COMPMID-845: Create a ConvolutionLayer for CL

Change-Id: Ifcc406d2d0a99c911d6b6c875657b0e0028255d5
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/119148
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

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();
 }