| /* | |
| * Copyright (c) 2017-2023 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/gpu/cl/kernels/ClDirectConv2dKernel.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/ITensor.h" | |
| #include "arm_compute/core/KernelDescriptors.h" | |
| #include "arm_compute/core/PixelValue.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/CLUtils.h" | |
| #include "src/core/CL/CLValidate.h" | |
| #include "src/core/helpers/AutoConfiguration.h" | |
| #include "src/core/helpers/WindowHelpers.h" | |
| #include "src/gpu/cl/kernels/gemm/ClGemmHelpers.h" | |
| #include "support/Cast.h" | |
| #include "support/StringSupport.h" | |
| namespace arm_compute | |
| { | |
| namespace opencl | |
| { | |
| namespace kernels | |
| { | |
| namespace | |
| { | |
| Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, | |
| const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, const DirectConvComputeKernelInfo &desc) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src); | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, DataType::F16, DataType::F32); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights); | |
| const DataLayout data_layout = src->data_layout(); | |
| const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); | |
| const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); | |
| const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(channel_idx) != src->dimension(channel_idx), "Weights feature map dimension should match the respective src's one"); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->num_dimensions() > 4, "Weights can be at most 4 dimensional"); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(desc.export_input_to_cl_image == true, "Export to CLImage is not supported for the input tensor"); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(desc.export_output_to_cl_image == true, "Export to CLImage is not supported for the output tensor"); | |
| if(data_layout == DataLayout::NCHW) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != weights->dimension(height_idx), "Weights should have same width and height"); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 1) && std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported for 1x1 convolution."); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 3 || weights->dimension(width_idx) == 5 || weights->dimension(width_idx) == 9) && std::get<0>(conv_info.stride()) > 2, | |
| "Strides larger than 2 not supported for 3x3, 5x5, 9x9 convolution."); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(act_info.enabled(), "Fused activation is not supported for NCHW layout"); | |
| if(is_data_type_quantized(src->data_type())) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5 && weights->dimension(width_idx) != 9, | |
| "Kernel sizes other than 1x1, 3x3, 5x5 or 9x9 are not supported with quantized data types"); | |
| } | |
| else | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5, | |
| "Kernel sizes other than 1x1, 3x3 or 5x5 are not supported with float data types"); | |
| } | |
| } | |
| if(data_layout == DataLayout::NHWC) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(act_info.enabled() && !is_data_type_float(src->data_type()), "Fused activation in NHWC is only supported for floating point."); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(desc.m0 <= 0 || desc.m0 > 8, "M0 can only be greater than 0 and less than or equal to 8"); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(desc.n0 != 1 && desc.n0 != 2 && desc.n0 != 3 && desc.n0 != 4 && desc.n0 != 8 && desc.n0 != 16, | |
| "N0 can only be: 1, 2, 3, 4, 8, and 16"); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(desc.k0 != 1 && desc.k0 != 2 && desc.k0 != 3 && desc.k0 != 4 && desc.k0 != 8 && desc.k0 != 16, | |
| "K0 can only be: 1, 2, 3, 4, 8, and 16"); | |
| if(desc.export_weights_to_cl_image) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(desc.k0 != 4 && desc.k0 != 8 && desc.k0 != 16, | |
| "K0 can only be: 4, 8, and 16"); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!export_to_cl_image(weights), | |
| "Export to CLImage is not supported for this weight configuration"); | |
| } | |
| } | |
| if(biases != nullptr) | |
| { | |
| if(is_data_type_quantized_asymmetric(src->data_type())) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); | |
