diff options
Diffstat (limited to 'src/runtime/CL/functions/CLFullyConnectedLayer.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLFullyConnectedLayer.cpp | 483 |
1 files changed, 74 insertions, 409 deletions
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp index 31c8908270..b30f9e701f 100644 --- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp +++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -23,472 +23,137 @@ */ #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/core/CL/CLKernelLibrary.h" #include "arm_compute/runtime/CL/CLScheduler.h" -#include "src/core/CL/kernels/CLFillBorderKernel.h" -#include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h" -#include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h" -#include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h" -#include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h" -#include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h" -#include "src/core/gpu/cl/kernels/ClTransposeKernel.h" -#include "support/Cast.h" -#include <algorithm> +#include "src/core/helpers/MemoryHelpers.h" +#include "src/gpu/cl/operators/ClFullyConnected.h" namespace arm_compute { -using namespace arm_compute::misc::shape_calculator; -using namespace arm_compute::utils::cast; +using namespace arm_compute::experimental; -namespace +struct CLFullyConnectedLayer::Impl { -Status construct_gemmlowp_output_stage(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo &output, - GEMMLowpOutputStageInfo &gemmlowp_output_stage, ActivationLayerInfo activation_info) -{ - gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; - gemmlowp_output_stage.gemmlowp_offset = 0; - gemmlowp_output_stage.gemmlowp_multiplier = 0; - gemmlowp_output_stage.gemmlowp_shift = 0; - - const auto data_type = input.data_type(); - - // Configure output stage for quantized case - if(is_data_type_quantized_asymmetric(data_type)) - { - const QuantizationInfo oq_info = output.quantization_info(); - const UniformQuantizationInfo iq_unif = input.quantization_info().uniform(); - const UniformQuantizationInfo wq_unif = weights.quantization_info().uniform(); - const UniformQuantizationInfo oq_unif = oq_info.uniform(); - - const auto output_quant_info = (output.total_size() == 0) ? iq_unif : oq_unif; - - const float multiplier = (iq_unif.scale * wq_unif.scale) / output_quant_info.scale; - int output_multiplier = 0; - int output_shift = 0; - ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); - - PixelValue type_min{}; - PixelValue type_max{}; - std::tie(type_min, type_max) = get_min_max(data_type); - - if(activation_info.enabled()) - { - std::tie(type_min, type_max) = get_quantized_activation_min_max(activation_info, data_type, output_quant_info); - } - - // Set the GEMMLowp output stage info - gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset; - gemmlowp_output_stage.gemmlowp_multiplier = output_multiplier; - gemmlowp_output_stage.gemmlowp_shift = output_shift; - gemmlowp_output_stage.gemmlowp_multipliers.push_back(output_multiplier); - gemmlowp_output_stage.gemmlowp_shifts.push_back(output_shift); - type_min.get(gemmlowp_output_stage.gemmlowp_min_bound); - type_max.get(gemmlowp_output_stage.gemmlowp_max_bound); - } - - return Status{}; -} - -Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo *bias, const ITensorInfo &output, const FullyConnectedLayerInfo &fc_info) -{ - GEMMLowpOutputStageInfo gemmlowp_output_stage; - ARM_COMPUTE_RETURN_ON_ERROR(construct_gemmlowp_output_stage(input, weights, output, gemmlowp_output_stage, fc_info.activation_info)); + MemoryGroup memory_group{}; + IWeightsManager *weights_manager{nullptr}; - const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped - false, // is_b_reshaped - true, // reshape_b_only_on_first_run - 0, // depth_output_gemm3d - false, // reinterpret_input_as_3d - fc_info.retain_internal_weights, // retain_internal_weights - gemmlowp_output_stage, // gemmlowp_output_stage - fc_info.fp_mixed_precision, // fp_mixed_precision - true, // broadcast_bias - ActivationLayerInfo()); // activation_info + std::unique_ptr<opencl::ClFullyConnected> op{nullptr}; - if(is_data_type_quantized_asymmetric(input.data_type())) - { - const UniformQuantizationInfo iq_info = input.quantization_info().uniform(); - const UniformQuantizationInfo wq_info = weights.quantization_info().uniform(); - - // 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(iq_info.scale, -iq_info.offset); - const QuantizationInfo