From 561c176598cd14245e2e7918fdf136d1c888d1da Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Fri, 16 Apr 2021 15:08:59 +0100 Subject: Rework OpenCL Depthwise Convolution - Remove dedicated kernels for NCHW. Now we only use NHWC with permute - Remove specialized kernels for 3x3 NHWC - Simplify CLDepthwiseConvolutionLayer.cpp to call just the native implementation for both floating-point and quantized data types - Develop two parametric opencl kernels for depthwise convolution layer NHWC (floating-point and quantized) - Add support to export the weights to cl_image - Extend test for depthwise convolution on opencl Resolves COMPMID-4417 Change-Id: I253dd5d959a70783c82e62b1771a5e9f91621cb0 Signed-off-by: Gian Marco Iodice Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5806 Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins Reviewed-by: Giorgio Arena --- .../CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp | 432 --------------------- .../CLDepthwiseConvolutionLayer3x3NCHWKernel.h | 131 ------- .../CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp | 238 ------------ .../CLDepthwiseConvolutionLayer3x3NHWCKernel.h | 110 ------ .../CLDepthwiseConvolutionLayerNativeKernel.cpp | 223 ++++++----- .../CLDepthwiseConvolutionLayerNativeKernel.h | 33 +- 6 files changed, 139 insertions(+), 1028 deletions(-) delete mode 100644 src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp delete mode 100644 src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h delete mode 100644 src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp delete mode 100644 src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h (limited to 'src/core/CL/kernels') diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp deleted file mode 100644 index dda70d2231..0000000000 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp +++ /dev/null @@ -1,432 +0,0 @@ -/* - * Copyright (c) 2018-2021 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 "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h" - -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/core/utils/quantization/AsymmHelpers.h" -#include "src/core/AccessWindowStatic.h" -#include "src/core/CL/CLValidate.h" -#include "src/core/CL/ICLKernel.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" -#include "support/StringSupport.h" - -namespace arm_compute -{ -using namespace arm_compute::misc::shape_calculator; - -namespace -{ -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D dilation, - const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) -{ - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.enabled()) && (input->data_type() == DataType::QASYMM8 || input->data_type() == DataType::QASYMM8_SIGNED) - && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) - && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU) - && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU) - && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC), - "For QASYMM8 only logistic, relu, lower bounded relu and lower-upper bounded relu are supported"); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != 3 || weights->dimension(1) != 3); - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3); - - ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); - - const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); - - if(biases != nullptr) - { - if(is_qasymm) - { - 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((biases->dimension(0) != weights->dimension(2)) && (weights->dimension(2) != 1 || biases->dimension(0) != weights->dimension(3))); - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - - if(is_qasymm) - { - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output_multipliers, output_shifts); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1); - ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1); - - if(is_data_type_quantized_per_channel(weights->data_type())) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != output_multipliers->dimension(0)); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != output_shifts->dimension(0)); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - ARM_COMPUTE_RETURN_ERROR_ON(1 != output_multipliers->dimension(0)); - ARM_COMPUTE_RETURN_ERROR_ON(1 != output_shifts->dimension(0)); - } - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - } - - if(output->total_size() != 0) - { - const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation }; - const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, info); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); - } - - return Status{}; -} - -std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, std::string &kernel_name, const Size2D dilation) -{ - // Output auto inizialitation if not yet initialized - const ConvolutionInfo info - { - conv_info, depth_multiplier, ActivationLayerInfo(), dilation - }; - const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, info); - auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_quantization_info(output->quantization_info())); - - const unsigned int conv_stride_x = conv_info.stride().first; - const unsigned int conv_stride_y = conv_info.stride().second; - const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); - - // Configure kernel window - 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; - - if(input->data_type() == DataType::F16) - { - kernel_name = "depthwise_convolution_3x3_f16"; - num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type()); - num_elems_written_per_iteration_y = 1; - num_elems_read_per_iteration_y = 3; - switch(conv_stride_x) - { - case 1: - num_elems_read_per_iteration_x = 8; - break; - case 2: - num_elems_read_per_iteration_x = 9; - break; - case 3: - num_elems_read_per_iteration_x = 16; - break; - default: - num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x; - break; - } - if(conv_stride_x == 1 && conv_stride_y == 1) - { - kernel_name = "depthwise_convolution_3x3_stridex1_stridey1_f16"; - num_elems_read_per_iteration_x = 8; - num_elems_written_per_iteration_x = 4; - num_elems_read_per_iteration_y = 6; - num_elems_written_per_iteration_y = 4; - } - else if(conv_stride_x == 2 && conv_stride_y == 2) - { - kernel_name = "depthwise_convolution_3x3_stridex2_stridey2_f16"; - num_elems_read_per_iteration_x = 10; - num_elems_written_per_iteration_x = 4; - num_elems_read_per_iteration_y = 5; - num_elems_written_per_iteration_y = 2; - } - } - else if(input->data_type() == DataType::F32) - { - if(conv_stride_x == 1 && conv_stride_y == 1) - { - kernel_name = "depthwise_convolution_3x3_stridex1_stridey1_f32"; - num_elems_read_per_iteration_x = 4; - num_elems_read_per_iteration_y = 6; - num_elems_written_per_iteration_x = 2; - num_elems_written_per_iteration_y = 4; - } - else if(conv_stride_x == 2 && conv_stride_y == 2) - { - kernel_name = "depthwise_convolution_3x3_stridex2_stridey2_f32"; - num_elems_read_per_iteration_x = 6; - num_elems_read_per_iteration_y = 5; - num_elems_written_per_iteration_x = 2; - num_elems_written_per_iteration_y = 2; - } - else - { - kernel_name = "depthwise_convolution_3x3"; - num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type()); - num_elems_written_per_iteration_y = 1; - num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x; - num_elems_read_per_iteration_y = 3; - } - } - else - { - const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()) && !is_data_type_quantized_per_channel(weights->data_type()); - - kernel_name = is_qasymm ? "dwc_3x3_native_quantized8" : "depthwise_convolution_3x3"; - kernel_name += (is_qasymm && is_dot8_supported ? "_dot8" : ""); - kernel_name += (is_qasymm ? "_nchw" : ""); - - num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type()); - num_elems_written_per_iteration_y = (is_qasymm && conv_stride_y == 1 && dilation.y() == 1) ? 