This commit unifies functions for elementwise ops in acl ex.
Signed-off-by: jiseob.jang <jiseob.jang@samsung.com>
--- /dev/null
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (c) 2016-2018 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.
+ */
+
+#ifndef __ARM_COMPUTE_NEELEMENTWISEOPERATIONFUNCS_H__
+#define __ARM_COMPUTE_NEELEMENTWISEOPERATIONFUNCS_H__
+
+#include <arm_neon.h>
+
+namespace arm_compute
+{
+class ITensor;
+class Window;
+class QuantizationInfo;
+} // namespace arm_compute
+
+namespace arm_compute
+{
+
+float32x4x4_t load_quantized(const uint8_t *input1_ptr, const int32x4_t &offset,
+ const float32x4_t &scale);
+
+void store_quantized(uint8_t *output_ptr, const float32x4x4_t &rf, const float32x4_t &offset,
+ const float32x4_t &invscale);
+
+float32x4x4_t dup_quantized(uint8_t broadcast_value, int offset, float scale);
+
+void elementwise_op_quantized(
+ const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
+ uint8_t (*scalar_func)(const float &, const float &, QuantizationInfo),
+ int (*broadcast_func)(int, int, int, const uint8_t *, float32x4x4_t, uint8_t *, int32x4_t,
+ float32x4_t, float32x4_t, float32x4_t, const bool),
+ int (*neon_func)(int, int, int, const uint8_t *, const uint8_t *, uint8_t *, int32x4_t,
+ int32x4_t, float32x4_t, float32x4_t, float32x4_t, float32x4_t));
+
+void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
+ float (*scalar_func)(const float &, const float &),
+ int (*broadcast_func)(int, int, int, const float *, const float &, float *,
+ const bool),
+ int (*neon_func)(int, int, int, const float *, const float *, float *));
+
+void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
+ uint8_t (*scalar_func)(const uint8_t &, const uint8_t &),
+ int (*broadcast_func)(int, int, int, const uint8_t *, const uint8_t &,
+ uint8_t *, const bool),
+ int (*neon_func)(int, int, int, const uint8_t *, const uint8_t *, uint8_t *));
+} // namespace arm_compute
+#endif // __ARM_COMPUTE_NEELEMENTWISEOPERATIONFUNCS_H__
--- /dev/null
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (c) 2016-2018 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/NEElementwiseOperationFuncs.h"
+
+#include <algorithm>
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/NEON/NEAsymm.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Window.h"
+
+namespace
+{
+void store_quantized_int32(uint8_t *output_ptr, const int32x4x4_t &out)
+{
+ const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[0]), vqmovn_s32(out.val[1])));
+ const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[2]), vqmovn_s32(out.val[3])));
+ vst1q_u8(output_ptr, vcombine_u8(pa, pb));
+}
+
+using namespace arm_compute;
+template <typename InputScalarType, typename OutputScalarType, typename InputVectorType>
+void elementwise_op_templ(
+ const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
+ OutputScalarType (*scalar_func)(const InputScalarType &, const InputScalarType &),
+ int (*broadcast_func)(int, int, int, const InputScalarType *, const InputScalarType &,
+ OutputScalarType *, const bool),
+ int (*neon_func)(int, int, int, const InputScalarType *, const InputScalarType *,
+ OutputScalarType *))
+{
+ // Create input windows
+ Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+
+ // Clear X Dimension on execution window as we handle manually
+ Window win = window;
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ const int window_step_x = std::min(16 / static_cast<int>(sizeof(OutputScalarType)), 8);
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+ const bool is_broadcast_across_x = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);
+
+ if (is_broadcast_across_x)
+ {
+ const bool is_broadcast_input_2 = input2_win.x().step() == 0;
+ Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
+ Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
+
+ // Clear X Dimension on execution window as we handle manually
+ non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator broadcast_input(broadcast_tensor, broadcast_win);
+ Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
