COMPMID-1073: CLDepthwiseConvolutionLayer uses the optimised path

Change-Id: Ibdb7d875f8ff89bc210c63d389abef1ea1fd51d5
Reviewed-on: https://review.mlplatform.org/330
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Anthony Barbier <Anthony.barbier@arm.com>

1 files changed, 201 insertions, 158 deletions

diff --git a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
index 497cdae85c..03cd5fd54f 100644
--- a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
@@ -89,9 +89,23 @@ void CLDepthwiseConvolutionLayer3x3::run()
     CLScheduler::get().enqueue(*_kernel);
 }
 
+namespace
+{
+inline bool can_run_optimised_3x3_kernel(const ITensorInfo *weights, unsigned int depth_multiplier)
+{
+    const DataLayout data_layout = weights->data_layout();
+    const size_t     idx_w       = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+    const size_t     idx_h       = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+    const Size2D     weights_size(weights->dimension(idx_w), weights->dimension(idx_h));
+    return weights_size == Size2D(3, 3) && (data_layout == DataLayout::NHWC && depth_multiplier <= 1);
+}
+
+} // namespace
+
 CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer()
     : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _vector_to_tensor_kernel(), _output_stage_kernel(), _activationlayer_function(), _v2mm_input_fill_border(), _v2mm_weights_fill_border(),
-      _input_reshaped(), _weights_reshaped(), _v2mm_output(), _output_reshaped(), _is_prepared(false), _is_quantized(false), _is_activationlayer_enabled(false), _original_weights(nullptr)
+      _input_reshaped(), _weights_reshaped(), _v2mm_output(), _output_reshaped(), _is_prepared(false), _is_quantized(false), _is_activationlayer_enabled(false), _original_weights(nullptr),
+      _optimised_function(nullptr)
 {
 }
 
@@ -102,157 +116,172 @@ void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *w
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
 
