| /* |
| * 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) */ |