| } | |
| else | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); | |
| } | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->dimension(0) != weights->dimension(3), | |
| "Biases size and number of dst feature maps should match"); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->num_dimensions() > 1, | |
| "Biases should be one dimensional"); | |
| } | |
| // Checks performed when dst is configured | |
| if(dst->total_size() != 0) | |
| { | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(), | |
| misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info)); | |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); | |
| } | |
| const auto data_type = src->data_type(); | |
| if(is_data_type_quantized(data_type)) | |
| { | |
| const UniformQuantizationInfo iqinfo = src->quantization_info().uniform(); | |
| const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform(); | |
| const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform(); | |
| float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale; | |
| int output_multiplier = 0; | |
| int output_shift = 0; | |
| ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); | |
| } | |
| return Status{}; | |
| } | |
| } // namespace | |
| ClDirectConv2dKernel::ClDirectConv2dKernel() | |
| { | |
| _type = CLKernelType::DIRECT; | |
| } | |
| void ClDirectConv2dKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, | |
| const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, const DirectConvComputeKernelInfo &desc) | |
| { | |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); | |
| // Perform validation | |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info, act_info, desc)); | |
| const int conv_stride_x = std::get<0>(conv_info.stride()); | |
| const int conv_stride_y = std::get<1>(conv_info.stride()); | |
| _data_layout = src->data_layout(); | |
| _conv_info = conv_info; | |
| const unsigned int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); | |
| const unsigned int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); | |
| const unsigned int channel_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL); | |
| const unsigned int kernel_size = weights->dimension(width_idx); | |
| const DataType data_type = src->data_type(); | |
| const GPUTarget gpu_target = get_target(); | |
| unsigned int _num_elems_processed_per_iteration = 0; | |
| // Get dst shape | |
| TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info); | |
| // Output auto inizialitation if not yet initialized | |
| auto_init_if_empty(*dst, output_shape, | |
| 1, | |
| src->data_type(), | |
| src->quantization_info()); | |
| // Configure kernel window | |
| Window win; | |
| if(_data_layout == DataLayout::NHWC) | |
| { | |
| output_shape.collapse(2U, 1U); | |
| const unsigned int n0 = adjust_vec_size(desc.n0, output_shape[0]); | |
| const unsigned int m0 = adjust_vec_size(desc.m0, output_shape[1]); | |
| // Create window and update padding | |
| win = calculate_max_window(output_shape, Steps(n0, m0)); | |
| } | |
| else if(_data_layout == DataLayout::NCHW) | |
| { | |
| _num_elems_processed_per_iteration = 1u; | |
| win = calculate_max_window(*dst, Steps(_num_elems_processed_per_iteration)); | |
| } | |
| ICLKernel::configure_internal(win); | |
| std::stringstream kernel_name; | |
| CLBuildOptions build_options; | |
| if(_data_layout == DataLayout::NHWC) | |
| { | |
| kernel_name << "direct_convolution_nhwc"; | |
| const unsigned int n0 = win.x().step(); | |
| const unsigned int m0 = win.y().step(); | |
| const unsigned int k0 = adjust_vec_size(desc.k0, src->dimension(channel_idx)); | |
| const unsigned int partial_store_n0 = dst->dimension(channel_idx) % n0; | |
| const unsigned int pad_left = conv_info.pad_left(); | |
| const unsigned int pad_top = conv_info.pad_top(); | |
| _export_weights_to_cl_image = desc.export_weights_to_cl_image; | |
| _export_input_to_cl_image = desc.export_input_to_cl_image; | |
| _export_output_to_cl_image = desc.export_output_to_cl_image; | |
| // Update the padding for the weights tensor if we can export to cl_image | |
| if(_export_weights_to_cl_image) | |
| { | |
| gemm::update_padding_for_cl_image(weights); | |
| } | |
| if(_export_output_to_cl_image) | |
| { | |
| gemm::update_padding_for_cl_image(dst); | |
| } | |
| if(_export_input_to_cl_image) | |
