From e4558b501bc4a8e4e731517916a29fb1594d2a78 Mon Sep 17 00:00:00 2001 From: Sang-Hoon Park Date: Thu, 1 Oct 2020 10:13:07 +0100 Subject: COMPMID-3163: Remove padding from NEDepthwiseConvolutionLayerNativeKernel Change-Id: Ibbd6bee5c6a4ce4f212b207d17a65b9c33bcfa78 Signed-off-by: Sang-Hoon Park Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4106 Comments-Addressed: Arm Jenkins Reviewed-by: Michele Di Giorgio Tested-by: Arm Jenkins --- .../NEDepthwiseConvolutionLayerNativeKernel.cpp | 597 ++++++++++++--------- 1 file changed, 358 insertions(+), 239 deletions(-) (limited to 'src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp') diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp index fb47879b17..0a34ee6a07 100644 --- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp +++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.cpp @@ -36,46 +36,103 @@ namespace arm_compute { namespace { -void pad_vectors(std::vector &mult, std::vector &shift, int vec_size) +constexpr auto data_layout = DataLayout::NHWC; +const size_t batch_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); +const size_t width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); +const size_t height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); +const size_t channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); + +constexpr auto dim_manual_loop = Window::Dimension(0, 0, 0); +constexpr auto dim_single_unit_step = Window::Dimension(0, 1, 1); +constexpr size_t vector_size = 8; + +struct DepthwiseConvolutionRunInfo { - ARM_COMPUTE_ERROR_ON(mult.size() != shift.size()); - while(mult.size() % vec_size != 0) +public: + const size_t num_read_elements_per_iteration; + const uint32_t x_start; + const uint32_t x_end; + const uint32_t x_step; + const uint32_t x_leftover_start; + const size_t input_stride_y; + const size_t input_stride_z; + const size_t input_max_offset; + const size_t weights_width; + const size_t weights_height; + const size_t weights_stride_y; + const size_t weights_stride_z; + const size_t conv_stride_x; + const size_t conv_stride_y; + const size_t conv_pad_left; + const size_t conv_pad_top; + const size_t input_height; + const size_t input_width; + const size_t input_depth; + + DepthwiseConvolutionRunInfo(const ITensorInfo &input, const ITensorInfo &weights, const PadStrideInfo &conv_info, const Window &w, uint32_t depth_multiplier = 1) + : num_read_elements_per_iteration((depth_multiplier == 1 ? (vector_size / element_size_from_data_type(input.data_type())) : 1)), + x_start(w.x().start()), + x_end(w.x().end()), + x_step(static_cast(num_read_elements_per_iteration * depth_multiplier)), + x_leftover_start(std::max(static_cast(w.x().end()) - static_cast(x_step) + 1, int32_t(0))), + input_stride_y(input.strides_in_bytes().y()), + input_stride_z(input.strides_in_bytes().z()), + input_max_offset(input.strides_in_bytes().z() * input.dimension(height_idx) - (input.padding().bottom + input.padding().top) * input.strides_in_bytes().y()), + weights_width(weights.dimension(width_idx)), + weights_height(weights.dimension(height_idx)), + weights_stride_y(weights.strides_in_bytes().y()), + weights_stride_z(weights.strides_in_bytes().z()), + conv_stride_x(conv_info.stride().first), + conv_stride_y(conv_info.stride().second), + conv_pad_left(conv_info.pad_left()), + conv_pad_top(conv_info.pad_top()), + input_height(input.dimension(height_idx)), + input_width(input.dimension(width_idx)), + input_depth(input.dimension(channel_idx)) { - mult.push_back(0); - shift.push_back(0); } +}; + +inline bool is_valid_input_region(int32_t base_w, uint32_t base_h, uint32_t w, uint32_t h, const DepthwiseConvolutionRunInfo &run_info, const Size2D &dilation) +{ + const int32_t current_h = base_h + h * dilation.y(); + const bool is_valid_h = current_h >= 0 && current_h < static_cast(run_info.input_height); + + const int32_t current_w = base_w + w * dilation.x(); + const bool is_valid_w = current_w >= 0 && current_w < static_cast(run_info.input_width); + + return is_valid_h && is_valid_w; } -template +template void depthwise_loop_multiplier1_fp(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const Size2D &dilation, const Window &window, bool has_biases) { - using VectorType = typename wrapper::traits::neon_vector::type; - using TagType = typename wrapper::traits::neon_vector::tag_type; - - const size_t input_stride_y = input->info()->strides_in_bytes().y(); - const size_t input_stride_z = input->info()->strides_in_bytes().z(); - const size_t input_max_offset = input->info()->strides_in_bytes().z() * input->info()->dimension(2) - (input->info()->padding().bottom + input->info()->padding().top) * - input->info()->strides_in_bytes().y(); - const size_t weights_width = weights->info()->dimension(1); - const size_t weights_height = weights->info()->dimension(2); - const size_t weights_stride_y = weights->info()->strides_in_bytes().y(); - const size_t weights_stride_z = weights->info()->strides_in_bytes().z(); - const size_t conv_stride_x = conv_info.stride().first; - const size_t conv_stride_y = conv_info.stride().second; - const size_t conv_pad_left = conv_info.pad_left(); - const size_t conv_pad_top = conv_info.pad_top(); + constexpr auto element_per_vector = vector_size / sizeof(T); + using VectorType = typename wrapper::traits::neon_vector::type; + using TagType = typename wrapper::traits::neon_vector::tag_type; + + const auto run_info = DepthwiseConvolutionRunInfo(*input->info(), *weights->info(), conv_info, window); + + const VectorType zero_vector = wrapper::vdup_n(static_cast(0), TagType{}); + + Window execution_window = window; + execution_window.set(Window::DimX, dim_single_unit_step); Window win_input = window; - win_input.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_input.set(Window::DimZ, Window::Dimension(0, 0, 0)); + win_input.set(Window::DimX, dim_manual_loop); + win_input.set(Window::DimY, dim_manual_loop); + win_input.set(Window::DimZ, dim_manual_loop); Window win_weights = win_input; - win_weights.set(3, Window::Dimension(0, 0, 0)); + win_weights.set(Window::DimW, dim_manual_loop); + + Window win_output = window; + win_output.set(Window::DimX, dim_manual_loop); Iterator input_it(input, win_input); Iterator weights_it(weights, win_weights); - Iterator output_it(output, window); + Iterator output_it(output, win_output); Iterator biases_it{}; if(has_biases) @@ -83,38 +140,80 @@ void depthwise_loop_multiplier1_fp(const ITensor *input, const ITensor *weights, biases_it = Iterator(biases, win_weights); } - execute_window_loop(window, [&](const Coordinates & id) + execute_window_loop(execution_window, [&](const Coordinates & id) { - VectorType acc = wrapper::vdup_n(static_cast(0), TagType{}); + const int32_t input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; + const int32_t input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; + const int64_t base_input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; - const int input_y = id.y() * conv_stride_x - conv_pad_left; - const int input_z = id.z() * conv_stride_y - conv_pad_top; - int input_offset = input_y * input_stride_y + input_z * input_stride_z; + auto const base_weights_ptr = weights_it.ptr(); + uint32_t x = run_info.x_start; - auto weights_ptr = weights_it.ptr(); - for(size_t h = 0; h < weights_height; ++h) + for(; x < run_info.x_leftover_start; x += run_info.x_step) { - int offs = input_offset; - for(size_t w = 0; w < weights_width; ++w) + VectorType acc = zero_vector; + auto weights_ptr = base_weights_ptr; + int64_t input_offset = base_input_offset; + + for(uint32_t h = 0; h < run_info.weights_height; ++h) { - const auto input_vals = wrapper::vload(reinterpret_cast(input_it.ptr() + std::min(static_cast(offs), input_max_offset))); - const auto weights_vals = wrapper::vload(reinterpret_cast(weights_ptr + w * weights_stride_y)); + int64_t offs = input_offset + x * sizeof(T); + for(uint32_t w = 0; w < run_info.weights_width; ++w) + { + const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); + const auto input_vals = is_valid_region ? + wrapper::vload(reinterpret_cast(input_it.ptr() + std::min(static_cast(offs), run_info.input_max_offset))) : + zero_vector; + const auto weights_vals = wrapper::vload(reinterpret_cast(weights_ptr + w * run_info.weights_stride_y) + x); + acc = wrapper::vmla(acc, weights_vals, input_vals); + + offs += dilation.x() * run_info.input_stride_y; + } + + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; + } - acc = wrapper::vmla(acc, weights_vals, input_vals); - offs += dilation.x() * input_stride_y; + if(has_biases) + { + const auto biases_vals = wrapper::vload(reinterpret_cast(biases_it.ptr()) + x); + acc = wrapper::vadd(acc, biases_vals); } - weights_ptr += weights_stride_z; - input_offset += dilation.y() * input_stride_z; + wrapper::vstore(reinterpret_cast(output_it.ptr()) + x, acc); } - if(has_biases) + for(; x < run_info.x_end; ++x) { - const auto biases_vals = wrapper::vload(reinterpret_cast(biases_it.ptr())); - acc = wrapper::vadd(acc, biases_vals); - } + auto acc_scalar = T{ 0 }; + auto weights_ptr = base_weights_ptr; + int64_t input_offset = base_input_offset; + + for(size_t h = 0; h < run_info.weights_height; ++h) + { + int64_t offs = input_offset + x * sizeof(T); + for(size_t w = 0; w < run_info.weights_width; ++w) + { + const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); + const auto input_vals = is_valid_region ? *reinterpret_cast(input_it.ptr() + std::min(static_cast(offs), run_info.input_max_offset)) : 0; + const auto weights_vals = *(reinterpret_cast(weights_ptr + w * run_info.weights_stride_y) + x); + + acc_scalar += (input_vals * weights_vals); + + offs += dilation.x() * run_info.input_stride_y; + } + + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; + } - wrapper::vstore(reinterpret_cast(output_it.ptr()), acc); + if(has_biases) + { + const auto biases_vals = *(reinterpret_cast(biases_it.ptr()) + x); + acc_scalar += biases_vals; + } + *(reinterpret_cast(output_it.ptr()) + x) = acc_scalar; + } }, input_it, weights_it, biases_it, output_it); } @@ -123,31 +222,28 @@ template void depthwise_loop_generic_fp(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const Size2D &dilation, unsigned int depth_multiplier, const Window &window, bool has_biases) { - const size_t input_stride_y = input->info()->strides_in_bytes().y(); - const size_t input_stride_z = input->info()->strides_in_bytes().z(); - const size_t input_max_offset = input->info()->strides_in_bytes().z() * input->info()->dimension(2) - (input->info()->padding().bottom + input->info()->padding().top) * - input->info()->strides_in_bytes().y(); - const size_t weights_width = weights->info()->dimension(1); - const size_t weights_height = weights->info()->dimension(2); - const size_t weights_stride_y = weights->info()->strides_in_bytes().y(); - const size_t weights_stride_z = weights->info()->strides_in_bytes().z(); - const size_t conv_stride_x = conv_info.stride().first; - const size_t conv_stride_y = conv_info.stride().second; - const size_t conv_pad_left = conv_info.pad_left(); - const size_t conv_pad_top = conv_info.pad_top(); + const auto run_info = DepthwiseConvolutionRunInfo(*input->info(), *weights->info(), conv_info, window, depth_multiplier); - Window win_input = window; - win_input.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_input.set(Window::DimZ, Window::Dimension(0, 0, 0)); + Window execution_window = window; + execution_window.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); - Window win_weights = win_input; - win_weights.set(3, Window::Dimension(0, 0, 0)); + Window win_input = execution_window; + win_input.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); + win_input.set(Window::DimY, dim_manual_loop); + win_input.set(Window::DimZ, dim_manual_loop); - win_input.set_dimension_step(Window::DimX, 1); + Window win_weights = window; + win_weights.set_dimension_step(Window::DimX, run_info.x_step); + win_weights.set(Window::DimY, dim_manual_loop); + win_weights.set(Window::DimZ, dim_manual_loop); + win_weights.set(Window::DimW, dim_manual_loop); + + Window win_output = window; + win_output.set_dimension_step(Window::DimX, run_info.x_step); Iterator input_it(input, win_input); Iterator weights_it(weights, win_weights); - Iterator output_it(output, window); + Iterator output_it(output, win_output); Iterator biases_it{}; if(has_biases) @@ -155,33 +251,34 @@ void depthwise_loop_generic_fp(const ITensor *input, const ITensor *weights, con biases_it = Iterator(biases, win_weights); } - execute_window_loop(window, [&](const Coordinates & id) + execute_window_loop(execution_window, [&](const Coordinates & id) { std::vector acc(depth_multiplier, static_cast(0)); - const int input_y = id.y() * conv_stride_x - conv_pad_left; - const int input_z = id.z() * conv_stride_y - conv_pad_top; - int input_offset = input_y * input_stride_y + input_z * input_stride_z; + const int input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; + const int input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; + int input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; auto weights_ptr = weights_it.ptr(); - for(size_t h = 0; h < weights_height; ++h) + for(size_t h = 0; h < run_info.weights_height; ++h) { int offs = input_offset; - for(size_t w = 0; w < weights_width; ++w) + for(size_t w = 0; w < run_info.weights_width; ++w) { - const auto input_val = *(reinterpret_cast(input_it.ptr() + std::min(static_cast(offs), input_max_offset))); + const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); + const auto input_val = is_valid_region ? *(reinterpret_cast(input_it.ptr() + std::min(static_cast(offs), run_info.input_max_offset))) : T(0); for(size_t m = 0; m < depth_multiplier; ++m) { - const auto weights_val = *(reinterpret_cast(weights_ptr + m * sizeof(T) + w * weights_stride_y)); + const auto weights_val = *(reinterpret_cast(weights_ptr + m * sizeof(T) + w * run_info.weights_stride_y)); acc.at(m) = support::cpp11::fma(weights_val, input_val, acc.at(m)); } - offs += dilation.x() * input_stride_y; + offs += dilation.x() * run_info.input_stride_y; } - weights_ptr += weights_stride_z; - input_offset += dilation.y() * input_stride_z; + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; } if(has_biases) @@ -203,41 +300,43 @@ void depthwise_loop_generic_fp(const ITensor *input, const ITensor *weights, con input_it, weights_it, biases_it, output_it); } -template +template void depthwise_loop_multiplier1_quantized(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const Size2D &dilation, std::vector output_multiplier, std::vector output_shift, const Window &window, bool has_biases) { - using VectorType = typename wrapper::traits::neon_vector::type; - using TagType = typename wrapper::traits::neon_vector::tag_type; - - const size_t input_stride_y = input->info()->strides_in_bytes().y(); - const size_t input_stride_z = input->info()->strides_in_bytes().z(); - const size_t input_max_offset = input->info()->strides_in_bytes().z() * input->info()->dimension(2) - (input->info()->padding().bottom + input->info()->padding().top) * - input->info()->strides_in_bytes().y(); - const size_t weights_width = weights->info()->dimension(1); - const size_t weights_height = weights->info()->dimension(2); - const size_t weights_stride_y = weights->info()->strides_in_bytes().y(); - const size_t weights_stride_z = weights->info()->strides_in_bytes().z(); - const size_t conv_stride_x = conv_info.stride().first; - const size_t conv_stride_y = conv_info.stride().second; - const size_t conv_pad_left = conv_info.pad_left(); - const size_t conv_pad_top = conv_info.pad_top(); + constexpr auto element_per_vector = vector_size / sizeof(T); + using VectorType = typename wrapper::traits::neon_vector::type; + using TagType = typename wrapper::traits::neon_vector::tag_type; + using AccType = int32_t; + using AccArrayType = std::array; + + const auto out_of_bound_value = PixelValue(static_cast(0), input->info()->data_type(), input->info()->quantization_info()).get(); + const auto out_of_bound_vector = wrapper::vdup_n(static_cast(out_of_bound_value), TagType{}); + + const auto run_info = DepthwiseConvolutionRunInfo(*input->info(), *weights->info(), conv_info, window); const int32_t input_qoffset = input->info()->quantization_info().uniform().offset; const int32_t weights_qoffset = weights->info()->quantization_info().uniform().offset; const int32_t output_qoffset = output->info()->quantization_info().uniform().offset; - const int32_t k_offset = weights_width * weights_height * input_qoffset * weights_qoffset; + const int32_t k_offset = run_info.weights_width * run_info.weights_height * input_qoffset * weights_qoffset; + + Window execution_window = window; + execution_window.set(Window::DimX, dim_single_unit_step); Window win_input = window; - win_input.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_input.set(Window::DimZ, Window::Dimension(0, 0, 0)); + win_input.set(Window::DimX, dim_manual_loop); + win_input.set(Window::DimY, dim_manual_loop); + win_input.set(Window::DimZ, dim_manual_loop); Window win_weights = win_input; - win_weights.set(3, Window::Dimension(0, 0, 0)); + win_weights.set(Window::DimW, dim_manual_loop); + + Window win_output = window; + win_output.set(Window::DimX, dim_manual_loop); Iterator input_it(input, win_input); Iterator weights_it(weights, win_weights); - Iterator output_it(output, window); + Iterator output_it(output, win_output); Iterator biases_it{}; if(has_biases) @@ -245,65 +344,134 @@ void depthwise_loop_multiplier1_quantized(const ITensor *input, const ITensor *w biases_it = Iterator(biases, win_weights); } - execute_window_loop(window, [&](const Coordinates & id) + execute_window_loop(execution_window, [&](const Coordinates & id) { - std::vector acc(S, 0); - std::vector in_sum(S, 0); - std::vector we_sum(S, 0); + const int32_t input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; + const int32_t input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; + const int64_t base_input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; + auto const base_weights_ptr = weights_it.ptr(); + size_t x = run_info.x_start; - const int input_y = id.y() * conv_stride_x - conv_pad_left; - const int input_z = id.z() * conv_stride_y - conv_pad_top; - int input_offset = input_y * input_stride_y + input_z * input_stride_z; - - auto weights_ptr = weights_it.ptr(); - for(size_t h = 0; h < weights_height; ++h) + for(; x < run_info.x_leftover_start; x += run_info.x_step) { - int offs = input_offset; - for(size_t w = 0; w < weights_width; ++w) + AccArrayType acc{}; + AccArrayType in_sum{}; + AccArrayType we_sum{}; + + auto weights_ptr = base_weights_ptr; + auto input_offset = base_input_offset; + + for(size_t h = 0; h < run_info.weights_height; ++h) + { + int64_t offs = input_offset + x * sizeof(T); + for(size_t w = 0; w < run_info.weights_width; ++w) + { + const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); + const auto input_vals = is_valid_region ? + wrapper::vload(reinterpret_cast(input_it.ptr() + std::min(static_cast(offs), run_info.input_max_offset))) : + out_of_bound_vector; + const auto weights_vals = wrapper::vload(reinterpret_cast(weights_ptr + w * run_info.weights_stride_y) + x); + + for(size_t i = 0; i < run_info.x_step; ++i) + { + acc.at(i) += input_vals[i] * weights_vals[i]; + in_sum.at(i) += input_vals[i]; + we_sum.at(i) += weights_vals[i]; + } + + offs += dilation.x() * run_info.input_stride_y; + } + + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; + } + + VectorType out_vals = wrapper::vdup_n(static_cast(0), TagType{}); + for(size_t i = 0; i < run_info.x_step; ++i) { - const auto input_vals = wrapper::vload(reinterpret_cast(input_it.ptr() + std::min(static_cast(offs), input_max_offset))); - const auto weights_vals = wrapper::vload(reinterpret_cast(weights_ptr + w * weights_stride_y)); + acc.at(i) -= in_sum.at(i) * weights_qoffset; + acc.at(i) -= we_sum.at(i) * input_qoffset; + acc.at(i) += k_offset; - for(int i = 0; i < S; ++i) + if(has_biases) { - acc.at(i) += input_vals[i] * weights_vals[i]; - in_sum.at(i) += input_vals[i]; - we_sum.at(i) += weights_vals[i]; + acc.at(i) += *(reinterpret_cast(biases_it.ptr() + i * sizeof(int32_t)) + x); } - offs += dilation.x() * input_stride_y; + const int32_t out_mul = output_multiplier.at(x + i); + const int32_t out_shift = output_shift.at(x + i); + if(out_shift < 0) + { + acc.at(i) = saturating_doubling_high_mul(acc.at(i) * (1 << (-out_shift)), out_mul) + output_qoffset; + } + else + { + acc.at(i) = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(i), out_mul), out_shift) + output_qoffset; + } + out_vals[i] = static_cast(utility::clamp(acc.at(i))); } - weights_ptr += weights_stride_z; - input_offset += dilation.y() * input_stride_z; + wrapper::vstore(reinterpret_cast(output_it.ptr()) + x, out_vals); } - VectorType out_vals = wrapper::vdup_n(static_cast(0), TagType{}); - for(int i = 0; i < S; ++i) + // left-over + for(; x < run_info.x_end; ++x) { - acc.at(i) -= in_sum.at(i) * weights_qoffset; - acc.at(i) -= we_sum.at(i) * input_qoffset; - acc.at(i) += k_offset; + AccType acc = 0; + AccType in_sum = 0; + AccType we_sum = 0; + + auto weights_ptr = base_weights_ptr; + auto input_offset = base_input_offset; + + for(size_t h = 0; h < run_info.weights_height; ++h) + { + int64_t offs = input_offset + x * sizeof(T); + for(size_t w = 0; w < run_info.weights_width; ++w) + { + const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); + const auto input_val = is_valid_region ? + *reinterpret_cast(input_it.ptr() + std::min(static_cast(offs), run_info.input_max_offset)) : + out_of_bound_value; + const auto weights_val = *(reinterpret_cast(weights_ptr + w * run_info.weights_stride_y) + x); + + acc += input_val * weights_val; + in_sum += input_val; + we_sum += weights_val; + + offs += dilation.x() * run_info.input_stride_y; + } + + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; + } + + T out_vals{ 0 }; + + acc -= in_sum * weights_qoffset; + acc -= we_sum * input_qoffset; + acc += k_offset; if(has_biases) { - acc.at(i) += *reinterpret_cast(biases_it.ptr() + i * sizeof(int32_t)); + acc += *(reinterpret_cast(biases_it.ptr()) + x); } - const int out_mul = output_multiplier.at(id.x() + i); - const int out_shift = output_shift.at(id.x() + i); + const int32_t out_mul = output_multiplier.at(x); + const int32_t out_shift = output_shift.at(x); + if(out_shift < 0) { - acc.at(i) = saturating_doubling_high_mul(acc.at(i) * (1 << (-out_shift)), out_mul) + output_qoffset; + acc = saturating_doubling_high_mul(acc * (1 << (-out_shift)), out_mul) + output_qoffset; } else { - acc.at(i) = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(i), out_mul), out_shift) + output_qoffset; + acc = rounding_divide_by_exp2(saturating_doubling_high_mul(acc, out_mul), out_shift) + output_qoffset; } - out_vals[i] = static_cast(utility::clamp(acc.at(i))); - } - wrapper::vstore(reinterpret_cast(output_it.ptr()), out_vals); + out_vals = static_cast(utility::clamp(acc)); + *(reinterpret_cast(output_it.ptr()) + x) = out_vals; + } }, input_it, weights_it, biases_it, output_it); } @@ -312,36 +480,36 @@ template void