2 : 1; - num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x + (conv_stride_x > 1 ? 1 : 0); - num_elems_read_per_iteration_y = num_elems_written_per_iteration_y + 2; - } - // The OpenCL routine convolution1x3 does loadn(addr), loadn(addr + dilation_x) and loadn(addr + 2 * dilation_x) on the input. - // Each of the three convolution1x3 gets called by passing addr, (addr + dilation_y) and (addr + 2 * dilation_y) - // Hence we must add 2 * dilation.x/y() to the number of elements read in those axes per thread - num_elems_read_per_iteration_x += 2 * dilation.x(); - num_elems_read_per_iteration_y += 2 * dilation.y(); - - // Create window and update padding - Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y)); - - AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.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, 3, 3); - AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y); - - bool window_changed = update_window_and_padding(win, input_access, weights_access, output_access); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} -} // namespace - -CLDepthwiseConvolutionLayer3x3NCHWKernel::CLDepthwiseConvolutionLayer3x3NCHWKernel() - : _border_size(0), _input(), _output(), _weights(), _biases(), _conv_stride_y(1), _output_multipliers(), _output_shifts(), _is_quantized(false), _conv_stride_x(0), _conv_pad_top(0), _conv_pad_left(0) -{ -} - -BorderSize CLDepthwiseConvolutionLayer3x3NCHWKernel::border_size() const -{ - return _border_size; -} - -void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation, - const ICLTensor *output_multipliers, const ICLTensor *output_shifts) -{ - configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation, output_multipliers, output_shifts); -} - -void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation, - const ICLTensor *output_multipliers, const ICLTensor *output_shifts) -{ - 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, act_info, dilation, - (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, - (output_shifts != nullptr) ? output_shifts->info() : nullptr)); - - _input = input; - _output = output; - _weights = weights; - _biases = biases; - _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(); - _output_multipliers = output_multipliers; - _output_shifts = output_shifts; - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); - - // Configure kernel window - std::string kernel_name; - - auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, kernel_name, dilation); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - ICLKernel::configure_internal(win_config.second); - - _border_size = BorderSize(input->info()->padding()); - - // Set build options - CLBuildOptions build_opts; - build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation()))); - build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(_output->info()->tensor_shape().z())); - build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier)); - build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x)); - build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x())); - build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); - build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); - - 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(); - - const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->info()->data_type()); - const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()) && !is_quantized_per_channel; - build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(_conv_stride_y)); - build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iq_info.offset)); - build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wq_info.offset)); - build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oq_info.offset)); - build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * iq_info.offset * wq_info.offset)); - build_opts.add_option_if(is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION"); - build_opts.add_option_if(is_dot8_supported, "-DIS_DOT8"); - - // Compute non-per-channel multiplier and shift anyway to make OpenCL kernel simpler - float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; - int output_multiplier = 0; - int output_shift = 0; - quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); - build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); - build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); - - if(act_info.enabled()) - { - int a_val{}; - int b_val{}; - std::tie(b_val, a_val) = get_quantized_activation_min_max(act_info, input->info()->data_type(), oq_info); - - const int o1 = oq_info.offset; - - build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val)); - build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val)); - build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1)); - - const float s1 = iq_info.scale; - build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1)); - build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1)); - } - - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); - build_opts.add_option("-DWEIGHTS_TYPE=" + get_cl_type_from_data_type(weights->info()->data_type())); - build_opts.add_option("-DWEIGHTS_PROMOTED_TYPE=" + get_cl_promoted_type_from_data_type(weights->info()->data_type())); - } - else - { - build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a())); - build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b())); - build_opts.add_option_if(act_info.enabled(), "-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); - build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(win_config.second.x().step())); - } - - build_opts.add_option_if(input->info()->data_type() == DataType::F16, "-DIS_F16"); - build_opts.add_option_if(input->info()->data_type() == DataType::F32, "-DIS_F32"); - - _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); - - // Set