Remove dynamic fusion prototype with tests and examples

Public headers of the new experimental dynamic fusion can be found in arm_compute/dynamic_fusion/
New examples on how to use the interface can be found in tests/validation/dynamic_fusion/gpu/Integration.cpp

Resolves COMPMID-5683

Change-Id: I7ccb902a227fb487562df15fc3c30118d1d95bbd
Signed-off-by: SiCong Li <sicong.li@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/8671
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

2 files changed, 0 insertions, 630 deletions

diff --git a/examples/dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp b/examples/dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp
deleted file mode 100644
index afbc55777b..0000000000
--- a/examples/dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp
+++ /dev/null
@@ -1,392 +0,0 @@
-/*
- * Copyright (c) 2022 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-
-/// @example dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp
-/// @copybrief example_dynamic_fusion_cl_conv2d_elementwise_add
-///
-/// @page example_dynamic_fusion_cl_conv2d_elementwise_add Dynamic Fusion Example: Conv2d + Elementwise Addition (OpenCL target)
-/// This example demonstrates how to fuse a Conv2d with an Addition using the new OperatorGraph API, and to run it with the Async Composite Operator
-
-#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION
-#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */
-#error "This example needs to be built with -DARM_COMPUTE_CL"
-#endif /* ARM_COMPUTE_CL */
-
-#include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/Types.h"
-#include "arm_compute/core/experimental/ClWorkload.h"
-#include "arm_compute/core/experimental/OperatorGraph.h"
-#include "arm_compute/runtime/CL/CLScheduler.h"
-#include "arm_compute/runtime/CL/CLTensor.h"
-#include "arm_compute/runtime/CL/CLTuner.h"
-#include "arm_compute/runtime/experimental/ClCompositeOperator.h"
-
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "utils/TypePrinter.h"
-
-#include "utils/Utils.h"
-
-#include <cstdlib>
-
-using namespace arm_compute;
-using namespace utils;
-using namespace arm_compute::experimental::dynamic_fusion;
-
-#define TICK(clock_name) \
-    auto clock_name##_tick = std::chrono::high_resolution_clock::now();
-#define TOCK(clock_name, measurement_map)                                               \
-    auto clock_name##_tock                 = std::chrono::high_resolution_clock::now(); \
-    measurement_map["\"" #clock_name "\""] = duration_cast<microseconds>(clock_name##_tock - clock_name##_tick);
-#define TOCK_AVG(clock_name, measurement_map, num_iterations)                           \
-    auto clock_name##_tock                 = std::chrono::high_resolution_clock::now(); \
-    measurement_map["\"" #clock_name "\""] = duration_cast<microseconds>((clock_name##_tock - clock_name##_tick) / (num_iterations));
-
-using std::chrono::duration_cast;
-using std::chrono::microseconds;
-
-class ClFusedConv2dEltwiseAddExample : public Example
-{
-public:
-    bool do_setup(int argc, char **argv) override
-    {
-        size_t       ih;
-        size_t       iw;
-        size_t       ifm;
-        size_t       wh;
-        size_t       ww;
-        size_t       ofm;
-        size_t       tuner_choice;
-        unsigned int pad_x;
-        unsigned int pad_y;
-        if(argc < 10)
-        {
-            // Print help
-            std::cout << "Usage:  ./cl_fused_conv2d_elementwise_add ih iw ifm wh ww ofm tuner_choice(0=Disable, 1=Rapid, 2=Normal, 3=Exhaustive) pad_x pad_y\n";
-            std::cout << "Too few or no input_matrices provided. Using shape config = SRGAN_0, tuner_choice=2\n\n";
-            ih           = 512;
-            iw           = 512;
-            ifm          = 64;
-            wh           = 1;
-            ww           = 1;
-            ofm          = 3;
-            tuner_choice = 2;
-            pad_x        = 0;
-            pad_y        = 0;
-        }
-        else
-        {
-            ih           = strtol(argv[1], nullptr, 10);
-            iw           = strtol(argv[2], nullptr, 10);
-            ifm          = strtol(argv[3], nullptr, 10);
-            wh           = strtol(argv[4], nullptr, 10);
-            ww           = strtol(argv[5], nullptr, 10);
-            ofm          = strtol(argv[6], nullptr, 10);
-            tuner_choice = strtol(argv[7], nullptr, 10);
-            pad_x        = strtol(argv[8], nullptr, 10);
-            pad_y        = strtol(argv[9], nullptr, 10);
-        }
