arm_compute v18.02
Change-Id: I7207aa488e5470f235f39b6c188b4678dc38d1a6
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index b3af11e..1a486ce 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -24,9 +24,9 @@
#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/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
@@ -35,319 +35,86 @@
#include <tuple>
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();
-}
+using namespace arm_compute::misc::shape_calculator;
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)
+ 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));
+
+ 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:
{
- ARM_COMPUTE_ERROR("Weights already reshaped are not suppported with gemmlowp");
+ 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:
{
- // 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*/));
-
- // 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);
+ 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;
}
- }
- else
- {
- if(are_weights_reshaped)
- {
- // Configure matrix multiply kernel
- _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed);
- }
- else
- {
- // 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*/));
- }
+ 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_RETURN_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
+ CLDirectConvolutionLayer::validate(input, weights, biases, output, conv_info);
+ break;
}
- else
+ case ConvolutionMethod::GEMM:
{
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+ // Validate gemm-based convolution layer
+ CLGEMMConvolutionLayer::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();
+ return Status{};
+}
- // 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());
+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(weights);
+ ARM_COMPUTE_UNUSED(biases);
+ ARM_COMPUTE_UNUSED(output);
+ ARM_COMPUTE_UNUSED(conv_info);
+ ARM_COMPUTE_UNUSED(weights_info);
+ ARM_COMPUTE_UNUSED(gpu_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);
- //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.
- //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
- _interleave_kernel.configure(&_im2col_output, &_interleave_output);
- _memory_group.manage(&_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);
- _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output->info()->quantization_info().offset);
- _gemm_output.allocator()->allocate();
- }
-
- // 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();
- }
- else
- {
- _gemm_output.allocator()->allocate();
- }
-
- 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");
-
- // Allocate intermediate tensor
- if(!_are_weights_reshaped)
- {
- _weights_reshaped.allocator()->allocate();
- }
+ 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();
}