config_id for enabling LWS tuning - _config_id = kernel_name; - _config_id += "_"; - _config_id += lower_string(string_from_data_type(input->info()->data_type())); - _config_id += "_"; - _config_id += support::cpp11::to_string(input->info()->dimension(0)); - _config_id += "_"; - _config_id += support::cpp11::to_string(input->info()->dimension(1)); - _config_id += "_"; - _config_id += support::cpp11::to_string(input->info()->dimension(2)); - _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(0)); - _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(1)); -} - -Status CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, - const Size2D &dilation, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) -{ - std::string kernel_name; - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation, output_multipliers, output_shifts)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), - conv_info, depth_multiplier, kernel_name, dilation) - .first); - - return Status{}; -} - -void CLDepthwiseConvolutionLayer3x3NCHWKernel::run(const Window &window, cl::CommandQueue &queue) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); - - Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); - - // Create input window and adjust - Window collapsed_in = collapsed; - collapsed_in.adjust(Window::DimX, -_conv_pad_left, true); - collapsed_in.adjust(Window::DimY, -_conv_pad_top, true); - collapsed_in.set_dimension_step(Window::DimX, collapsed_in.x().step() * _conv_stride_x); - collapsed_in.set_dimension_step(Window::DimY, collapsed_in.y().step() * _conv_stride_y); - - Window slice_in = collapsed_in.first_slice_window_3D(); - Window slice_out = collapsed.first_slice_window_3D(); - Window slice_weights = window.first_slice_window_3D(); - slice_weights.set_dimension_step(Window::DimX, 0); - slice_weights.set_dimension_step(Window::DimY, 0); - - unsigned int idx = 3 * num_arguments_per_3D_tensor(); - - // Set output multipliers in case of quantized data type - if(_is_quantized) - { - Window slice; - slice.use_tensor_dimensions(_output_multipliers->info()->tensor_shape()); - add_1D_tensor_argument(idx, _output_multipliers, slice); - add_1D_tensor_argument(idx, _output_shifts, slice); - } - - // Set biases - if(_biases != nullptr) - { - Window slice_biases; - slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape()); - add_1D_tensor_argument(idx, _biases, slice_biases); - } - - do - { - idx = 0; - add_3D_tensor_argument(idx, _input, slice_in); - add_3D_tensor_argument(idx, _output, slice_out); - add_3D_tensor_argument(idx, _weights, slice_weights); - - enqueue(queue, *this, slice_out, lws_hint()); - } - while(collapsed.slide_window_slice_3D(slice_out) && collapsed_in.slide_window_slice_3D(slice_in)); -} -} // namespace arm_compute diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h deleted file mode 100644 index c4e475f6f2..0000000000 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h +++ /dev/null @@ -1,131 +0,0 @@ -/* - * Copyright (c) 2018-2021 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. - */ -#ifndef ARM_COMPUTE_CLDEPTHWISECONVOLUTIONNCHWKERNEL3x3_H -#define ARM_COMPUTE_CLDEPTHWISECONVOLUTIONNCHWKERNEL3x3_H - -#include "src/core/CL/ICLKernel.h" - -namespace arm_compute -{ -class ICLTensor; - -/** Interface for the kernel to run a 3x3 depthwise convolution on a tensor when the data layout is NCHW. - */ -class CLDepthwiseConvolutionLayer3x3NCHWKernel : public ICLKernel -{ -public: - /** Default constructor */ - CLDepthwiseConvolutionLayer3x3NCHWKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDepthwiseConvolutionLayer3x3NCHWKernel(const CLDepthwiseConvolutionLayer3x3NCHWKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDepthwiseConvolutionLayer3x3NCHWKernel &operator=(const CLDepthwiseConvolutionLayer3x3NCHWKernel &) = delete; - /** Default Move Constructor. */ - CLDepthwiseConvolutionLayer3x3NCHWKernel(CLDepthwiseConvolutionLayer3x3NCHWKernel &&) = default; - /** Default move assignment operator */ - CLDepthwiseConvolutionLayer3x3NCHWKernel &operator=(CLDepthwiseConvolutionLayer3x3NCHWKernel &&) = default; - /** Initialize the function's source, destination, conv and border_size. - * - * @param[in] input Source tensor. DataType supported: QASYMM8/QASYMM8_SIGNED/F16/F32. - * @param[in] weights Weights tensor. A 3D tensor with dimensions [3, 3, IFM]. - * Data type supported: Same as @p input or QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when @p input is QASYMM8/QASYMM8_SIGNED. - * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. - * @param[out] output Destination tensor. Data type supported: Same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for QASYMM8 supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * @param[in] output_multipliers (Optional) Output multipliers tensor for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - * @param[in] output_shifts (Optional) Output shifts tensor for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - */ - void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U), - const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); - /** Initialize the function's source, destination, conv and border_size. - * - * @param[in] compile_context The compile context to be used. - * @param[in] input Source tensor. DataType supported: QASYMM8/QASYMM8_SIGNED/F16/F32. - * @param[in] weights Weights tensor. A 3D tensor with dimensions [3, 3, IFM]. - * Data type supported: Same as @p input or QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when @p input is QASYMM8/QASYMM8_SIGNED. - * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. - * @param[out] output Destination tensor. Data type supported: Same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for QASYMM8 supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * @param[in] output_multipliers (Optional) Output multipliers tensor for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - * @param[in] output_shifts (Optional) Output shifts tensor for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - */ - void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U), - const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); - /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayer3x3NCHWKernel - * - * @param[in] input Source tensor info. DataType supported: QASYMM8/QASYMM8_SIGNED/F16/F32. - * @param[in] weights Weights tensor info. A 3D tensor with dimensions [3, 3, IFM]. - * Data type supported: Same as @p input or QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL when @p input is QASYMM8/QASYMM8_SIGNED. - * @param[in] biases Biases tensor info. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. - * @param[in] output Destination tensor. Data type supported: Same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * @param[in] output_multipliers (Optional) Output multipliers tensor info for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - * @param[in] output_shifts (Optional) Output shifts tensor for quantized computations. In case of per-channel quantization, - * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), - const Size2D &dilation = Size2D(1U, 1U), const ITensorInfo *output_multipliers = nullptr, const ITensorInfo *output_shifts = nullptr); - - void run(const Window &window, cl::CommandQueue &queue) override; - BorderSize border_size() const override; - -private: - BorderSize _border_size; - const ICLTensor *_input; - ICLTensor *_output; - const ICLTensor *_weights; - const ICLTensor *_biases; - unsigned int _conv_stride_y; - const ICLTensor *_output_multipliers; - const ICLTensor *_output_shifts; - bool _is_quantized; - - unsigned int _conv_stride_x; - unsigned int _conv_pad_top; - unsigned int _conv_pad_left; -}; -} // namespace arm_compute -#endif /*ARM_COMPUTE_CLDEPTHWISECONVOLUTIONNCHWKERNEL3x3_H */ diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp deleted file mode 100644 index 91a2f5745a..0000000000 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp +++ /dev/null @@ -1,238 +0,0 @@ -/* - * Copyright (c) 2018-2021 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 "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h" - -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "src/core/AccessWindowStatic.h" -#include "src/core/CL/CLValidate.h" -#include "src/core/CL/ICLKernel.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.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, unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) -{ - ARM_COMPUTE_UNUSED(act_info); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier > 1); - - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1); - ARM_COMPUTE_RETURN_ERROR_ON(std::max(conv_info.pad_top(), conv_info.pad_bottom()) > 4); - - ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); - - const size_t weights_width = 3; - const size_t weights_height = 3; - - const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation }; - - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape( - *input, TensorInfo(TensorShape(weights_width, weights_height), 1, weights->data_type()).set_data_layout(DataLayout::NCHW), info); - - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(1) != weights_width) || (weights->dimension(2) != weights_height)); - - if(biases != nullptr) - { - ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != output_shape[0]); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); - - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - - if(output->total_size() != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); - } - - return Status{}; -} - -std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) -{ - ARM_COMPUTE_UNUSED(weights, bias); - ARM_COMPUTE_UNUSED(depth_multiplier); - - const bool is_stride_1_dilation_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1) && dilation.x() == 1 && dilation.y() == 1); - unsigned int num_rows_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1; - - Window win{}; - Status err{}; - - unsigned int num_elems_accessed_per_iteration = adjust_vec_size(4 / input->element_size(), input->dimension(0)); - win = calculate_max_window(*output, Steps(num_elems_accessed_per_iteration, num_rows_processed_per_iteration)); - - return std::make_pair(err, win); -} -} // namespace - -CLDepthwiseConvolutionLayer3x3NHWCKernel::CLDepthwiseConvolutionLayer3x3NHWCKernel() - : _input(), _output(), _weights(), _biases(), _num_planes_processed_per_iteration(1) -{ -} - -void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) -{ - configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); -} - -void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, 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, act_info, dilation)); - - auto padding_info = get_padding_info({ input, weights, biases, output }); - - auto win_config = validate_and_configure_window(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), - conv_info, depth_multiplier, dilation); - - const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); - const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1); - - _input = input; - _output = output; - _weights = weights; - _biases = biases; - _num_planes_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1; - - unsigned int num_elems_accessed_per_iteration = adjust_vec_size(4 / input->info()->element_size(), input->info()->dimension(0)); - unsigned int num_rows_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1; - - CLBuildOptions build_opts; - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type())); - build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation()))); - build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_accessed_per_iteration)); - build_opts.add_option("-DSRC_DIM_1=" + support::cpp11::to_string(_input->info()->dimension(1))); - build_opts.add_option("-DSRC_DIM_2=" + support::cpp11::to_string(_input->info()->dimension(2))); - build_opts.add_option("-DCONV_PAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); - build_opts.add_option("-DCONV_PAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); - build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(input->info()->dimension(0) % num_elems_accessed_per_iteration)); - build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); - build_opts.add_option_if(_input->info()->tensor_shape().total_size_upper(3) > 1, - "-DDST_DEPTH=" + support::cpp11::to_string(static_cast(std::ceil(_output->info()->dimension(2) / static_cast(_num_planes_processed_per_iteration))))); - build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a())); - build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b())); - - if(is_stride_1_dilation_1) - { - build_opts.add_option("-DNUM_ROWS_PROCESSED=" + support::cpp11::to_string(num_rows_processed_per_iteration)); - build_opts.add_option("-DNUM_PLANES_PROCESSED=" + support::cpp11::to_string(_num_planes_processed_per_iteration)); - build_opts.add_option("-DDST_DIM_1=" + support::cpp11::to_string(_output->info()->dimension(1))); - build_opts.add_option("-DDST_DIM_2=" + support::cpp11::to_string(_output->info()->dimension(2))); - build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string((input->info()->dimension(1) + conv_info.pad_left() + conv_info.pad_right()) % num_rows_processed_per_iteration)); - } - else - { - build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(conv_info.stride().first)); - build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second)); - build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x())); - build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); - } - - // Create kernel - std::string kernel_name; - kernel_name = std::string("depthwise_convolution_3x3_nhwc"); - kernel_name += (is_stride_1_dilation_1 ? "_stride1" : ""); - - ICLKernel::configure_internal(win_config.second); - _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); - - ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); - - // Set config_id for enabling LWS tuning - _config_id = kernel_name; - _config_id += "_"; - _config_id += support::cpp11::to_string(input->info()->dimension(0)); - _config_id += "_"; - _config_id += support::cpp11::to_string(input->info()->dimension(1)); - _config_id += "_"; - _config_id += support::cpp11::to_string(input->info()->dimension(2)); - _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(0)); - _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(1)); - _config_id += "_"; - _config_id += string_from_data_type(input->info()->data_type()); -} - -Status CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, 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 CLDepthwiseConvolutionLayer3x3NHWCKernel::run(const Window &window, cl::CommandQueue &queue) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); - - const size_t total_batches = _input->info()->tensor_shape().total_size_upper(3); - - Window win = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); - win.set(Window::DimZ, Window::Dimension(0, std::ceil(_output->info()->dimension(2) / static_cast(_num_planes_processed_per_iteration)) * total_batches, 1)); - - unsigned int idx = 2 * num_arguments_per_4D_tensor() + num_arguments_per_3D_tensor(); - - if(_biases != nullptr) - { - Window win_biases; - win_biases.use_tensor_dimensions(_biases->info()->tensor_shape()); - win_biases.set_dimension_step(Window::DimX, window.x().step()); - add_1D_tensor_argument(idx, _biases, win_biases); - } - - Window slice = win.first_slice_window_4D(); - do - { - unsigned int idx = 0; - add_4D_tensor_argument(idx, _input, slice); - add_4D_tensor_argument(idx, _output, slice); - add_3D_tensor_argument(idx, _weights, slice); - - enqueue(queue, *this, slice, lws_hint()); - } - while(win.slide_window_slice_4D(slice)); -} -} // namespace arm_compute diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h deleted file mode 100644 index ee47d98807..0000000000 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h +++ /dev/null @@ -1,110 +0,0 @@ -/* - * Copyright (c) 2018-2021 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. - */ -#ifndef ARM_COMPUTE_CLDEPTHWISECONVOLUTIONNHWCKERNEL3x3_H -#define ARM_COMPUTE_CLDEPTHWISECONVOLUTIONNHWCKERNEL3x3_H - -#include "src/core/CL/ICLKernel.h" - -namespace arm_compute -{ -class ICLTensor; - -/** Interface for the kernel to run a 3x3 depthwise convolution on a tensor when the data layout is NHWC. - */ -class CLDepthwiseConvolutionLayer3x3NHWCKernel : public ICLKernel -{ -public: - /** Default constructor */ - CLDepthwiseConvolutionLayer3x3NHWCKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDepthwiseConvolutionLayer3x3NHWCKernel(const CLDepthwiseConvolutionLayer3x3NHWCKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLDepthwiseConvolutionLayer3x3NHWCKernel &operator=(const CLDepthwiseConvolutionLayer3x3NHWCKernel &) = delete; - /** Default Move Constructor. */ - CLDepthwiseConvolutionLayer3x3NHWCKernel(CLDepthwiseConvolutionLayer3x3NHWCKernel &&) = default; - /** Default move assignment operator */ - CLDepthwiseConvolutionLayer3x3NHWCKernel &operator=(CLDepthwiseConvolutionLayer3x3NHWCKernel &&) = default; - /** Default move assignment operator. */ - /** Initialize the function's source, destination, conv and border_size. - * - * @param[in] input Source tensor. DataType supported: F16/F32. - * @param[in] weights Weights tensor. A 3D tensor with dimensions [IFM, 3, 3]. - * Data type supported: Same as @p input. - * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input. - * @param[out] output Destination tensor. Data type supported: Same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - */ - void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - /** Initialize the function's source, destination, conv and border_size. - * - * @param[in] compile_context The compile context to be used. - * @param[in] input Source tensor. DataType supported: F16/F32. - * @param[in] weights Weights tensor. A 3D tensor with dimensions [IFM, 3, 3]. - * Data type supported: Same as @p input. - * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input. - * @param[out] output Destination tensor. Data type supported: Same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - */ - void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayer3x3NHWCKernel - * - * @param[in] input Source tensor info. DataType supported: F16/F32. - * @param[in] weights Weights tensor info. A 3D tensor with dimensions [IFM, 3, 3]. - * Data type supported: Same as @p input. - * @param[in] biases Biases tensor info. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. - * Data type supported: Same as @p input. - * @param[in] output Destination tensor info. Data type supported: Same as @p input. - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); - - // Inherited methods overridden: - void run(const Window &window, cl::CommandQueue &queue) override; - -private: - const ICLTensor *_input; - ICLTensor *_output; - const ICLTensor *_weights; - const ICLTensor *_biases; - - unsigned int _num_planes_processed_per_iteration; -}; -} // namespace arm_compute -#endif /*ARM_COMPUTE_CLDEPTHWISECONVOLUTIONNHWCKERNEL3x3_H */ diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp index fcfa7f878d..67ca341b0d 100644 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp @@ -31,8 +31,10 @@ #include "arm_compute/core/Utils.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "src/core/CL/CLUtils.h" #include "src/core/CL/CLValidate.h" #include "src/core/CL/ICLKernel.h" +#include "src/core/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "support/StringSupport.h" @@ -41,25 +43,28 @@ namespace arm_compute { namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const DWCComputeKernelInfo &dwc_info, + const ConvolutionInfo &conv_info, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) { - ARM_COMPUTE_UNUSED(dwc_info); ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(input, DataLayout::NHWC); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier > 1 && dwc_weights_info.n0 != 1); - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1); - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().second < 1); - ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.depth_multiplier > 1 && dwc_info.n0 != 1); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().first > 1 && dwc_info.m0 != 1); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.dilation.x() > 1 && dwc_info.m0 != 1); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((dwc_info.export_weights_to_cl_image == true) && (export_weights_to_cl_image(weights) == false), "Export to cl_image not supported!"); + ARM_COMPUTE_RETURN_ERROR_ON((dwc_info.export_weights_to_cl_image == true) && (conv_info.depth_multiplier > 1)); + ARM_COMPUTE_RETURN_ERROR_ON((dwc_info.export_weights_to_cl_image == true) && ((dwc_info.n0 % 4) != 0)); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().first < 1); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().second < 1); + ARM_COMPUTE_RETURN_ERROR_ON((conv_info.dilation.x() < 1) || (conv_info.dilation.y() < 1)); const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); ARM_COMPUTE_UNUSED(idx_c); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_c) != (input->dimension(idx_c) * depth_multiplier)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_c) != (input->dimension(idx_c) * conv_info.depth_multiplier)); - const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation }; - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, info); + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info); const bool is_quantized = is_data_type_quantized(input->data_type()); @@ -134,111 +139,131 @@ CLDepthwiseConvolutionLayerNativeKernel::CLDepthwiseConvolutionLayerNativeKernel _depth_multiplier(1), _output_multipliers(nullptr), _output_shifts(nullptr), + _export_to_cl_image(false), _is_quantized(false) { } -void CLDepthwiseConvolutionLayerNativeKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, +void CLDepthwiseConvolutionLayerNativeKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, + const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info, const ICLTensor *output_multipliers, const ICLTensor *output_shifts) { - configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation, output_multipliers, output_shifts); + configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, dwc_info, conv_info, output_multipliers, output_shifts); } void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation, + const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info, const ICLTensor *output_multipliers, const ICLTensor *output_shifts) { 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(), - dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation, - (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, (output_shifts != nullptr) ? output_shifts->info() : nullptr)); + dwc_info, conv_info, (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, (output_shifts != nullptr) ? output_shifts->info() : nullptr)); auto padding_info = get_padding_info({ input, output }); - const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation }; - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*(input->info()), *(weights->info()), info); + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*(input->info()), *(weights->info()), conv_info); auto_init_if_empty(*(output->info()), input->info()->clone()->set_tensor_shape(output_shape).set_quantization_info(output->info()->quantization_info())); _input = input; _output = output; _weights = weights; _biases = biases; - _depth_multiplier = depth_multiplier; + _depth_multiplier = conv_info.depth_multiplier; _output_multipliers = output_multipliers; _output_shifts = output_shifts; + _export_to_cl_image = dwc_info.export_weights_to_cl_image; _is_quantized = is_data_type_quantized(input->info()->data_type()); - const unsigned int n0 = adjust_vec_size(dwc_weights_info.n0, input->info()->dimension(0)); + const unsigned int n0 = adjust_vec_size(dwc_info.n0, input->info()->dimension(0)); + const unsigned int m0 = std::min(dwc_info.m0, (unsigned int)output->info()->dimension(1)); + std::string kernel_name = ""; CLBuildOptions build_opts; - build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); - build_opts.add_option_if(_input->info()->tensor_shape().total_size_upper(3) > 1, "-DDST_DEPTH=" + support::cpp11::to_string(static_cast(_output->info()->dimension(2)))); - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type())); - build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(dwc_info.activation_info.activation()))); - build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier)); + + // Update the padding for the weights tensor if we can export to cl_image + if(_export_to_cl_image) + { + arm_compute::opencl::kernels::gemm::update_padding_for_cl_image(weights->info()); + } + + build_opts.add_option("-cl-fast-relaxed-math"); + build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(conv_info.act_info.activation()))); + build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(conv_info.depth_multiplier)); + build_opts.add_option("-DSRC_TENSOR_TYPE=BUFFER"); + build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(_input->info()->dimension(1))); + build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(_input->info()->dimension(2))); + // Note: SRC_DATA_TYPE must have the same data type of WEI_DATA_TYPE. In quantized, we could + // have a case where the data types for the activation and weights are different. However, since the implementation + // only works when both have same data type, we have to change the offset to take into account this aspect + build_opts.add_option("-DSRC_DATA_TYPE=" + get_cl_type_from_data_type(_weights->info()->data_type())); + build_opts.add_option("-DDST_TENSOR_TYPE=BUFFER"); + build_opts.add_option("-DDST_WIDTH=" + support::cpp11::to_string(_output->info()->dimension(1))); + build_opts.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(_output->info()->dimension(2))); + build_opts.add_option("-DDST_DATA_TYPE=" + get_cl_type_from_data_type(_output->info()->data_type())); + build_opts.add_option_if_else(_export_to_cl_image, "-DWEI_TENSOR_TYPE=IMAGE", "-DWEI_TENSOR_TYPE=BUFFER"); + build_opts.add_option("-DWEI_WIDTH=" + support::cpp11::to_string(weights->info()->dimension(1))); + build_opts.add_option("-DWEI_HEIGHT=" + support::cpp11::to_string(weights->info()->dimension(2))); + build_opts.add_option("-DWEI_DATA_TYPE=" + get_cl_type_from_data_type(weights->info()->data_type())); + build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_stride_info.pad_top())); + build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_stride_info.pad_left())); + build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.pad_stride_info.stride().first)); + build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.pad_stride_info.stride().second)); + build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(conv_info.dilation.x())); + build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(conv_info.dilation.y())); build_opts.add_option("-DN0=" + support::cpp11::to_string(n0)); - build_opts.add_option("-DSRC_DIM1=" + support::cpp11::to_string(_input->info()->dimension(1))); - build_opts.add_option("-DSRC_DIM2=" + support::cpp11::to_string(_input->info()->dimension(2))); - build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(weights->info()->dimension(1))); - build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(weights->info()->dimension(2))); - build_opts.add_option("-DCONV_PAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); - build_opts.add_option("-DCONV_PAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); - build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(conv_info.stride().first)); - build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second)); - build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x())); - build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); - build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(_input->info()->dimension(0) % n0)); - - std::string