| { | |
| gemm::update_padding_for_cl_image(src); | |
| } | |
| if(biases != nullptr) | |
| { | |
| build_options.add_option(std::string("-DHAS_BIAS")); | |
| build_options.add_option(std::string("-DBIA_DATA_TYPE=" + get_cl_type_from_data_type(biases->data_type()))); | |
| } | |
| // Conditions of -cl-fast-relaxed-math causing accuracy issues can be traced from COMPMID-5324 | |
| const auto act_function = act_info.activation(); | |
| const auto dst_data_type = dst->data_type(); | |
| if((gpu_target != GPUTarget::G71 && (gpu_target & GPUTarget::GPU_ARCH_MASK) == GPUTarget::BIFROST) | |
| && (act_function == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU || act_function == ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) | |
| && (dst_data_type == DataType::F32 || dst_data_type == DataType::F16)) | |
| { | |
| // -cl-fast-relaxed-math also sets -cl-finite-math-only and -cl-unsafe-math-optimizations | |
| // to disable -cl-finite-math-only, we only include -cl-unsafe-math-optimizations | |
| build_options.add_option("-cl-unsafe-math-optimizations"); | |
| } | |
| else | |
| { | |
| build_options.add_option("-cl-fast-relaxed-math"); | |
| } | |
| build_options.add_option_if_else(_export_input_to_cl_image, "-DSRC_TENSOR_TYPE=IMAGE", "-DSRC_TENSOR_TYPE=BUFFER"); | |
| build_options.add_option("-DSRC_DATA_TYPE=" + get_cl_type_from_data_type(src->data_type())); | |
| build_options.add_option("-DSRC_CHANNELS=" + support::cpp11::to_string(src->dimension(0))); | |
| build_options.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(1))); | |
| build_options.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(2))); | |
| build_options.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(dst->dimension(0))); | |
| build_options.add_option("-DDST_WIDTH=" + support::cpp11::to_string(dst->dimension(1))); | |
| build_options.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(dst->dimension(2))); | |
| build_options.add_option_if_else(_export_output_to_cl_image, "-DDST_TENSOR_TYPE=IMAGE", "-DDST_TENSOR_TYPE=BUFFER"); | |
| build_options.add_option("-DDST_DATA_TYPE=" + get_cl_type_from_data_type(dst_data_type)); | |
| build_options.add_option_if_else(_export_weights_to_cl_image, "-DWEI_TENSOR_TYPE=IMAGE", "-DWEI_TENSOR_TYPE=BUFFER"); | |
| build_options.add_option("-DWEI_WIDTH=" + support::cpp11::to_string(weights->dimension(width_idx))); | |
| build_options.add_option("-DWEI_HEIGHT=" + support::cpp11::to_string(weights->dimension(height_idx))); | |
| build_options.add_option("-DWEI_DATA_TYPE=" + get_cl_type_from_data_type(weights->data_type())); | |
| build_options.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_stride_x)); | |
| build_options.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_stride_y)); | |
| build_options.add_option("-DPAD_LEFT=" + support::cpp11::to_string(pad_left)); | |
| build_options.add_option("-DPAD_TOP=" + support::cpp11::to_string(pad_top)); | |
| build_options.add_option("-DN0=" + support::cpp11::to_string(n0)); | |
| build_options.add_option("-DM0=" + support::cpp11::to_string(m0)); | |
| build_options.add_option("-DK0=" + support::cpp11::to_string(k0)); | |
| build_options.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0)); | |
| build_options.add_option_if((src->dimension(channel_idx) % k0) != 0, "-DLEFTOVER_LOOP"); | |
| build_options.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_function))); | |
| if(is_data_type_quantized(data_type)) | |
| { | |
| const UniformQuantizationInfo iqinfo = src->quantization_info().uniform(); | |
| const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform(); | |
| const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform(); | |
| PixelValue zero_value = PixelValue(0, src->data_type(), src->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_options.add_option("-DIS_QUANTIZED"); | |
| build_options.add_option("-DDST_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); | |
| build_options.add_option("-DDST_SHIFT=" + support::cpp11::to_string(output_shift)); | |
| build_options.add_option("-DSRC_OFFSET=" + support::cpp11::to_string(-iqinfo.offset)); | |
| build_options.add_option("-DWEI_OFFSET=" + support::cpp11::to_string(-wqinfo.offset)); | |
| build_options.add_option("-DDST_OFFSET=" + support::cpp11::to_string(oqinfo.offset)); | |