-    const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
-    const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
-    const size_t idx_c = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::CHANNEL);
-
-    const size_t weights_w = weights->info()->dimension(idx_w);
-    const size_t weights_h = weights->info()->dimension(idx_h);
-    const size_t weights_z = weights->info()->dimension(idx_c);
-
-    _is_prepared      = false;
-    _original_weights = weights;
-    _is_quantized     = is_data_type_quantized_asymmetric(input->info()->data_type());
-
-    bool            append_bias = (biases != nullptr) && !_is_quantized;
-    const GPUTarget gpu_target  = CLScheduler::get().target();
-
-    // Calculate output shape
-    TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier);
-
-    // Output auto inizialitation if not yet initialized
-    auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
-
-    // Output width and height
-    const unsigned int conv_w = output_shape[idx_w];
-    const unsigned int conv_h = output_shape[idx_h];
-
-    // Set up intermediate tensors
-    const size_t patch_size = weights_w * weights_h + ((append_bias) ? 1 : 0);
-    const size_t conv_size  = conv_w * conv_h;
-
-    // Im2Col configuration
-    TensorShape shape_im2col = input->info()->tensor_shape();
-    shape_im2col.set(0, patch_size);
-    shape_im2col.set(1, conv_size);
-    shape_im2col.set(2, weights_z);
-    _input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
-    _im2col_kernel.set_target(gpu_target);
-    _im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier);
-    CLScheduler::get().tune_kernel_static(_im2col_kernel);
-
-    // Weights reshape configuration
-    const TensorShape shape_weights_reshape(patch_size, weights_z);
-    _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape));
-    _weights_reshape_kernel.configure(weights, &_weights_reshaped, append_bias ? biases : nullptr);
-
-    // GEMV configuration
-    DataType    v2mm_dt        = (input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : input->info()->data_type();
-    TensorShape shape_v2mm_out = input->info()->tensor_shape();
-    shape_v2mm_out.set(0, conv_size * weights_z);
-    shape_v2mm_out.set(1, 1);
-    shape_v2mm_out.set(2, 1);
-    _v2mm_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out));
-    _v2mm_kernel.set_target(gpu_target);
-    _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output);
-    CLScheduler::get().tune_kernel_static(_v2mm_kernel);
-    _output_reshaped.allocator()->init(_v2mm_output.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
-    _vector_to_tensor_kernel.configure(&_v2mm_output, (_is_quantized) ? &_output_reshaped : output, conv_w, conv_h);
-
-    // Output staged configuration
-    if(_is_quantized)
+    if(can_run_optimised_3x3_kernel(weights->info(), depth_multiplier))
     {
-        const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
-
-        float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
-        int   output_multiplier, output_shift;
-        quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
-        _output_stage_kernel.configure(&_output_reshaped, biases, output, output_multiplier, output_shift, output_quant_info.offset);
-        _output_reshaped.allocator()->allocate();
+        auto f = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3>();
+        f->configure(input, weights, biases, output, conv_info, depth_multiplier, act_info);
+        _optimised_function = std::move(f);
     }
-
-    // Fill borders on inputs
-    PixelValue zero_in(static_cast<int32_t>(0));
-    PixelValue zero_w(static_cast<int32_t>(0));
-    if(_is_quantized)
-    {
-        zero_in = PixelValue(static_cast<int32_t>(input->info()->quantization_info().offset));
-        zero_w  = PixelValue(static_cast<int32_t>(weights->info()->quantization_info().offset));
-    }
-    BorderSize border_size = _v2mm_kernel.border_size();
-    _v2mm_input_fill_border.configure(&_input_reshaped, border_size, BorderMode::CONSTANT, zero_in);
-
-    border_size.bottom = 0;
-    _v2mm_weights_fill_border.configure(&_weights_reshaped, border_size, BorderMode::CONSTANT, zero_w);
-
-    // Allocate intermediate tensors
-    _input_reshaped.allocator()->allocate();
-    _v2mm_output.allocator()->allocate();
-
-    //Configure Activation Layer
-    _is_activationlayer_enabled = act_info.enabled();
-
-    if(_is_activationlayer_enabled)
+    else
     {
-        _activationlayer_function.configure(output, nullptr, act_info);
+        const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
+        const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
+        const size_t idx_c = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::CHANNEL);
+
+        const size_t weights_w = weights->info()->dimension(idx_w);
+        const size_t weights_h = weights->info()->dimension(idx_h);
+        const size_t weights_z = weights->info()->dimension(idx_c);
+
+        _is_prepared      = false;
+        _original_weights = weights;
+        _is_quantized     = is_data_type_quantized_asymmetric(input->info()->data_type());
+
+        bool            append_bias = (biases != nullptr) && !_is_quantized;
+        const GPUTarget gpu_target  = CLScheduler::get().target();
+
+        // Calculate output shape
+        TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier);
+
+        // Output auto inizialitation if not yet initialized
+        auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape));
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
+
+        // Output width and height
+        const unsigned int conv_w = output_shape[idx_w];
+        const unsigned int conv_h = output_shape[idx_h];
+
+        // Set up intermediate tensors
+        const size_t patch_size = weights_w * weights_h + ((append_bias) ? 1 : 0);
+        const size_t conv_size  = conv_w * conv_h;
+
+        // Im2Col configuration
+        TensorShape shape_im2col = input->info()->tensor_shape();
+        shape_im2col.set(0, patch_size);
+        shape_im2col.set(1, conv_size);
+        shape_im2col.set(2, weights_z);
+        _input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+        _im2col_kernel.set_target(gpu_target);
+        _im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier);
+        CLScheduler::get().tune_kernel_static(_im2col_kernel);
+
+        // Weights reshape configuration
+        const TensorShape shape_weights_reshape(patch_size, weights_z);
+        _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape));
+        _weights_reshape_kernel.configure(weights, &_weights_reshaped, append_bias ? biases : nullptr);
+
+        // GEMV configuration
+        DataType    v2mm_dt        = (input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : input->info()->data_type();
+        TensorShape shape_v2mm_out = input->info()->tensor_shape();
+        shape_v2mm_out.set(0, conv_size * weights_z);
+        shape_v2mm_out.set(1, 1);
+        shape_v2mm_out.set(2, 1);
+        _v2mm_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out));
+        _v2mm_kernel.set_target(gpu_target);
+        _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output);
+        CLScheduler::get().tune_kernel_static(_v2mm_kernel);
+        _output_reshaped.allocator()->init(_v2mm_output.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
+        _vector_to_tensor_kernel.configure(&_v2mm_output, (_is_quantized) ? &_output_reshaped : output, conv_w, conv_h);
+
+        // Output staged configuration
+        if(_is_quantized)
+        {
+            const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
+
+            float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
+            int   output_multiplier, output_shift;
+            quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+            _output_stage_kernel.configure(&_output_reshaped, biases, output, output_multiplier, output_shift, output_quant_info.offset);
+            _output_reshaped.allocator()->allocate();
+        }
+
+        // Fill borders on inputs
+        PixelValue zero_in(static_cast<int32_t>(0));
+        PixelValue zero_w(static_cast<int32_t>(0));
+        if(_is_quantized)
+        {
+            zero_in = PixelValue(static_cast<int32_t>(input->info()->quantization_info().offset));
+            zero_w  = PixelValue(static_cast<int32_t>(weights->info()->quantization_info().offset));
+        }
+        BorderSize border_size = _v2mm_kernel.border_size();
+        _v2mm_input_fill_border.configure(&_input_reshaped, border_size, BorderMode::CONSTANT, zero_in);
+
+        border_size.bottom = 0;
+        _v2mm_weights_fill_border.configure(&_weights_reshaped, border_size, BorderMode::CONSTANT, zero_w);
+
+        // Allocate intermediate tensors
+        _input_reshaped.allocator()->allocate();
+        _v2mm_output.allocator()->allocate();
+
+        //Configure Activation Layer
+        _is_activationlayer_enabled = act_info.enabled();
+
+        if(_is_activationlayer_enabled)
+        {
+            _activationlayer_function.configure(output, nullptr, act_info);
+        }
     }
 }
 
