From 44f5572f3d6ba8e39c4a18a991049992d590ce39 Mon Sep 17 00:00:00 2001 From: Giorgio Arena Date: Fri, 12 Jul 2019 14:49:49 +0100 Subject: COMPMID-2179 New generic depthwise convolution for NEON F32 NHWC Change-Id: I2b883785c0500d4bdb6ee4700382ee058be2cd36 Signed-off-by: Giorgio Arena Reviewed-on: https://review.mlplatform.org/c/1538 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Gian Marco Iodice --- .../kernels/NEDepthwiseConvolutionLayerKernel.cpp | 330 ++++++++++++++++ .../NEON/functions/NEDepthwiseConvolutionLayer.cpp | 414 +++++++++++---------- 2 files changed, 551 insertions(+), 193 deletions(-) create mode 100644 src/core/NEON/kernels/NEDepthwiseConvolutionLayerKernel.cpp (limited to 'src') diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayerKernel.cpp new file mode 100644 index 0000000000..feb2071d47 --- /dev/null +++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayerKernel.cpp @@ -0,0 +1,330 @@ +/* + * Copyright (c) 2019 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. + */ +#include "arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayerKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/NEON/wrapper/traits.h" +#include "arm_compute/core/NEON/wrapper/wrapper.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" + +namespace arm_compute +{ +namespace +{ +template +void depthwise_loop_multiplier1(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, + const Size2D &dilation, const Window &window) +{ + 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(); + + 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 win_weights = win_input; + win_weights.set(3, Window::Dimension(0, 0, 0)); + + Iterator input_it(input, win_input); + Iterator weights_it(weights, win_weights); + Iterator output_it(output, window); + Iterator biases_it{}; + + if(has_biases) + { + biases_it = Iterator(biases, win_weights); + } + + execute_window_loop(window, [&](const Coordinates & id) + { + VectorType acc = wrapper::vdup_n(static_cast(0), TagType{}); + + 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) + { + int offs = input_offset; + for(size_t w = 0; w < weights_width; ++w) + { + 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 = wrapper::vmla(acc, weights_vals, input_vals); + offs += dilation.x() * input_stride_y; + } + + weights_ptr += weights_stride_z; + input_offset += dilation.y() * input_stride_z; + } + + if(has_biases) + { + const auto biases_vals = wrapper::vload(reinterpret_cast(biases_it.ptr())); + acc = wrapper::vadd(acc, biases_vals); + } + + wrapper::vstore(reinterpret_cast(output_it.ptr()), acc); + }, + input_it, weights_it, biases_it, output_it); +} + +template +void depthwise_loop_generic(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) +{ + 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(); + + 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 win_weights = win_input; + win_weights.set(3, Window::Dimension(0, 0, 0)); + + win_input.set_dimension_step(Window::DimX, 1); + + Iterator input_it(input, win_input); + Iterator weights_it(weights, win_weights); + Iterator output_it(output, window); + Iterator biases_it{}; + + if(has_biases) + { + biases_it = Iterator(biases, win_weights); + } + + execute_window_loop(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; + + auto weights_ptr = weights_it.ptr(); + for(size_t h = 0; h < weights_height; ++h) + { + int offs = input_offset; + for(size_t w = 0; w < weights_width; ++w) + { + const auto input_val = *(reinterpret_cast(input_it.ptr() + std::min(static_cast(offs), input_max_offset))); + + for(size_t m = 0; m < depth_multiplier; ++m) + { + const auto weights_val = *(reinterpret_cast(weights_ptr + m * sizeof(T) + w * weights_stride_y)); + acc.at(m) = std::fma(weights_val, input_val, acc.at(m)); + } + + offs += dilation.x() * input_stride_y; + } + + weights_ptr += weights_stride_z; + input_offset += dilation.y() * input_stride_z; + } + + if(has_biases) + { + for(size_t m = 0; m < depth_multiplier; ++m) + { + const auto biases_val = *(reinterpret_cast(biases_it.ptr() + m * sizeof(T))); + *(reinterpret_cast(output_it.ptr() + m * sizeof(T))) = acc.at(m) + biases_val; + } + } + else + { + for(size_t m = 0; m < depth_multiplier; ++m) + { + *(reinterpret_cast(output_it.ptr() + m * sizeof(T))) = acc.at(m); + } + } + }, + input_it, weights_it, biases_it, output_it); +} + +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, + const Size2D &dilation) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); + ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier == 0); + ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(0) * depth_multiplier) != weights->dimension(0)); + ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); + ARM_COMPUTE_RETURN_ERROR_ON((conv_info.stride().first < 1) || (conv_info.stride().second < 1)); + + if(biases != