ break;
+ }
+ case DataType::QASYMM8_SIGNED:
+ {
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel>();
+ k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+ _kernel = std::move(k);
+ break;
+ }
+ case DataType::QSYMM16:
+ {
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel>();
+ k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+ _kernel = std::move(k);
+ break;
+ }
+ default:
+ {
+ ARM_COMPUTE_ERROR("Unsupported output data type.");
+ break;
+ }
}
- default:
- ARM_COMPUTE_ERROR("Unsupported output data type.");
+ break;
}
- }
- else if(info.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
- {
- switch(output->info()->data_type())
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN:
{
- case DataType::QASYMM8:
+ switch(info.output_data_type)
{
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>();
- k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- _kernel = std::move(k);
- break;
+ case DataType::QASYMM8:
+ case DataType::QASYMM8_SIGNED:
+ {
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ScaleKernel>();
+ k->configure(input, bias, output, &info);
+ _kernel = std::move(k);
+ break;
+ }
+ default:
+ {
+ ARM_COMPUTE_ERROR("Unsupported output data type.");
+ break;
+ }
}
- case DataType::QASYMM8_SIGNED:
- {
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel>();
- k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_offset, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- _kernel = std::move(k);
- break;
- }
- case DataType::QSYMM16:
- {
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel>();
- k->configure(input, bias, output, info.gemmlowp_multiplier, info.gemmlowp_shift, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- _kernel = std::move(k);
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Unsupported output data type.");
+ break;
}
- }
- else
- {
- ARM_COMPUTE_ERROR("Unsupported output stage quantization type.");
+ default:
+ ARM_COMPUTE_ERROR("Unsupported GEMMLowpOutputStage type.");
}
}
@@ -147,29 +168,35 @@
ARM_COMPUTE_RETURN_ERROR_ON((info.type != GEMMLowpOutputStageType::QUANTIZE_DOWN) && (info.type != GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT));
- if(info.type == GEMMLowpOutputStageType::QUANTIZE_DOWN)
+ switch(info.type)
{
- switch(output->data_type())
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT:
{
- case DataType::QASYMM8:
- return NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- default:
- return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
+ switch(output->data_type())
+ {
+ case DataType::QASYMM8:
+ return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+ case DataType::QASYMM8_SIGNED:
+ return NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+ case DataType::QSYMM16:
+ return NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
+ default:
+ return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
+ }
}
- }
- else
- {
- switch(output->data_type())
+ case GEMMLowpOutputStageType::QUANTIZE_DOWN:
{
- case DataType::QASYMM8:
- return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- case DataType::QASYMM8_SIGNED:
- return NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- case DataType::QSYMM16:
- return NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::validate(input, bias, output, info.gemmlowp_min_bound, info.gemmlowp_max_bound);
- default:
- return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
+ switch(output->data_type())
+ {
+ case DataType::QASYMM8:
+ case DataType::QASYMM8_SIGNED:
+ return NEGEMMLowpQuantizeDownInt32ScaleKernel::validate(input, bias, output, &info);
+ default:
+ return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported output data type.");
+ }
}
+ default:
+ return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Unsupported GEMMLowpOutputStage type.");
}
}
} // namespace arm_compute