kernel_name = (_is_quantized) ? "dwc_MxN_native_quantized8_nhwc" : "dwc_MxN_native_fp_nhwc"; + build_opts.add_option("-DM0=" + support::cpp11::to_string(m0)); + build_opts.add_option("-DM0_A=" + support::cpp11::to_string(weights->info()->dimension(1) + m0 - 1)); + build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(_input->info()->dimension(0) % n0)); + build_opts.add_option_if(_input->info()->num_dimensions() > 3, "-DBATCHED_EXECUTION"); + if(biases != nullptr) + { + build_opts.add_option(std::string("-DHAS_BIAS")); + build_opts.add_option(std::string("-DBIA_DATA_TYPE=" + get_cl_type_from_data_type(biases->info()->data_type()))); + } 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(); + kernel_name = "dwc_native_quantized_nhwc"; + const UniformQuantizationInfo iqinfo = input->info()->quantization_info().uniform(); + const UniformQuantizationInfo wqinfo = weights->info()->quantization_info().uniform(); + const UniformQuantizationInfo oqinfo = output->info()->quantization_info().uniform(); - build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iq_info.offset)); - build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wq_info.offset)); - build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oq_info.offset)); - build_opts.add_option_if(is_data_type_quantized_per_channel(weights->info()->data_type()), "-DPER_CHANNEL_QUANTIZATION"); + PixelValue zero_value = PixelValue(0, input->info()->data_type(), input->info()->quantization_info()); + int zero_value_s32; + zero_value.get(zero_value_s32); - // Compute non-per-channel multiplier and shift anyway to make OpenCL kernel simpler - float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; + 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_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); - build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); - - if(dwc_info.activation_info.enabled()) - { - int a_val{}; - int b_val{}; - std::tie(b_val, a_val) = get_quantized_activation_min_max(dwc_info.activation_info, input->info()->data_type(), oq_info); - - const int o1 = oq_info.offset; - - build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val)); - build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val)); - build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1)); - - const float s1 = iq_info.scale; - build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1)); - build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1)); - } - - build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); - build_opts.add_option("-DWEIGHTS_TYPE=" + get_cl_type_from_data_type(weights->info()->data_type())); + build_opts.add_option("-DDST_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); + build_opts.add_option("-DDST_SHIFT=" + support::cpp11::to_string(output_shift)); + build_opts.add_option("-DSRC_OFFSET=" + support::cpp11::to_string(-iqinfo.offset)); + build_opts.add_option("-DWEI_OFFSET=" + support::cpp11::to_string(-wqinfo.offset)); + build_opts.add_option("-DDST_OFFSET=" + support::cpp11::to_string(oqinfo.offset)); + build_opts.add_option("-DZERO_VALUE=" + support::cpp11::to_string(zero_value_s32)); + build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(DataType::S32)); + build_opts.add_option("-DDST_MULTIPLIERS_DATA_TYPE=" + get_cl_type_from_data_type(_output_multipliers->info()->data_type())); + build_opts.add_option("-DDST_SHIFTS_DATA_TYPE=" + get_cl_type_from_data_type(_output_shifts->info()->data_type())); + build_opts.add_option_if_else(weights->info()->data_type() == DataType::QSYMM8_PER_CHANNEL, "-DQUANTIZATION_TYPE=PER_CHANNEL", "-DQUANTIZATION_TYPE=PER_TENSOR"); + // Note: We expect the input and output tensors to always adopt a per-tensor quantization approach + int a_val{}; + int b_val{}; + std::tie(b_val, a_val) = get_quantized_activation_min_max(conv_info.act_info, input->info()->data_type(), oqinfo); + + build_opts.add_option_if(conv_info.act_info.enabled(), "-DA_VAL=" + support::cpp11::to_string(a_val)); + build_opts.add_option_if(conv_info.act_info.enabled(), "-DB_VAL=" + support::cpp11::to_string(b_val)); } else { - build_opts.add_option_if(dwc_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(dwc_info.activation_info.a())); - build_opts.add_option_if(dwc_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(dwc_info.activation_info.b())); + kernel_name = "dwc_native_fp_nhwc"; + build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.add_option("-DZERO_VALUE=" + support::cpp11::to_string(0)); + build_opts.add_option_if(conv_info.act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(conv_info.act_info.a())); + build_opts.add_option_if(conv_info.act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(conv_info.act_info.b())); } - Window win = calculate_max_window(*(output->info()), Steps(n0)); + Window win = calculate_max_window(*(output->info()), Steps(n0, m0)); ICLKernel::configure_internal(win); _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); @@ -264,10 +289,9 @@ void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext & } Status CLDepthwiseConvolutionLayerNativeKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const DWCWeightsKernelInfo &dwc_weights_info, const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const Size2D &dilation, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) + const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation, output_multipliers, output_shifts)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, dwc_info, conv_info, output_multipliers, output_shifts)); return Status{}; } @@ -278,37 +302,46 @@ void CLDepthwiseConvolutionLayerNativeKernel::run(const Window &window, cl::Comm // Collapse window Window window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ); - Window slice_in = window.first_slice_window_4D(); - Window slice_out = window_collapsed.first_slice_window_4D(); + + Window slice = window_collapsed.first_slice_window_4D(); if(_depth_multiplier != 1) { - ARM_COMPUTE_ERROR_ON(slice_out.x().step() != 1); - slice_out.set(Window::DimX, Window::Dimension(0, _input->info()->tensor_shape()[0], 