-
-        CLTuner *tuner_to_use;
-        switch(tuner_choice)
-        {
-            case 0:
-            {
-                tuner_to_use = nullptr;
-                break;
-            }
-            case 1:
-            {
-                tuner.set_tuner_mode(CLTunerMode::RAPID);
-                tuner_to_use = &tuner;
-                break;
-            }
-            case 3:
-            {
-                tuner.set_tuner_mode(CLTunerMode::EXHAUSTIVE);
-                tuner_to_use = &tuner;
-                break;
-            }
-            case 2:
-            default:
-            {
-                tuner.set_tuner_mode(CLTunerMode::NORMAL);
-                tuner_to_use = &tuner;
-                break;
-            }
-        }
-        CLScheduler::get().default_init(tuner_to_use);
-
-        TICK(startup_time);
-        TICK(configure);
-        /* Computation:
-         * out = add_desc(addend, conv2d1x1(direct_conv)(input, weights, bias))
-         */
-        const auto data_type   = DataType::F32;
-        const auto data_layout = DataLayout::NHWC;
-
-        const auto t_input_shape     = TensorShape(ifm, iw, ih);
-        const auto t_weight_shape    = TensorShape(ifm, ww, wh, ofm);
-        const auto t_bias_shape      = TensorShape(ofm);
-        const auto t_l1_addend_shape = TensorShape(ofm, iw);
-
-        std::cout << "input_shape: " << t_input_shape << std::endl;
-        std::cout << "weight_shape: " << t_weight_shape << std::endl;
-        std::cout << "bias_shape: " << t_bias_shape << std::endl;
-        std::cout << "addend_shape: " << t_l1_addend_shape << std::endl;
-
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// @section describe_workload_using_operator_graph Describe the workload to run using OperatorGraph
-        /// OperatorGraph is a graph of Tensors and Operators. Let's first default-construct it
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp Construct OperatorGraph
-        // [Construct OperatorGraph]
-        OperatorGraph op_graph;
-        // [Construct OperatorGraph]
-
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// @subsection add_conv2d Add the first operator (root operator) Conv2d
-        /// The first operator to be added to the graph is called the "root operator" of the entire graph.
-        /// @note As of now, operators need to be inserted according to their dependency order. This is because output tensor auto-initialization occurs during construction time.
-        ///       Later this might be changed to allow out-of-order insertion.
-
-        /// Before we insert the operator, we need to initialize the required TensorInfo objects.
-        /// We can choose not to initialize an output TensorInfo; if so, they will be auto-initialized during the construction of the OperatorGraph
-        /// The "t_acc_info" is the TensorInfo of the accumulator tensor, which is the output tensor of our first operator conv2d
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp  Initialize Conv2d TensorInfo
-        // [Initialize Conv2d TensorInfo]
-        auto t_input_info  = TensorInfo(t_input_shape, 1, data_type, data_layout);
-        auto t_weight_info = TensorInfo(t_weight_shape, 1, data_type, data_layout);
-        auto t_bias_info   = TensorInfo(t_bias_shape, 1, data_type, data_layout);
-        auto t_acc_info    = TensorInfo();
-        // [Initialize Conv2d TensorInfo]
-
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// Next we associate the TensorInfo with the OpTensor s created in the op_graph.
-        /// @note The associated TensorInfo objects must be in scope and remain valid until the ClWorkload building is completed
-
-        /// @note The associated TensorInfo objects must be declard as non-const, since they may be updated during the OperatorGraph construction
-
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp  Add OpTensors
-        // [Add OpTensors]
-        const auto op_t_input  = add_tensor(op_graph, t_input_info);
-        const auto op_t_weight = add_tensor(op_graph, t_weight_info);
-        const auto op_t_bias   = add_tensor(op_graph, t_bias_info);
-        const auto op_t_acc    = add_tensor(op_graph, t_acc_info);
-        // [Add OpTensors]
-
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// Finally we add the Conv2d operator to op_graph. The Conv2dDescriptor contains all the TOSA-compliant attribute parameters
-        /// The add_op... group of functions accept the OpTensors created by the add_tensor function, and return an Operator handle.
