diff options
Diffstat (limited to 'src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp | 172 |
1 files changed, 112 insertions, 60 deletions
diff --git a/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp b/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp index 84683ea69f..4466c24604 100644 --- a/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp +++ b/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp @@ -1018,33 +1018,115 @@ void mul_F32_F32_F32(const ITensor *in1, const ITensor *in2, ITensor *out, const } } -void c_mul_F32_F32_F32_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr) +void c_mul_F32_F32_F32_n(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window) { - const auto input1 = static_cast<const float *__restrict>(input1_ptr); - const auto input2 = static_cast<const float *__restrict>(input2_ptr); - const auto output = static_cast<float *__restrict>(output_ptr); + // 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)); + + constexpr int window_step_x = 8 / sizeof(float); + 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; - const float32x4_t a = wrapper::vloadq(input1); - float32x4_t b = wrapper::vloadq(input2); + // Clear X Dimension on execution window as we handle manually + non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); - using ExactTagType = typename wrapper::traits::neon_vector<float, 2>::tag_type; + Iterator broadcast_input(broadcast_tensor, broadcast_win); + Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); + Iterator output(out, win); - const float32x4_t mask = { -1.0f, 1.0f, -1.0f, 1.0f }; - const float32x2_t tmp00 = wrapper::vdup_n(wrapper::vgetlane(a, 0), ExactTagType{}); - const float32x2_t tmp01 = wrapper::vdup_n(wrapper::vgetlane(a, 1), ExactTagType{}); - const float32x2_t tmp10 = wrapper::vdup_n(wrapper::vgetlane(a, 2), ExactTagType{}); - const float32x2_t tmp11 = wrapper::vdup_n(wrapper::vgetlane(a, 3), ExactTagType{}); + execute_window_loop(win, [&](const Coordinates &) + { + const auto non_broadcast_input_ptr = reinterpret_cast<const float *>(non_broadcast_input.ptr()); + const auto output_ptr = reinterpret_cast<float *>(output.ptr()); - const float32x4_t tmp0 = wrapper::vcombine(tmp00, tmp10); - const float32x4_t tmp1 = wrapper::vcombine(tmp01, tmp11); + const float broadcast_value = *reinterpret_cast<const float *>(broadcast_input.ptr()); - float32x4_t res = wrapper::vmul(tmp0, b); + int x = window_start_x; + // Compute left-over elements + for(; x < window_end_x; ++x) + { + const auto broadcast_value0 = *(non_broadcast_input_ptr + 2 * x); + const auto broadcast_value1 = *(non_broadcast_input_ptr + 2 * x + 1); + auto res1 = broadcast_value * (broadcast_value0 - broadcast_value1); + auto res2 = broadcast_value * (broadcast_value1 + broadcast_value0); + *(output_ptr + 2 * x) = res1; + *(output_ptr + 2 * x + 1) = res2; + } + }, + 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)); - b = wrapper::vrev64(b); - b = wrapper::vmul(b, mask); + Iterator input1(in1, input1_win); + Iterator input2(in2, input2_win); + Iterator output(out, win); - res = wrapper::vmla(res, tmp1, b); - wrapper::vstore(output, res); + execute_window_loop(win, [&](const Coordinates &) + { + const auto input1_ptr = reinterpret_cast<const float *>(input1.ptr()); + const auto input2_ptr = reinterpret_cast<const float *>(input2.ptr()); + const auto output_ptr = reinterpret_cast<float *>(output.ptr()); + + using ExactTagType = typename wrapper::traits::neon_vector<float, 2>::tag_type; + + // 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 float32x4_t a = wrapper::vloadq(input1_ptr + 2 * x); + float32x4_t b = wrapper::vloadq(input2_ptr + 2 * x); + + const float32x4_t mask = { -1.0f, 1.0f, -1.0f, 1.0f }; + const float32x2_t tmp00 = wrapper::vdup_n(wrapper::vgetlane(a, 0), ExactTagType{}); + const float32x2_t tmp01 = wrapper::vdup_n(wrapper::vgetlane(a, 1), ExactTagType{}); + const float32x2_t tmp10 = wrapper::vdup_n(wrapper::vgetlane(a, 2), ExactTagType{}); + const float32x2_t tmp11 = wrapper::vdup_n(wrapper::vgetlane(a, 3), ExactTagType{}); + + const float32x4_t tmp0 = wrapper::vcombine(tmp00, tmp10); + const float32x4_t tmp1 = wrapper::vcombine(tmp01, tmp11); + + float32x4_t