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(0)); + } + + if(output->total_size() != 0) + { + const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); + } + + return Status{}; +} +} // namespace + +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)); + + // 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); +} + +NEDepthwiseConvolutionLayerKernel::NEDepthwiseConvolutionLayerKernel() + : _func(), _border_size(0), _input(), _weights(), _biases(), _output(), _conv_info(), _depth_multiplier(1), _dilation() +{ +} + +BorderSize NEDepthwiseConvolutionLayerKernel::border_size() const +{ + return _border_size; +} + +void NEDepthwiseConvolutionLayerKernel::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, + const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, dilation)); + + _input = input; + _weights = weights; + _biases = biases; + _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; + + switch(_input->info()->data_type()) + { + case DataType::F32: + _func = (biases != nullptr) ? &NEDepthwiseConvolutionLayerKernel::run_depthwise : &NEDepthwiseConvolutionLayerKernel::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); +} + +Status NEDepthwiseConvolutionLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + unsigned int depth_multiplier, + 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{}; +} + +void NEDepthwiseConvolutionLayerKernel::run(const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); + + (this->*_func)(window); +} + +template +void NEDepthwiseConvolutionLayerKernel::run_depthwise(const Window &window) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); + + if(_depth_multiplier == 1) + { + depthwise_loop_multiplier1(_input, _weights, _biases, _output, _conv_info, _dilation, window); + } + else + { + depthwise_loop_generic(_input, _weights, _biases, _output, _conv_info, _dilation, _depth_multiplier, window); + } +} +} // namespace arm_compute diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp index 001bece933..c2ed901169 100644 --- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp @@ -689,9 +689,10 @@ void NEDepthwiseConvolutionLayerOptimized::prepare() } NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayer() - : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _vector_to_tensor_kernel(), _output_stage_kernel(), _v2mm_input_fill_border(), _v2mm_weights_fill_border(), _permute_input(), - _permute_weights(), _permute_output(), _activationlayer_function(), _input_reshaped(), _weights_reshaped(), _v2mm_output(), _output_reshaped(), _permuted_input(), _permuted_weights(), - _permuted_output(), _is_prepared(false), _is_quantized(false), _is_nhwc(false), _is_activationlayer_enabled(false), _original_weights(nullptr) + : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _depthwise_conv_kernel(), _vector_to_tensor_kernel(), _output_stage_kernel(), _fill_border(), _v2mm_input_fill_border(), + _v2mm_weights_fill_border(), _permute_input(), _permute_weights(), _permute_output(), _activationlayer_function(), _input_reshaped(), _weights_reshaped(), _v2mm_output(), _output_reshaped(), + _permuted_input(), _permuted_weights(), _permuted_output(), _is_prepared(false), _is_quantized(false), _is_nhwc(false), _is_activationlayer_enabled(false), _is_optimized(false), + _original_weights(nullptr) { } @@ -703,123 +704,135 @@ void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weigh ARM_COMPUTE_ERROR_THROW_ON(NEDepthwiseConvolutionLayer::validate(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), output->info(), conv_info, depth_multiplier, act_info, dilation)); - _is_nhwc = input->info()->data_layout() == DataLayout::NHWC; + _is_nhwc = input->info()->data_layout() == DataLayout::NHWC; + _is_optimized = _is_nhwc && input->info()->data_type() == DataType::F32; - ITensor *input_to_use = input; - const ITensor *weights_to_use = weights; - ITensor *output_to_use = output; - - if(_is_nhwc) + if(!