From cc1f6c94f1fc3b5d5ccbd5aa43e2a08487664f50 Mon Sep 17 00:00:00 2001 From: morgolock Date: Tue, 24 Mar 2020 09:26:48 +0000 Subject: MLCE-166: Add support for extracting indices in NEPoolingLayer 2x2 NCHW * Added initial support for pooling indices * Only supported for NCHW Poolsize 2 Change-Id: I92ce767e64fcc01aae89411064b4cb2be272a1e9 Signed-off-by: morgolock Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2927 Comments-Addressed: Arm Jenkins Reviewed-by: Georgios Pinitas Reviewed-by: Sang-Hoon Park Tested-by: Arm Jenkins --- src/core/NEON/kernels/NEPoolingLayerKernel.cpp | 230 +++++++++++++++++-------- 1 file changed, 155 insertions(+), 75 deletions(-) (limited to 'src/core/NEON/kernels') diff --git a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp index d6a3fadd33..fdbba815b4 100644 --- a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp +++ b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp @@ -123,7 +123,8 @@ inline void scale_vector_q16x8(bool exclude_padding, TVec &v, const Coordinates v = wrapper::vsetlane(elems[7], v, 7); } -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, unsigned int &pooled_w, unsigned int pooled_h) +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, + unsigned int &pooled_w, unsigned int pooled_h, const ITensorInfo *indices, Size2D pool_size) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); @@ -134,6 +135,11 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); + if(indices) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(indices, 1, DataType::U32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_type != PoolingType::MAX, "Pooling indices only supported for MAX pooling method"); + } ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON(pool_type == PoolingType::L2 && is_data_type_quantized(input->data_type())); ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_quantized(input->data_type()) && !pool_info.exclude_padding && (pool_info.pool_type == PoolingType::AVG) && pool_info.pad_stride_info.has_padding() @@ -146,6 +152,14 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); ARM_COMPUTE_RETURN_ERROR_ON((output->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH)) != pooled_w) || (output->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT)) != pooled_h)); + + if(indices) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_size != Size2D(2, 2)), "Pooling indices only supported for pool size 2x2"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() == DataLayout::NHWC, "Pool indices only supported in NCHW"); + ARM_COMPUTE_RETURN_ERROR_ON((indices->dimension(get_data_layout_dimension_index(indices->data_layout(), DataLayoutDimension::WIDTH)) != pooled_w) + || (indices->dimension(get_data_layout_dimension_index(indices->data_layout(), DataLayoutDimension::HEIGHT)) != pooled_h)); + } } return Status{}; @@ -159,13 +173,18 @@ Status validate_arguments_pool_info(const unsigned int pool_size_x, const unsign return Status{}; } -std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const PoolingLayerInfo &pool_info, unsigned int &num_elems_processed_per_iteration, - BorderSize &border_size, +std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *indices, const PoolingLayerInfo &pool_info, + unsigned int &num_elems_processed_per_iteration, + BorderSize &border_size, unsigned int pooled_w, unsigned int pooled_h, int pool_size_x, int pool_size_y) { // Output auto inizialitation if not yet initialized auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_pool_shape(*input, pool_info))); - + if(indices) + { + // Indices auto inizialitation if not yet initialized + auto_init_if_empty(*indices, (input->clone()->set_tensor_shape(compute_pool_shape(*input, pool_info))).set_data_type(DataType::U32) /* we store the offset to the element */); + } const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->data_layout() : pool_info.data_layout; unsigned int num_elems_read_per_iteration = 0; unsigned int num_elems_horizontal_window = 0; @@ -286,25 +305,28 @@ std::pair validate_and_configure_window(ITensorInfo *input, ITen { // Number of iterations in X dimension const int num_iterations_x = (pooled_w + num_elems_processed_per_iteration - 1) / num_elems_processed_per_iteration; - // Upper limit for the number of right/bottom border elements that are accessed const int