diff options
Diffstat (limited to 'src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp | 696 |
1 files changed, 87 insertions, 609 deletions
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp index 3184d5dfe0..aef7cddd7a 100644 --- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -23,22 +23,17 @@ */ #include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/PixelValue.h" #include "arm_compute/core/Size2D.h" #include "arm_compute/core/Utils.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/Validate.h" #include "arm_compute/runtime/CL/CLScheduler.h" -#include "src/core/CL/kernels/CLCol2ImKernel.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/CL/kernels/CLIm2ColKernel.h" -#include "src/core/CL/kernels/CLWeightsReshapeKernel.h" -#include "src/core/helpers/AutoConfiguration.h" + +#include "src/core/helpers/MemoryHelpers.h" +#include "src/gpu/cl/operators/ClGemmConv2d.h" #include "support/Cast.h" #include <cmath> @@ -49,634 +44,117 @@ namespace arm_compute { using namespace arm_compute::misc::shape_calculator; using namespace arm_compute::utils::cast; +using namespace arm_compute::experimental; -CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights() - : _weights_reshape_kernel(std::make_unique<CLWeightsReshapeKernel>()) -{ -} - -CLConvolutionLayerReshapeWeights::~CLConvolutionLayerReshapeWeights() = default; - -void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, unsigned int num_groups) -{ - configure(CLKernelLibrary::get().get_compile_context(), weights, biases, output, num_groups); -} - -void CLConvolutionLayerReshapeWeights::configure(const CLCompileContext &compile_context, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, unsigned int num_groups) -{ - // Perform validation step - ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output); - ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayerReshapeWeights::validate(weights->info(), - (biases != nullptr) ? biases->info() : nullptr, - output->info(), - num_groups)); - - const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type()); - const ICLTensor *biases_to_use = (append_biases) ? biases : nullptr; - - _weights_reshape_kernel->configure(compile_context, weights, biases_to_use, output, num_groups); - - output->info()->set_quantization_info(weights->info()->quantization_info()); -} - -Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, unsigned int num_groups) +struct CLGEMMConvolutionLayer::Impl { - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); - - if(biases != nullptr) - { - const int idx_kernels = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::BATCHES); - ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized(weights->data_type())); - - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); - ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels)); - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - - if((output != nullptr) && (output->total_size() != 0)) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); - CLWeightsReshapeKernel::validate(weights, biases, output, num_groups); - } - - return Status{}; -} - -void CLConvolutionLayerReshapeWeights::run() -{ - CLScheduler::get().enqueue(*_weights_reshape_kernel); -} - -CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager) - : _memory_group(memory_manager), _weights_manager(weights_manager), _reshape_weights(), _reshape_weights_managed(), _im2col_kernel(std::make_unique<CLIm2ColKernel>()), _mm_gemm(memory_manager, - weights_manager), _mm_gemmlowp(memory_manager), _col2im_kernel(std::make_unique<CLCol2ImKernel>()), _activationlayer_function(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), - _gemm_output(), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _fuse_activation(true), _is_prepared(false) + const ITensor *weights{nullptr}; + std::unique_ptr<opencl::ClGemmConv2d> op{nullptr}; + ITensorPack run_pack{}; + ITensorPack prep_pack{}; + MemoryGroup memory_group{}; + IWeightsManager *weights_manager{nullptr}; + MemoryRequirements aux_mem_req{}; + WorkspaceData<CLTensor> workspace_tensors{}; + bool is_prepared{false}; +}; + +CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager, + IWeightsManager *weights_manager) + : _impl(std::make_unique<Impl>()) { + _impl->memory_group = MemoryGroup(memory_manager); + _impl->weights_manager = weights_manager; } CLGEMMConvolutionLayer::~CLGEMMConvolutionLayer() = default; -void CLGEMMConvolutionLayer::configure_mm(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const GEMMLowpOutputStageInfo &gemmlowp_output_stage, - int gemm_3d_depth, const ActivationLayerInfo &act_info) +void CLGEMMConvolutionLayer::configure(const ICLTensor *input, + const ICLTensor *weights, + const ICLTensor *biases, + ICLTensor *output, + const PadStrideInfo &conv_info, + const WeightsInfo &weights_info, + const Size2D &dilation, + const ActivationLayerInfo &act_info, + unsigned int num_groups) { - ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); - ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), gemmlowp_output_stage, gemm_3d_depth, _skip_im2col, act_info)); - - const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped - false, // is_b_reshaped - true, // reshape_b_only_on_first_run - gemm_3d_depth, // depth_output_gemm3d - _skip_im2col, // reinterpret_input_as_3d - false, // retain_internal_weights - gemmlowp_output_stage, // gemmlowp_output_stage - false, // fp_mixed_precision - true, // broadcast_bias - act_info); // activation_info - - 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)); - - _mm_gemmlowp.configure(compile_context, input, weights, biases, output, gemm_info); - - // Revert back QuantizatioInfo as input and weights could be used in other convolution layers - input->info()->set_quantization_info(input_quantization_info); - weights->info()->set_quantization_info(weights_quantization_info); - } - else - { - // Configure matrix multiply function - _mm_gemm.configure(compile_context, input, weights, biases, output, 1.0f, 1.0f, gemm_info); - } + configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, weights_info, + dilation, act_info, num_groups); } -Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, - const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth, bool skip_im2col, const ActivationLayerInfo &act_info) -{ - const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); - - const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped - false, // is_b_reshaped - true, // reshape_b_only_on_first_run - gemm_3d_depth, // depth_output_gemm3d - skip_im2col, // reinterpret_input_as_3d - false, // retain_internal_weights - gemmlowp_output_stage, // gemmlowp_output_stage - false, // fp_mixed_precision - true, // broadcast_bias - act_info); // activation_info - - 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->quantization_info(); - const QuantizationInfo weights_quantization_info = weights->quantization_info(); - - std::unique_ptr<ITensorInfo> input_qa = input->clone(); - std::unique_ptr<ITensorInfo> weights_qa = weights->clone(); - input_qa->set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset)); - weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset)); - - // Perform validation step on GEMMLowp - return CLGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), biases, output, gemm_info); - } - else - { - // Perform validation step on Matrix multiply function - return CLGEMM::validate(input, weights, biases, output, 1.0f, 1.0f, gemm_info); - } -} - -void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, - const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups) -{ - configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, weights_info, dilation, act_info, num_groups); -} - -void CLGEMMConvolutionLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups) +void CLGEMMConvolutionLayer::configure(const CLCompileContext &compile_context, + const ICLTensor *input, + const ICLTensor *weights, + const ICLTensor *biases, + ICLTensor *output, + const PadStrideInfo &conv_info, + const WeightsInfo &weights_info, + const Size2D &dilation, + const ActivationLayerInfo &act_info, + unsigned int num_groups) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - - ARM_COMPUTE_ERROR_THROW_ON(CLGEMMConvolutionLayer::validate(input->info(), - weights->info(), - biases != nullptr ? biases->info() : nullptr, - output->info(), - conv_info, - weights_info, - dilation, - act_info, - num_groups)); - - const DataType data_type = input->info()->data_type(); - const DataLayout data_layout = input->info()->data_layout(); - const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); - - const unsigned int kernel_width = weights->info()->dimension(idx_width); - const unsigned int kernel_height = weights->info()->dimension(idx_height); - const unsigned int num_kernels = weights->info()->dimension(idx_kernels); - - const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); - - _is_prepared = weights_info.retain_internal_weights(); - _original_weights = weights; - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); - _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); - _skip_col2im = data_layout == DataLayout::NHWC; - - // Only for quantize there are few cases where we cannot fuse the activation function in GEMM - _fuse_activation = true; - - // Set the GPU target for im2col and col2im - _im2col_kernel->set_target(CLScheduler::get().target()); - _col2im_kernel->set_target(CLScheduler::get().target()); - - const ICLTensor *gemm_input_to_use = input; - ICLTensor *gemm_output_to_use = output; - - // Get parameters from conv_info - unsigned int stride_x = 0; - unsigned int stride_y = 0; - std::tie(stride_x, stride_y) = conv_info.stride(); - - // Get convolved dimensions - unsigned int conv_w = 0; - unsigned int conv_h = 0; - std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(idx_width), - input->info()->dimension(idx_height), - kernel_width, - kernel_height, - conv_info, - dilation); - - unsigned int mat_weights_cols = num_kernels / num_groups; - - const ICLTensor *biases_to_use = biases; - bool append_bias = false; - - ICLTensor *weights_to_use = &_weights_reshaped; - if(num_groups != 1 && biases != nullptr) - { - // num_groups != 1 can only be for NCHW - // Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor - biases_to_use = nullptr; - append_bias = true; - - if(_weights_manager && _weights_manager->are_weights_managed(weights)) - { - _reshape_weights_managed.configure(compile_context, weights, biases, num_groups); - weights_to_use = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(weights, &_reshape_weights_managed)); - } - else - { - _reshape_weights.configure(compile_context, weights, biases, &_weights_reshaped, num_groups); - } - } - else - { - if(_weights_manager && _weights_manager->are_weights_managed(weights)) - { - _reshape_weights_managed.configure(compile_context, weights, nullptr, num_groups); - weights_to_use = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(weights, &_reshape_weights_managed)); - } - else - { - _reshape_weights.configure(compile_context, weights, nullptr, &_weights_reshaped, num_groups); - } - } - - // Create tensor to store im2col reshaped inputs - if(!_skip_im2col) - { - _memory_group.manage(&_im2col_output); - - // Configure and tune im2col. im2col output shape is auto-initialized - _im2col_kernel->configure(compile_context, input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation, num_groups); - - // Set quantization info - _im2col_output.info()->set_quantization_info(input->info()->quantization_info()); - CLScheduler::get().tune_kernel_static(*_im2col_kernel); - - // Update GEMM input - gemm_input_to_use = &_im2col_output; - } - - // Create GEMM output tensor - if(!_skip_col2im) - { - TensorShape shape_gemm; - - // If we cannot skip col2im it means we run im2col as well - shape_gemm = _im2col_output.info()->tensor_shape(); - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, conv_w * conv_h); - - TensorInfo info_gemm(shape_gemm, 1, data_type); - info_gemm.set_quantization_info(output->info()->quantization_info()).set_data_layout(input->info()->data_layout()); - _gemm_output.allocator()->init(info_gemm); - _memory_group.manage(&_gemm_output); - - // Update GEMM output - gemm_output_to_use = &_gemm_output; - } - - GEMMLowpOutputStageInfo gemmlowp_output_stage; - gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; - gemmlowp_output_stage.gemmlowp_offset = 0; - - // Configure output stage for quantized case - if(_is_quantized) - { - const auto output_quant_info = (output->info()->total_size() == 0) ? iq_info : oq_info; - const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->info()->data_type()); - const unsigned int num_filters = (is_quantized_per_channel) ? num_kernels : 1; - - gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel; - - gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters); - gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters); - quantization::compute_quantized_multipliers_and_shifts(input->info(), - weights->info(), - output->info(), - idx_kernels, - gemmlowp_output_stage.gemmlowp_multipliers.data(), - gemmlowp_output_stage.gemmlowp_shifts.data()); - gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0]; - gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0]; - - PixelValue min_val{}; - PixelValue max_val{}; - std::tie(min_val, max_val) = get_min_max(output->info()->data_type()); - - auto min_activation = min_val.get<int32_t>(); - auto max_activation = max_val.get<int32_t>(); - - const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, - ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, - ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU - }; - - if(act_info.enabled()) - { - if(supported_acts.count(act_info.activation()) != 0) - { - std::tie(min_activation, max_activation) = get_quantized_activation_min_max(act_info, data_type, output_quant_info); - } - else - { - _fuse_activation = false; - } - } - - // Set the GEMMLowp output stage info - gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset; - gemmlowp_output_stage.gemmlowp_min_bound = min_activation; - gemmlowp_output_stage.gemmlowp_max_bound = max_activation; - } - - // Configure and tune GEMM - // In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix - const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0; - - configure_mm(compile_context, gemm_input_to_use, weights_to_use, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, act_info); - - if(!_skip_im2col) - { - _im2col_output.allocator()->allocate(); - } - - if(!_skip_col2im) - { - // Configure and tune Col2Im - _col2im_kernel->configure(compile_context, gemm_output_to_use, output, Size2D(conv_w, conv_h), num_groups); - CLScheduler::get().tune_kernel_static(*_col2im_kernel.get()); - } - - if(!_skip_col2im) - { - _gemm_output.allocator()->allocate(); - } - - ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(idx_width) != conv_w) || (output->info()->dimension(idx_height) != conv_h), - "Output shape does not match the expected one"); - - if(!