_is_optimized) { - _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U)); - _permuted_input.info()->set_data_layout(DataLayout::NCHW); - input_to_use = &_permuted_input; + ITensor *input_to_use = input; + const ITensor *weights_to_use = weights; + ITensor *output_to_use = output; - _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U)); - _permuted_weights.info()->set_data_layout(DataLayout::NCHW); - weights_to_use = &_permuted_weights; - } + if(_is_nhwc) + { + _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U)); + _permuted_input.info()->set_data_layout(DataLayout::NCHW); + input_to_use = &_permuted_input; - const size_t weights_w = weights_to_use->info()->dimension(0); - const size_t weights_h = weights_to_use->info()->dimension(1); - const size_t weights_z = weights_to_use->info()->dimension(2); + _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U)); + _permuted_weights.info()->set_data_layout(DataLayout::NCHW); + weights_to_use = &_permuted_weights; + } - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); - _is_prepared = false; - _original_weights = weights_to_use; + const size_t weights_w = weights_to_use->info()->dimension(0); + const size_t weights_h = weights_to_use->info()->dimension(1); + const size_t weights_z = weights_to_use->info()->dimension(2); - // Should bias be appended ? - bool append_bias = (biases != nullptr) && !_is_quantized; + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); + _is_prepared = false; + _original_weights = weights_to_use; - // Calculate output shape - TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier, dilation); + // Should bias be appended ? + bool append_bias = (biases != nullptr) && !_is_quantized; - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); + // Calculate output shape + TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier, dilation); - if(_is_nhwc) - { - permute(output_shape, PermutationVector(1U, 2U, 0U)); - _permuted_output.allocator()->init(output->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape)); - _permuted_output.info()->set_data_layout(DataLayout::NCHW); - _permuted_output.info()->set_quantization_info(output->info()->quantization_info()); - output_to_use = &_permuted_output; - } - - // Output width and height - const unsigned int conv_w = output_shape.x(); - const unsigned int conv_h = output_shape.y(); - - // Set up intermediate tensors - const size_t patch_size = weights_w * weights_h + (append_bias ? 1 : 0); - const size_t conv_size = conv_w * conv_h; - - // Im2Col configuration - TensorShape shape_im2col = input_to_use->info()->tensor_shape(); - shape_im2col.set(0, patch_size); - shape_im2col.set(1, conv_size); - shape_im2col.set(2, weights_z); - _input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW)); - _im2col_kernel.configure(input_to_use, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation); - - // Weights reshape configuration - const TensorShape shape_weights_reshape(patch_size, weights_z); - _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape).set_data_layout(DataLayout::NCHW)); - _weights_reshape_kernel.configure(weights_to_use, &_weights_reshaped, append_bias ? biases : nullptr); - - // GEMV configuration - DataType v2mm_dt = (input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : input->info()->data_type(); - TensorShape shape_v2mm_out = input_to_use->info()->tensor_shape(); - shape_v2mm_out.set(0, conv_size * weights_z); - shape_v2mm_out.set(1, 1); - shape_v2mm_out.set(2, 1); - _v2mm_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out).set_data_layout(DataLayout::NCHW)); - _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output); - _output_reshaped.allocator()->init(_v2mm_output.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape)); - _vector_to_tensor_kernel.configure(&_v2mm_output, (_is_quantized) ? &_output_reshaped : output_to_use, conv_w, conv_h); - - // Output staged configuration - if(_is_quantized) - { - const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); - float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; - int output_multiplier; - int output_shift; - quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - _output_stage_kernel.configure(&_output_reshaped, biases, output_to_use, output_multiplier, output_shift, oq_info.offset); - _output_reshaped.allocator()->allocate(); - } + if(_is_nhwc) + { + permute(output_shape, PermutationVector(1U, 2U, 0U)); + _permuted_output.allocator()->init(output->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape)); + _permuted_output.info()->set_data_layout(DataLayout::NCHW); + _permuted_output.info()->set_quantization_info(output->info()->quantization_info()); + output_to_use = &_permuted_output; + } - if(_is_nhwc) - { - _permute_output.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U)); + // Output width and height + const unsigned int conv_w = output_shape.x(); + const unsigned int conv_h = output_shape.y(); + + // Set up intermediate tensors + const size_t patch_size = weights_w * weights_h + (append_bias ? 