Rewrite dynamic fusion

The new version introduces the following major changes:

* Change public interface to simplify and standardize the user experience
    - Use the term "Workload" uniformly
    - Simplify operator interface to be a set of static methods:
      validate_op(), create_op()

* Separate the kernel writing into its own component (template_writer).
  This is to allow the co-development of GpuKernelWriter, and to allow
  easy replacement once GpuKernelWriter is mature.

* Optimize the core fusion algorithm used by the component graph. The
  details can be found in GpuKernelComponentGraph::fuse()

* Use Gpu instead of Cl prefixes for most of the Workload interfaces
  (except for runtime and kernel components, which have to be language specific)
  This allows the potential extension to other Gpu langauges in the
  future.

* Refactor runtime memory interface so that auxiliary tensor handling
  is separate from the user tensor passing. This is because the former
  is less stable and may require extension in the future.

* Hide source code object from the user as it is not required at the
  moment

* Deprecate the old prototype entirely by disabling it in SCons build

Resolves COMPMID-5510, COMPMID-5512, COMPMID-5513

Change-Id: If69d2362856f2de4503546b7b6cf48a525cf3079
Signed-off-by: SiCong Li <sicong.li@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/8406
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 200 insertions, 0 deletions

diff --git a/src/dynamic_fusion/runtime/gpu/cl/ClKernelRuntime.cpp b/src/dynamic_fusion/runtime/gpu/cl/ClKernelRuntime.cpp
new file mode 100644
index 0000000000..93fbdfed63
--- /dev/null
+++ b/src/dynamic_fusion/runtime/gpu/cl/ClKernelRuntime.cpp
@@ -0,0 +1,200 @@
+/*
+ * 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.
+ */
+#include "ClKernelRuntime.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "src/core/CL/CLUtils.h"
+#include "src/dynamic_fusion/sketch/gpu/GpuKernelSourceCode.h"
+#include "src/gpu/cl/ClKernelLibrary.h"
+
+#include "support/Cast.h"
+namespace arm_compute
+{
+namespace experimental
+{
+namespace dynamic_fusion
+{
+using namespace arm_compute::opencl;
+
+void ClKernelRuntime::configure(const ClCompileContext &compile_ctx, const GpuKernelSourceCode &code)
+{
+    // Create kernel from kernel source string
+    opencl::ClKernelLibrary &klib = opencl::ClKernelLibrary::get();
+    _kernel                       = static_cast<cl::Kernel>(compile_ctx.create_kernel(code.name(),
+                                                                                      "" /* Program name: Used to as part of a unique string for built kernel cache. Not needed */,
+                                                                                      code.code(),
+                                                                                      klib.kernel_path() /* Kernel path: Used in cases of embedded kernels */,
+                                                                                      code.build_options().options(),
+                                                                                      false /* Is source binary */));
+
+    // Configure execution window
+    IClKernel::configure_internal(code.window());
+
+    // Set config id for lws tuning
+    _config_id = code.config_id();
+
+    // Set kernel arguments
+    _arguments = code.arguments();
+}
+
+inline void ClKernelRuntime::add_tensor_argument(unsigned int &idx, const GpuKernelArgumentInfo &arg, const ICLTensor *tensor, const Window &arg_slice, std::vector<cl::Image2D> &cl_images)
+{
+    switch(arg.type)
+    {
+        case GpuKernelArgumentInfo::Type::Scalar:
+        {
+            ARM_COMPUTE_ERROR("Unsupported yet");
+            break;
+        }
+
+        case GpuKernelArgumentInfo::Type::Vector:
+        {
+            add_1D_tensor_argument(idx, tensor, arg_slice);
+            break;
+        }
+
+        case GpuKernelArgumentInfo::Type::Image:
+        {
+            add_2D_tensor_argument(idx, tensor, arg_slice);
+            break;
+        }
+        case GpuKernelArgumentInfo::Type::Image_Reinterpret_As_3D:
