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/*
* Copyright (c) 2023 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.
*/
#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/TensorInfo.h"
#include "src/core/CPP/Validate.h"
#include "src/core/NEON/wrapper/wrapper.h"
#include "src/cpu/CpuTypes.h"
namespace arm_compute
{
namespace cpu
{
void mul_F16_F16_F16(const ITensor *src1, const ITensor *src2, ITensor *out, const Window &window, float scale)
{
// Create input windows
Window input1_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
Window input2_win = window.broadcast_if_dimension_le_one(src2->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));
constexpr 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 = src1->info()->tensor_shape().x() != src2->info()->tensor_shape().x();
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 ? src2 : src1;
const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src2 : src1;
// 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 dst(out, win);
execute_window_loop(
win,
[&](const Coordinates &)
{
const auto non_broadcast_input_ptr = reinterpret_cast<const float16_t *>(non_broadcast_input.ptr());
const auto output_ptr = reinterpret_cast<float16_t *>(dst.ptr());
const auto broadcast_value = *reinterpret_cast<const float16_t *>(broadcast_input.ptr());
const float16x8x2_t broadcast_value_vec = {{
vdupq_n_f16(broadcast_value),
vdupq_n_f16(broadcast_value),
}};
const auto scale_vec = vdupq_n_f16(scale);
// Compute window_step_x elements per iteration
int x = window_start_x;
for (; x <= (window_end_x - window_step_x); x += window_step_x)
{
const float16x8x2_t non_broadcast_v = {{
vld1q_f16(non_broadcast_input_ptr + x),
vld1q_f16(non_broadcast_input_ptr + x + 8),
}};
const float16x8x2_t result = {{
vmulq_f16(vmulq_f16(broadcast_value_vec.val[0], non_broadcast_v.val[0]), scale_vec),
vmulq_f16(vmulq_f16(broadcast_value_vec.val[1], non_broadcast_v.val[1]), scale_vec),
}};
vst1q_f16(output_ptr + x, result.val[0]);
vst1q_f16(output_ptr + x + 8, result.val[1]);
}
// Compute left-over elements
for (; x < window_end_x; ++x)
{
const auto non_broadcast_v = *(non_broadcast_input_ptr + x);
*(output_ptr + x) = broadcast_value * non_broadcast_v * scale;
}
},
broadcast_input, non_broadcast_input, dst);
}
else
{
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator input1(src1, input1_win);
Iterator input2(src2, input2_win);
Iterator dst(out, win);
execute_window_loop(
win,
[&](const Coordinates &)
{
const auto input1_ptr = reinterpret_cast<const float16_t *>(input1.ptr());
const auto input2_ptr = reinterpret_cast<const float16_t *>(input2.ptr());
const auto output_ptr = reinterpret_cast<float16_t *>(dst.ptr());
// Compute window_step_x elements per iteration
int x = window_start_x;
for (; x <= (window_end_x - window_step_x); x += window_step_x)
{
const float16x8x2_t ta1 = {{
vld1q_f16(input1_ptr + x),
vld1q_f16(input1_ptr + x + 8),
}};
const float16x8x2_t ta2 = {{
vld1q_f16(input2_ptr + x),
vld1q_f16(input2_ptr + x + 8),
}};
const float16x8_t scale_vec = vdupq_n_f16(scale);
const float16x8x2_t result = {{
vmulq_f16(vmulq_f16(ta1.val[0], ta2.val[0]), scale_vec),
vmulq_f16(vmulq_f16(ta1.val[1], ta2.val[1]), scale_vec),
}};
vst1q_f16(output_ptr + x, result.val[0]);
vst1q_f16(output_ptr + x + 8, result.val[1]);
}
// Compute left-over elements
for (; x < window_end_x; ++x)
{
const auto ta1 = *(input1_ptr + x);
const auto ta2 = *(input2_ptr + x);
*(output_ptr + x) = ta1 * ta2 * scale;
}
},
input1, input2, dst);
}
}
} // namespace cpu
} // namespace arm_compute
#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */
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