res = wrapper::vmul(tmp0, b); + + b = wrapper::vrev64(b); + b = wrapper::vmul(b, mask); + + res = wrapper::vmla(res, tmp1, b); + wrapper::vstore(output_ptr + 2 * x, res); + } + + // Compute left-over elements + for(; x < window_end_x; ++x) + { + const auto a0 = *(input1_ptr + 2 * x); + const auto a1 = *(input1_ptr + 2 * x + 1); + const auto b0 = *(input2_ptr + 2 * x); + const auto b1 = *(input2_ptr + 2 * x + 1); + auto res1 = a0 * b0 - a1 * b1; + auto res2 = a0 * b1 + a1 * b0; + *(output_ptr + 2 * x) = res1; + *(output_ptr + 2 * x + 1) = res2; + } + }, + input1, input2, output); + } } #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC @@ -1507,8 +1589,6 @@ void NEPixelWiseMultiplicationKernel::run_op(ITensorPack &tensors, const Window } namespace { -constexpr unsigned int num_elems_processed_per_iteration_complex = 2; - Status validate_arguments_complex(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 2, DataType::F32); @@ -1527,9 +1607,13 @@ Status validate_arguments_complex(const ITensorInfo *input1, const ITensorInfo * return Status{}; } +} // namespace -std::pair<Status, Window> validate_and_configure_window_complex(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output) +void NEComplexPixelWiseMultiplicationKernel::configure(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output) { + ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_complex(input1, input2, output)); + const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2); const TensorShape &out_shape = broadcast_pair.first; const ValidRegion &valid_region = broadcast_pair.second; @@ -1538,43 +1622,19 @@ std::pair<Status, Window> validate_and_configure_window_complex(ITensorInfo *inp const TensorInfo out_info(out_shape, input1->num_channels(), input1->data_type()); auto_init_if_empty(*output, out_info); - Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration_complex)); - Window win_input1 = win.broadcast_if_dimension_le_one(*input1); - Window win_input2 = win.broadcast_if_dimension_le_one(*input2); - - AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration_complex); - AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration_complex); - AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration_complex); - - bool window_changed = update_window_and_padding(win_input1, input1_access) - || update_window_and_padding(win_input2, input2_access) - || update_window_and_padding(win, output_access); - - output_access.set_valid_region(win, valid_region); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} -} // namespace - -void NEComplexPixelWiseMultiplicationKernel::configure(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_complex(input1, input2, output)); - // Configure kernel window - auto win_config = validate_and_configure_window_complex(input1, input2, output); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + Coordinates coord; + coord.set_num_dimensions(output->num_dimensions()); + output->set_valid_region(valid_region); + Window win = calculate_max_window(valid_region, Steps()); - // Create kernel - INEKernel::configure(win_config.second); + INEKernel::configure(win); } Status NEComplexPixelWiseMultiplicationKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) { ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_complex(input1, input2, output)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_complex(input1->clone().get(), input2->clone().get(), output->clone().get()).first); return Status{}; } @@ -1589,14 +1649,6 @@ void NEComplexPixelWiseMultiplicationKernel::run_op(ITensorPack &tensors, const auto input2 = tensors.get_const_tensor(TensorType::ACL_SRC_1); auto output = tensors.get_tensor(TensorType::ACL_DST); - Iterator input1_it(input1, window.broadcast_if_dimension_le_one(input1->info()->tensor_shape())); - Iterator input2_it(input2, window.broadcast_if_dimension_le_one(input2->info()->tensor_shape())); - Iterator output_it(output, window); - - execute_window_loop(window, [&](const Coordinates &) - { - c_mul_F32_F32_F32_n(input1_it.ptr(), input2_it.ptr(), output_it.ptr()); - }, - input1_it, input2_it, output_it); + c_mul_F32_F32_F32_n(input1, input2, output, window); } } // namespace arm_compute |