diff options
Diffstat (limited to 'src/runtime/CL/functions/CLConvolutionLayer.cpp')
-rw-r--r-- | src/runtime/CL/functions/CLConvolutionLayer.cpp | 332 |
1 files changed, 51 insertions, 281 deletions
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp index d1533b6f24..c430174fe7 100644 --- a/src/runtime/CL/functions/CLConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -24,10 +24,8 @@ #include "arm_compute/runtime/CL/functions/CLConvolutionLayer.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/quantization/AsymmHelpers.h" #include "arm_compute/runtime/CL/CLScheduler.h" #include <cmath> @@ -36,315 +34,87 @@ using namespace arm_compute; -CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false) -{ -} - -void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW) -{ - ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); - - if(biases != nullptr) - { - ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->info()->data_type())); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); - ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3)); - ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); - } - - const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type()); - const unsigned bias_element = (append_biases) ? 1 : 0; - const ICLTensor *biases_to_use = (append_biases) ? biases : nullptr; - - _transpose1xW = transpose1xW; - - if(transpose1xW) - { - // Create tensor to store the reshaped weights - const unsigned int mat_weights_cols = weights->info()->dimension(3); - const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element; - TensorShape shape_wr(mat_weights_cols, mat_weights_rows); - const DataType dt = weights->info()->data_type(); - const int fixed_point_position = weights->info()->fixed_point_position(); - TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position); - - _weights_reshaped.allocator()->init(info_wr); - _memory_group.manage(&_weights_reshaped); - _weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped); - _weights_transposed_kernel.configure(&_weights_reshaped, output); - _weights_reshaped.allocator()->allocate(); - } - else - { - _weights_reshape_kernel.configure(weights, biases_to_use, output); - } - - output->info()->set_quantization_info(weights->info()->quantization_info()); -} - -void CLConvolutionLayerReshapeWeights::run() -{ - _memory_group.acquire(); - - CLScheduler::get().enqueue(_weights_reshape_kernel); - if(_transpose1xW) - { - CLScheduler::get().enqueue(_weights_transposed_kernel); - } - - _memory_group.release(); -} - CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _interleave_kernel(), _mm_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), - _col2im_kernel(), _im2col_output(), _interleave_output(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _are_weights_reshaped(false), _is_quantized(false), - _is_interleaved_transposed(false) + : _memory_manager(std::move(memory_manager)), _function() { } -void CLConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed, bool are_weights_reshaped) +void CLConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) { - if(_is_quantized) - { - if(are_weights_reshaped) - { - ARM_COMPUTE_ERROR("Weights already reshaped are not suppported with gemmlowp"); - } - else - { - // 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)); - - _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_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 + switch(CLConvolutionLayer::get_convolution_method(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, + weights_info, CLScheduler::get().target())) { - if(are_weights_reshaped) + case ConvolutionMethod::DIRECT: { - // Configure matrix multiply kernel - _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed); + auto f = arm_compute::support::cpp14::make_unique<CLDirectConvolutionLayer>(); + f->configure(input, weights, biases, output, conv_info); + _function = std::move(f); + break; } - else + case ConvolutionMethod::GEMM: { - // Configure matrix multiply function - _mm_gemm.configure(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); + auto f = arm_compute::support::cpp14::make_unique<CLGEMMConvolutionLayer>(_memory_manager); + f->configure(input, weights, biases, output, conv_info, weights_info); + _function = std::move(f); + break; } + default: + ARM_COMPUTE_ERROR("Not supported."); + break; } } -void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) +Status CLConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + const WeightsInfo &weights_info) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights); - ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && CLScheduler::get().target() == GPUTarget::BIFROST); - ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2)); - ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); - ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->info()->data_type())); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); + //Configure if the parameters match the direct convolution or the gemm-based + const GPUTarget gpu_target = CLScheduler::get().target(); - if(biases != nullptr) + switch(CLConvolutionLayer::get_convolution_method(input, weights, biases, output, conv_info, weights_info, gpu_target)) { - if(_is_quantized) + case ConvolutionMethod::DIRECT: { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); + // Validate direct convolution layer + CLDirectConvolutionLayerKernel::validate(input, weights, biases, output, conv_info, gpu_target); + break; } - else + case ConvolutionMethod::GEMM: { - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + // Validate gemm-based convolution layer + /* TODO COMPMID-754: Add validation methods for CLGEMMConvolutionLayer + CLGEMMConvolutionLayerKernel::validate(input, weights, biases, output, conv_info, weights_info); */ + break; } - ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases); - ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3)); - ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); + default: + ARM_COMPUTE_ERROR("Not supported."); + break; } - const DataType dt = input->info()->data_type(); - - // Set the GPU target for matrix multiply and im2col and col2im - _mm_kernel.set_target(CLScheduler::get().target()); - _im2col_kernel.set_target(CLScheduler::get().target()); - _col2im_kernel.set_target(CLScheduler::get().target()); - - const bool append_bias = (biases != nullptr) && (!