+ Iterator output(out, win);
+
+ execute_window_loop(win,
+ [&](const Coordinates &) {
+ auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr());
+ const auto non_broadcast_input_ptr =
+ reinterpret_cast<const InputScalarType *>(non_broadcast_input.ptr());
+ const InputScalarType broadcast_value =
+ *reinterpret_cast<const InputScalarType *>(broadcast_input.ptr());
+
+ int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x,
+ non_broadcast_input_ptr, broadcast_value,
+ output_ptr, !is_broadcast_input_2);
+ for (; x < window_end_x; ++x)
+ {
+ const auto a = *(non_broadcast_input_ptr + x);
+ *(output_ptr + x) =
+ (*scalar_func)(!is_broadcast_input_2 ? broadcast_value : a,
+ !is_broadcast_input_2 ? a : broadcast_value);
+ }
+ },
+ broadcast_input, non_broadcast_input, output);
+ }
+ else
+ {
+ // Clear X Dimension on execution window as we handle manually
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator input1(in1, input1_win);
+ Iterator input2(in2, input2_win);
+ Iterator output(out, win);
+
+ execute_window_loop(win,
+ [&](const Coordinates &) {
+ auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr());
+ const auto input1_ptr =
+ reinterpret_cast<const InputScalarType *>(input1.ptr());
+ const auto input2_ptr =
+ reinterpret_cast<const InputScalarType *>(input2.ptr());
+
+ int x = (*neon_func)(window_start_x, window_end_x, window_step_x,
+ input1_ptr, input2_ptr, output_ptr);
+ for (; x < window_end_x; ++x)
+ {
+ const auto a = *(input1_ptr + x);
+ const auto b = *(input2_ptr + x);
+ *(output_ptr + x) = (*scalar_func)(a, b);
+ }
+ },
+ input1, input2, output);
+ }
+}
+
+} // namespace
+
+namespace arm_compute
+{
+
+float32x4x4_t load_quantized(const uint8_t *input1_ptr, const int32x4_t &offset,
+ const float32x4_t &scale)
+{
+ qasymm8x16_t x = vld1q_u8(input1_ptr);
+ const float32x4x4_t out = {{
+ vmulq_f32(
+ vcvtq_f32_s32(vsubq_s32(
+ vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(x))))), offset)),
+ scale),
+ vmulq_f32(
+ vcvtq_f32_s32(vsubq_s32(
+ vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(x))))), offset)),
+ scale),
+ vmulq_f32(
+ vcvtq_f32_s32(vsubq_s32(
+ vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(x))))), offset)),
+ scale),
+ vmulq_f32(
+ vcvtq_f32_s32(vsubq_s32(
+ vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(x))))), offset)),
+ scale),
+ }};
+ return out;
+}
+
+void store_quantized(uint8_t *output_ptr, const float32x4x4_t &rf, const float32x4_t &offset,
+ const float32x4_t &invscale)
+{
+ int32x4x4_t out = {{
+ vcvtq_s32_f32(vmlaq_f32(offset, rf.val[0], invscale)),
+ vcvtq_s32_f32(vmlaq_f32(offset, rf.val[1], invscale)),
+ vcvtq_s32_f32(vmlaq_f32(offset, rf.val[2], invscale)),
+ vcvtq_s32_f32(vmlaq_f32(offset, rf.val[3], invscale)),
+ }};
+ store_quantized_int32(output_ptr, out);
+}
+
+float32x4x4_t dup_quantized(uint8_t broadcast_value, int offset, float scale)
+{
+ const qasymm8x16_t broadcast_value_vec = vdupq_n_u8(broadcast_value);
+ const int32x4_t voffset = vdupq_n_s32(offset);
+ const float32x4_t vscale = vdupq_n_f32(scale);
+
+ const float32x4x4_t broadcast_vector = {{
+ vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(
+ vmovl_u8(vget_low_u8(broadcast_value_vec))))),
+ voffset)),
+ vscale),
+ vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(
+ vmovl_u8(vget_low_u8(broadcast_value_vec))))),
+ voffset)),
+ vscale),
+ vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(
+ vmovl_u8(vget_high_u8(broadcast_value_vec))))),
+ voffset)),
+ vscale),
+ vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(
+ vmovl_u8(vget_high_u8(broadcast_value_vec))))),
+ voffset)),
+ vscale),
+ }};
+ return broadcast_vector;
+}
+
+void elementwise_op_quantized(
+ const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
+ uint8_t (*scalar_func)(const float &, const float &, QuantizationInfo),
+ int (*broadcast_func)(int, int, int, const uint8_t *, float32x4x4_t, uint8_t *, int32x4_t,
+ float32x4_t, float32x4_t, float32x4_t, const bool),
+ int (*neon_func)(int, int, int, const uint8_t *, const uint8_t *, uint8_t *, int32x4_t,