| build_options.add_option("-DZERO_VALUE=" + support::cpp11::to_string(zero_value_s32)); | |
| build_options.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(DataType::S32)); | |
| } | |
| else | |
| { | |
| build_options.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(data_type)); | |
| build_options.add_option("-DZERO_VALUE=" + support::cpp11::to_string(0)); | |
| build_options.add_option("-DSRC_OFFSET=" + support::cpp11::to_string(0)); | |
| build_options.add_option("-DWEI_OFFSET=" + support::cpp11::to_string(0)); | |
| build_options.add_option("-DDST_OFFSET=" + support::cpp11::to_string(0)); | |
| build_options.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a())); | |
| build_options.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b())); | |
| } | |
| if(compile_context.get_ddk_version() >= 30) | |
| { | |
| build_options.add_option("-fregister-allocation=64"); | |
| } | |
| } | |
| else | |
| { | |
| _export_weights_to_cl_image = false; | |
| kernel_name << "direct_convolution_nchw"; | |
| build_options.add_option_if(biases != nullptr, std::string("-DHAS_BIAS")); | |
| build_options.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(width_idx))); | |
| build_options.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(height_idx))); | |
| build_options.add_option("-DSRC_CHANNELS=" + support::cpp11::to_string(src->dimension(channel_idx))); | |
| build_options.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); | |
| build_options.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); | |
| build_options.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_stride_x)); | |
| build_options.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_stride_y)); | |
| build_options.add_option("-DWEI_WIDTH=" + support::cpp11::to_string(weights->dimension(width_idx))); | |
| build_options.add_option("-DWEI_HEIGHT=" + support::cpp11::to_string(weights->dimension(height_idx))); | |
| build_options.add_option(std::string("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type))); | |
| build_options.add_option(std::string("-DDATA_SIZE=" + get_data_size_from_data_type(data_type))); | |
| build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(weights->dimension(channel_idx)))); | |
| build_options.add_option(std::string("-DSTRIDE_X=" + support::cpp11::to_string(conv_stride_x))); | |
| build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(data_type))); | |
| build_options.add_option(std::string("-DVEC_SIZE=" + support::cpp11::to_string(_num_elems_processed_per_iteration))); | |
| build_options.add_option(std::string("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(src->dimension(0) % _num_elems_processed_per_iteration))); | |
| if(is_data_type_quantized(data_type)) | |
| { | |
| const UniformQuantizationInfo iqinfo = src->quantization_info().uniform(); | |
| const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform(); | |
| const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform(); | |
| float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale; | |
| int output_multiplier = 0; | |
| int output_shift = 0; | |
| quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); | |
| build_options.add_option("-DIS_QUANTIZED"); | |
| build_options.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); | |
| build_options.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); | |
| build_options.add_option("-DKERNEL_SIZE=" + support::cpp11::to_string(kernel_size)); | |
| build_options.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iqinfo.offset)); | |
| build_options.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wqinfo.offset)); | |
| build_options.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oqinfo.offset)); | |
| } | |
| } | |
| _kernel = create_kernel(compile_context, kernel_name.str(), build_options.options()); | |
| // Set config_id for enabling LWS tuning | |
| _config_id = kernel_name.str(); | |
| _config_id += "_"; | |
| _config_id += lower_string(string_from_data_type(data_type)); | |
| _config_id += "_"; | |
| _config_id += support::cpp11::to_string(kernel_size); | |
| _config_id += "_"; | |
| _config_id += support::cpp11::to_string(border_size().left); | |
| _config_id += "_"; | |
| _config_id += support::cpp11::to_string(border_size().top); | |
