| /* |
| * Copyright (c) 2017-2018 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/runtime/CL/functions/CLFullyConnectedLayer.h" |
| |
| #include "arm_compute/core/Size2D.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| #include "support/ToolchainSupport.h" |
| |
| #include <algorithm> |
| |
| using namespace arm_compute; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| namespace |
| { |
| Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo &output) |
| { |
| if(is_data_type_quantized_asymmetric(input.data_type())) |
| { |
| // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() |
| // Extract and negate input and weights offset |
| const QuantizationInfo input_quantization_info(input.quantization_info().scale, -input.quantization_info().offset); |
| const QuantizationInfo weights_quantization_info(weights.quantization_info().scale, -weights.quantization_info().offset); |
| |
| // Validate gemmlowp function |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyCore::validate(&input.clone()->set_quantization_info(input_quantization_info), |
| &weights.clone()->set_quantization_info(weights_quantization_info), |
| &output)); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input, &weights, nullptr, &output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */))); |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| void CLFullyConnectedLayerReshapeWeights::configure(const ICLTensor *input, ICLTensor *output) |
| { |
| auto k = arm_compute::support::cpp14::make_unique<CLTransposeKernel>(); |
| k->configure(input, output); |
| _kernel = std::move(k); |
| } |
| |
| Status CLFullyConnectedLayerReshapeWeights::validate(const ITensorInfo *input, const ITensorInfo *output) |
| { |
| return CLTransposeKernel::validate(input, output); |
| } |
| |
| CLFullyConnectedLayer::CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(memory_manager), _im2col_kernel(), _reshape_weights_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _accumulate_biases_kernel(), |
| _im2col_output(), _gemmlowp_output(), _reshape_weights_output(), _are_weights_reshaped(true), _is_fc_after_conv(true), _accumulate_biases(false), _is_quantized(false), _original_weights(nullptr) |
| { |
| } |
| |
| void CLFullyConnectedLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output) |
| { |
| if(_is_quantized) |
| { |
| // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() |
| // Extract and negate input and weights offset |
| const QuantizationInfo input_quantization_info = input->info()->quantization_info(); |
| const QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); |
| |
| input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); |
| weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); |
| |
| // Configure gemmlowp function |
| _mm_gemmlowp.configure(input, weights, output); |
| |
| // Revert back QuantizatioInfo as input and weights could be used in other fully connected layers |
| input->info()->set_quantization_info(input_quantization_info); |
| weights->info()->set_quantization_info(weights_quantization_info); |
| } |
| else |
| { |
| // Configure matrix multiply kernel |
| _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */)); |
| } |
| } |
| |
| void CLFullyConnectedLayer::configure_conv_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)))); |
| |
| // If the fully connected layer is called after a convolution layer, the input tensor must be linearized |
| |
| // Initialize output tensor for im2col |
| TensorShape shape_im2col = compute_im2col_fc_shape(input->info()); |
| _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col)); |
| |
| // Configure im2col kernel |
| _memory_group.manage(&_im2col_output); |
| _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false); |
| |
| // Configure matrix multiply kernel |
| configure_mm(&_im2col_output, weights, output); |
| |
| // Allocate the output tensor for im2col once all the configure methods have been called |
| _im2col_output.allocator()->allocate(); |
| } |
| |
| void CLFullyConnectedLayer::configure_fc_fc(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1)); |
| |
| // Configure matrix multiply kernel |
| configure_mm(input, weights, output); |
| } |
| |
| void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose_weights, bool are_weights_reshaped, |
| bool retain_internal_weights) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| |
| // Perform validate step |
| ARM_COMPUTE_ERROR_THROW_ON(CLFullyConnectedLayer::validate(input->info(), |
| weights->info(), |
| biases != nullptr ? biases->info() : nullptr, |
| output->info(), |
| transpose_weights, |
| are_weights_reshaped, |
| retain_internal_weights)); |
| |
| _are_weights_reshaped = transpose_weights ? are_weights_reshaped : true; |
| _is_fc_after_conv = true; |
| _accumulate_biases = false; |
| _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); |
| _original_weights = weights; |
| |
| // Configure gemmlowp output |
| if(_is_quantized) |
| { |
| _gemmlowp_output.allocator()->init(output->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32)); |
| } |
| |
| // Configure accumulate biases kernel for non quantized asymmetric types |
| if(biases != nullptr && !_is_quantized) |
| { |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); |
| |
| _accumulate_biases = true; |
| |
| // Configure accumulate biases kernel |
| _accumulate_biases_kernel.set_target(CLScheduler::get().target()); |
| _accumulate_biases_kernel.configure(output, biases); |
| } |
| |
| // With the Fully Connected layer we can have 4 different cases: |
| // 1) Convolution layer -> Fully Connected layer without batches |
| // 2) Fully Connected layer -> Fully Connected layer without batches |
| // 3) Convolution layer -> Fully Connected layer with batches |
| // 4) Fully Connected layer -> Fully Connected layer with batches |
| |
| const ICLTensor *weights_to_use = weights; |
| |
| if(!_are_weights_reshaped) |
| { |