+ int32x4_t, float32x4_t, float32x4_t, float32x4_t, float32x4_t))
+{
+ // Create input windows
+ Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+
+ // Clear X Dimension on execution window as we handle manually
+ Window win = window;
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ 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 bool is_broadcast_across_x = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);
+
+ const float output_scale = out->info()->quantization_info().scale;
+ const int output_offset = out->info()->quantization_info().offset;
+
+ // Output quantization info (add 0.5 to round toward the nearest integer - 0.5 rounds away from
+ // zero)
+ const float32x4_t voffseto = vdupq_n_f32(output_offset + 0.5f);
+ const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_scale);
+
+ if (is_broadcast_across_x)
+ {
+ // Select the broadcast input on the X axis
+ const bool is_broadcast_input_2 = input2_win.x().step() == 0;
+ Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
+ Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
+
+ const QuantizationInfo broadcast_qinfo = broadcast_tensor->info()->quantization_info();
+ const QuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info();
+
+ const int32x4_t voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset);
+ const float32x4_t vscale_non_broadcast = vdupq_n_f32(non_broadcast_qinfo.scale);
+
+ // Clear X Dimension on execution window as we handle manually
+ non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator broadcast_input(broadcast_tensor, broadcast_win);
+ Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
+ Iterator output(out, win);
+
+ execute_window_loop(
+ win,
+ [&](const Coordinates &) {
+ const auto non_broadcast_input_ptr =
+ reinterpret_cast<const uint8_t *>(non_broadcast_input.ptr());
+ const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr());
+
+ const uint8_t broadcast_value = *reinterpret_cast<const uint8_t *>(broadcast_input.ptr());
+ const float32x4x4_t broadcast_vector =
+ dup_quantized(broadcast_value, broadcast_qinfo.offset, broadcast_qinfo.scale);
+
+ int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x,
+ non_broadcast_input_ptr, broadcast_vector, output_ptr,
+ voffset_non_broadcast, vscale_non_broadcast, voffseto,
+ invvscaleo, !is_broadcast_input_2);
+ for (; x < window_end_x; ++x)
+ {
+ const float afs =
+ scvt_f32_qasymm8(*(non_broadcast_input_ptr + x), non_broadcast_qinfo.scale,
+ non_broadcast_qinfo.offset);
+ const float bfs =
+ scvt_f32_qasymm8(broadcast_value, broadcast_qinfo.scale, broadcast_qinfo.offset);
+ *(output_ptr + x) =
+ (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs,
+ out->info()->quantization_info());
+ }
+ },
+ broadcast_input, non_broadcast_input, output);
+ }
+ else
+ {
+ // Input1 quantization info
+ const int32x4_t voffset1 = vdupq_n_s32(in1->info()->quantization_info().offset);
+ const float32x4_t vscale1 = vdupq_n_f32(in1->info()->quantization_info().scale);
+
+ // Input2 quantization info
+ const int32x4_t voffset2 = vdupq_n_s32(in2->info()->quantization_info().offset);
+ const float32x4_t vscale2 = vdupq_n_f32(in2->info()->quantization_info().scale);
+
+ // Clear X Dimension on execution window as we handle manually
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ const QuantizationInfo input1_qinfo = in1->info()->quantization_info();
+ const QuantizationInfo input2_qinfo = in2->info()->quantization_info();
+
+ Iterator input1(in1, input1_win);
+ Iterator input2(in2, input2_win);
+ Iterator output(out, win);
+
+ execute_window_loop(
+ win,
+ [&](const Coordinates &) {
+ const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr());
+ const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
+ const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr());
+
+ int x =
+ (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr,
+ output_ptr, voffset1, voffset2, vscale1, vscale2, voffseto, invvscaleo);
+ for (; x < window_end_x; ++x)
+ {
+ const float afs =
+ scvt_f32_qasymm8(*(input1_ptr + x), input1_qinfo.scale, input1_qinfo.offset);