1 : 0); + const size_t conv_size = conv_w * conv_h; + + // Im2Col configuration + TensorShape shape_im2col = input_to_use->info()->tensor_shape(); + shape_im2col.set(0, patch_size); + shape_im2col.set(1, conv_size); + shape_im2col.set(2, weights_z); + _input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW)); + _im2col_kernel.configure(input_to_use, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation); + + // Weights reshape configuration + const TensorShape shape_weights_reshape(patch_size, weights_z); + _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape).set_data_layout(DataLayout::NCHW)); + _weights_reshape_kernel.configure(weights_to_use, &_weights_reshaped, append_bias ? biases : nullptr); + + // GEMV configuration + DataType v2mm_dt = (input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : input->info()->data_type(); + TensorShape shape_v2mm_out = input_to_use->info()->tensor_shape(); + shape_v2mm_out.set(0, conv_size * weights_z); + shape_v2mm_out.set(1, 1); + shape_v2mm_out.set(2, 1); + _v2mm_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out).set_data_layout(DataLayout::NCHW)); + _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output); + _output_reshaped.allocator()->init(_v2mm_output.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape)); + _vector_to_tensor_kernel.configure(&_v2mm_output, (_is_quantized) ? &_output_reshaped : output_to_use, conv_w, conv_h); + + // Output staged configuration + if(_is_quantized) + { + const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); + const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); + const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); - _permuted_input.allocator()->allocate(); - _permuted_weights.allocator()->allocate(); - _permuted_output.allocator()->allocate(); - } + float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; + int output_multiplier; + int output_shift; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + _output_stage_kernel.configure(&_output_reshaped, biases, output_to_use, output_multiplier, output_shift, oq_info.offset); + _output_reshaped.allocator()->allocate(); + } - // Fill borders on inputs - PixelValue zero_in(static_cast(0)); - PixelValue zero_w(static_cast(0)); - if(_is_quantized) - { - zero_in = PixelValue(static_cast(input->info()->quantization_info().uniform().offset)); - zero_w = PixelValue(static_cast(weights->info()->quantization_info().uniform().offset)); - } - BorderSize border_size = _v2mm_kernel.border_size(); - _v2mm_input_fill_border.configure(&_input_reshaped, border_size, BorderMode::CONSTANT, zero_in); + if(_is_nhwc) + { + _permute_output.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U)); + + _permuted_input.allocator()->allocate(); + _permuted_weights.allocator()->allocate(); + _permuted_output.allocator()->allocate(); + } + + // Fill borders on inputs + PixelValue zero_in(static_cast(0)); + PixelValue zero_w(static_cast(0)); + if(_is_quantized) + { + zero_in = PixelValue(static_cast(input->info()->quantization_info().uniform().offset)); + zero_w = PixelValue(static_cast(weights->info()->quantization_info().uniform().offset)); + } + BorderSize border_size = _v2mm_kernel.border_size(); + _v2mm_input_fill_border.configure(&_input_reshaped, border_size, BorderMode::CONSTANT, zero_in); + + border_size.bottom = 0; + _v2mm_weights_fill_border.configure(&_weights_reshaped, border_size, BorderMode::CONSTANT, zero_w); - border_size.bottom = 0; - _v2mm_weights_fill_border.configure(&_weights_reshaped, border_size, BorderMode::CONSTANT, zero_w); + // Allocate intermediate tensors + _input_reshaped.allocator()->allocate(); + _v2mm_output.allocator()->allocate(); + } + else + { + // Configure kernel + _depthwise_conv_kernel.configure(input, weights, biases, output, conv_info, depth_multiplier, dilation); - // Allocate intermediate tensors - _input_reshaped.allocator()->allocate(); - _v2mm_output.allocator()->allocate(); + // Fill input borders + _fill_border.configure(input, _depthwise_conv_kernel.border_size(), BorderMode::CONSTANT, PixelValue(static_cast(0), input->info()->data_type())); + } //Configure Activation Layer _is_activationlayer_enabled = act_info.enabled(); @@ -845,89 +858,96 @@ Status NEDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITe ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(height_idx) + (weights->dimension(height_idx) - 1) * (dilation.y() - 1) > input->dimension(height_idx) + conv_info.pad_top() + conv_info.pad_bottom()); ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(channel_idx) * depth_multiplier) != weights->dimension(channel_idx)); - // Clone output to use auto init - auto output_clone = output->clone(); - - const ITensorInfo *input_to_use = input; - const ITensorInfo *weights_to_use = weights; - const ITensorInfo *output_to_use = output_clone.get(); - - TensorShape permuted_input_shape = input->tensor_shape(); - TensorShape permuted_weights_shape = weights->tensor_shape(); - TensorInfo