| weights_to_use = &_reshape_weights_output; |
| |
| // Reshape the weights |
| _reshape_weights_kernel.configure(weights, &_reshape_weights_output); |
| } |
| |
| // Check if we have a fully connected layer with batches |
| const bool is_batched_fc_layer = output->info()->dimension(1) > 1; |
| |
| if(is_batched_fc_layer) |
| { |
| _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3, |
| input->info()->tensor_shape().cend(), |
| output->info()->tensor_shape().cbegin() + 1)); |
| } |
| else |
| { |
| _is_fc_after_conv = input->info()->num_dimensions() > 1; |
| } |
| |
| ICLTensor *tmp_output = (_is_quantized) ? &_gemmlowp_output : output; |
| if(_is_fc_after_conv) |
| { |
| // Fully Connected layer after a Convolution Layer without batches |
| configure_conv_fc(input, weights_to_use, tmp_output); |
| } |
| else |
| { |
| // Fully Connected layer after a Fully Connected Layer without batches |
| configure_fc_fc(input, weights_to_use, tmp_output); |
| } |
| |
| // Configure output stage for asymmetric quantized types |
| if(_is_quantized) |
| { |
| float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_info().scale; |
| int output_multiplier, output_shift; |
| quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); |
| _gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, output_multiplier, output_shift, output->info()->quantization_info().offset); |
| _gemmlowp_output.allocator()->allocate(); |
| } |
| |
| _are_weights_reshaped = _are_weights_reshaped || retain_internal_weights; |
| } |
| |
| Status CLFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose_weights, bool are_weights_reshaped, |
| bool retain_internal_weights) |
| { |
| ARM_COMPUTE_UNUSED(retain_internal_weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2); |
| |
| bool weights_reshaped = transpose_weights ? are_weights_reshaped : true; |
| bool is_fc_after_conv = true; |
| bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| |
| const ITensorInfo &im2col_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_im2col_fc_shape(input))); |
| const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights))); |
| const ITensorInfo &gemmlowp_output = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32)); |
| |
| // Configure accumulate biases kernel for non quantized asymmetric types |
| if(biases != nullptr && !is_quantized) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixAccumulateBiasesKernel::validate(output, biases, gpu_target)); |
| } |
| |
| // With the Fully Connected layer we can have 4 different cases: |
| // 1) Convolution layer -> Fully Connected layer without batches |
| // 2) Fully Connected layer -> Fully Connected layer without batches |
| // 3) Convolution layer -> Fully Connected layer with batches |
| // 4) Fully Connected layer -> Fully Connected layer with batches |
| |
| const ITensorInfo *input_to_use = input; |
| const ITensorInfo *weights_to_use = weights; |
| const ITensorInfo *tmp_output = (is_quantized) ? &gemmlowp_output : output; |
| |
| if(!weights_reshaped) |
| { |
| // Validate reshape weights kernel |
| ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayerReshapeWeights::validate(weights, &reshaped_weights)); |
| weights_to_use = &reshaped_weights; |
| } |
| |
| // Check if we have a fully connected layer with batches |
| const bool is_batched_fc_layer = output->dimension(1) > 1; |
| |
| if(is_batched_fc_layer) |
| { |
| is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->tensor_shape().cbegin() + 3, |
| input->tensor_shape().cend(), |
| output->tensor_shape().cbegin() + 1)); |
| } |
| else |
| { |
| is_fc_after_conv = input->num_dimensions() > 1; |
| } |
| |
| if(is_fc_after_conv) |
| { |
| // Fully Connected layer after a Convolution Layer without batches |
| ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (input->dimension(0) * input->dimension(1) * input->dimension(2)))); |
| |
| // Validate im2col kernel |
| ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_input, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false)); |
| input_to_use = &im2col_input; |
| } |
| else |
| { |
| // Fully Connected layer after a Fully Connected Layer without batches |
| ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != weights_to_use->dimension(1)); |
| } |
| // Validate matrix multiply kernel |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(*input_to_use, *weights_to_use, *tmp_output)); |
| |
| // Validate output stage for asymmetric quantized types |
| if(is_quantized) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&gemmlowp_output, biases, output)); |
| } |
| |
| return Status{}; |
| } |
| |
| void CLFullyConnectedLayer::run() |
| { |
| prepare(); |
| |
| _memory_group.acquire(); |
| |
| // Linearize input if it comes from a convolutional layer |
| if(_is_fc_after_conv) |
| { |
| CLScheduler::get().enqueue(_im2col_kernel, false); |
| } |
| |
| // Run matrix multiply |
| if(_is_quantized) |
| { |
| _mm_gemmlowp.run(); |
| } |
| else |
| { |
| _mm_gemm.run(); |
| } |
| |
| // Accumulate biases if provided |
| if(_is_quantized) |
| { |
| _gemmlowp_output_stage.run(); |
| } |
| else |
| { |
| if(_accumulate_biases) |
| { |
| CLScheduler::get().enqueue(_accumulate_biases_kernel); |
| } |
| } |
| |
| _memory_group.release(); |
| } |
| |
| void CLFullyConnectedLayer::prepare() |
| { |
| // Reshape of the weights (happens only once) |
| if(!_are_weights_reshaped) |
| { |
| ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); |
| |
| // Run reshape weights kernel and mark weights as unused |
| _reshape_weights_output.allocator()->allocate(); |
| _reshape_weights_kernel.run(); |
| _original_weights->mark_as_unused(); |
| |
| // Prepare GEMM prepare and release unused weights |
| if(!_is_quantized) |
| { |
| _mm_gemm.prepare(); |
| if(!_reshape_weights_output.is_used()) |
| { |
| _reshape_weights_output.allocator()->free(); |
| } |
| } |
| |
| CLScheduler::get().queue().finish(); |
| _are_weights_reshaped = true; |
| } |
| } |