COMPMID-754: Add validation to kernels.

Adds validation method to:
- CLConvolutionLayer

Change-Id: I95516e20cfb71c1e603c60fc6491ac695883a856
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/117355
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 184 insertions, 161 deletions

diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index 60e1bde4e2..23c3050476 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -27,6 +27,7 @@
 #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"
 
@@ -35,53 +36,52 @@
 #include <tuple>
 
 using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
 
 CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
+    : _memory_group(std::move(memory_manager)), _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped()
 {
 }
 
-void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW)
+void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output)
 {
+    // Perform validation step
     ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
-    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);
-    }
+    ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayerReshapeWeights::validate(weights->info(),
+                                                                          (biases != nullptr) ? biases->info() : nullptr,
+                                                                          output->info()));
 
     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;
+    _weights_reshape_kernel.configure(weights, biases_to_use, output);
+
+    output->info()->set_quantization_info(weights->info()->quantization_info());
+}
+
+Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
 
-    if(transpose1xW)
+    if(biases != nullptr)
     {
-        // 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();
+        ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type()));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }
-    else
+
+    if((output != nullptr) && (output->total_size() != 0))
     {
-        _weights_reshape_kernel.configure(weights, biases_to_use, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
+
+        CLWeightsReshapeKernel::validate(weights, biases, output);
     }
 
-    output->info()->set_quantization_info(weights->info()->quantization_info());
+    return Status{};
 }
 
 void CLConvolutionLayerReshapeWeights::run()
@@ -89,99 +89,92 @@ void CLConvolutionLayerReshapeWeights::run()
     _memory_group.acquire();
 
     CLScheduler::get().enqueue(_weights_reshape_kernel);
-    if(_transpose1xW)
-    {
-        CLScheduler::get().enqueue(_weights_transposed_kernel);
-    }
 
     _memory_group.release();
 }
 
 CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(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_group(memory_manager), _reshape_weights(), _im2col_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(), _is_quantized(false)
 {
 }
 
-void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed, bool are_weights_reshaped)
+void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
+    ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->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();
+        // 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));
+        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*/));
+        _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);
-        }
+        // 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
     {
-        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*/));
-        }
+        // 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*/));
     }
 }
 
+Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output)
+{
+    const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+
+    const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */);
+    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.scale, -input_quantization_info.offset));
+        weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
+
+        // Perform validation step on GEMMLowp
+        CLGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), output, gemm_info);
+    }
+    else
+    {
+        // Perform validation step on Matrix multiply function
+        CLGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info);
+    }
+    return Status{};
+}
+
 void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
-    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()));
 
-    _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
+    ARM_COMPUTE_ERROR_THROW_ON(CLGEMMConvolutionLayer::validate(input->info(),
+                                                                weights->info(),
+                                                                biases != nullptr ? biases->info() : nullptr,
+                                                                output->info(),
+                                                                conv_info,
+                                                                weights_info));
 
-    if(biases != nullptr)
-    {
-        if(_is_quantized)
-        {
-            ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
-        }
-        else
-        {
-            ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-        }
-        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);
-    }
+    _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
 
     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());
+    // Set the GPU target for im2col and col2im
     _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;
@@ -195,41 +188,19 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
     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);
+    const unsigned int kernel_width  = weights->info()->dimension(0);
+    const unsigned int kernel_height = 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_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);
 
-        weights = &_weights_reshaped;
-    }
+    weights = &_weights_reshaped;
 
     // Create tensor to store im2col reshaped inputs
     const unsigned int mat_input_cols = mat_weights_rows;
@@ -259,21 +230,9 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
     // 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(&_im2col_output, weights, &_gemm_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
@@ -299,53 +258,117 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
     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();
+
+    ARM_COMPUTE_UNUSED(weights_info);
+}
+
+Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                        const WeightsInfo &weights_info)
+{
+    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::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != input->dimension(2));
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+
+    const bool     is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+    const bool     append_bias  = (biases != nullptr) && (!is_quantized);
+    const unsigned bias_element = (append_bias) ? 1 : 0;
+    const DataType dt           = input->data_type();
+
+    // Get convolved dimensions
+    unsigned int conv_w = 0;
+    unsigned int conv_h = 0;
+
+    const unsigned int kernel_width  = weights->dimension(0);
+    const unsigned int kernel_height = weights->dimension(1);
+
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height, conv_info);
+
+    unsigned int mat_weights_cols = weights->dimension(3);
+    unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + bias_element;
+
+    CLConvolutionLayerReshapeWeights::validate(weights, biases, nullptr);
+
+    // Create tensor info for 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->tensor_shape();
+    shape_im2col.set(0, mat_input_cols);
+    shape_im2col.set(1, mat_input_rows);
+    shape_im2col.set(2, 1);
+    TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->fixed_point_position());
+    im2col_reshaped_info.set_quantization_info(input->quantization_info());
+    CLIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias);
+
+    // Create GEMM output tensor
+    TensorShape shape_gemm = im2col_reshaped_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.
+    TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->fixed_point_position());
+    info_gemm.set_quantization_info(output->quantization_info());
+
+    validate_mm(&im2col_reshaped_info, weights, &info_gemm);
+
+    TensorInfo tmp_info(input->tensor_shape(), 1, DataType::QASYMM8, input->fixed_point_position());
+    if(is_quantized)
     {
-        _weights_reshaped.allocator()->allocate();
+        float multiplier = input->quantization_info().scale * weights->quantization_info().scale / output->quantization_info().scale;
+        int   output_multiplier, output_shift;
+        quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+        // Validate output stage for quantized case
+        CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&info_gemm, biases, &tmp_info, output->quantization_info().offset);
     }
+
+    // Validate Col2Im
+    CLCol2ImKernel::validate(is_quantized ? &tmp_info : &info_gemm, output, std::make_pair(conv_w, conv_h));
+
+    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_MISMATCHING_FIXED_POINT(input, biases);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    return Status{};
 }
 
 void CLGEMMConvolutionLayer::run()
 {
     // Run weights reshaping (Runs once for every configure)
-    if(!_are_weights_reshaped)
-    {
-        _are_weights_reshaped = true;
-        _reshape_weights.run();
-    }
+    _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)
+    // Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions
+    if(_is_quantized)
     {
-        // Run interleave4x4 kernel
-        CLScheduler::get().enqueue(_interleave_kernel);
+        // Run gemmlowp
+        _mm_gemmlowp.run();
 
-        // Run matrix multiply kernel
-        CLScheduler::get().enqueue(_mm_kernel);
+        // Run output stage
+        _gemmlowp_output_stage.run();
     }
     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();
-        }
+        // Run gemm
+        _mm_gemm.run();
     }
 
     // Reshape output matrix