permuted_input; - TensorInfo permuted_weights; - - if(input->data_layout() == DataLayout::NHWC) + if(input->data_layout() != DataLayout::NHWC || input->data_type() != DataType::F32) { - permute(permuted_input_shape, PermutationVector(1U, 2U, 0U)); - permute(permuted_weights_shape, PermutationVector(1U, 2U, 0U)); + // Clone output to use auto init + auto output_clone = output->clone(); - permuted_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NCHW)); - permuted_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NCHW)); + const ITensorInfo *input_to_use = input; + const ITensorInfo *weights_to_use = weights; + const ITensorInfo *output_to_use = output_clone.get(); - input_to_use = &permuted_input; - weights_to_use = &permuted_weights; - } + TensorShape permuted_input_shape = input->tensor_shape(); + TensorShape permuted_weights_shape = weights->tensor_shape(); + TensorInfo permuted_input; + TensorInfo permuted_weights; - const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); - const bool append_bias = (biases != nullptr) && !is_quantized; - TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); - const size_t weights_w = weights_to_use->dimension(0); - const size_t weights_h = weights_to_use->dimension(1); - const size_t weights_z = weights_to_use->dimension(2); - const unsigned int conv_w = output_shape[width_idx]; - const unsigned int conv_h = output_shape[height_idx]; - const size_t patch_size = weights_w * weights_h + (append_bias ? 1 : 0); - const size_t conv_size = conv_w * conv_h; + if(input->data_layout() == DataLayout::NHWC) + { + permute(permuted_input_shape, PermutationVector(1U, 2U, 0U)); + permute(permuted_weights_shape, PermutationVector(1U, 2U, 0U)); - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output_clone, input->clone()->set_tensor_shape(output_shape)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); + permuted_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NCHW)); + permuted_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NCHW)); - TensorInfo permuted_output; - if(input->data_layout() == DataLayout::NHWC) - { - permute(output_shape, PermutationVector(1U, 2U, 0U)); - permuted_output = TensorInfo(output_clone->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_layout(DataLayout::NCHW)); - output_to_use = &permuted_output; - } - - // Im2Col configuration - TensorShape shape_im2col = input_to_use->tensor_shape(); - shape_im2col.set(0, patch_size); - shape_im2col.set(1, conv_size); - shape_im2col.set(2, weights_z); - TensorInfo input_reshaped(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW)); - ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseIm2ColKernel::validate(input_to_use, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation)); + input_to_use = &permuted_input; + weights_to_use = &permuted_weights; + } - // Weights reshape configuration - const TensorShape shape_weights_reshape(patch_size, weights_z); - TensorInfo weights_reshaped(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape).set_data_layout(DataLayout::NCHW)); - ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseWeightsReshapeKernel::validate(weights_to_use, &weights_reshaped, append_bias ? biases : nullptr)); + const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); + const bool append_bias = (biases != nullptr) && !is_quantized; + TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); + const size_t weights_w = weights_to_use->dimension(0); + const size_t weights_h = weights_to_use->dimension(1); + const size_t weights_z = weights_to_use->dimension(2); + const unsigned int conv_w = output_shape[width_idx]; + const unsigned int conv_h = output_shape[height_idx]; + const size_t patch_size = weights_w * weights_h + (append_bias ? 1 : 0); + const size_t conv_size = conv_w * conv_h; + + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output_clone, input->clone()->set_tensor_shape(output_shape)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); + + TensorInfo permuted_output; + if(input->data_layout() == DataLayout::NHWC) + { + permute(output_shape, PermutationVector(1U, 2U, 0U)); + permuted_output = TensorInfo(output_clone->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_layout(DataLayout::NCHW)); + output_to_use = &permuted_output; + } - // GEMV configuration - DataType v2mm_dt = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type(); - TensorShape shape_v2mm_out = input_to_use->tensor_shape(); - shape_v2mm_out.set(0, conv_size * weights_z); - shape_v2mm_out.set(1, 1); - shape_v2mm_out.set(2, 1); - TensorInfo v2mm_output(input->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out).set_data_layout(DataLayout::NCHW)); - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixVectorMultiplyKernel::validate(&input_reshaped, &weights_reshaped, &v2mm_output)); + // Im2Col