| /* |
| * Copyright (c) 2019-2021 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/CLDepthwiseConvolutionLayerNativeKernel.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/CL/CLUtils.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 "src/gpu/cl/kernels/gemm/ClGemmHelpers.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 DWCComputeKernelInfo &dwc_info, |
| const ConvolutionInfo &conv_info, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) |
| { |
| ARM_COMPUTE_UNUSED(dwc_info); |
| bool in_place = false; |
| if(output == nullptr || output == input) |
| { |
| in_place = true; |
| output = input; |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights); |
| 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)); |
| 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)); |
| |
| // In place restrictions |
| if(in_place) |
| { |
| const int weights_width_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::WIDTH); |
| const int weights_height_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::HEIGHT); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->tensor_shape()[weights_width_idx] != 1U || weights->tensor_shape()[weights_height_idx] != 1U); |
| ARM_COMPUTE_RETURN_ERROR_ON(conv_info.depth_multiplier != 1U); |
| ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride() != std::make_pair(1U, 1U)); |
| ARM_COMPUTE_RETURN_ERROR_ON(conv_info.dilation != Size2D(1U, 1U)); |
| ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.has_padding()); // Note that in princple padding can be supported with in_place but we choose not to support it |
| } |
| |
| const ConvolutionInfo info{ conv_info.pad_stride_info, conv_info.depth_multiplier, ActivationLayerInfo(), conv_info.dilation }; |
| 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()); |
| |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != output_shape[idx_c]); |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); |
| |
| if(is_quantized) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); |
| } |
| } |
| |
| if(is_quantized) |
| { |
| 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(output_shape[idx_c] != output_multipliers->dimension(0)); |
| ARM_COMPUTE_RETURN_ERROR_ON(output_shape[idx_c] != 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) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| |
| if(is_data_type_quantized(input->data_type())) |
| { |
| const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); |
| const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = (output->total_size() != 0) ? output->quantization_info().uniform() : iq_info; |
| |
| float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; |
| int output_multiplier = 0; |
| int output_shift = 0; |
| ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| CLDepthwiseConvolutionLayerNativeKernel::CLDepthwiseConvolutionLayerNativeKernel() |
| : _input(nullptr), |
| _weights(nullptr), |
| _biases(nullptr), |
| _output(nullptr), |
| _depth_multiplier(1), |
| _output_multipliers(nullptr), |
| _output_shifts(nullptr), |
| _export_to_cl_image(false), |
| _is_quantized(false) |
| { |
| _type = CLKernelType::DEPTHWISE; |
| } |
| |
| void CLDepthwiseConvolutionLayerNativeKernel::configure(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_info, conv_info, output_multipliers, output_shifts); |
| } |
| |
| void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &compile_context, 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) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); |
| if(output == nullptr) |
| { |
| // In-place |
| output = input; |
| } |
| 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)); |
| |
| 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); |
| 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; |
| _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 = ""; |
| |
| 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"); |
| // 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(_input->info()->data_type())); |
| build_opts.add_option("-DDST_TENSOR_TYPE=BUFFER"); |
| 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("-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"); |
| |
| // Force unroll with pragma when any of the following values exceed the maximum number of manual unroll |
| set_unroll_with_pragma(build_opts, { static_cast<int>(_weights->info()->dimension(1) + m0 - 1), |
| static_cast<int>(_weights->info()->dimension(1)), |
| static_cast<int>(_weights->info()->dimension(2)) |
| }); |
| |
| 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) |
| { |
| 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(); |
| |
| PixelValue zero_value = PixelValue(0, input->info()->data_type(), input->info()->quantization_info()); |
| int zero_value_s32; |
| zero_value.get(zero_value_s32); |
| |
| 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("-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 |
| { |
| 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_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, m0)); |
| ICLKernel::configure_internal(win); |
| |
| _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 += support::cpp11::to_string(output->info()->dimension(2)); |
| _config_id += "_"; |
| _config_id += string_from_data_type(input->info()->data_type()); |
| } |
| |
| 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) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, dwc_info, conv_info, output_multipliers, output_shifts)); |
| return Status{}; |
| } |
| |
| void CLDepthwiseConvolutionLayerNativeKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| // Collapse window |
| Window window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ); |
| |
| Window slice = 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)); |
| } |
| |
| cl::Image2D weights_cl_image; |
| |
| if(_export_to_cl_image) |
| { |
| 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); |
| } |
| |
| unsigned int idx = 0; |
| add_4d_tensor_nhwc_argument(idx, _input); |
| add_4d_tensor_nhwc_argument(idx, _output); |
| |
| if(_export_to_cl_image) |
| { |
| _kernel.setArg(idx++, weights_cl_image); |
| } |
| 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) |
| { |
| add_1D_tensor_argument(idx, _biases, slice); |
| } |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| } // namespace arm_compute |