-        /// This handle can be used to further query and modify the operator inside the OperatorGraph after its creation
-        /// For example, here we use the handle to force the ConvolutionMethod to be Direct Convolution
-        /// @note The force_conv2d_method is only for debug purpose for now, as the end user is not expected to decide on the ConvolutionMethod
-
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp  Add Conv2d Operator
-        // [Add Conv2d Operator]
-        Conv2dDescriptor conv2d_desc{ Padding2D{ pad_x, pad_x, pad_y, pad_y } };
-        auto             conv2d = add_op_conv2d(op_graph, conv2d_desc, op_t_input, op_t_weight, op_t_bias, op_t_acc);
-        force_conv2d_method(op_graph, conv2d, ConvolutionMethod::DIRECT); // Only for debug purposes
-        // [Add Conv2d Operator]
-
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// @subsection add_elementwise_add Add the second operator Elementwise Add
-        /// This is similar to adding the first operator to op_graph, except that we link the two operators together by their common tensor,
-        /// namely the accumulator tensor op_t_acc, which is the output of conv2d and the input (lhs) of the addition
-        /// @note At the moment, it is recommended to always declare a separate TensorInfo (even if empty) for each OpTensor.
-        ///       For example, here op_t_dst could be associated with op_t_acc info as they are the same,
-        ///       but we still recommend creating a separate object.
-
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp  Add Elementwise Add Operator
-        // [Add Elementwise Add Operator]
-        auto                  t_l1_addend_info = TensorInfo(t_l1_addend_shape, 1, data_type, data_layout);
-        auto                  t_dst_info       = TensorInfo();
-        const auto            op_t_l1_addend   = add_tensor(op_graph, t_l1_addend_info);
-        const auto            op_t_dst         = add_tensor(op_graph, t_dst_info);
-        ElementwiseDescriptor add_desc{ ArithmeticOperation::ADD };
-        add_op_elementwise_op(op_graph, add_desc, op_t_acc, op_t_l1_addend, op_t_dst);
-        // [Add Elementwise Add Operator]
-
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// @section build_clworkload Build ClWorkload
-        /// ClWorkload is an intermediate object which contains all the built kernel codes and all other descriptors on how to schedule them
-        /// We build ClWorkload from the op_graph object that we just described
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp  Build ClWorkload
-        // [Build ClWorkload]
-        const ClWorkloadContext workload_ctx
-        {
-            GpuInfo{ CLScheduler::get().target() }
-        };
-        ClWorkload workload;
-        build(workload, op_graph, workload_ctx);
-        // [Build ClWorkload]
-
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// @section run_fused_op_with_clcompositeoperator Run the fused operator workload with ClCompositeOperator
-        /// @subsection configure_and_validate_clcompositeoperator Validate ClWorkload and Configure ClCompositeOperator
-        /// After ClWorkload is built, we need to configure it with the Compute Library runtime ClCompositeOperator to run it.
-        /// Optionally we can explicitly validate the workload to check if the workload has been built successfully.
-        /// The validate is automatically run inside configure and would throw if it fails.
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp Construct ClCompositeOperator
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp  Validate and configure ClCompositeOperator
-        // [Validate and configure ClCompositeOperator]
-        const auto success = ClCompositeOperator::validate(workload); // Optional
-        op.configure(CLKernelLibrary::get().get_compile_context(), workload);
-        // [Validate and configure ClCompositeOperator]
-        TOCK(configure, measurements);
-
-        TICK(tensor_allocation);
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// @subsection run_clcompositeoperator Run ClCompositeOperator
-        /// Construct the runtime CLTensor s with backing memory
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp Construct CLTensor objects
-
-        /// Initialize, allocate and fill the CLTensor objects
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp Initialize, Allocate and Fill CLTensor objects
-        // [Initialize, Allocate and Fill CLTensor objects]
-        t_input.allocator()->init(t_input_info);
-        t_weight.allocator()->init(t_weight_info);
-        t_bias.allocator()->init(t_bias_info);
-        t_l1_addend.allocator()->init(t_dst_info);
-        t_dst.allocator()->init(t_dst_info);
-
-        t_input.allocator()->allocate();
-        t_weight.allocator()->allocate();
-        t_bias.allocator()->allocate();
-        t_l1_addend.allocator()->allocate();
-        t_dst.allocator()->allocate();
-
-        fill_random_tensor(t_input, -1.f, 1.f);
-        fill_random_tensor(t_weight, -1.f, 1.f);
-        fill_random_tensor(t_l1_addend, -1.f, 1.f);
-        // [Initialize, Allocate and Fill CLTensor objects]
-
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// The OpTensorBinding creates a mapping from the OpTensor handles that we created early to the real CLTensors
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp Create OpTensorBinding
-        // [Create OpTensorBinding]
-        OpTensorBinding op_tensors({ { op_t_input, &t_input },
-            { op_t_weight, &t_weight },
-            { op_t_bias, &t_bias },
-            { op_t_l1_addend, &t_l1_addend },
-            { op_t_dst, &t_dst }
-        });
-        // [Create OpTensorBinding]
-
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// Bind the CLTensor objects to the prepare_pack_map and run_pack_map, which are used to prepare and run the op
-        /// This step additionally creates empty auxiliary CLTensor objects if any, and contain them inside a ClAuxTensorData aux_tensor_data
-        /// @note This step associates all the CLTensors contained in op_tensors and aux_tensor_data, with prepare_pack_map and run_pack_map
-        ///       Make sure these CLTensors remain valid as long as the two pack_maps are still in use
-
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp Construct ClAuxTensorData
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp Construct TensorPackMaps
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp Bind Tensors
-        // [Bind Tensors]
-        bind_tensors(aux_tensor_data, prepare_pack_map, run_pack_map, workload, op_tensors);
-        // [Bind Tensors]
-
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// Initialize and Allocate Auxiliary CLTensor objects.
