| /* |
| * Copyright (c) 2018-2020 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 "arm_compute/core/CL/kernels/CLFuseBatchNormalizationKernel.h" |
| |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/CLValidate.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/Window.h" |
| |
| #include "support/StringSupport.h" |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, |
| const ITensorInfo *fused_weights, const ITensorInfo *fused_bias, |
| const ITensorInfo *input_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma, |
| float epsilon, FuseBatchNormalizationType fbn_type) |
| { |
| ARM_COMPUTE_UNUSED(epsilon); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input_weights, bn_mean, bn_var); |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input_weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input_weights, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_var); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, bn_mean, bn_var); |
| ARM_COMPUTE_RETURN_ERROR_ON(input_bias == nullptr && fused_bias == nullptr); |
| ARM_COMPUTE_RETURN_ERROR_ON(bn_mean->num_dimensions() > 1); |
| |
| if(fbn_type == FuseBatchNormalizationType::CONVOLUTION) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(input_weights->dimension(3) != bn_mean->dimension(0)); |
| } |
| else |
| { |
| const size_t channel_idx = get_data_layout_dimension_index(input_weights->data_layout(), DataLayoutDimension::CHANNEL); |
| ARM_COMPUTE_RETURN_ERROR_ON(input_weights->dimension(channel_idx) != bn_mean->dimension(0)); |
| } |
| |
| // Validate bias |
| if(input_bias != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, input_bias); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, input_bias); |
| } |
| // Validate beta |
| if(bn_beta != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_beta); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, bn_beta); |
| } |
| // Validate gamma |
| if(bn_gamma != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_gamma); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, bn_gamma); |
| } |
| // Validate output weights |
| if(fused_weights != nullptr && fused_weights->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input_weights, fused_weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input_weights, fused_weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, fused_weights); |
| } |
| // Validate output bias |
| if(fused_bias != nullptr && fused_bias->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, fused_bias); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input_weights, fused_bias); |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| CLFuseBatchNormalizationKernel::CLFuseBatchNormalizationKernel() |
| : _input_weights(nullptr), _input_bias(nullptr), _bn_mean(nullptr), _bn_var(nullptr), _bn_gamma(nullptr), _bn_beta(nullptr), _fused_weights(nullptr), _fused_bias(nullptr), _epsilon(), |
| _run_in_place_weights(false), _run_in_place_bias(false) |
| { |
| } |
| |
| void CLFuseBatchNormalizationKernel::configure(const ICLTensor *input_weights, const ICLTensor *bn_mean, const ICLTensor *bn_var, |
| ICLTensor *fused_weights, ICLTensor *fused_bias, |
| const ICLTensor *input_bias, const ICLTensor *bn_beta, const ICLTensor *bn_gamma, |
| float epsilon, FuseBatchNormalizationType fbn_type) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input_weights, bn_mean, bn_var); |
| |
| _input_weights = input_weights; |
| _input_bias = input_bias; |
| _bn_mean = bn_mean; |
| _bn_var = bn_var; |
| _bn_beta = bn_beta; |
| _bn_gamma = bn_gamma; |
| _fused_weights = fused_weights; |
| _fused_bias = fused_bias; |
| _epsilon = epsilon; |
| |
| _run_in_place_weights = (fused_weights == nullptr) || (fused_weights == input_weights); |
| _run_in_place_bias = (input_bias != nullptr && fused_bias == nullptr) || (input_bias != nullptr && fused_bias == input_bias); |
| |
| // Auto initialize outputs |
| if(_fused_weights != nullptr) |
| { |
| // Output tensor auto initialization if not yet initialized |
| auto_init_if_empty(*_fused_weights->info(), *_input_weights->info()->clone()); |
| } |
| if(_fused_bias != nullptr) |
| { |
| // Output tensor auto initialization if not yet initialized |
| auto_init_if_empty(*_fused_bias->info(), *_bn_mean->info()->clone()); |
| } |
| |
| // Validate arguments |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input_weights->info(), bn_mean->info(), bn_var->info(), |
| (fused_weights != nullptr) ? fused_weights->info() : nullptr, |
| (fused_bias != nullptr) ? fused_bias->info() : nullptr, |
| (input_bias != nullptr) ? input_bias->info() : nullptr, |
| (bn_beta != nullptr) ? bn_beta->info() : nullptr, |
| (bn_gamma != nullptr) ? bn_gamma->info() : nullptr, |
| epsilon, fbn_type)); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input_weights->info()); |
| ICLKernel::configure_internal(win); |
| |
| // Set build options |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input_weights->info()->data_type())); |
| build_opts.add_option_if(fbn_type == FuseBatchNormalizationType::CONVOLUTION, "-DDIM2=" + support::cpp11::to_string(input_weights->info()->dimension(2))); |
| build_opts.add_option("-DEPSILON=" + float_to_string_with_full_precision(epsilon)); |
| build_opts.add_option_if(_input_weights->info()->data_layout() == DataLayout::NHWC, "-DNHWC"); |
| build_opts.add_option_if(_run_in_place_weights, "-DIN_PLACE_W"); |
| build_opts.add_option_if(_run_in_place_bias, "-DIN_PLACE_B"); |
| build_opts.add_option_if(input_bias != nullptr, "-DBIAS"); |
| build_opts.add_option_if(bn_beta != nullptr, "-DBETA"); |
| build_opts.add_option_if(bn_gamma != nullptr, "-DGAMMA"); |
| |
| // Create kernel |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("fuse_batchnormalization_layer", build_opts.options())); |
| } |
| |
| Status CLFuseBatchNormalizationKernel::validate(const ITensorInfo *input_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, |
| const ITensorInfo *fused_weights, const ITensorInfo *fused_bias, |
| const ITensorInfo *input_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma, |
| float epsilon, FuseBatchNormalizationType fbn_type) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input_weights, bn_mean, bn_var, fused_weights, fused_bias, input_bias, bn_beta, bn_gamma, epsilon, fbn_type)); |
| return Status{}; |
| } |
| |
| void CLFuseBatchNormalizationKernel::run(const arm_compute::Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| // Create window slice |
| Window collapsed_window = window.collapse(window, Window::DimZ); |
| Window slice_1d = window.first_slice_window_1D(); |
| Window slice_3d = collapsed_window.first_slice_window_3D(); |
| |
| // Add kernel arguments |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, _input_weights, slice_3d); |
| if(_input_bias != nullptr) |
| { |
| add_1D_tensor_argument(idx, _input_bias, slice_1d); |
| } |
| add_1D_tensor_argument(idx, _bn_mean, slice_1d); |
| add_1D_tensor_argument(idx, _bn_var, slice_1d); |
| if(!_run_in_place_weights) |
| { |
| add_3D_tensor_argument(idx, _fused_weights, slice_3d); |
| } |
| if(!_run_in_place_bias) |
| { |
| add_1D_tensor_argument(idx, _fused_bias, slice_1d); |
| } |
| if(_bn_beta != nullptr) |
| { |
| add_1D_tensor_argument(idx, _bn_beta, slice_1d); |
| } |
| if(_bn_gamma != nullptr) |
| { |
| add_1D_tensor_argument(idx, _bn_gamma, slice_1d); |
| } |
| enqueue(queue, *this, slice_3d, lws_hint()); |
| } |
| } // namespace arm_compute |