depthwise_loop_generic_quantized(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const Size2D &dilation, unsigned int depth_multiplier, std::vector output_multiplier, std::vector output_shift, const Window &window, bool has_biases) { - const size_t input_stride_y = input->info()->strides_in_bytes().y(); - const size_t input_stride_z = input->info()->strides_in_bytes().z(); - const size_t input_max_offset = input->info()->strides_in_bytes().z() * input->info()->dimension(2) - (input->info()->padding().bottom + input->info()->padding().top) * - input->info()->strides_in_bytes().y(); - const size_t weights_width = weights->info()->dimension(1); - const size_t weights_height = weights->info()->dimension(2); - const size_t weights_stride_y = weights->info()->strides_in_bytes().y(); - const size_t weights_stride_z = weights->info()->strides_in_bytes().z(); - const size_t conv_stride_x = conv_info.stride().first; - const size_t conv_stride_y = conv_info.stride().second; - const size_t conv_pad_left = conv_info.pad_left(); - const size_t conv_pad_top = conv_info.pad_top(); + using AccType = int32_t; + + const auto run_info = DepthwiseConvolutionRunInfo(*input->info(), *weights->info(), conv_info, window, depth_multiplier); + + const auto out_of_bound_value = PixelValue(static_cast(0), input->info()->data_type(), input->info()->quantization_info()).get(); const int32_t input_qoffset = input->info()->quantization_info().uniform().offset; const int32_t weights_qoffset = weights->info()->quantization_info().uniform().offset; const int32_t output_qoffset = output->info()->quantization_info().uniform().offset; - const int32_t k_offset = weights_width * weights_height * input_qoffset * weights_qoffset; + const int32_t k_offset = run_info.weights_width * run_info.weights_height * input_qoffset * weights_qoffset; - Window win_input = window; - win_input.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_input.set(Window::DimZ, Window::Dimension(0, 0, 0)); + Window execution_window = window; + execution_window.set(Window::DimX, Window::Dimension(0, run_info.input_depth, 1)); - Window win_weights = win_input; - win_weights.set(3, Window::Dimension(0, 0, 0)); + Window win_input = execution_window; + win_input.set(Window::DimY, dim_manual_loop); + win_input.set(Window::DimZ, dim_manual_loop); - win_input.set_dimension_step(Window::DimX, 1); + Window win_weights = window; + win_weights.set_dimension_step(Window::DimX, run_info.x_step); + win_weights.set(Window::DimY, dim_manual_loop); + win_weights.set(Window::DimZ, dim_manual_loop); + win_weights.set(Window::DimW, dim_manual_loop); + + Window win_output = window; + win_output.set_dimension_step(Window::DimX, run_info.x_step); Iterator input_it(input, win_input); Iterator weights_it(weights, win_weights); - Iterator output_it(output, window); + Iterator output_it(output, win_output); Iterator biases_it{}; if(has_biases) @@ -349,38 +517,39 @@ void depthwise_loop_generic_quantized(const ITensor *input, const ITensor *weigh biases_it = Iterator(biases, win_weights); } - execute_window_loop(window, [&](const Coordinates & id) + execute_window_loop(execution_window, [&](const Coordinates & id) { - std::vector acc(depth_multiplier, 0); - std::vector we_sum(depth_multiplier, 0); - int32_t in_sum = 0; + std::vector acc(depth_multiplier, 0); + std::vector we_sum(depth_multiplier, 0); + AccType in_sum = 0; - const int input_y = id.y() * conv_stride_x - conv_pad_left; - const int input_z = id.z() * conv_stride_y - conv_pad_top; - int input_offset = input_y * input_stride_y + input_z * input_stride_z; + const int32_t input_y = id.y() * run_info.conv_stride_x - run_info.conv_pad_left; + const int32_t input_z = id.z() * run_info.conv_stride_y - run_info.conv_pad_top; + int64_t input_offset = input_y * run_info.input_stride_y + input_z * run_info.input_stride_z; auto weights_ptr = weights_it.ptr(); - for(size_t h = 0; h < weights_height; ++h) + for(size_t h = 0; h < run_info.weights_height; ++h) { int offs = input_offset; - for(size_t w = 0; w < weights_width; ++w) + for(size_t w = 0; w < run_info.weights_width; ++w) { - const auto input_val = *(reinterpret_cast(input_it.ptr() + std::min(static_cast(offs), input_max_offset))); + const bool is_valid_region = is_valid_input_region(input_y, input_z, w, h, run_info, dilation); + const auto input_val = is_valid_region ? *(reinterpret_cast(input_it.ptr() + std::min(static_cast(offs), run_info.input_max_offset))) : out_of_bound_value; for(size_t m = 0; m < depth_multiplier; ++m) { - const auto weights_val = *(reinterpret_cast(weights_ptr + m * sizeof(T) + w * weights_stride_y)); + const auto weights_val = *(reinterpret_cast(weights_ptr + m * sizeof(T) + w * run_info.weights_stride_y)); acc.at(m) += input_val * weights_val; we_sum.at(m) += weights_val; } - offs += dilation.x() * input_stride_y; + offs += dilation.x() * run_info.input_stride_y; in_sum += input_val; } - weights_ptr += weights_stride_z; - input_offset += dilation.y() * input_stride_z; + weights_ptr += run_info.weights_stride_z; + input_offset += dilation.y() * run_info.input_stride_z; } for(size_t m = 0; m < depth_multiplier; ++m) @@ -394,8 +563,8 @@ void depthwise_loop_generic_quantized(const ITensor *input, const ITensor *weigh acc.at(m) += *(reinterpret_cast(biases_it.ptr() + m * sizeof(int32_t))); } - const int out_mul = output_multiplier.at(id.x() + m); - const int out_shift = output_shift.at(id.x() + m); + const int32_t out_mul = output_multiplier.at(id.x() * depth_multiplier + m); + const int32_t out_shift = output_shift.at(id.x() * depth_multiplier + m); if(out_shift < 0) { acc.at(m) = saturating_doubling_high_mul(acc.at(m) * (1 << (-out_shift)), out_mul) + output_qoffset; @@ -404,7 +573,7 @@ void depthwise_loop_generic_quantized(const ITensor *input, const ITensor *weigh { acc.at(m) = rounding_divide_by_exp2(saturating_doubling_high_mul(acc.at(m), out_mul), out_shift) + output_qoffset; } - *(reinterpret_cast(output_it.ptr() + m * sizeof(T))) = static_cast(utility::clamp(acc.at(m))); + *(reinterpret_cast(output_it.ptr() + m * sizeof(T))) = static_cast(utility::clamp(acc.at(m))); } }, input_it, weights_it, biases_it, output_it); @@ -458,54 +627,13 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, return Status{}; } - -std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *biases, - ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const Size2D &dilation) -{ - // Get convolved dimensions - const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); - - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output, input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_quantization_info(output->quantization_info())); - - // Configure kernel window (generic) - const unsigned int num_elems_read_per_iteration = (depth_multiplier == 1) ? 8 / element_size_from_data_type(input->data_type()) : 1; - const unsigned int num_elems_written_per_iteration = num_elems_read_per_iteration * depth_multiplier; - - // Configure kernel window - Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration)); - - AccessWindowStatic input_access(input, 0, -conv_info.pad_left(), ceil_to_multiple(num_elems_read_per_iteration, input->dimension(0)), - input->dimension(1) + std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top())); - AccessWindowHorizontal weights_access(weights, 0, num_elems_written_per_iteration); - AccessWindowHorizontal output_access(output, 0, num_elems_written_per_iteration); - - bool window_changed = update_window_and_padding(win, input_access, weights_access, output_access); - - if(biases != nullptr) - { - AccessWindowHorizontal biases_access(biases, 0, num_elems_written_per_iteration); - window_changed |= update_window_and_padding(win, biases_access); - } - - output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} } // namespace NEDepthwiseConvolutionLayerNativeKernel::NEDepthwiseConvolutionLayerNativeKernel() - : _func(), _border_size(0), _input(), _weights(), _biases(), _output(), _conv_info(), _depth_multiplier(1), _dilation(), _output_multiplier(), _output_shift(), _has_biases() + : _func(), _input(), _weights(), _biases(), _output(), _conv_info(), _depth_multiplier(1), _dilation(), _output_multiplier(), _output_shift(), _has_biases() { } -BorderSize NEDepthwiseConvolutionLayerNativeKernel::border_size() const -{ - return _border_size; -} - void NEDepthwiseConvolutionLayerNativeKernel::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) { @@ -518,7 +646,6 @@ void NEDepthwiseConvolutionLayerNativeKernel::configure(const ITensor *input, co _output = output; _conv_info = conv_info; _depth_multiplier = depth_multiplier; - _border_size = BorderSize(_conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0); _dilation = dilation; _has_biases = (biases != nullptr); @@ -530,17 +657,17 @@ void NEDepthwiseConvolutionLayerNativeKernel::configure(const ITensor *input, co auto weights_scale = weights->info()->quantization_info().scale(); if(!is_data_type_quantized_per_channel(_weights->info()->data_type())) { - for(size_t i = 1; i < _weights->info()->dimension(0); ++i) + for(size_t i = 1; i < _weights->info()->dimension(channel_idx); ++i) { weights_scale.push_back(weights_scale.front()); } } - for(size_t i = 0; i < weights_scale.size(); ++i) + for(const auto &s : weights_scale) { int32_t out_mult = 