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp Initialize and Allocate Auxiliary CLTensor objects
-        // [Initialize and Allocate Auxiliary CLTensor objects]
-        for(auto tensor_data : aux_tensor_data.get_tensors())
-        {
-            tensor_data.tensor->allocator()->init(tensor_data.tensor_info);
-            tensor_data.tensor->allocator()->allocate();
-        }
-        // [Initialize and Allocate Auxiliary CLTensor objects]
-        TOCK(tensor_allocation, measurements);
-
-        TICK(dummy_run);
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// Run the ClCompositeOperator prepare job. This performs any jobs that are required for the first run, like
-        /// reshaping tensors for a more performant format.
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp Prepare ClCompositeOperator
-        // [Prepare ClCompositeOperator]
-        op.prepare(prepare_pack_map);
-        // [Prepare ClCompositeOperator]
-
-        /// @page example_dynamic_fusion_cl_conv2d_elementwise_add
-        /// At last, we run our operator
-        /// @snippet dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp Run ClCompositeOperator
-        // [Run ClCompositeOperator]
-        op.run(run_pack_map);
-        // [Run ClCompositeOperator]
-        CLScheduler::get().sync();
-        TOCK(dummy_run, measurements);
-        TOCK(startup_time, measurements);
-        return true;
-    }
-    void do_run() override
-    {
-        // Run the fused op
-        op.run(run_pack_map);
-
-        // Make sure all the OpenCL jobs are done executing:
-        CLScheduler::get().sync();
-    }
-
-    void do_teardown() override
-    {
-        for(const auto &m : measurements)
-        {
-            std::cout << m.first << ": " << m.second.count() << "us" << std::endl;
-        }
-    }
-
-private:
-    // [Construct CLTensor objects]
-    CLTensor t_input{};
-    CLTensor t_weight{};
-    CLTensor t_bias{};
-    CLTensor t_l1_addend{};
-    CLTensor t_dst{};
-    // [Construct CLTensor objects]
-    // [Construct ClAuxTensorData]
-    ClAuxTensorData aux_tensor_data{};
-    // [Construct ClAuxTensorData]
-    // [Construct TensorPackMaps]
-    TensorPackMap prepare_pack_map{};
-    TensorPackMap run_pack_map{};
-    // [Construct TensorPackMaps]
-    // [Construct ClCompositeOperator]
-    ClCompositeOperator op{};
-    // [Construct ClCompositeOperator]
-    CLTuner tuner{};
-    std::map<std::string, std::chrono::microseconds> measurements{};
-};
-
-/** Main program for sgemm test
- *
- * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Matrix A, [optional] Matrix B, [optional] Matrix C, [optional] alpha, [optional] beta )
- */
-int main(int argc, char **argv)
-{
-    return utils::run_example<ClFusedConv2dEltwiseAddExample>(argc, argv);
-}
-
-#undef TICK
-#undef TOCK
-#undef TOCK_AVG
-#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
diff --git a/examples/dynamic_fusion/cl_ref_conv2d_elementwise_add.cpp b/examples/dynamic_fusion/cl_ref_conv2d_elementwise_add.cpp
deleted file mode 100644
index 3aedcc0f41..0000000000
--- a/examples/dynamic_fusion/cl_ref_conv2d_elementwise_add.cpp
+++ /dev/null
@@ -1,238 +0,0 @@
-/*
- * Copyright (c) 2022 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */
-#error "This example needs to be built with -DARM_COMPUTE_CL"
-#endif /* ARM_COMPUTE_CL */
-
-#include "arm_compute/core/Types.h"
-#include "arm_compute/runtime/CL/CLScheduler.h"
-#include "arm_compute/runtime/CL/CLTensor.h"
-#include "arm_compute/runtime/CL/CLTuner.h"
-#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
-#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
-
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "utils/TypePrinter.h"
-#include "utils/Utils.h"
-
-#include <cstdlib>
-
-using namespace arm_compute;
-using namespace utils;
-
-#define TICK(clock_name) \