_is_quantized); - _are_weights_reshaped = weights_info.are_reshaped(); - - const unsigned bias_element = (append_bias) ? 1 : 0; - const ICLTensor *biases_to_use = (append_bias) ? biases : nullptr; - - // 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; - - const unsigned int kernel_width = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0); - const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1); - std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height, - conv_info); - - // Check if its a "fully connected" convolution - const bool is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); - _is_interleaved_transposed = (!is_fully_connected_convolution) && (!_is_quantized) && (_are_weights_reshaped); - - unsigned int mat_weights_cols = weights->info()->dimension(3); - unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element; - - // Reshape weights if needed - if(_are_weights_reshaped) - { - if(is_fully_connected_convolution || _is_quantized) - { - mat_weights_cols = weights->info()->dimension(0); - mat_weights_rows = weights->info()->dimension(1); - } - else - { - mat_weights_cols = weights_info.num_kernels(); - const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4; - mat_weights_rows = quarter_reshaped_cols + bias_element; - } - } - else - { - // _weights_reshaped will be auto configured in the kernel. - // Just append biases and do not transpose 1xW as it will be reshaped in CLGEMM - _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false); - - weights = &_weights_reshaped; - } - - // Create tensor to store im2col reshaped inputs - const unsigned int mat_input_cols = mat_weights_rows; - const unsigned int mat_input_rows = conv_w * conv_h; - TensorShape shape_im2col = input->info()->tensor_shape(); - shape_im2col.set(0, mat_input_cols); - shape_im2col.set(1, mat_input_rows); - shape_im2col.set(2, 1); - // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. - TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->info()->fixed_point_position()); - im2col_reshaped_info.set_quantization_info(input->info()->quantization_info()); - _im2col_output.allocator()->init(im2col_reshaped_info); - _memory_group.manage(&_im2col_output); - - // Create GEMM output tensor - TensorShape shape_gemm = _im2col_output.info()->tensor_shape(); - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, mat_input_rows); - const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt; - // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. - // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. - TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position()); - info_gemm.set_quantization_info(output->info()->quantization_info()); - _gemm_output.allocator()->init(info_gemm); - _memory_group.manage(&_gemm_output); - - // Configure im2col - _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias); - - // Configure matrix multiply - if(_is_interleaved_transposed) - { - // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel - _memory_group.manage(&_interleave_output); - _interleave_kernel.configure(&_im2col_output, &_interleave_output); - - // Configure GEMM - configure_mm(&_interleave_output, weights, &_gemm_output, true, _are_weights_reshaped); - _interleave_output.allocator()->allocate(); - } - else - { - configure_mm(&_im2col_output, weights, &_gemm_output, false, _are_weights_reshaped); - } - _im2col_output.allocator()->allocate(); - - // Configure output stage for quantized case - 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); - _memory_group.manage(&_tmp_output); - _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output->info()->quantization_info().offset); - } - - // Configure Col2Im - _col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h)); - if(_is_quantized) - { - _tmp_output.allocator()->allocate(); - } - _gemm_output.allocator()->allocate(); + return Status{}; +} - ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one"); +ConvolutionMethod CLConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + const WeightsInfo &weights_info, const GPUTarget gpu_target) +{ + ARM_COMPUTE_UNUSED(input); + ARM_COMPUTE_UNUSED(biases); + ARM_COMPUTE_UNUSED(output); + ARM_COMPUTE_UNUSED(conv_info); + ARM_COMPUTE_UNUSED(weights_info); - // Allocate intermediate tensor - if(!_are_weights_reshaped) + if((gpu_target == GPUTarget::BIFROST) && (weights->dimension(0) == 5) && (weights->dimension(1) == 5)) { - _weights_reshaped.allocator()->allocate(); + return ConvolutionMethod::DIRECT; } + return ConvolutionMethod::GEMM; } void CLConvolutionLayer::run() { - // Run weights reshaping (Runs once for every configure) - if(!_are_weights_reshaped) - { - _are_weights_reshaped = true; - _reshape_weights.run(); - } - - _memory_group.acquire(); - - // Run im2col - CLScheduler::get().enqueue(_im2col_kernel); - - // Note: _is_interleaved_transposed is true only if the weights passed to the function have been passed already reshaped - // and if we do not have QASYMM8 data type. If this flag is true, we need to run the - // gemm kernel instead of gemm function - if(_is_interleaved_transposed) - { - // Run interleave4x4 kernel - CLScheduler::get().enqueue(_interleave_kernel); - - // Run matrix multiply kernel - CLScheduler::get().enqueue(_mm_kernel); - } - else - { - // Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions - if(_is_quantized) - { - // Run gemmlowp - _mm_gemmlowp.run(); - - // Run output stage - _gemmlowp_output_stage.run(); - } - else - { - // Run gemm - _mm_gemm.run(); - } - } - - // Reshape output matrix - CLScheduler::get().enqueue(_col2im_kernel, false); - - _memory_group.release(); + _function->run(); } |