diff options
Diffstat (limited to 'src/core/CL/kernels')
6 files changed, 1028 insertions, 139 deletions
diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp new file mode 100644 index 0000000000..dda70d2231 --- /dev/null +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp @@ -0,0 +1,432 @@ +/* + * 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<Status, Window> 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 new file mode 100644 index 0000000000..c4e475f6f2 --- /dev/null +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h @@ -0,0 +1,131 @@ +/* + * 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 new file mode 100644 index 0000000000..91a2f5745a --- /dev/null +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp @@ -0,0 +1,238 @@ +/* + * 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<Status, Window> 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<int>(std::ceil(_output->info()->dimension(2) / static_cast<float>(_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<float>(_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 new file mode 100644 index 0000000000..ee47d98807 --- /dev/null +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h @@ -0,0 +1,110 @@ +/* + * 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 65c4b8568c..4cc0e462c4 100644 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp @@ -31,10 +31,8 @@ #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" @@ -43,28 +41,25 @@ namespace arm_compute { namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const DWCComputeKernelInfo &dwc_info, - const ConvolutionInfo &conv_info, +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, 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(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)); + 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)); 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) * conv_info.depth_multiplier)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_c) != (input->dimension(idx_c) * depth_multiplier)); - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info); + 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 bool is_quantized = is_data_type_quantized(input->data_type()); @@ -139,132 +134,112 @@ CLDepthwiseConvolutionLayerNativeKernel::CLDepthwiseConvolutionLayerNativeKernel _depth_multiplier(1), _output_multipliers(nullptr), _output_shifts(nullptr), - _export_to_cl_image(false), _is_quantized(false) { _type = CLKernelType::DEPTHWISE; } -void CLDepthwiseConvolutionLayerNativeKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info, +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, const ICLTensor *output_multipliers, const ICLTensor *output_shifts) { - configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, dwc_info, conv_info, output_multipliers, 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); } void CLDepthwiseConvolutionLayerNativeKernel::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 DWCWeightsKernelInfo &dwc_weights_info, + const DWCKernelInfo &dwc_info, const PadStrideInfo &conv_info, unsigned int depth_multiplier, 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(), - dwc_info, conv_info, (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, (output_shifts != nullptr) ? output_shifts->info() : nullptr)); + dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation, + (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, (output_shifts != nullptr) ? output_shifts->info() : nullptr)); auto padding_info = get_padding_info({ input, output }); - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*(input->info()), *(weights->info()), conv_info); + 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); 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 = conv_info.depth_multiplier; + _depth_multiplier = 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_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 = ""; + const unsigned int n0 = adjust_vec_size(dwc_weights_info.n0, input->info()->dimension(0)); CLBuildOptions build_opts; - - // 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_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<int>(_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)); build_opts.add_option("-DN0=" + support::cpp11::to_string(n0)); - 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()))); - } + 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"; if(_is_quantized) { - 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(); + 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(); - PixelValue zero_value = PixelValue(0, input->info()->data_type(), input->info()->quantization_info()); - int zero_value_s32; - zero_value.get(zero_value_s32); + 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"); - float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale; + // 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("-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)); + 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())); } else { - 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())); + 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())); } - Window win = calculate_max_window(*(output->info()), Steps(n0, m0)); + Window win = calculate_max_window(*(output->info()), Steps(n0)); ICLKernel::configure_internal(win); _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); @@ -290,9 +265,10 @@ void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext & } Status CLDepthwiseConvolutionLayerNativeKernel::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, const ITensorInfo *output_shifts) + 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) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, dwc_info, conv_info, output_multipliers, 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)); return Status{}; } @@ -303,46 +279,37 @@ void CLDepthwiseConvolutionLayerNativeKernel::run(const Window &window, cl::Comm // Collapse window Window window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ); - - Window slice = window_collapsed.first_slice_window_4D(); + Window slice_in = window.first_slice_window_4D(); + Window slice_out = window_collapsed.first_slice_window_4D(); if(_depth_multiplier != 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)); + ARM_COMPUTE_ERROR_ON(slice_out.x().step() != 1); + slice_out.set(Window::DimX, Window::Dimension(0, _input->info()->tensor_shape()[0], 1)); } - cl::Image2D weights_cl_image; + unsigned int idx = 2 * num_arguments_per_4D_tensor() + num_arguments_per_3D_tensor(); - if(_export_to_cl_image) + // Set output multipliers in case of quantized data type + if(_is_quantized) { - 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); + add_1D_tensor_argument(idx, _output_multipliers, slice_in); + add_1D_tensor_argument(idx, _output_shifts, slice_in); } - unsigned int idx = 0; - add_4D_tensor_argument(idx, _input, slice); - add_4D_tensor_argument(idx, _output, slice); - if(_export_to_cl_image) - { - _kernel.setArg(idx++, weights_cl_image); - } - add_4D_tensor_argument(idx, _weights, slice); - if(_is_quantized) + if(_biases != nullptr) { - add_1D_tensor_argument(idx, _output_multipliers, slice); - add_1D_tensor_argument(idx, _output_shifts, slice); + add_1D_tensor_argument(idx, _biases, slice_in); } - if(_biases != nullptr) + + do { - add_1D_tensor_argument(idx, _biases, slice); + 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()); } - enqueue(queue, *this, slice, lws_hint()); + while(window_collapsed.slide_window_slice_4D(slice_out) && window.slide_window_slice_4D(slice_in)); } } // namespace arm_compute diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h index 68e4ccfc1e..325f4e7067 100644 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2021 Arm Limited. + * Copyright (c) 2019-2020 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -55,15 +55,19 @@ 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 Convolution info (padding, stride, dilation, ...) + * @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] 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 DWCComputeKernelInfo &dwc_info, - const ConvolutionInfo &conv_info, const ICLTensor *output_multipliers = nullptr, const ICLTensor *output_shifts = nullptr); + 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); /** Initialize the function's source, destination and parameters * * @param[in] compile_context The compile context to be used. @@ -73,15 +77,19 @@ 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 Convolution info (padding, stride, dilation, ...) + * @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] 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 DWCComputeKernelInfo &dwc_info, - const ConvolutionInfo &conv_info, 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 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); /** 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 @@ -90,8 +98,11 @@ 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 Convolution info (padding, stride, dilation, ...) + * @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] 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, @@ -99,8 +110,9 @@ public: * * @return a status */ - 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); + 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); // Inherited methods overridden: void run(const Window &window, cl::CommandQueue &queue) override; @@ -113,7 +125,6 @@ private: unsigned int _depth_multiplier; const ICLTensor *_output_multipliers; const ICLTensor *_output_shifts; - bool _export_to_cl_image; bool _is_quantized; }; } // namespace arm_compute |