-    auto clock_name##_tick = std::chrono::high_resolution_clock::now();
-#define TOCK(clock_name, measurement_map)                                               \
-    auto clock_name##_tock                 = std::chrono::high_resolution_clock::now(); \
-    measurement_map["\"" #clock_name "\""] = duration_cast<microseconds>(clock_name##_tock - clock_name##_tick);
-#define TOCK_AVG(clock_name, measurement_map, num_iterations)                           \
-    auto clock_name##_tock                 = std::chrono::high_resolution_clock::now(); \
-    measurement_map["\"" #clock_name "\""] = duration_cast<microseconds>((clock_name##_tock - clock_name##_tick) / (num_iterations));
-
-using std::chrono::duration_cast;
-using std::chrono::microseconds;
-/** A reference for comparing against the fusion of a direct convolution with an elementwise addition:
- *  examples/dynamic_fusion/cl_fused_conv2d_elementwise_add.cpp
- */
-class ClRefConv2dEltwiseAddExample : public Example
-{
-public:
-    bool do_setup(int argc, char **argv) override
-    {
-        size_t       ih;
-        size_t       iw;
-        size_t       ifm;
-        size_t       wh;
-        size_t       ww;
-        size_t       ofm;
-        size_t       tuner_choice;
-        unsigned int pad_x;
-        unsigned int pad_y;
-        if(argc < 10)
-        {
-            // Print help
-            std::cout << "Usage:  ./cl_ref_conv2d_elementwise_add ih iw ifm wh ww ofm tuner_choice(0=Disable, 1=Rapid, 2=Normal, 3=Exhaustive) pad_x pad_y\n";
-            std::cout << "Too few or no input_matrices provided. Using shape config = SRGAN_0, tuner_choice=2\n\n";
-            ih           = 512;
-            iw           = 512;
-            ifm          = 64;
-            wh           = 1;
-            ww           = 1;
-            ofm          = 3;
-            tuner_choice = 2;
-            pad_x        = 0;
-            pad_y        = 0;
-        }
-        else
-        {
-            ih           = strtol(argv[1], nullptr, 10);
-            iw           = strtol(argv[2], nullptr, 10);
-            ifm          = strtol(argv[3], nullptr, 10);
-            wh           = strtol(argv[4], nullptr, 10);
-            ww           = strtol(argv[5], nullptr, 10);
-            ofm          = strtol(argv[6], nullptr, 10);
-            tuner_choice = strtol(argv[7], nullptr, 10);
-            pad_x        = strtol(argv[8], nullptr, 10);
-            pad_y        = strtol(argv[9], nullptr, 10);
-        }
-
-        CLTuner *tuner_to_use;
-        switch(tuner_choice)
-        {
-            case 0:
-            {
-                tuner_to_use = nullptr;
-                break;
-            }
-            case 1:
-            {
-                tuner.set_tuner_mode(CLTunerMode::RAPID);
-                tuner_to_use = &tuner;
-                break;
-            }
-            case 3:
-            {
-                tuner.set_tuner_mode(CLTunerMode::EXHAUSTIVE);
-                tuner_to_use = &tuner;
-                break;
-            }
-            case 2:
-            default:
-            {
-                tuner.set_tuner_mode(CLTunerMode::NORMAL);
-                tuner_to_use = &tuner;
-                break;
-            }
-        }
-
-        CLScheduler::get().default_init(tuner_to_use);
-
-        TICK(startup_time);
-        TICK(configure);
-
-        /* Computation:
-         * out = add_desc(addend, conv2d1x1(direct_conv)(input, weights, bias))
-         */
-        const auto          data_type   = DataType::F32;
-        const auto          data_layout = DataLayout::NHWC;
-        const PadStrideInfo conv_info{ 1, 1, pad_x, pad_y };
-        const auto          t_input_shape     = TensorShape(ifm, iw, ih);
-        const auto          t_weight_shape    = TensorShape(ifm, ww, wh, ofm);