weights_quantization_info(wq_info.scale, -wq_info.offset); + const ITensor *original_weights{nullptr}; - // 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), - bias, - &output, - gemm_info)); - } - else - { - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input, &weights, bias, &output, 1.f, 1.f, gemm_info)); - } + ITensorPack run_pack{}; + WorkspaceData<CLTensor> workspace{}; + experimental::MemoryRequirements aux_mem_req{}; - return Status{}; -} -} // namespace + bool is_prepared{false}; + bool dynamic_weights{false}; +}; -CLFullyConnectedLayer::CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager) - : _memory_group(memory_manager), _weights_manager(weights_manager), _convert_weights(), _convert_weights_managed(), _reshape_weights_managed_function(), _flatten_layer(), _reshape_weights_function(), - _mm_gemm(memory_manager, weights_manager), _mm_gemmlowp(memory_manager), _flatten_output(), _converted_weights_output(), _reshape_weights_output(), _are_weights_converted(true), - _are_weights_reshaped(true), _is_fc_after_conv(true), _is_quantized(false), _is_prepared(false), _original_weights(nullptr) +CLFullyConnectedLayer::CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager, + IWeightsManager *weights_manager) + : _impl(std::make_unique<Impl>()) { + _impl->memory_group = MemoryGroup(std::move(memory_manager)); + _impl->weights_manager = weights_manager; } -void CLFullyConnectedLayer::configure_mm(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, - const FullyConnectedLayerInfo &fc_info) -{ - GEMMLowpOutputStageInfo gemmlowp_output_stage; - construct_gemmlowp_output_stage(*input->info(), *weights->info(), *output->info(), gemmlowp_output_stage, fc_info.activation_info); - - const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped - false, // is_b_reshaped - true, // reshape_b_only_on_first_run - 0, // depth_output_gemm3d - false, // reinterpret_input_as_3d - fc_info.retain_internal_weights, // retain_internal_weights - gemmlowp_output_stage, // gemmlowp_output_stage - fc_info.fp_mixed_precision, // fp_mixed_precision - true, // broadcast_bias - fc_info.activation_info, // activation_info - fc_info.constant_weights); // constant_weights - - 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.uniform().scale, -input_quantization_info.uniform().offset)); - weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset)); +CLFullyConnectedLayer::~CLFullyConnectedLayer() = default; - // Configure gemmlowp function - _mm_gemmlowp.configure(compile_context, input, weights, bias, output, gemm_info); - - // 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(compile_context, input, weights, bias, output, 1.f, 1.f, gemm_info); - } -} - -void CLFullyConnectedLayer::configure_conv_fc(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, - const FullyConnectedLayerInfo &fc_info) -{ - 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 flatten - TensorShape shape_flatten = compute_flatten_shape(input->info()); - _flatten_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten).set_data_layout(DataLayout::NCHW)); - - // Configure flatten kernel - _memory_group.manage(&_flatten_output); - _flatten_layer.configure(compile_context, input, &_flatten_output); - - // Configure matrix multiply kernel - configure_mm(compile_context, &_flatten_output, weights, bias, output, fc_info); - - // Allocate the output tensor for flatten once all the configure methods have been called - _flatten_output.allocator()->allocate(); -} - -void CLFullyConnectedLayer::configure_fc_fc(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, - const FullyConnectedLayerInfo &fc_info) -{ - ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1)); - - // Configure matrix multiply kernel - configure_mm(compile_context, input, weights, bias, output, fc_info); -} - -void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, +void CLFullyConnectedLayer::configure(const ICLTensor *input, + const ICLTensor *weights, + const ICLTensor *biases, + ICLTensor *output, FullyConnectedLayerInfo fc_info) { configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, fc_info); } -void CLFullyConnectedLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, +void CLFullyConnectedLayer::configure(const CLCompileContext &compile_context, + const ICLTensor *input, + const ICLTensor *weights, + const ICLTensor *biases, + ICLTensor *output, FullyConnectedLayerInfo