configuration + TensorShape shape_im2col = input_to_use->tensor_shape(); + shape_im2col.set(0, patch_size); + shape_im2col.set(1, conv_size); + shape_im2col.set(2, weights_z); + TensorInfo input_reshaped(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW)); + ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseIm2ColKernel::validate(input_to_use, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation)); + + // Weights reshape configuration + const TensorShape shape_weights_reshape(patch_size, weights_z); + TensorInfo weights_reshaped(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape).set_data_layout(DataLayout::NCHW)); + ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseWeightsReshapeKernel::validate(weights_to_use, &weights_reshaped, append_bias ? biases : nullptr)); + + // GEMV configuration + DataType v2mm_dt = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type(); + TensorShape shape_v2mm_out = input_to_use->tensor_shape(); + shape_v2mm_out.set(0, conv_size * weights_z); + shape_v2mm_out.set(1, 1); + shape_v2mm_out.set(2, 1); + TensorInfo v2mm_output(input->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out).set_data_layout(DataLayout::NCHW)); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixVectorMultiplyKernel::validate(&input_reshaped, &weights_reshaped, &v2mm_output)); + + TensorInfo output_reshaped(v2mm_output.clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_to_use->tensor_shape())); + ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseVectorToTensorKernel::validate(&v2mm_output, (is_quantized) ? &output_reshaped : output_to_use, conv_w, conv_h)); - TensorInfo output_reshaped(v2mm_output.clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_to_use->tensor_shape())); - ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseVectorToTensorKernel::validate(&v2mm_output, (is_quantized) ? &output_reshaped : output_to_use, conv_w, conv_h)); + if(is_quantized) + { + const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); + const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); + const UniformQuantizationInfo oq_info = output->quantization_info().uniform(); - if(is_quantized) + float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; + int output_multiplier; + int output_shift; + ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift)); + ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&output_reshaped, biases, output_to_use, output_multiplier, output_shift, oq_info.offset)); + } + } + else { - const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = output->quantization_info().uniform(); - - float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; - int output_multiplier; - int output_shift; - ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift)); - ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&output_reshaped, biases, output_to_use, output_multiplier, output_shift, oq_info.offset)); + ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayerKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, dilation)); } // Validate Activation Layer @@ -941,25 +961,33 @@ Status NEDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITe void NEDepthwiseConvolutionLayer::run() { - prepare(); - - if(_is_nhwc) + if(!_is_optimized) { - _permute_input.run(); - } + prepare(); - NEScheduler::get().schedule(&_im2col_kernel, Window::DimX); - NEScheduler::get().schedule(&_v2mm_input_fill_border, Window::DimX); - NEScheduler::get().schedule(&_v2mm_kernel, Window::DimX); - NEScheduler::get().schedule(&_vector_to_tensor_kernel, Window::DimX); - if(_is_quantized) - { - NEScheduler::get().schedule(&_output_stage_kernel, Window::DimX); - } + if(_is_nhwc) + { + _permute_input.run(); + } + + NEScheduler::get().schedule(&_im2col_kernel, Window::DimX); + NEScheduler::get().schedule(&_v2mm_input_fill_border, Window::DimX); + NEScheduler::get().schedule(&_v2mm_kernel, Window::DimX); + NEScheduler::get().schedule(&_vector_to_tensor_kernel, Window::DimX); + if(_is_quantized) + { + NEScheduler::get().schedule(&_output_stage_kernel, Window::DimX); + } - if(_is_nhwc) + if(_is_nhwc) + { + _permute_output.run(); + } + } + else { - _permute_output.run(); + NEScheduler::get().schedule(&_fill_border, Window::DimX); + NEScheduler::get().schedule(&_depthwise_conv_kernel, Window::DimY); } if(_is_activationlayer_enabled) @@ -970,7 +998,7 @@ void NEDepthwiseConvolutionLayer::run() void NEDepthwiseConvolutionLayer::prepare() { - if(!_is_prepared) + if(!_is_prepared && !_is_optimized) { ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); -- cgit v1.2.1