| _config_id += "_"; | |
| _config_id += support::cpp11::to_string(border_size().right); | |
| _config_id += "_"; | |
| _config_id += support::cpp11::to_string(border_size().bottom); | |
| _config_id += "_"; | |
| _config_id += support::cpp11::to_string(conv_stride_x); | |
| _config_id += "_"; | |
| _config_id += support::cpp11::to_string(conv_stride_y); | |
| _config_id += "_"; | |
| _config_id += support::cpp11::to_string(dst->dimension(width_idx)); | |
| _config_id += "_"; | |
| _config_id += support::cpp11::to_string(dst->dimension(height_idx)); | |
| _config_id += "_"; | |
| _config_id += lower_string(string_from_data_layout(_data_layout)); | |
| } | |
| Status ClDirectConv2dKernel::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, | |
| const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, const DirectConvComputeKernelInfo &desc) | |
| { | |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info, act_info, desc)); | |
| return Status{}; | |
| } | |
| void ClDirectConv2dKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) | |
| { | |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); | |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); | |
| // Get initial windows | |
| Window slice = window.first_slice_window_3D(); | |
| const auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); | |
| const auto weights = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); | |
| const auto biases = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); | |
| auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); | |
| if(_data_layout == DataLayout::NHWC) | |
| { | |
| cl::Image2D weights_cl_image; | |
| cl::Image2D output_cl_image; | |
| cl::Image2D input_cl_image; | |
| if(_export_weights_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, CLImage2DType::ReadOnly); | |
| } | |
| if(_export_output_to_cl_image) | |
| { | |
| const size_t image_w = dst->info()->dimension(0) / 4; | |
| const size_t image_h = dst->info()->dimension(1) * dst->info()->dimension(2) * dst->info()->dimension(3); | |
| const TensorShape shape2d(image_w, image_h); | |
| const size_t image_row_pitch = dst->info()->strides_in_bytes()[1]; | |
| // Export cl_buffer to cl_image | |
| output_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), dst->cl_buffer(), shape2d, dst->info()->data_type(), image_row_pitch, CLImage2DType::WriteOnly); | |
| } | |
| if(_export_input_to_cl_image) | |
| { | |
| const size_t image_w = src->info()->dimension(0) / 4; | |
| const size_t image_h = src->info()->dimension(1) * src->info()->dimension(2) * src->info()->dimension(3); | |
| const TensorShape shape2d(image_w, image_h); | |
| const size_t image_row_pitch = src->info()->strides_in_bytes()[1]; | |
| // Export cl_buffer to cl_image | |
| input_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), src->cl_buffer(), shape2d, src->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly); | |
| } | |
| unsigned int idx = 0; | |
| if(_export_input_to_cl_image) | |
| { | |
| _kernel.setArg(idx++, input_cl_image); | |
| } | |
| add_4d_tensor_nhwc_argument(idx, src); | |
| if(_export_output_to_cl_image) | |
| { | |
| _kernel.setArg(idx++, output_cl_image); | |
| } | |
| add_4d_tensor_nhwc_argument(idx, dst); | |
| if(_export_weights_to_cl_image) | |
| { | |
| _kernel.setArg(idx++, weights_cl_image); | |
| } | |
| add_4d_tensor_nhwc_argument(idx, weights); | |
| if(biases != nullptr) | |
| { | |
| add_1D_tensor_argument(idx, biases, slice); | |
| } | |
| enqueue(queue, *this, slice, lws_hint()); | |
| } | |
| else | |
| { | |
| unsigned int idx1 = 2 * num_arguments_per_3D_tensor(); | |
| add_3D_tensor_argument(idx1, weights, slice); | |
| if(biases != nullptr) | |
| { | |
| Window slice_biases; | |
| slice_biases.use_tensor_dimensions(biases->info()->tensor_shape()); | |
| add_1D_tensor_argument(idx1, biases, slice_biases); | |
| } | |
| _kernel.setArg(idx1++, static_cast<unsigned int>(weights->info()->strides_in_bytes()[3])); | |
| do | |
| { | |
| unsigned int idx = 0; | |
| add_3D_tensor_argument(idx, src, slice); | |
| add_3D_tensor_argument(idx, dst, slice); | |
| enqueue(queue, *this, slice, lws_hint()); | |
| } | |
| while(window.slide_window_slice_3D(slice)); | |
| } | |
| } | |
| } // namespace kernels | |
| } // namespace opencl | |
| } // namespace arm_compute |