+ const float bfs =
+ scvt_f32_qasymm8(*(input2_ptr + x), input2_qinfo.scale, input2_qinfo.offset);
+ *(output_ptr + x) = (*scalar_func)(afs, bfs, out->info()->quantization_info());
+ }
+ },
+ input1, input2, output);
+ }
+}
+
+void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
+ float (*scalar_func)(const float &, const float &),
+ int (*broadcast_func)(int, int, int, const float *, const float &, float *,
+ const bool),
+ int (*neon_func)(int, int, int, const float *, const float *, float *))
+{
+ elementwise_op_templ<float, float, float32x4_t>(in1, in2, out, window, scalar_func,
+ broadcast_func, neon_func);
+}
+
+void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
+ uint8_t (*scalar_func)(const uint8_t &, const uint8_t &),
+ int (*broadcast_func)(int, int, int, const uint8_t *, const uint8_t &,
+ uint8_t *, const bool),
+ int (*neon_func)(int, int, int, const uint8_t *, const uint8_t *, uint8_t *))
+{
+ elementwise_op_templ<uint8_t, uint8_t, uint8x16_t>(in1, in2, out, window, scalar_func,
+ broadcast_func, neon_func);
+}
+} // namespace arm_compute
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
+#include "arm_compute/core/NEON/NEElementwiseOperationFuncs.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Validate.h"
return x;
}
-template <typename InputScalarType, typename OutputScalarType, typename InputVectorType>
-void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
- OutputScalarType (*scalar_func)(const InputScalarType &,
- const InputScalarType &),
- int (*broadcast_func)(int, int, int, const InputScalarType *,
- const InputScalarType &, OutputScalarType *, const bool),
- int (*neon_func)(int, int, int, const InputScalarType *,
- const InputScalarType *, OutputScalarType *))
-{
- // Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
-
- // Clear X Dimension on execution window as we handle manually
- Window win = window;
- win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- const int window_step_x = std::min(16 / static_cast<int>(sizeof(OutputScalarType)), 8);
- const auto window_start_x = static_cast<int>(window.x().start());
- const auto window_end_x = static_cast<int>(window.x().end());
- const bool is_broadcast_across_x = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);
-
- if (is_broadcast_across_x)
- {
- const bool is_broadcast_input_2 = input2_win.x().step() == 0;
- Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
- Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
-
- // Clear X Dimension on execution window as we handle manually
- non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator broadcast_input(broadcast_tensor, broadcast_win);
- Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator output(out, win);
-
- execute_window_loop(win,
- [&](const Coordinates &) {
- auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr());
- const auto non_broadcast_input_ptr =
- reinterpret_cast<const InputScalarType *>(non_broadcast_input.ptr());
- const InputScalarType broadcast_value =
- *reinterpret_cast<const InputScalarType *>(broadcast_input.ptr());
-
- int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x,
- non_broadcast_input_ptr, broadcast_value,
- output_ptr, !is_broadcast_input_2);
- for (; x < window_end_x; ++x)
- {
- const auto a = *(non_broadcast_input_ptr + x);
- *(output_ptr + x) =
- (*scalar_func)(!is_broadcast_input_2 ? broadcast_value : a,
- !is_broadcast_input_2 ? a : broadcast_value);
- }
- },
- broadcast_input, non_broadcast_input, output);
- }
- else
- {
- // Clear X Dimension on execution window as we handle manually
- input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
-
- execute_window_loop(win,
- [&](const Coordinates &) {
- auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr());
- const auto input1_ptr =
- reinterpret_cast<const InputScalarType *>(input1.ptr());
- const auto input2_ptr =
- reinterpret_cast<const InputScalarType *>(input2.ptr());
-
- int x = (*neon_func)(window_start_x, window_end_x, window_step_x,
- input1_ptr, input2_ptr, output_ptr);