 Status CLDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                                              unsigned int depth_multiplier, const ActivationLayerInfo &act_info)
 {
-    const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
-    const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
-    const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
-
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
-    ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(idx_c) * depth_multiplier) != weights->dimension(idx_c));
-
-    const bool         is_quantized = is_data_type_quantized_asymmetric(input->data_type());
-    const bool         append_bias  = (biases != nullptr) && !is_quantized;
-    const TensorShape  output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
-    const size_t       weights_w    = weights->dimension(idx_w);
-    const size_t       weights_h    = weights->dimension(idx_h);
-    const size_t       weights_z    = weights->dimension(idx_c);
-    const unsigned int conv_w       = output_shape[idx_w];
-    const unsigned int conv_h       = output_shape[idx_h];
-    const size_t       patch_size   = weights_w * weights_h + ((append_bias) ? 1 : 0);
-    const size_t       conv_size    = conv_w * conv_h;
-
-    TensorShape shape_im2col = input->tensor_shape();
-    shape_im2col.set(0, patch_size);
-    shape_im2col.set(1, conv_size);
-    shape_im2col.set(2, weights_z);
-    TensorInfo input_reshaped(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseIm2ColKernel::validate(input, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier));
-
-    const TensorShape shape_weights_reshape(patch_size, weights_z);
-    TensorInfo        weights_reshaped(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseWeightsReshapeKernel::validate(weights, &weights_reshaped, append_bias ? biases : nullptr));
-
-    DataType    v2mm_dt        = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type();
-    TensorShape shape_v2mm_out = input->tensor_shape();
-    shape_v2mm_out.set(0, conv_size * weights_z);
-    shape_v2mm_out.set(1, 1);
-    shape_v2mm_out.set(2, 1);
-    TensorInfo v2mm_output(input->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixVectorMultiplyKernel::validate(&input_reshaped, &weights_reshaped, &v2mm_output));
-
-    TensorInfo output_reshaped(v2mm_output.clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseVectorToTensorKernel::validate(&v2mm_output, (is_quantized) ? &output_reshaped : output, conv_w, conv_h));
-
-    if(is_quantized)
+    if(can_run_optimised_3x3_kernel(weights, depth_multiplier))
     {
-        ARM_COMPUTE_RETURN_ON_ERROR(CLDirectConvolutionLayerOutputStageKernel::validate(&output_reshaped, biases, output));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info));
     }
-
-    // Validate Activation Layer
-    if(act_info.enabled())
+    else
     {
-        ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output, nullptr, act_info));
+        const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+        const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+        const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
+
+        ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+        ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(idx_c) * depth_multiplier) != weights->dimension(idx_c));
+
+        const bool         is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+        const bool         append_bias  = (biases != nullptr) && !is_quantized;
+        const TensorShape  output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier);
+        const size_t       weights_w    = weights->dimension(idx_w);
+        const size_t       weights_h    = weights->dimension(idx_h);
+        const size_t       weights_z    = weights->dimension(idx_c);
+        const unsigned int conv_w       = output_shape[idx_w];
+        const unsigned int conv_h       = output_shape[idx_h];
+        const size_t       patch_size   = weights_w * weights_h + ((append_bias) ? 1 : 0);
+        const size_t       conv_size    = conv_w * conv_h;
+
+        TensorShape shape_im2col = input->tensor_shape();
+        shape_im2col.set(0, patch_size);
+        shape_im2col.set(1, conv_size);
+        shape_im2col.set(2, weights_z);
+        TensorInfo input_reshaped(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseIm2ColKernel::validate(input, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier));
+
+        const TensorShape shape_weights_reshape(patch_size, weights_z);
+        TensorInfo        weights_reshaped(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseWeightsReshapeKernel::validate(weights, &weights_reshaped, append_bias ? biases : nullptr));
+
+        DataType    v2mm_dt        = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type();
+        TensorShape shape_v2mm_out = input->tensor_shape();
+        shape_v2mm_out.set(0, conv_size * weights_z);
+        shape_v2mm_out.set(1, 1);
+        shape_v2mm_out.set(2, 1);
+        TensorInfo v2mm_output(input->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixVectorMultiplyKernel::validate(&input_reshaped, &weights_reshaped, &v2mm_output));
+
+        TensorInfo output_reshaped(v2mm_output.clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape));
+        ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseVectorToTensorKernel::validate(&v2mm_output, (is_quantized) ? &output_reshaped : output, conv_w, conv_h));
+
+        if(is_quantized)
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(CLDirectConvolutionLayerOutputStageKernel::validate(&output_reshaped, biases, output));
+        }
+
+        // Validate Activation Layer
+        if(act_info.enabled())
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output, nullptr, act_info));
+        }
     }
-
     return Status{};
 }
 
