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>

1 files changed, 321 insertions, 0 deletions

diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp
new file mode 100644
index 0000000000..80ba2aff93
--- /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