_fuse_activation) - { - _activationlayer_function.configure(compile_context, output, nullptr, act_info); - } - - ARM_COMPUTE_UNUSED(weights_info); + _impl->weights = weights; + _impl->op = std::make_unique<opencl::ClGemmConv2d>(); + const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, false, num_groups); + _impl->op->configure(compile_context, input->info(), weights->info(), + (biases != nullptr ? biases->info() : nullptr), output->info(), conv2d_info, weights_info); + + _impl->run_pack = {{TensorType::ACL_SRC_0, input}, + {TensorType::ACL_SRC_1, weights}, + {TensorType::ACL_SRC_2, biases}, + {TensorType::ACL_DST, output}}; + _impl->prep_pack = { + {TensorType::ACL_SRC_1, weights}, + {TensorType::ACL_SRC_2, biases}, + }; + _impl->aux_mem_req = _impl->op->workspace(); + _impl->workspace_tensors = + manage_workspace<CLTensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->prep_pack); } -Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups) +Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, + const ITensorInfo *weights, + const ITensorInfo *biases, + const ITensorInfo *output, + const PadStrideInfo &conv_info, + const WeightsInfo &weights_info, + const Size2D &dilation, + const ActivationLayerInfo &act_info, + unsigned int num_groups) { - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!"); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->data_type()); - - if(!is_quantized_per_channel) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - } - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((num_groups != 1) && (input->data_layout() != DataLayout::NCHW), "Grouping (num_groups != 1) with NHWC data layout is not supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((num_groups != 1) && (input->data_type() == DataType::QASYMM8), "Grouping (num_groups != 1) is not supported with QASYMM8"); - ARM_COMPUTE_RETURN_ERROR_ON(((input->dimension(2) / weights->dimension(2)) != num_groups) && (input->data_layout() == DataLayout::NCHW)); - - const DataLayout data_layout = input->data_layout(); - const DataType data_type = input->data_type(); - const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); - const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); - - const unsigned int kernel_width = weights->dimension(idx_width); - const unsigned int kernel_height = weights->dimension(idx_height); - const unsigned int num_kernels = weights->dimension(idx_kernels); - - TensorInfo im2col_reshaped_info{}; - TensorInfo info_gemm{}; - TensorInfo weights_reshaped_info{}; - const ITensorInfo *gemm_input_to_use = input; - const ITensorInfo *gemm_output_to_use = output; - const ITensorInfo *weights_to_use = weights; - const bool is_quantized = is_data_type_quantized_asymmetric(data_type); - const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1); - const bool skip_col2im = data_layout == DataLayout::NHWC; - bool fuse_activation = true; - - ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * num_groups) != input->dimension(idx_channel)); - ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); - - // Validate biases - if(biases != nullptr) - { - if(is_quantized) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); - } - ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels)); - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - - if(act_info.enabled()) - { - ARM_COMPUTE_ERROR_ON(act_info.b() > act_info.a()); - } - - // Get convolved dimensions - unsigned int conv_w = 0; - unsigned int conv_h = 0; - - std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(idx_width), - input->dimension(idx_height), - kernel_width, - kernel_height, - conv_info, - dilation); - - unsigned int mat_weights_cols = num_kernels / num_groups; - - const ITensorInfo *biases_to_use = biases; - bool append_bias = false; - - if(num_groups != 1 && biases != nullptr) - { - // num_groups != 1 can only be for NCHW - // Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor - biases_to_use = nullptr; - append_bias = true; - - ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayerReshapeWeights::validate(weights, biases, nullptr, num_groups)); - weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, true, num_groups), 1, data_type); - } - else - { - ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayerReshapeWeights::validate(weights, nullptr, nullptr, num_groups)); - weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, false, num_groups), 1, data_type); - } - - weights_to_use = &weights_reshaped_info; - - if(!skip_im2col) - { - const Size2D kernel_dims(kernel_width, kernel_height); - - // Output tensor auto initialization if not yet initialized - TensorShape expected_output_shape = compute_im2col_conv_shape(input, kernel_dims, conv_info, append_bias, dilation, num_groups == 1, num_groups); - - auto_init_if_empty(im2col_reshaped_info, input->clone()->set_tensor_shape(expected_output_shape)); - - ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_reshaped_info, kernel_dims, conv_info, append_bias, dilation, num_groups)); - gemm_input_to_use = &im2col_reshaped_info; - } - - // Create GEMM output tensor - if(!skip_col2im) - { - TensorShape shape_gemm; - - shape_gemm = gemm_input_to_use->tensor_shape(); - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, conv_w * conv_h); - - info_gemm = TensorInfo(shape_gemm, 1, data_type); - info_gemm.set_quantization_info(output->quantization_info()).set_data_layout(input->data_layout()); - gemm_output_to_use = &info_gemm; - } - - GEMMLowpOutputStageInfo gemmlowp_output_stage; - gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; - gemmlowp_output_stage.gemmlowp_offset = 0; - gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel; - - if(is_quantized) - { - const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = output->quantization_info().uniform(); - const auto output_quant_info = (output->total_size() == 0) ? iq_info : oq_info; - const unsigned int num_filters = (is_quantized_per_channel) ? num_kernels : 1; - - gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters); - gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters); - quantization::compute_quantized_multipliers_and_shifts(input, - weights, - output, - idx_kernels, - gemmlowp_output_stage.gemmlowp_multipliers.data(), - gemmlowp_output_stage.gemmlowp_shifts.data()); - gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0]; - gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0]; - - int min_activation = 0; - int max_activation = 0; - - const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, - ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, - ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU - }; - - if(act_info.enabled()) - { - if(supported_acts.count(act_info.activation()) != 0) - { - std::tie(min_activation, max_activation) = get_quantized_activation_min_max(act_info, data_type, output_quant_info); - } - else - { - fuse_activation = false; - } - } - - // Set the GEMMLowp output stage info - gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset; - gemmlowp_output_stage.gemmlowp_min_bound = min_activation; - gemmlowp_output_stage.gemmlowp_max_bound = max_activation; - } - - // In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix - const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0; - - ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, skip_im2col, act_info)); - - // Validate Col2Im - if(!skip_col2im) - { - ARM_COMPUTE_RETURN_ON_ERROR(CLCol2ImKernel::validate(gemm_output_to_use, output, Size2D(conv_w, conv_h), num_groups)); - } - - //Validate Activation Layer - if(!fuse_activation) - { - ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output, nullptr, act_info)); - } - - return Status{}; + const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, false, num_groups); + return opencl::ClGemmConv2d::validate(input, weights, biases, output, conv2d_info, weights_info); } void CLGEMMConvolutionLayer::run() { prepare(); - - MemoryGroupResourceScope scope_mg(_memory_group); - - // Run im2col - if(!_skip_im2col) - { - CLScheduler::get().enqueue(*_im2col_kernel); - } - - // Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions - if(_is_quantized) - { - // Run gemmlowp - _mm_gemmlowp.run(); - } - else - { - // Run gemm - _mm_gemm.run(); - } - - // Reshape output matrix - if(!_skip_col2im) - { - CLScheduler::get().enqueue(*_col2im_kernel.get(), false); - } - - //Run Activation Layer if we cannot fuse in GEMM - if(!_fuse_activation) - { - _activationlayer_function.run(); - } + MemoryGroupResourceScope scope_mg(_impl->memory_group); + _impl->op->run(_impl->run_pack); } void CLGEMMConvolutionLayer::prepare() { - if(!_is_prepared) + if (!_impl->is_prepared) { - ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); - if(_weights_manager && _weights_manager->are_weights_managed(_original_weights)) + _impl->op->prepare(_impl->prep_pack); + auto has_reshape = + std::find_if(_impl->aux_mem_req.begin(), _impl->aux_mem_req.end(), + [](const MemoryInfo &m) -> bool { return m.lifetime == MemoryLifetime::Persistent; }); + + if (has_reshape != std::end(_impl->aux_mem_req)) { - _weights_manager->run(_original_weights, &_reshape_weights_managed); + _impl->weights->mark_as_unused(); } else { - // Run weights reshaping and mark original weights tensor as unused - _weights_reshaped.allocator()->allocate(); - _reshape_weights.run(); - _original_weights->mark_as_unused(); - } - - // Prepare GEMM - _is_quantized ? _mm_gemmlowp.prepare() : _mm_gemm.prepare(); - if(!_weights_reshaped.is_used()) - { - _weights_reshaped.allocator()->free(); + // Pack the B matrix to be used as the underlying GEMM performs no reshapes + _impl->run_pack.add_const_tensor(ACL_SRC_1, _impl->weights); } - - CLScheduler::get().queue().finish(); - _is_prepared = true; + release_temporaries(_impl->aux_mem_req, _impl->workspace_tensors); + _impl->is_prepared = true; } } } // namespace arm_compute |