@@ -260,33 +289,47 @@ void CLDepthwiseConvolutionLayer::run()
 {
     prepare();
 
-    CLScheduler::get().enqueue(_im2col_kernel);
-    CLScheduler::get().enqueue(_v2mm_input_fill_border);
-    CLScheduler::get().enqueue(_v2mm_kernel);
-    CLScheduler::get().enqueue(_vector_to_tensor_kernel);
-    if(_is_quantized)
+    if(_optimised_function != nullptr)
     {
-        CLScheduler::get().enqueue(_output_stage_kernel);
+        _optimised_function->run();
     }
-    if(_is_activationlayer_enabled)
+    else
     {
-        _activationlayer_function.run();
+        CLScheduler::get().enqueue(_im2col_kernel);
+        CLScheduler::get().enqueue(_v2mm_input_fill_border);
+        CLScheduler::get().enqueue(_v2mm_kernel);
+        CLScheduler::get().enqueue(_vector_to_tensor_kernel);
+        if(_is_quantized)
+        {
+            CLScheduler::get().enqueue(_output_stage_kernel);
+        }
+        if(_is_activationlayer_enabled)
+        {
+            _activationlayer_function.run();
+        }
     }
 }
 
 void CLDepthwiseConvolutionLayer::prepare()
 {
-    if(!_is_prepared)
+    if(_optimised_function != nullptr)
     {
-        ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
-
-        // Run weights reshaping and mark original weights tensor as unused
-        _weights_reshaped.allocator()->allocate();
-        CLScheduler::get().enqueue(_weights_reshape_kernel);
-        CLScheduler::get().enqueue(_v2mm_weights_fill_border);
-        _original_weights->mark_as_unused();
-
-        CLScheduler::get().queue().finish();
-        _is_prepared = true;
+        _optimised_function->prepare();
+    }
+    else
+    {
+        if(!_is_prepared)
+        {
+            ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
+
+            // Run weights reshaping and mark original weights tensor as unused
+            _weights_reshaped.allocator()->allocate();
+            CLScheduler::get().enqueue(_weights_reshape_kernel);
+            CLScheduler::get().enqueue(_v2mm_weights_fill_border);
+            _original_weights->mark_as_unused();
+
+            CLScheduler::get().queue().finish();
+            _is_prepared = true;
+        }
     }
 }