/* * Copyright (c) 2022 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/ClTransposedConvolutionKernel.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "src/core/CL/CLValidate.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "support/Cast.h" #include "arm_compute/core/utils/quantization/AsymmHelpers.h" namespace arm_compute { namespace opencl { namespace kernels { namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &deconv_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, DataType::QASYMM8_SIGNED, DataType::QASYMM8); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(input, DataLayout::NHWC); ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(weights, DataLayout::NHWC); constexpr unsigned int channel_idx = 0; constexpr unsigned int width_idx = 1; constexpr unsigned int height_idx = 2; constexpr unsigned int batch_idx = 3; ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(channel_idx) != input->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"); if(biases != nullptr) { if(is_data_type_quantized_asymmetric(input->data_type())) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); } else { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); } ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->dimension(channel_idx) != weights->dimension(batch_idx), "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"); ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(input, DataLayout::NHWC); } // Checks performed when output is configured if(output->total_size() != 0) { const size_t input_width = input->dimension(width_idx); const size_t input_height = input->dimension(height_idx); const size_t weights_width = weights->dimension(width_idx); const size_t weights_height = weights->dimension(height_idx); auto out_dims = deconvolution_output_dimensions(input_width, input_height, weights_width, weights_height, deconv_info); TensorShape output_shape = misc::shape_calculator::compute_deconvolution_output_shape(out_dims, *input, *weights); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(output, DataLayout::NHWC); } return Status{}; } } // namespace void ClTransposedConvolutionKernel::configure(const CLCompileContext &compile_context, const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *output, const PadStrideInfo &deconv_info) { ARM_COMPUTE_UNUSED(biases, deconv_info); ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); // Perform validation ARM_COMPUTE_ERROR_THROW_ON(validate(input, weights, biases, output, deconv_info)); constexpr unsigned int channel_idx = 0; constexpr unsigned int width_idx = 1; constexpr unsigned int height_idx = 2; const size_t input_channels = input->dimension(channel_idx); // same as weight channels const size_t input_width = input->dimension(width_idx); const size_t input_height = input->dimension(height_idx); const size_t weights_width = weights->dimension(width_idx); const size_t weights_height = weights->dimension(height_idx); const size_t output_width = output->dimension(width_idx); const size_t output_height = output->dimension(height_idx); const size_t output_channels = output->dimension(channel_idx); // Calculate output shape auto out_dims = deconvolution_output_dimensions(input_width, input_height, weights_width, weights_height, deconv_info); TensorShape output_shape = misc::shape_calculator::compute_deconvolution_output_shape(out_dims, *input, *weights); auto_init_if_empty(*output, output_shape, 1, input->data_type(), input->quantization_info()); // Calculate updated paddings // p' = k - p - 1 (k: kernel dimensions) const uint32_t pad_left = weights_width - deconv_info.pad_left() - 1; const uint32_t pad_top = weights_height - deconv_info.pad_top() - 1; // Configure kernel window Window win; output_shape.collapse(2U, 1U); // Collapse width and height into single dimension const unsigned int n0 = adjust_vec_size(16 / output->element_size(), output_channels); const unsigned int m0 = 1; const unsigned int k0 = adjust_vec_size(16 / input->element_size(), input_channels); const unsigned int partial_store_n0 = output_channels % n0; // Create window and update padding win = calculate_max_window(output_shape, Steps(n0, m0)); ICLKernel::configure_internal(win); const std::string kernel_name = "transposed_convolution_nhwc"; CLBuildOptions build_options; const DataType input_data_type = input->data_type(); const PaddingInfo strides = deconv_info.stride(); 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()))); } const auto output_data_type = output->data_type(); build_options.add_option("-cl-fast-relaxed-math"); build_options.add_option("-DSRC_TENSOR_TYPE=BUFFER"); build_options.add_option("-DSRC_DATA_TYPE=" + get_cl_type_from_data_type(input_data_type)); build_options.add_option("-DSRC_CHANNELS=" + support::cpp11::to_string(input_channels)); build_options.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input_width)); build_options.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input_height)); build_options.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(output_channels)); build_options.add_option("-DDST_WIDTH=" + support::cpp11::to_string(output_width)); build_options.add_option("-DDST_HEIGHT=" + support::cpp11::to_string(output_height)); build_options.add_option("-DDST_TENSOR_TYPE=BUFFER"); build_options.add_option("-DDST_DATA_TYPE=" + get_cl_type_from_data_type(output_data_type)); build_options.add_option("-DWEI_TENSOR_TYPE=BUFFER"); build_options.add_option("-DWEI_WIDTH=" + support::cpp11::to_string(weights_width)); build_options.add_option("-DWEI_HEIGHT=" + support::cpp11::to_string(weights_height)); 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(strides.first)); build_options.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(strides.second)); 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((input_channels % k0) != 0, "-DLEFTOVER_LOOP"); if(is_data_type_quantized(output_data_type)) { const UniformQuantizationInfo iqinfo = input->quantization_info().uniform(); const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform(); const UniformQuantizationInfo oqinfo = output->quantization_info().uniform(); PixelValue zero_value = PixelValue(0, input->data_type(), input->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(input_data_type)); build_options.add_option("-DZERO_VALUE=" + support::cpp11::to_string(0)); } if(compile_context.get_ddk_version() >= 30) { build_options.add_option("-fregister-allocation=64"); } _kernel = create_kernel(compile_context, kernel_name, build_options.options()); // Set config_id for enabling LWS tuning _config_id = kernel_name; _config_id += "_"; _config_id += lower_string(string_from_data_type(input_data_type)); _config_id += "_"; _config_id += support::cpp11::to_string(weights_width); _config_id += "_"; _config_id += support::cpp11::to_string(strides.first); _config_id += "_"; _config_id += support::cpp11::to_string(strides.second); _config_id += "_"; _config_id += support::cpp11::to_string(output_width); _config_id += "_"; _config_id += support::cpp11::to_string(m0); _config_id += "_"; _config_id += support::cpp11::to_string(n0); } Status ClTransposedConvolutionKernel::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &deconv_info) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, deconv_info)); return Status{}; } void ClTransposedConvolutionKernel::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(tensors.get_const_tensor(TensorType::ACL_SRC_0)); const auto weights = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SRC_1)); const auto biases = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SRC_2)); auto dst = utils::cast::polymorphic_downcast(tensors.get_tensor(TensorType::ACL_DST)); unsigned int idx = 0; add_4d_tensor_nhwc_argument(idx, src); add_4d_tensor_nhwc_argument(idx, dst); add_4d_tensor_nhwc_argument(idx, weights); if(biases != nullptr) { add_1D_tensor_argument(idx, biases, slice); } enqueue(queue, *this, slice, lws_hint()); } } // namespace kernels } // namespace opencl } // namespace arm_compute