1)); + // If the depth multiplier > 1, we need to use the input channels rather than the output channels + ARM_COMPUTE_ERROR_ON(slice.x().step() != 1); + slice.set(Window::DimX, Window::Dimension(0, _input->info()->tensor_shape()[0], 1)); } - unsigned int idx = 2 * num_arguments_per_4D_tensor() + num_arguments_per_3D_tensor(); + cl::Image2D weights_cl_image; - // Set output multipliers in case of quantized data type - if(_is_quantized) + if(_export_to_cl_image) { - add_1D_tensor_argument(idx, _output_multipliers, slice_in); - add_1D_tensor_argument(idx, _output_shifts, slice_in); + const size_t image_w = _weights->info()->dimension(0) / 4; + const size_t image_h = _weights->info()->dimension(1) * _weights->info()->dimension(2) * _weights->info()->dimension(3); + const TensorShape shape2d(image_w, image_h); + const size_t image_row_pitch = _weights->info()->strides_in_bytes()[1]; + + // Export cl_buffer to cl_image + weights_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), _weights->cl_buffer(), shape2d, _weights->info()->data_type(), image_row_pitch); } - if(_biases != nullptr) + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, slice); + add_4D_tensor_argument(idx, _output, slice); + if(_export_to_cl_image) { - add_1D_tensor_argument(idx, _biases, slice_in); + _kernel.setArg(idx++, weights_cl_image); } - - do + add_4D_tensor_argument(idx, _weights, slice); + if(_is_quantized) + { + add_1D_tensor_argument(idx, _output_multipliers, slice); + add_1D_tensor_argument(idx, _output_shifts, slice); + } + if(_biases != nullptr) { - idx = 0; - add_4D_tensor_argument(idx, _input, slice_in); - add_4D_tensor_argument(idx, _output, slice_out); - add_3D_tensor_argument(idx, _weights, slice_out); - enqueue(queue, *this, slice_out, lws_hint()); + add_1D_tensor_argument(idx, _biases, slice); } - while(window_collapsed.slide_window_slice_4D(slice_out) && window.slide_window_slice_4D(slice_in)); + enqueue(queue, *this, slice, lws_hint()); } } // namespace arm_compute diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h index 325f4e7067..68e4ccfc1e 100644 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2020 Arm Limited. + * Copyright (c) 2019-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -55,19 +55,15 @@ public: * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. * @param[out] output Destination tensor. Data type supported: Same as @p input. - * @param[in] dwc_weights_info Depthwise convolution layer weights info to retrieve the number of output elements processed by each thread * @param[in] dwc_info Depthwise convolution layer info - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). + * @param[in] conv_info Convolution info (padding, stride, dilation, ...) * @param[in] output_multipliers (Optional) Output multipliers tensor for quantized computations. In case of per-channel quantization, * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 * @param[in] output_shifts (Optional) Output shifts tensor for quantized computations. In case of per-channel quantization, * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 */ - void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, const Size2D &dilation = Size2D(1U, 1U), - const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); + void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const DWCComputeKernelInfo &dwc_info, + const ConvolutionInfo &conv_info, const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); /** Initialize the function's source, destination and parameters * * @param[in] compile_context The compile context to be used. @@ -77,19 +73,15 @@ public: * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. * @param[out] output Destination tensor. Data type supported: Same as @p input. - * @param[in] dwc_weights_info Depthwise convolution layer weights info to retrieve the number of output elements processed by each thread * @param[in] dwc_info Depthwise convolution layer info - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). + * @param[in] conv_info Convolution info (padding, stride, dilation, ...) * @param[in] output_multipliers (Optional) Output multipliers tensor for quantized computations. In case of per-channel quantization, * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 * @param[in] output_shifts (Optional) Output shifts tensor for quantized computations. In case of per-channel quantization, * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 */ - void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, const Size2D &dilation = Size2D(1U, 1U), - const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); + void configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const DWCComputeKernelInfo &dwc_info, + const ConvolutionInfo &conv_info, const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayerNativeKernel * * @param[in] input Source tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/FP32/FP16. Data layout supported: NHWC @@ -98,11 +90,8 @@ public: * @param[in] biases Biases tensor info. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input, S32 when input is QASYMM8/QASYMM8_SIGNED. * @param[in] output Destination tensor info. Data type supported: Same as @p input. - * @param[in] dwc_weights_info Depthwise convolution layer weights info to retrieve the number of output elements processed by each thread * @param[in] dwc_info Depthwise convolution layer info - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. - * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). + * @param[in] conv_info Convolution info (padding, stride, dilation, ...) * @param[in] output_multipliers (Optional) Output multipliers tensor for quantized computations. In case of per-channel quantization, * the number of multipliers must be equal to the number of filters (IFM). Supported data types: S32 * @param[in] output_shifts (Optional) Output shifts tensor for quantized computations. In case of per-channel quantization, @@ -110,9 +99,8 @@ public: * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const DWCWeightsKernelInfo &dwc_weights_info, - const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, const Size2D &dilation = Size2D(1U, 1U), - const ITensorInfo *output_multipliers = nullptr, const ITensorInfo *output_shifts = nullptr); + static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const DWCComputeKernelInfo &dwc_info, + const ConvolutionInfo &conv_info, const ITensorInfo *output_multipliers = nullptr, const ITensorInfo *output_shifts = nullptr); // Inherited methods overridden: void run(const Window &window, cl::CommandQueue &queue) override; @@ -125,6 +113,7 @@ private: unsigned int _depth_multiplier; const ICLTensor *_output_multipliers; const ICLTensor *_output_shifts; + bool _export_to_cl_image; bool _is_quantized; }; } // namespace arm_compute -- cgit v1.2.1