- for (; x < window_end_x; ++x)
- {
- const auto a = *(input1_ptr + x);
- const auto b = *(input2_ptr + x);
- *(output_ptr + x) = (*scalar_func)(a, b);
- }
- },
- input1, input2, output);
- }
-}
-
template <BinaryLogicalOperation op, typename ScalarType, typename VectorType>
void elementwise_logic_op(const ITensor *in1, const ITensor *in2, ITensor *out,
const Window &window)
{
- elementwise_op<ScalarType, ScalarType, VectorType>(
- in1, in2, out, window, &elementwise_logic_op_scalar<op, ScalarType>,
- &elementwise_logic_op_broadcast_loop<op, ScalarType, VectorType>,
- &elementwise_logic_op_loop<op, ScalarType, VectorType>);
+ elementwise_op(in1, in2, out, window, &elementwise_logic_op_scalar<op, ScalarType>,
+ &elementwise_logic_op_broadcast_loop<op, ScalarType, VectorType>,
+ &elementwise_logic_op_loop<op, ScalarType, VectorType>);
}
std::function<void(const ITensor *, const ITensor *, ITensor *, const Window &)> configure_func(
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/NEAsymm.h"
+#include "arm_compute/core/NEON/NEElementwiseOperationFuncs.h"
#include "arm_compute/core/NEON/wrapper/wrapper.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Window.h"
PRELU, /**< (x * y) for x < 0, x for x >= 0 */
};
-// TODO Unify Elementwise ops if possible
-float32x4x4_t load_quantized(const uint8_t *input1_ptr, const int32x4_t &offset,
- const float32x4_t &scale)
-{
- qasymm8x16_t x = vld1q_u8(input1_ptr);
- const float32x4x4_t out = {{
- vmulq_f32(
- vcvtq_f32_s32(vsubq_s32(
- vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(x))))), offset)),
- scale),
- vmulq_f32(
- vcvtq_f32_s32(vsubq_s32(
- vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(x))))), offset)),
- scale),
- vmulq_f32(
- vcvtq_f32_s32(vsubq_s32(
- vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(x))))), offset)),
- scale),
- vmulq_f32(
- vcvtq_f32_s32(vsubq_s32(
- vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(x))))), offset)),
- scale),
- }};
- return out;
-}
-
-void store_quantized(uint8_t *output_ptr, const int32x4x4_t &out)
-{
- const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[0]), vqmovn_s32(out.val[1])));
- const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(out.val[2]), vqmovn_s32(out.val[3])));
- vst1q_u8(output_ptr, vcombine_u8(pa, pb));
-}
-
-void store_quantized(uint8_t *output_ptr, const float32x4x4_t &rf, const float32x4_t &offset,
- const float32x4_t &invscale)
-{
- int32x4x4_t out = {{
- vcvtq_s32_f32(vmlaq_f32(offset, rf.val[0], invscale)),
- vcvtq_s32_f32(vmlaq_f32(offset, rf.val[1], invscale)),
- vcvtq_s32_f32(vmlaq_f32(offset, rf.val[2], invscale)),
- vcvtq_s32_f32(vmlaq_f32(offset, rf.val[3], invscale)),
- }};
- store_quantized(output_ptr, out);
-}
-
-float32x4x4_t dup_quantized(qasymm8_t broadcast_value, int offset, float scale)
-{
- const qasymm8x16_t broadcast_value_vec = vdupq_n_u8(broadcast_value);
- const int32x4_t voffset = vdupq_n_s32(offset);
- const float32x4_t vscale = vdupq_n_f32(scale);
-
- const float32x4x4_t broadcast_vector = {{
- vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(
- vmovl_u8(vget_low_u8(broadcast_value_vec))))),
- voffset)),
- vscale),
- vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(
- vmovl_u8(vget_low_u8(broadcast_value_vec))))),
- voffset)),
- vscale),
- vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(
- vmovl_u8(vget_high_u8(broadcast_value_vec))))),
- voffset)),
- vscale),
- vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(
- vmovl_u8(vget_high_u8(broadcast_value_vec))))),
- voffset)),
- vscale),
- }};
- return broadcast_vector;
-}
-
template <ConditionalOperation op, typename ScalarType>
inline ScalarType elementwise_conditional_op_scalar(const ScalarType &a, const ScalarType &b)
{
return x;
}
-template <typename InputScalarType, typename OutputScalarType, typename InputVectorType>
-void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
- OutputScalarType (*scalar_func)(const InputScalarType &,
- const InputScalarType &),
- int (*broadcast_func)(int, int, int, const InputScalarType *,
