| /* |
| * Copyright (c) 2017-2020 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/CL/kernels/CLDirectConvolutionLayerKernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/CLValidate.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/IAccessWindow.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "support/StringSupport.h" |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| |
| const DataLayout data_layout = input->data_layout(); |
| const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != weights->dimension(height_idx), "Weights should have same width and height"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5 && weights->dimension(width_idx) != 9, |
| "Kernel sizes other than 1x1, 3x3, 5x5 or 9x9 are not supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(channel_idx) != input->dimension(channel_idx), |
| "Weights feature map dimension should match the respective input's one"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->num_dimensions() > 4, "Weights can be at most 4 dimensional"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 1) && std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported for 1x1 convolution."); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 3 || weights->dimension(width_idx) == 5) && std::get<0>(conv_info.stride()) > 2, |
| "Strides larger than 2 not supported for 3x3 convolution."); |
| |
| const auto data_type = input->data_type(); |
| |
| if(weights->dimension(width_idx) == 9) |
| { |
| const auto supported_data_layout = is_data_type_quantized(data_type) ? DataLayout::NCHW : DataLayout::NHWC; |
| const auto error_message = std::string("Only " + string_from_data_layout(supported_data_layout) + " layout is supported for 9x9 convolution with " + string_from_data_type( |
| data_type) |
| + " type"); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((supported_data_layout != data_layout), error_message.c_str()); |
| } |
| |
| if(biases != nullptr) |
| { |
| if(is_data_type_quantized_asymmetric(input->data_type())) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->dimension(0) != weights->dimension(3), |
| "Biases size and number of input feature maps should match"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->num_dimensions() > 1, |
| "Biases should be one dimensional"); |
| } |
| |
| // Checks performed when output is configured |
| if(output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), |
| misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| |
| if(is_data_type_quantized(data_type)) |
| { |
| const UniformQuantizationInfo iqinfo = input->quantization_info().uniform(); |
| const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform(); |
| const UniformQuantizationInfo oqinfo = output->quantization_info().uniform(); |
| |
| float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale; |
| int output_multiplier = 0; |
| int output_shift = 0; |
| ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); |
| } |
| return Status{}; |
| } |
| |
| inline bool can_run_optimized_kernel_for_bifrost(GPUTarget gpu_target, unsigned int conv_stride_x, unsigned int conv_stride_y, unsigned int kernel_size, |
| DataType data_type, DataLayout data_layout) |
| { |
| return gpu_target_is_in(gpu_target, |
| GPUTarget::G71, GPUTarget::G72, GPUTarget::G76, |
| GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, |
| GPUTarget::G52, GPUTarget::G52LIT) |
| && (kernel_size <= 5) |
| && (conv_stride_x == 1) && (conv_stride_y == 1) |
| && (data_type == DataType::F32) |
| && (data_layout == DataLayout::NCHW); |
| } |
| |
| inline bool can_run_optimized_kernel_for_bifrost_nhwc(GPUTarget gpu_target, unsigned int conv_stride_x, unsigned int conv_stride_y, unsigned int kernel_size, |
| DataType data_type, DataLayout data_layout) |
| { |
| return gpu_target_is_in(gpu_target, |
| GPUTarget::G71, GPUTarget::G72, GPUTarget::G76, |
| GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, |
| GPUTarget::G52, GPUTarget::G52LIT) |
| && (kernel_size == 9) |
| && (conv_stride_x == 1) && (conv_stride_y == 1) |
| && (data_type == DataType::F32) |
| && (data_layout == DataLayout::NHWC); |
| } |
| |
| inline void setup_num_elems(unsigned int &num_elems_read_per_iteration_x, unsigned int &num_elems_read_per_iteration_y, |
| unsigned int &num_elems_written_per_iteration_x, unsigned int &num_elems_written_per_iteration_y, |
| unsigned int kernel_size, const PadStrideInfo &conv_info, const GPUTarget target, ITensorInfo *input) |
| { |
| const DataType data_type = input->data_type(); |
| const DataLayout data_layout = input->data_layout(); |
| unsigned int conv_stride_x = std::get<0>(conv_info.stride()); |
| unsigned int conv_stride_y = std::get<1>(conv_info.stride()); |
| |
| const bool run_optimized_bifrost = can_run_optimized_kernel_for_bifrost(target, conv_stride_x, conv_stride_y, kernel_size, data_type, data_layout); |
| |
| if(run_optimized_bifrost) |
| { |
| // Configure kernel window |
| switch(kernel_size) |
| { |
| case 1: |
| { |
| num_elems_read_per_iteration_x = 4; |
| num_elems_read_per_iteration_y = 4; |
| num_elems_written_per_iteration_x = 4; |