upper_bound_w = ((num_iterations_x - 1) * num_elems_processed_per_iteration * pool_stride_x - pool_pad_left + num_elems_read_per_iteration) - input_width; const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_top + pool_size_y) - input_height; - - border_size = BorderSize(pool_pad_top, pool_pad_right, pool_pad_bottom, pool_pad_left); - border_size.right = std::max(upper_bound_w, pool_pad_right); - border_size.bottom = std::max(upper_bound_h, pool_pad_bottom); - + border_size = BorderSize(pool_pad_top, pool_pad_right, pool_pad_bottom, pool_pad_left); + border_size.right = std::max(upper_bound_w, pool_pad_right); + border_size.bottom = std::max(upper_bound_h, pool_pad_bottom); TensorShape output_shape{ input->tensor_shape() }; output_shape.set(0, pooled_w); output_shape.set(1, pooled_h); TensorInfo output_info(input->clone()->set_tensor_shape(output_shape)); - win = calculate_max_window(output_info, Steps(num_elems_processed_per_iteration)); - AccessWindowStatic input_access(input, -pool_pad_left, -pool_pad_top, input_width + border_size.right, input_height + border_size.bottom); - + AccessWindowStatic input_access(input, -pool_pad_left, -pool_pad_top, input_width + border_size.right, input_height + border_size.bottom); AccessWindowHorizontal output_access(output, 0, num_elems_horizontal_window); - window_changed = update_window_and_padding(win, input_access, output_access); + if(indices) + { + AccessWindowHorizontal indices_access(indices, 0, num_elems_horizontal_window); + window_changed = update_window_and_padding(win, input_access, output_access, indices_access); + } + else + { + window_changed = update_window_and_padding(win, input_access, output_access); + } output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); } else @@ -313,12 +335,18 @@ std::pair validate_and_configure_window(ITensorInfo *input, ITen output_shape.set(1, pooled_w); output_shape.set(2, pooled_h); TensorInfo output_info(input->clone()->set_tensor_shape(output_shape)); - win = calculate_max_window(output_info, Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); - window_changed = update_window_and_padding(win, input_access, output_access); + if(indices) + { + AccessWindowHorizontal indices_access(indices, 0, num_elems_processed_per_iteration); + window_changed = update_window_and_padding(win, input_access, output_access, indices_access); + } + else + { + window_changed = update_window_and_padding(win, input_access, output_access); + } output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); } @@ -438,7 +466,7 @@ inline int8x8_t vrequantize_pooling(int8x8_t &vec, const UniformQuantizationInfo } // namespace NEPoolingLayerKernel::NEPoolingLayerKernel() - : _func(nullptr), _input(nullptr), _output(nullptr), _pool_info(), _data_layout(DataLayout::UNKNOWN), _num_elems_processed_per_iteration(0), _border_size(0), _is_square(false) + : _func(nullptr), _input(nullptr), _output(nullptr), _indices(nullptr), _pool_info(), _data_layout(DataLayout::UNKNOWN), _num_elems_processed_per_iteration(0), _border_size(0), _is_square(false) { } @@ -447,10 +475,9 @@ BorderSize NEPoolingLayerKernel::border_size() const return _border_size; } -void NEPoolingLayerKernel::configure(const ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info) +void NEPoolingLayerKernel::configure(const ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info, ITensor *indices) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - const PadStrideInfo pad_stride_info = pool_info.pad_stride_info; const bool is_global_pooling = pool_info.is_global_pooling; const int pool_stride_x = pad_stride_info.stride().first; @@ -478,11 +505,12 @@ void NEPoolingLayerKernel::configure(const ITensor *input, ITensor *output, cons pad_stride_info); // Perform validation step - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info, pooled_w, pooled_h)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info, pooled_w, pooled_h, (indices) ? indices->info() : nullptr, pool_size)); // Set instance variables _input = input; _output = output; + _indices = indices; _pool_info = pool_info; _data_layout = input->info()->data_layout(); _is_square = (pool_size.x() == pool_size.y()); @@ -690,7 +718,8 @@ void NEPoolingLayerKernel::configure(const ITensor *input, ITensor *output, cons } // Configure kernel window - auto win_config = validate_and_configure_window(input->info(), output->info(), pool_info, _num_elems_processed_per_iteration, _border_size, pooled_w, pooled_h, pool_size.x(), pool_size.y()); + auto win_config = validate_and_configure_window(input->info(), output->info(), (indices) ? indices->info() : nullptr, + pool_info, _num_elems_processed_per_iteration, _border_size, pooled_w, pooled_h, pool_size.x(), pool_size.y()); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); INEKernel::configure(win_config.second); } @@ -1435,7 +1464,6 @@ void NEPoolingLayerKernel::poolingMxN_f32_nchw(const Window &window_input, const res = std::max(res, data); } } - #if defined(__aarch64__) // Reduction operation available on 64 bit architectures only res = std::max(vmaxvq_f32(vres), res); @@ -1459,66 +1487,117 @@ void NEPoolingLayerKernel::poolingMxN_f32_nchw(const Window &window_input, const input, output); } -void NEPoolingLayerKernel::pooling2_f32_nchw(const Window &window_input, const Window &window, PoolingType pooling_type, bool exclude_padding) +void NEPoolingLayerKernel::pooling2_f32_nchw_maxpool_indices(const Window &window_input, const Window &window) { - Iterator input(_input, window_input); - Iterator output(_output, window); - - constexpr int pool_size = 2; - const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); - const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); - const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); - const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); - int pool_stride_x = 0; - int pool_stride_y = 0; + Iterator input(_input, window_input); + Iterator output(_output, window); + Iterator indices(_indices, window); + int final_index = 0; + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + int pool_stride_x = 0; + int pool_stride_y = 0; std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); - const int upper_bound_w = _input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); - const int upper_bound_h = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); - const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top))); const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + 1)); - execute_window_loop(window, [&](const Coordinates & id) + const Strides &input_strides = _input->info()->strides_in_bytes(); + const auto in_stridew = input_strides[1]; + + execute_window_loop(window, [&](const Coordinates &) { - float32x2_t top_data = vld1_f32(reinterpret_cast(input_top_ptr + input.offset())); - float32x2_t bottom_data = vld1_f32(reinterpret_cast(input_bottom_ptr + input.offset())); - float32x2_t res = {}; - float final_res = 0; + const auto input_offset_top = input_top_ptr + input.offset(); + const auto input_offset_bottom = input_bottom_ptr + input.offset(); + const auto in_top_ptr = reinterpret_cast(input_offset_top); + const auto in_bottom_ptr = reinterpret_cast(input_offset_bottom); + float32x2_t top_data = vld1_f32(in_top_ptr); + float32x2_t bottom_data = vld1_f32(in_bottom_ptr); + float32x2_t res = {}; + float final_res = 0; + const float32x2_t max_data = vmax_f32(top_data, bottom_data); + res = vpmax_f32(max_data, max_data); + final_res = vget_lane_f32(res, 0); + // Store result + *(reinterpret_cast(output.ptr())) = final_res; + const uint32_t offset_top = (uint32_t)(input.offset() / sizeof(float)); + const uint32_t offset_bottom = (uint32_t)offset_top + (in_stridew / sizeof(float)); + const uint32x2_t voffset_top = { offset_top, offset_top + 1u }; + const uint32x2_t voffset_bottom = { offset_bottom, offset_bottom + 1u }; + const uint32x2_t tmp_indices = vbsl_u32(vcgt_f32(top_data, bottom_data), voffset_top, voffset_bottom); + final_index = vget_lane_u32(vbsl_u32(vcgt_f32(max_data, vrev64_f32(max_data)), tmp_indices, vrev64_u32(tmp_indices)), 0); + *(reinterpret_cast(indices.ptr())) = final_index; + }, + input, output, indices); +} - // Get power of 2 in case of l2 pooling - if(pooling_type == PoolingType::L2) +void NEPoolingLayerKernel::pooling2_f32_nchw(const Window &window_input, const Window &window, PoolingType pooling_type, + bool exclude_padding) +{ + if(pooling_type == PoolingType::MAX && _indices) + { + pooling2_f32_nchw_maxpool_indices(window_input, window); + } + else + { + Iterator input(_input, window_input); + Iterator output(_output, window); + constexpr int pool_size = 2; + const