fc_info) { + // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_ERROR_THROW_ON(CLFullyConnectedLayer::validate( + input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), fc_info)); - // Perform validate step - ARM_COMPUTE_ERROR_THROW_ON(CLFullyConnectedLayer::validate(input->info(), - weights->info(), - biases != nullptr ? biases->info() : nullptr, - output->info(), - fc_info)); + _impl->op = std::make_unique<opencl::ClFullyConnected>(); + _impl->original_weights = weights; + _impl->is_prepared = fc_info.retain_internal_weights; - _are_weights_converted = true; - _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; - _is_fc_after_conv = true; - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); - _is_prepared = fc_info.retain_internal_weights; - _original_weights = weights; + _impl->op->configure(compile_context, input->info(), weights->info(), + (biases != nullptr) ? biases->info() : nullptr, output->info(), fc_info); - if(_weights_manager) + if (_impl->weights_manager != nullptr) { - _weights_manager->manage(weights); + _impl->weights_manager->manage(_impl->original_weights); } - const ICLTensor *weights_to_use = weights; - - // 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 - - // 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) + if (!_impl->is_prepared) { - _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)); + _impl->aux_mem_req = _impl->op->workspace(); + _impl->run_pack = {{ACL_SRC_0, input}, {ACL_SRC_1, weights}, {ACL_SRC_2, biases}, {ACL_DST, output}}; + _impl->workspace = + manage_workspace<CLTensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->run_pack); } else { - _is_fc_after_conv = input->info()->num_dimensions() > 1; - } - - // Reshape weights if needed - if(!_are_weights_reshaped) - { - if(_weights_manager && _weights_manager->are_weights_managed(weights)) - { - _reshape_weights_managed_function.configure(compile_context, weights); - weights_to_use = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(weights, &_reshape_weights_managed_function)); - } - else - { - // Reshape the weights - _reshape_weights_function.configure(compile_context, weights, &_reshape_weights_output); - weights_to_use = &_reshape_weights_output; - } - } - - // Convert weights if needed - if(_is_fc_after_conv && (input->info()->data_layout() != fc_info.weights_trained_layout)) - { - if(_weights_manager && _weights_manager->are_weights_managed(weights_to_use)) - { - _convert_weights_managed.configure(compile_context, weights_to_use, - input->info()->tensor_shape(), - fc_info.weights_trained_layout); - weights_to_use = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(weights, &_convert_weights_managed)); - } - else - { - // Convert weights - _convert_weights.configure(compile_context, weights_to_use, - &_converted_weights_output, - input->info()->tensor_shape(), - fc_info.weights_trained_layout); - - weights_to_use = &_converted_weights_output; - } - _are_weights_converted = false; + _impl->run_pack.add_tensor(ACL_SRC_0, input); + _impl->run_pack.add_tensor(ACL_DST, output); } - if(_is_fc_after_conv) - { - // Fully Connected layer after a Convolution Layer without batches - configure_conv_fc(compile_context, input, weights_to_use, biases, output, fc_info); - } - else - { - // Fully Connected layer after a Fully Connected Layer without batches - configure_fc_fc(compile_context, input, weights_to_use, biases, output, fc_info); - } + _impl->dynamic_weights = !weights->info()->are_values_constant() && fc_info.transpose_weights && + !fc_info.are_weights_reshaped && !fc_info.retain_internal_weights; } -Status CLFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, +Status CLFullyConnectedLayer::validate(const ITensorInfo *input, + const ITensorInfo *weights, + const ITensorInfo *biases, + const ITensorInfo *output, FullyConnectedLayerInfo fc_info) { - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); - 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_MISMATCHING_DATA_TYPES(input, weights, output); - ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2); - ARM_COMPUTE_RETURN_ERROR_ON(fc_info.activation_info.enabled() && is_data_type_quantized(input->data_type()) && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::RELU - && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU); - ARM_COMPUTE_RETURN_ERROR_ON(!fc_info.constant_weights && (!fc_info.are_weights_reshaped || fc_info.transpose_weights)); - - bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; - bool is_fc_after_conv = true; - - const ITensorInfo &flatten_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input)).set_data_layout(DataLayout::NCHW)); - const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights))); - const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone()); - - // 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; - - // 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(!weights_reshaped) - { - // Validate reshape weights kernel - ARM_COMPUTE_RETURN_ON_ERROR(CLTranspose::validate(weights, &reshaped_weights)); - weights_to_use = &reshaped_weights; - } - - if(is_fc_after_conv && (input->data_layout() != fc_info.weights_trained_layout)) - { - // Validate convert weights kernel - ARM_COMPUTE_RETURN_ON_ERROR(CLConvertFullyConnectedWeights::validate(weights_to_use, - &converted_weights, - input->tensor_shape(), - fc_info.weights_trained_layout)); - weights_to_use = &converted_weights; - } - - 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 flatten kernel - ARM_COMPUTE_RETURN_ON_ERROR(CLFlattenLayer::validate(input, &flatten_input)); - input_to_use = &flatten_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, biases, *output, fc_info)); - - return Status{}; + return opencl::ClFullyConnected::validate(input, weights, biases, output, fc_info); } void CLFullyConnectedLayer::run() { - prepare(); - - MemoryGroupResourceScope scope_mg(_memory_group); - - // Linearize input if it comes from a convolutional layer - if(_is_fc_after_conv) + if (!_impl->dynamic_weights) { - _flatten_layer.run(); + prepare(); } - // Run matrix multiply - if(_is_quantized) - { - _mm_gemmlowp.run(); - } - else - { - _mm_gemm.run(); - } + MemoryGroupResourceScope scope_mg(_impl->memory_group); + _impl->op->run(_impl->run_pack); } void CLFullyConnectedLayer::prepare() { - if(!_is_prepared) + if (!_impl->is_prepared) { - if(!_weights_manager) - { - ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); - } - - auto release_unused = [](CLTensor * w) - { - if(!w->is_used()) - { - CLScheduler::get().queue().finish(); - w->allocator()->free(); - } - }; + _impl->op->prepare(_impl->run_pack); - // Pointer to current weights - const ICLTensor *cur_weights = _original_weights; + // Release temporary tensors that are only used in prepare stage + release_temporaries<CLTensor>(_impl->aux_mem_req, _impl->workspace); + _impl->is_prepared = true; - // Reshape of the weights if needed (happens only once) - if(!_are_weights_reshaped) + // Handle weights managed infrastructure + if (_impl->weights_manager != nullptr && _impl->weights_manager->are_weights_managed(_impl->original_weights)) { - if(_weights_manager && _weights_manager->are_weights_managed(_original_weights)) + // Ensure that b gets marked as unused (memory released) only after the last function which uses b also finishes its prepare + // This is for cases where multiple functions share the same b (weights) + // Therefore when a function marks original b as unused, we pre-mark it in weights manager, and mark it back to used so that it doesn't get released before its last reference + const ITensor *original_b = _impl->original_weights; + if (!original_b->is_used()) { - cur_weights = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->run(cur_weights, &_reshape_weights_managed_function)); + _impl->weights_manager->pre_mark_as_unused(original_b); } - else - { - // Run reshape weights kernel and mark weights as unused - _reshape_weights_output.allocator()->allocate(); - _reshape_weights_function.run(); - - cur_weights->mark_as_unused(); - cur_weights = &_reshape_weights_output; - } - _are_weights_reshaped = true; + _impl->original_weights->mark_as_used(); + _impl->weights_manager->release(_impl->original_weights); } - - // Convert weights if needed (happens only once) - if(!_are_weights_converted) - { - if(_weights_manager && _weights_manager->are_weights_managed(cur_weights)) - { - _weights_manager->run(cur_weights, &_convert_weights_managed); - } - else - { - _converted_weights_output.allocator()->allocate(); - _convert_weights.run(); - cur_weights->mark_as_unused(); - } - - _are_weights_converted = true; - } - - // Release reshaped weights if unused - release_unused(&_reshape_weights_output); - - // Prepare GEMM prepare and release unused weights - if(!_is_quantized) - { - _mm_gemm.prepare(); - } - - // Release converted weights if unused - release_unused(&_reshape_weights_output); - release_unused(&_converted_weights_output); - - _is_prepared = true; } } } // namespace arm_compute |