| num_elems_written_per_iteration_y = 4; |
| break; |
| } |
| case 3: |
| { |
| num_elems_read_per_iteration_x = 6; |
| num_elems_read_per_iteration_y = 5; |
| num_elems_written_per_iteration_x = 4; |
| num_elems_written_per_iteration_y = 3; |
| break; |
| } |
| case 5: |
| { |
| num_elems_read_per_iteration_x = 8; |
| num_elems_read_per_iteration_y = 6; |
| num_elems_written_per_iteration_x = 4; |
| num_elems_written_per_iteration_y = 2; |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("Kernel size not optimized for Bifrost"); |
| } |
| } |
| } |
| else if(data_layout == DataLayout::NCHW) |
| { |
| num_elems_read_per_iteration_y = kernel_size; |
| num_elems_written_per_iteration_x = 8; |
| num_elems_written_per_iteration_y = 1; |
| switch(kernel_size) |
| { |
| case 1: |
| switch(conv_stride_x) |
| { |
| case 1: |
| num_elems_read_per_iteration_x = 8; |
| break; |
| case 2: |
| num_elems_read_per_iteration_x = 16; |
| break; |
| case 3: |
| switch(input->element_size()) |
| { |
| case 1: |
| num_elems_read_per_iteration_x = 28; |
| break; |
| case 2: |
| num_elems_read_per_iteration_x = 24; |
| break; |
| case 4: |
| num_elems_read_per_iteration_x = 22; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Invalid data size"); |
| } |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Invalid convolution stride X"); |
| } |
| break; |
| case 3: |
| switch(conv_stride_x) |
| { |
| case 1: |
| num_elems_read_per_iteration_x = 10; |
| break; |
| case 2: |
| num_elems_read_per_iteration_x = 17; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Invalid convolution stride X"); |
| } |
| break; |
| case 5: |
| switch(conv_stride_x) |
| { |
| case 1: |
| num_elems_read_per_iteration_x = 12; |
| break; |
| case 2: |
| num_elems_read_per_iteration_x = 20; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Invalid convolution stride X"); |
| } |
| break; |
| case 9: |
| switch(conv_stride_x) |
| { |
| case 1: |
| num_elems_read_per_iteration_x = 16; |
| break; |
| case 2: |
| num_elems_read_per_iteration_x = 24; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Invalid convolution stride X"); |
| } |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Invalid direct convolution size"); |
| } |
| } |
| else // data_layout == NHWC |
| { |
| const bool run_optimized_bifrost_nhwc = can_run_optimized_kernel_for_bifrost_nhwc(target, conv_stride_x, conv_stride_y, kernel_size, data_type, data_layout); |
| |
| num_elems_written_per_iteration_x = 1; |
| |
| if(run_optimized_bifrost_nhwc) |
| { |
| num_elems_read_per_iteration_x = 4; |
| } |
| else |
| { |
| num_elems_read_per_iteration_x = 1; |
| } |
| |
| switch(kernel_size) |
| { |
| case 1: |
| switch(conv_stride_x) |
| { |
| case 1: |
| num_elems_read_per_iteration_y = 8; |
| num_elems_written_per_iteration_y = 8; |
| break; |
| case 2: |
| num_elems_read_per_iteration_y = 16; |
| num_elems_written_per_iteration_y = 8; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Invalid convolution stride X"); |
| } |
| break; |
| case 3: |
| switch(conv_stride_x) |
| { |
| case 1: |
| num_elems_read_per_iteration_y = 10; |
| num_elems_written_per_iteration_y = 8; |
| break; |
| case 2: |
| num_elems_read_per_iteration_y = 17; |
| num_elems_written_per_iteration_y = 8; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Invalid convolution stride X"); |
| } |
| break; |
| case 5: |
| switch(conv_stride_x) |
| { |
| case 1: |
| num_elems_read_per_iteration_y = 12; |
| num_elems_written_per_iteration_y = 8; |
| break; |
| case 2: |
| num_elems_read_per_iteration_y = 20; |
| num_elems_written_per_iteration_y = 8; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Invalid convolution stride X"); |
| } |
| break; |
| case 9: |
| switch(conv_stride_x) |
| { |
| case 1: |
| num_elems_read_per_iteration_y = 16; |
| num_elems_written_per_iteration_y = 8; |
| break; |
| case 2: |
| num_elems_read_per_iteration_y = 24; |
| num_elems_written_per_iteration_y = 8; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Invalid convolution stride X"); |
| } |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not implemented."); |
| break; |
| } |
| } |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, const GPUTarget target) |
| { |
| const DataLayout data_layout = input->data_layout(); |
| const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const unsigned int kernel_size = weights->dimension(width_idx); |
| |
| // Get convolved dimensions |
| TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info); |
| |
| // Output auto inizialitation if not yet initialized |
| // TODO(COMPMID-2078): input->clone()->set_tensor_shape(output_shape) doesn't work with subtensors for grouped direct convolutions (AlexNet). |
| auto_init_if_empty(*output, output_shape, |
| 1, |
| input->data_type(), |
| input->quantization_info()); |
| |
| unsigned int num_elems_read_per_iteration_x = 0; |
| unsigned int num_elems_read_per_iteration_y = 0; |
| unsigned int num_elems_written_per_iteration_x = 0; |
| unsigned int num_elems_written_per_iteration_y = 0; |
| |
| unsigned int conv_pad_left = conv_info.pad_left(); |
