1 files changed, 207 insertions, 0 deletions
diff --git a/src/core/NEON/kernels/NEDirectConvolutionLayerBiasAccumulateKernel.cpp b/src/core/NEON/kernels/NEDirectConvolutionLayerBiasAccumulateKernel.cpp
new file mode 100644
index 0000000000..effc50e7c0
--- /dev/null
+++ b/src/core/NEON/kernels/NEDirectConvolutionLayerBiasAccumulateKernel.cpp
@@ -0,0 +1,207 @@
+/*
+ * Copyright (c) 2017 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/NEDirectConvolutionLayerBiasAccumulateKernel.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/NEFixedPoint.h"
+#include "arm_compute/core/Types.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
+{
+// Internal load
+inline float32x4_t internal_vld1q(const float *in)
+{
+    return vld1q_f32(in);
+}
+inline qint8x16_t internal_vld1q(const qint8_t *in)
+{
+    return vld1q_qs8(in);
+}
+inline qint16x8_t internal_vld1q(const qint16_t *in)
+{
+    return vld1q_qs16(in);
+}
+
+// Internal store
+inline void internal_vst1q(float *p, const float32x4_t &v)
+{
+    vst1q_f32(p, v);
+}
+inline void internal_vst1q(qint8_t *p, const qint8x16_t &v)
+{
+    vst1q_qs8(p, v);
+}
+inline void internal_vst1q(qint8_t *p, const qint16x8_t &v)
+{
+    vst1_qs8(p, vqmovn_s16(v));
+}
+inline void internal_vst1q(qint16_t *p, const qint16x8_t &v)
+{
+    vst1q_qs16(p, v);
+}
+
+// Internal vdup
+inline float32x4_t internal_vdupq_n(float v)
+{
+    return vdupq_n_f32(v);
+}
+inline qint8x16_t internal_vdupq_n(qint8_t v)
+{
+    return vdupq_n_qs8(v);
+}
+inline qint16x8_t internal_vdupq_n(qint16_t v)
+{
+    return vdupq_n_qs16(v);
+}
+
+// Internal vadd
+inline float32x4_t internal_vqaddq(const float32x4_t &x, const float32x4_t &y)
+{
+    return vaddq_f32(x, y);
+}
+inline qint8x16_t internal_vqaddq(const qint8x16_t &x, const qint8x16_t &y)
+{
+    return vqaddq_qs8(x, y);
+}
+inline qint16x8_t internal_vqaddq(const qint16x8_t &x, const qint16x8_t &y)
+{
+    return vqaddq_qs16(x, y);
+}
+
+template <typename T1, typename T2, bool in_place>
+void accumulate_bias(ITensor *input, const ITensor *bias, const Window window, ITensor *output)
+{
+    Iterator in(input, window);
+
+    if(in_place) // In place accumulate
+    {
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            // Get bias and pointer to input
+            const auto in_ptr = reinterpret_cast<T1 *>(in.ptr());
+            const auto vb     = internal_vdupq_n(static_cast<T1>(*reinterpret_cast<const T2 *>(bias->ptr_to_element(Coordinates(id.z())))));
+
+            // Accumulate bias
+            internal_vst1q(in_ptr, internal_vqaddq(internal_vld1q(in_ptr), vb));
+        },
+        in);
+    }
+    else // Out of place accumulate
+    {
+        Iterator out(output, window);
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            // Get bias and pointer to input
+            const auto in_ptr  = reinterpret_cast<const T1 *>(in.ptr());
+            const auto out_ptr = reinterpret_cast<T2 *>(out.ptr());
+            const auto vb      = internal_vdupq_n(static_cast<T1>(*reinterpret_cast<const T2 *>(bias->ptr_to_element(Coordinates(id.z())))));
+
+            // Accumulate bias
+            internal_vst1q(out_ptr, internal_vqaddq(internal_vld1q(in_ptr), vb));
+        },
+        in, out);
+    }
+}
+} // namespace
+
+NEDirectConvolutionLayerBiasAccumulateKernel::NEDirectConvolutionLayerBiasAccumulateKernel()
+    : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr)
+{
+}
+
+void NEDirectConvolutionLayerBiasAccumulateKernel::configure(ITensor *input, const ITensor *bias, ITensor *output)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::QS8, DataType::QS16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON(input->info()->fixed_point_position() != bias->info()->fixed_point_position());
+    if(output != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::QS16, DataType::F32);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(bias, output);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(bias, output);
+    }
+    ARM_COMPUTE_ERROR_ON(bias->info()->num_dimensions() > 1);
+
+    _func   = nullptr;
+    _bias   = bias;
+    _input  = input;
+    _output = output;
+
+    const unsigned int num_elems_processed_per_iteration = 16 / element_size_from_data_type(input->info()->data_type());
+
+    // Configure kernel window
+    Window                 win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration));
+    AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration);
+    AccessWindowStatic     bias_access(bias->info(), 0, 0, bias->info()->dimension(0), bias->info()->dimension(1));
+    if(output != nullptr)
+    {
+        AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+        update_window_and_padding(win, input_access, output_access, bias_access);
+        output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
+    }
+    else
+    {
+        update_window_and_padding(win, input_access, bias_access);
+        input_access.set_valid_region(win, ValidRegion(Coordinates(), input->info()->tensor_shape()));
+    }
+    INEKernel::configure(win);
+
+    // Set appropriate function
+    if(input->info()->data_type() == DataType::F32)
+    {
+        _func = (output == nullptr) ? &accumulate_bias<float, float, true> : &accumulate_bias<float, float, false>;
+    }
+    else if(input->info()->data_type() == DataType::QS8)
+    {
+        _func = (output == nullptr) ? &accumulate_bias<qint8_t, qint8_t, true> : &accumulate_bias<qint8_t, qint8_t, false>;
+    }
+    else if(input->info()->data_type() == DataType::QS16 && bias->info()->data_type() == DataType::QS8)
+    {
+        _func = (output == nullptr) ? &accumulate_bias<qint16_t, qint8_t, true> : &accumulate_bias<qint16_t, qint8_t, false>;
+    }
+    else
+    {
+        ARM_COMPUTE_ERROR("Unsupported combination of types among the inputs.");
+    }
+}
+
+void NEDirectConvolutionLayerBiasAccumulateKernel::run(const Window &window)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    (*_func)(_input, _bias, window, _output);
+}