int pool_pad_right = _pool_info.pad_stride_info.pad_right(); + const int pool_pad_top = _pool_info.pad_stride_info.pad_top(); + const int pool_pad_left = _pool_info.pad_stride_info.pad_left(); + const int pool_pad_bottom = _pool_info.pad_stride_info.pad_bottom(); + int pool_stride_x = 0; + int pool_stride_y = 0; + std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); + const int upper_bound_w = _input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_right); + const int upper_bound_h = _input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_bottom); + + const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top))); + const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast(pool_pad_left), -static_cast(pool_pad_top) + 1)); + + execute_window_loop(window, [&](const Coordinates & id) { - top_data = vmul_f32(top_data, top_data); - bottom_data = vmul_f32(bottom_data, bottom_data); - } + const auto in_top_ptr = reinterpret_cast(input_top_ptr + input.offset()); + const auto in_bottom_ptr = reinterpret_cast(input_bottom_ptr + input.offset()); + float32x2_t top_data = vld1_f32(in_top_ptr); + float32x2_t bottom_data = vld1_f32(in_bottom_ptr); + float32x2_t res = {}; + float final_res = 0; + // Get power of 2 in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + top_data = vmul_f32(top_data, top_data); + bottom_data = vmul_f32(bottom_data, bottom_data); + } - if(pooling_type != PoolingType::MAX) - { - // Calculate scale - float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); - const float32x2_t scale_v = vdup_n_f32(scale); + if(pooling_type != PoolingType::MAX) + { + // Calculate scale + float scale = calculate_avg_scale(exclude_padding, DataLayout::NCHW, id, pool_size, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y); + const float32x2_t scale_v = vdup_n_f32(scale); - // Perform pooling - const float32x2_t sum_data = vadd_f32(top_data, bottom_data); - res = vmul_f32(vpadd_f32(sum_data, sum_data), scale_v); - } - else - { - const float32x2_t max_data = vmax_f32(top_data, bottom_data); - res = vpmax_f32(max_data, max_data); - } - final_res = vget_lane_f32(res, 0); + // Perform pooling + const float32x2_t sum_data = vadd_f32(top_data, bottom_data); + res = vmul_f32(vpadd_f32(sum_data, sum_data), scale_v); + } + else + { + const float32x2_t max_data = vmax_f32(top_data, bottom_data); + res = vpmax_f32(max_data, max_data); + } + final_res = vget_lane_f32(res, 0); - // Calculate square-root in case of l2 pooling - if(pooling_type == PoolingType::L2) - { - final_res = sqrt(final_res); - } + // Calculate square-root in case of l2 pooling + if(pooling_type == PoolingType::L2) + { + final_res = sqrt(final_res); + } - // Store result - *(reinterpret_cast(output.ptr())) = final_res; - }, - input, output); + // Store result + *(reinterpret_cast(output.ptr())) = final_res; + }, + input, output); + } } void NEPoolingLayerKernel::pooling3_f32_nchw(const Window &window_input, const Window &window, PoolingType pooling_type, bool exclude_padding) @@ -2001,7 +2080,7 @@ void NEPoolingLayerKernel::poolingMxN_q8_nhwc(const Window &window_input, const input, output); } -Status NEPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info) +Status NEPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, const ITensorInfo *indices) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); @@ -2032,8 +2111,9 @@ Status NEPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInf pool_size_y, pool_info.pad_stride_info); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info, pooled_w, pooled_h)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), pool_info, num_elems_processed_per_iteration, border_size, pooled_w, pooled_h, + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info, pooled_w, pooled_h, indices, Size2D(pool_size_x, pool_size_y))); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), + (indices) ? indices->clone().get() : nullptr, pool_info, num_elems_processed_per_iteration, border_size, pooled_w, pooled_h, pool_size_x, pool_size_y) .first); @@ -2094,4 +2174,4 @@ void NEPoolingLayerKernel::run(const Window &window, const ThreadInfo &info) // Run function (this->*_func)(window_input, window, _pool_info.pool_type, exclude_padding); } -} // namespace arm_compute \ No newline at end of file +} // namespace arm_compute -- cgit v1.2.1