- const InputScalarType &, OutputScalarType *, const bool),
- int (*neon_func)(int, int, int, const InputScalarType *,
- const InputScalarType *, OutputScalarType *))
-{
- // Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
-
- // Clear X Dimension on execution window as we handle manually
- Window win = window;
- win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- const int window_step_x = std::min(16 / static_cast<int>(sizeof(OutputScalarType)), 8);
- const auto window_start_x = static_cast<int>(window.x().start());
- const auto window_end_x = static_cast<int>(window.x().end());
- const bool is_broadcast_across_x = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);
-
- if (is_broadcast_across_x)
- {
- const bool is_broadcast_input_2 = input2_win.x().step() == 0;
- Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
- Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
-
- // Clear X Dimension on execution window as we handle manually
- non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator broadcast_input(broadcast_tensor, broadcast_win);
- Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator output(out, win);
-
- execute_window_loop(win,
- [&](const Coordinates &) {
- auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr());
- const auto non_broadcast_input_ptr =
- reinterpret_cast<const InputScalarType *>(non_broadcast_input.ptr());
- const InputScalarType broadcast_value =
- *reinterpret_cast<const InputScalarType *>(broadcast_input.ptr());
-
- int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x,
- non_broadcast_input_ptr, broadcast_value,
- output_ptr, !is_broadcast_input_2);
- for (; x < window_end_x; ++x)
- {
- const auto a = *(non_broadcast_input_ptr + x);
- *(output_ptr + x) =
- (*scalar_func)(!is_broadcast_input_2 ? broadcast_value : a,
- !is_broadcast_input_2 ? a : broadcast_value);
- }
- },
- broadcast_input, non_broadcast_input, output);
- }
- else
- {
- // Clear X Dimension on execution window as we handle manually
- input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
-
- execute_window_loop(win,
- [&](const Coordinates &) {
- auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr());
- const auto input1_ptr =
- reinterpret_cast<const InputScalarType *>(input1.ptr());
- const auto input2_ptr =
- reinterpret_cast<const InputScalarType *>(input2.ptr());
-
- int x = (*neon_func)(window_start_x, window_end_x, window_step_x,
- input1_ptr, input2_ptr, output_ptr);
- for (; x < window_end_x; ++x)
- {
- const auto a = *(input1_ptr + x);
- const auto b = *(input2_ptr + x);
- *(output_ptr + x) = (*scalar_func)(a, b);
- }
- },
- input1, input2, output);
- }
-}
-
-void elementwise_op_quantized(
- const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
- uint8_t (*scalar_func)(const float &, const float &, QuantizationInfo),
- int (*broadcast_func)(int, int, int, const uint8_t *, float32x4x4_t, uint8_t *, int32x4_t,
- float32x4_t, float32x4_t, float32x4_t, const bool),
- int (*neon_func)(int, int, int, const uint8_t *, const uint8_t *, uint8_t *, int32x4_t,
- int32x4_t, float32x4_t, float32x4_t, float32x4_t, float32x4_t))
-{
- // Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
-
- // Clear X Dimension on execution window as we handle manually
- Window win = window;
- win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- 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 bool is_broadcast_across_x = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);
-
- const float output_scale = out->info()->quantization_info().scale;
- const int output_offset = out->info()->quantization_info().offset;
-
- // Output quantization info (add 0.5 to round toward the nearest integer - 0.5 rounds away from
- // zero)
- const float32x4_t voffseto = vdupq_n_f32(output_offset + 0.5f);
- const float32x4_t invvscaleo = vdupq_n_f32(1.f / output_scale);
-
- if (is_broadcast_across_x)
- {
- // Select the broadcast input on the X axis
- const bool is_broadcast_input_2 = input2_win.x().step() == 0;
- Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
- Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