| unsigned int conv_pad_top = conv_info.pad_top(); |
| unsigned int conv_stride_x = std::get<0>(conv_info.stride()); |
| unsigned int conv_stride_y = std::get<1>(conv_info.stride()); |
| |
| setup_num_elems(num_elems_read_per_iteration_x, num_elems_read_per_iteration_y, |
| num_elems_written_per_iteration_x, num_elems_written_per_iteration_y, |
| kernel_size, conv_info, target, input); |
| |
| // Create window and update padding |
| bool window_changed = false; |
| Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y)); |
| |
| if(data_layout == DataLayout::NHWC) |
| { |
| AccessWindowStatic input_access(input, 0, -conv_pad_left, |
| ceil_to_multiple(input->dimension(0), num_elems_read_per_iteration_x), |
| ceil_to_multiple(input->dimension(1) + conv_info.pad_right(), num_elems_read_per_iteration_y)); |
| AccessWindowStatic weights_access(weights, 0, 0, weights->dimension(0), weights->dimension(1)); |
| AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y); |
| window_changed = update_window_and_padding(win, input_access, weights_access, output_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); |
| } |
| else if(data_layout == DataLayout::NCHW) |
| { |
| AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration_x, num_elems_read_per_iteration_y, conv_stride_x, conv_stride_y); |
| AccessWindowStatic weights_access(weights, 0, 0, kernel_size, kernel_size); |
| AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y); |
| window_changed = update_window_and_padding(win, input_access, weights_access, output_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); |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| } |
| } // namespace |
| |
| CLDirectConvolutionLayerKernel::CLDirectConvolutionLayerKernel() |
| : _input(nullptr), _biases(nullptr), _weights(nullptr), _output(nullptr), _data_layout(DataLayout::UNKNOWN), _border_size(0), _conv_stride_x(0), _conv_stride_y(0) |
| { |
| } |
| |
| BorderSize CLDirectConvolutionLayerKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| |
| _data_layout = input->info()->data_layout(); |
| const int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); |
| const int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); |
| const int channel_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL); |
| |
| const unsigned int kernel_size = weights->info()->dimension(width_idx); |
| const DataType data_type = input->info()->data_type(); |
| |
| // Get convolved dimensions |
| TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*input->info(), *weights->info(), conv_info); |
| |
| // Output auto inizialitation if not yet initialized |
| // TODO(COMPMID-2078): input->clone()->set_tensor_shape(output_shape) doesn't work with subtensors for grouped direct convolutions (AlexNet). |
| auto_init_if_empty(*output->info(), |
| output_shape, |
| 1, |
| input->info()->data_type(), |
| input->info()->quantization_info()); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), |
| weights->info(), |
| (biases != nullptr) ? biases->info() : nullptr, |
| output->info(), |
| conv_info)); |
| |
| _conv_stride_x = std::get<0>(conv_info.stride()); |
| _conv_stride_y = std::get<1>(conv_info.stride()); |
| |
| if(_data_layout == DataLayout::NHWC) |
| { |
| _border_size = BorderSize(conv_info.pad_left(), 0, conv_info.pad_right(), 0); |
| } |
| else if(_data_layout == DataLayout::NCHW) |
| { |
| _border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()); |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| |
| _input = input; |
| _weights = weights; |
| _output = output; |
| _biases = biases; |
| |
| const GPUTarget gpu_target = get_target(); |
| |
| std::stringstream kernel_name; |
| kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size; |
| if(_data_layout == DataLayout::NHWC) |
| { |
| kernel_name << "_" << lower_string(string_from_data_layout(_data_layout)); |
| } |
| |
| CLBuildOptions build_options; |
| build_options.add_option_if(_biases != nullptr, std::string("-DHAS_BIAS")); |
| |
| const bool run_optimized_for_bifrost = can_run_optimized_kernel_for_bifrost(gpu_target, _conv_stride_x, _conv_stride_y, kernel_size, data_type, _data_layout); |
| |
| if(run_optimized_for_bifrost) |
| { |
| build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(channel_idx)))); |
| |
| kernel_name << "_f32_bifrost"; |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), build_options.options())); |
| } |
| else |
| { |
| build_options.add_option(std::string("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type))); |
| build_options.add_option(std::string("-DDATA_SIZE=" + get_data_size_from_data_type(data_type))); |
| build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(channel_idx)))); |
| build_options.add_option(std::string("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x))); |
| if(_data_layout == DataLayout::NHWC) |
| { |
| const bool run_optimized_for_bifrost_nhwc = can_run_optimized_kernel_for_bifrost_nhwc(gpu_target, _conv_stride_x, _conv_stride_y, kernel_size, data_type, _data_layout); |
| build_options.add_option(std::string("-DDATA_LAYOUT_NHWC=1")); |