-        const auto          t_bias_shape      = TensorShape(ofm);
-        const auto          t_l1_addend_shape = TensorShape(ofm, iw);
-        const auto          t_dst_shape       = misc::shape_calculator::compute_deep_convolution_shape(t_input_shape, data_layout, t_weight_shape, conv_info);
-        std::cout << "input_shape: " << t_input_shape << std::endl;
-        std::cout << "weight_shape: " << t_weight_shape << std::endl;
-        std::cout << "bias_shape: " << t_bias_shape << std::endl;
-        std::cout << "addend_shape: " << t_l1_addend_shape << std::endl;
-        std::cout << "dst_shape: " << t_dst_shape << std::endl;
-        auto t_input_info     = TensorInfo(t_input_shape, 1, data_type, data_layout);
-        auto t_weight_info    = TensorInfo(t_weight_shape, 1, data_type, data_layout);
-        auto t_bias_info      = TensorInfo(t_bias_shape, 1, data_type, data_layout);
-        auto t_l0_dst_info    = TensorInfo(t_dst_shape, 1, data_type, data_layout); // Intermediate tensor for cond3
-        auto t_l1_addend_info = TensorInfo(t_l1_addend_shape, 1, data_type, data_layout);
-        auto t_dst_info       = TensorInfo(t_dst_shape, 1, data_type, data_layout);
-
-        // Init tensors
-        {
-            t_input.allocator()->init(t_input_info);
-            t_weight.allocator()->init(t_weight_info);
-            t_bias.allocator()->init(t_bias_info);
-            t_l1_addend.allocator()->init(t_dst_info);
-            t_l0_dst.allocator()->init(t_l0_dst_info);
-            t_dst.allocator()->init(t_dst_info);
-        }
-
-        op0.configure(&t_input, &t_weight, &t_bias, &t_l0_dst, conv_info);
-        op1.configure(&t_l0_dst, &t_l1_addend, &t_dst, ConvertPolicy{});
-        TOCK(configure, measurements);
-
-        TICK(tensor_allocation);
-        // Construct tensors
-        // Allocate and fill tensors
-        {
-            t_input.allocator()->allocate();
-            t_weight.allocator()->allocate();
-            t_bias.allocator()->allocate();
-            t_l1_addend.allocator()->allocate();
-            t_l0_dst.allocator()->allocate();
-            t_dst.allocator()->allocate();
-            fill_random_tensor(t_input, -1.f, 1.f);
-            fill_random_tensor(t_weight, -1.f, 1.f);
-            fill_random_tensor(t_bias, -1.f, 1.f);
-            fill_random_tensor(t_l1_addend, -1.f, 1.f);
-        }
-        TOCK(tensor_allocation, measurements);
-        // Dummy run for CLTuner
-        TICK(dummy_run);
-        op0.run();
-        CLScheduler::get().sync();
-        TOCK(dummy_run, measurements);
-        TOCK(startup_time, measurements);
-        return true;
-    }
-    void do_run() override
-    {
-        // Run the ops
-        op0.run();
-        op1.run();
-
-        // Make sure all the OpenCL jobs are done executing:
-        CLScheduler::get().sync();
-    }
-
-    void do_teardown() override
-    {
-        for(auto m : measurements)
-        {
-            std::cout << m.first << ": " << m.second.count() << "us" << std::endl;
-        }
-    }
-
-private:
-    CLTensor                 t_input{};
-    CLTensor                 t_weight{};
-    CLTensor                 t_bias{};
-    CLTensor                 t_l1_addend{};
-    CLTensor                 t_l0_dst{};
-    CLTensor                 t_dst{};
-    CLDirectConvolutionLayer op0{};
-    CLArithmeticAddition     op1{};
-    CLTuner                  tuner{};
-    std::map<std::string, std::chrono::microseconds> measurements{};
-};
-
-/** Main program for sgemm test
- *
- * @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Matrix A, [optional] Matrix B, [optional] Matrix C, [optional] alpha, [optional] beta )
- */
-int main(int argc, char **argv)
-{
-    return utils::run_example<ClRefConv2dEltwiseAddExample>(argc, argv);
-}
-
-#undef TICK
-#undef TOCK
-#undef TOCK_AVG
\ No newline at end of file