-
- const QuantizationInfo broadcast_qinfo = broadcast_tensor->info()->quantization_info();
- const QuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info();
-
- const int32x4_t voffset_non_broadcast = vdupq_n_s32(non_broadcast_qinfo.offset);
- const float32x4_t vscale_non_broadcast = vdupq_n_f32(non_broadcast_qinfo.scale);
-
- // Clear X Dimension on execution window as we handle manually
- non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Iterator broadcast_input(broadcast_tensor, broadcast_win);
- Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator output(out, win);
-
- execute_window_loop(
- win,
- [&](const Coordinates &) {
- const auto non_broadcast_input_ptr =
- reinterpret_cast<const uint8_t *>(non_broadcast_input.ptr());
- const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr());
-
- const uint8_t broadcast_value = *reinterpret_cast<const uint8_t *>(broadcast_input.ptr());
- const float32x4x4_t broadcast_vector =
- dup_quantized(broadcast_value, broadcast_qinfo.offset, broadcast_qinfo.scale);
-
- int x = (*broadcast_func)(window_start_x, window_end_x, window_step_x,
- non_broadcast_input_ptr, broadcast_vector, output_ptr,
- voffset_non_broadcast, vscale_non_broadcast, voffseto,
- invvscaleo, !is_broadcast_input_2);
- for (; x < window_end_x; ++x)
- {
- const float afs =
- scvt_f32_qasymm8(*(non_broadcast_input_ptr + x), non_broadcast_qinfo.scale,
- non_broadcast_qinfo.offset);
- const float bfs =
- scvt_f32_qasymm8(broadcast_value, broadcast_qinfo.scale, broadcast_qinfo.offset);
- *(output_ptr + x) =
- (*scalar_func)(!is_broadcast_input_2 ? bfs : afs, !is_broadcast_input_2 ? afs : bfs,
- out->info()->quantization_info());
- }
- },
- broadcast_input, non_broadcast_input, output);
- }
- else
- {
- // Input1 quantization info
- const int32x4_t voffset1 = vdupq_n_s32(in1->info()->quantization_info().offset);
- const float32x4_t vscale1 = vdupq_n_f32(in1->info()->quantization_info().scale);
-
- // Input2 quantization info
- const int32x4_t voffset2 = vdupq_n_s32(in2->info()->quantization_info().offset);
- const float32x4_t vscale2 = vdupq_n_f32(in2->info()->quantization_info().scale);
-
- // Clear X Dimension on execution window as we handle manually
- input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- const QuantizationInfo input1_qinfo = in1->info()->quantization_info();
- const QuantizationInfo input2_qinfo = in2->info()->quantization_info();
-
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
-
- execute_window_loop(
- win,
- [&](const Coordinates &) {
- const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr());
- const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
- const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr());
-
- int x =
- (*neon_func)(window_start_x, window_end_x, window_step_x, input1_ptr, input2_ptr,
- output_ptr, voffset1, voffset2, vscale1, vscale2, voffseto, invvscaleo);
- for (; x < window_end_x; ++x)
- {
- const float afs =
- scvt_f32_qasymm8(*(input1_ptr + x), input1_qinfo.scale, input1_qinfo.offset);
- const float bfs =
- scvt_f32_qasymm8(*(input2_ptr + x), input2_qinfo.scale, input2_qinfo.offset);
- *(output_ptr + x) = (*scalar_func)(afs, bfs, out->info()->quantization_info());
- }
- },
- input1, input2, output);
- }
-}
-
template <ConditionalOperation op, typename ScalarType, typename VectorType>
void elementwise_conditional_op(const ITensor *in1, const ITensor *in2, ITensor *out,
const Window &window)
{
- elementwise_op<ScalarType, ScalarType, VectorType>(
- in1, in2, out, window, &elementwise_conditional_op_scalar<op, ScalarType>,
- &elementwise_conditional_op_broadcast_loop<op, ScalarType, VectorType>,
- &elementwise_conditional_op_loop<op, ScalarType, VectorType>);
+ elementwise_op(in1, in2, out, window, &elementwise_conditional_op_scalar<op, ScalarType>,
+ &elementwise_conditional_op_broadcast_loop<op, ScalarType, VectorType>,
+ &elementwise_conditional_op_loop<op, ScalarType, VectorType>);
}
template <ConditionalOperation op>
projects / platform / core / ml / nnfw.git / commitdiff