| build_options.add_option(std::string("-DDST_HEIGHT=" + support::cpp11::to_string(_output->info()->dimension(height_idx)))); |
| build_options.add_option(std::string("-DDST_WIDTH=" + support::cpp11::to_string(_output->info()->dimension(width_idx)))); |
| build_options.add_option(std::string("-DSRC_HEIGHT=" + support::cpp11::to_string(_input->info()->dimension(height_idx)))); |
| build_options.add_option(std::string("-DSRC_WIDTH=" + support::cpp11::to_string(_input->info()->dimension(width_idx)))); |
| build_options.add_option(std::string("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()))); |
| build_options.add_option(std::string("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()))); |
| build_options.add_option(std::string("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom()))); |
| build_options.add_option(std::string("-DSTRIDE_Y=" + support::cpp11::to_string(_conv_stride_y))); |
| if(run_optimized_for_bifrost_nhwc) |
| { |
| const unsigned int num_elems_read_per_iteration_x = 4; |
| _border_size.right = num_elems_read_per_iteration_x; |
| build_options.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_read_per_iteration_x)); |
| } |
| } |
| build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(data_type))); |
| |
| if(is_data_type_quantized(data_type)) |
| { |
| const UniformQuantizationInfo iqinfo = _input->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo wqinfo = _weights->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo oqinfo = _output->info()->quantization_info().uniform(); |
| |
| float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale; |
| int output_multiplier = 0; |
| int output_shift = 0; |
| quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); |
| build_options.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); |
| build_options.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); |
| build_options.add_option("-DKERNEL_SIZE=" + support::cpp11::to_string(kernel_size)); |
| |
| // Create kernel |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("direct_convolution_quantized", build_options.options())); |
| |
| // Set static kernel arguments |
| unsigned int idx = 3 * num_arguments_per_3D_tensor() + ((_biases != nullptr) ? num_arguments_per_1D_tensor() : 0) + 1; |
| _kernel.setArg(idx++, -iqinfo.offset); |
| _kernel.setArg(idx++, -wqinfo.offset); |
| _kernel.setArg(idx++, oqinfo.offset); |
| } |
| else |
| { |
| // Create kernel |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), build_options.options())); |
| } |
| } |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, gpu_target); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| ICLKernel::configure_internal(win_config.second); |
| |
| // Set config_id for enabling LWS tuning |
| _config_id = "direct_convolution_"; |
| _config_id += lower_string(string_from_data_type(data_type)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(kernel_size); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(border_size().left); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(border_size().top); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(border_size().right); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(border_size().bottom); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(_conv_stride_x); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(_conv_stride_y); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(width_idx)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(height_idx)); |
| _config_id += "_"; |
| _config_id += lower_string(string_from_data_layout(_data_layout)); |
| } |
| |
| Status CLDirectConvolutionLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, |
| const GPUTarget target) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, target).first); |
| |
| return Status{}; |
| } |
| |
| void CLDirectConvolutionLayerKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| // Get initial windows |
| Window slice = window.first_slice_window_3D(); |
| Window win_in = window; |
| |
| win_in.adjust(Window::DimX, -_border_size.left, true); |
| win_in.adjust(Window::DimY, -_border_size.top, true); |
| |
| const int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); |
| const int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); |
| |
| win_in.set_dimension_step(width_idx, window[width_idx].step() * _conv_stride_x); |
| win_in.set_dimension_step(height_idx, window[height_idx].step() * _conv_stride_y); |
| |
| Window slice_in = win_in.first_slice_window_3D(); |
| unsigned int idx1 = 2 * num_arguments_per_3D_tensor(); |
| add_3D_tensor_argument(idx1, _weights, slice); |
| |
| if(_biases != nullptr) |
| { |
| Window slice_biases; |
| slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape()); |
| add_1D_tensor_argument(idx1, _biases, slice_biases); |
| } |
| |
| _kernel.setArg(idx1++, static_cast<unsigned int>(_weights->info()->strides_in_bytes()[3])); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, _input, slice_in); |
| add_3D_tensor_argument(idx, _output, slice); |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in)); |
| } |
| } // namespace arm_compute |