0; int32_t out_shift = 0; - const float multiplier = input_scale * weights_scale.at(i) / output_scale; + const float multiplier = input_scale * s / output_scale; arm_compute::quantization::calculate_quantized_multiplier(multiplier, &out_mult, &out_shift); _output_multiplier.push_back(out_mult); @@ -551,42 +678,42 @@ void NEDepthwiseConvolutionLayerNativeKernel::configure(const ITensor *input, co switch(_weights->info()->data_type()) { case DataType::QASYMM8: - _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise; - pad_vectors(_output_multiplier, _output_shift, 8); + _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise; break; case DataType::QASYMM8_SIGNED: - _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise; - pad_vectors(_output_multiplier, _output_shift, 8); + _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise; break; case DataType::QSYMM8_PER_CHANNEL: if(_input->info()->data_type() == DataType::QASYMM8) { - _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise; + _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise; } else { - _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise; + _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise; } - pad_vectors(_output_multiplier, _output_shift, 8); break; #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: - _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise; - pad_vectors(_output_multiplier, _output_shift, 4); + _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise; break; #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F32: - _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise; - pad_vectors(_output_multiplier, _output_shift, 2); + _func = &NEDepthwiseConvolutionLayerNativeKernel::run_depthwise; break; default: ARM_COMPUTE_ERROR("Data type not supported"); break; } - auto win_config = validate_and_configure_window(_input->info(), _weights->info(), (biases != nullptr) ? biases->info() : nullptr, _output->info(), _conv_info, _depth_multiplier, dilation); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - INEKernel::configure(win_config.second); + const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier, dilation); + auto_init_if_empty(*output->info(), input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_quantization_info(output->info()->quantization_info())); + + Window win = calculate_max_window(*output->info(), Steps()); + Coordinates coord; + coord.set_num_dimensions(output->info()->num_dimensions()); + output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); + INEKernel::configure(win); } Status NEDepthwiseConvolutionLayerNativeKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, @@ -594,9 +721,6 @@ Status NEDepthwiseConvolutionLayerNativeKernel::validate(const ITensorInfo *inpu const Size2D &dilation) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, dilation)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), (biases != nullptr) ? biases->clone().get() : nullptr, output->clone().get(), conv_info, - depth_multiplier, dilation) - .first); return Status{}; } @@ -609,12 +733,7 @@ void NEDepthwiseConvolutionLayerNativeKernel::run(const Window &window, const Th (this->*_func)(window, _has_biases); } -template < typename T, typename TW, int S, typename std::enable_if < std::is_same::value -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - || std::is_same::value -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - , - int >::type > +template > void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window, bool has_biases) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); @@ -622,7 +741,7 @@ void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window if(_depth_multiplier == 1) { - depthwise_loop_multiplier1_fp(_input, _weights, _biases, _output, _conv_info, _dilation, window, has_biases); + depthwise_loop_multiplier1_fp(_input, _weights, _biases, _output, _conv_info, _dilation, window, has_biases); } else { @@ -630,7 +749,7 @@ void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window } } -template +template > void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window, bool has_biases) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); @@ -638,7 +757,7 @@ void NEDepthwiseConvolutionLayerNativeKernel::run_depthwise(const Window &window if(_depth_multiplier == 1) { - depthwise_loop_multiplier1_quantized(_input, _weights, _biases, _output, _conv_info, _dilation, _output_multiplier, _output_shift, window, has_biases); + depthwise_loop_multiplier1_quantized(_input, _weights, _biases, _output, _conv_info, _dilation, _output_multiplier, _output_shift, window, has_biases); } else { -- cgit v1.2.1