+        {
+            add_2D_tensor_argument(idx, tensor, arg_slice);
+            const unsigned int total_cross_plane_pad = tensor->info()->padding().top + tensor->info()->padding().bottom;
+            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad));
+            break;
+        }
+        case GpuKernelArgumentInfo::Type::Image_Export_To_ClImage2D:
+        {
+            const TensorShape shape2d(tensor->info()->dimension(0) / 4, tensor->info()->dimension(1) * tensor->info()->dimension(2) * tensor->info()->dimension(3));
+            const size_t      image_row_pitch = tensor->info()->strides_in_bytes()[1];
+            cl::Image2D       tensor_image2d  = create_image2d_from_buffer(CLKernelLibrary::get().context(), tensor->cl_buffer(), shape2d, tensor->info()->data_type(), image_row_pitch);
+            cl_images.push_back(tensor_image2d);
+            _kernel.setArg(idx++, tensor_image2d);
+            break;
+        }
+
+        case GpuKernelArgumentInfo::Type::Image_3D:
+        {
+            add_2D_tensor_argument(idx, tensor, arg_slice);
+            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(tensor->info()->strides_in_bytes()[2]));
+            break;
+        }
+        case GpuKernelArgumentInfo::Type::Image_3D_Export_To_ClImage2D:
+        {
+            const TensorShape shape2d(tensor->info()->dimension(0) / 4, tensor->info()->dimension(1) * tensor->info()->dimension(2) * tensor->info()->dimension(3));
+            const size_t      image_row_pitch = tensor->info()->strides_in_bytes()[1];
+            cl::Image2D       tensor_image2d  = create_image2d_from_buffer(CLKernelLibrary::get().context(), tensor->cl_buffer(), shape2d, tensor->info()->data_type(), image_row_pitch);
+            cl_images.push_back(tensor_image2d);
+            _kernel.setArg(idx++, tensor_image2d);
+            _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(tensor->info()->strides_in_bytes()[2]));
+            break;
+        }
+
+        case GpuKernelArgumentInfo::Type::Tensor_3D:
+        {
+            add_3D_tensor_argument(idx, tensor, arg_slice);
+            break;
+        }
+
+        case GpuKernelArgumentInfo::Type::Tensor_4D:
+        {
+            add_4D_tensor_argument(idx, tensor, arg_slice);
+            break;
+        }
+        case GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer:
+        {
+            add_4d_tensor_nhwc_argument(idx, tensor);
+            break;
+        }
+        case GpuKernelArgumentInfo::Type::Tensor_4D_t_Image:
+        {
+            const size_t image_w        = tensor->info()->dimension(0) / 4;
+            const size_t image_h        = tensor->info()->tensor_shape().total_size_upper(1);
+            const size_t image_stride_y = tensor->info()->strides_in_bytes()[1];
+
+            cl::Image2D tensor_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), tensor->cl_buffer(),
+                                                                    TensorShape(image_w, image_h), tensor->info()->data_type(), image_stride_y);
+            cl_images.push_back(tensor_image2d);
+
+            _kernel.setArg(idx++, tensor_image2d);
+            add_4d_tensor_nhwc_argument(idx, tensor);
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_ERROR("Unsupported");
+        }
+    }
+}
+
+void ClKernelRuntime::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
+
+    Window slice = window.first_slice_window_3D();
+    // Don't slice matrix along the z dimension if matrix has just 2 dimensions and matrix A more than 2
+    // This scenario can happen when the matrix multiplication is used to perform a convolution operation
+    Window slice_fixed_z = slice;
+    slice_fixed_z.set(Window::DimX, Window::Dimension(0, 1, 1));
+    slice_fixed_z.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+    /// NOTE: Parameters extracted from old kernels. So far they seem to be constant
+    /// but we may need to make them into another configuration passed from GpuWorkloadSourceCode if needed in the future
+    constexpr bool slide_along_dimz     = true;
+    constexpr bool skip_sliding_window  = false;
+    constexpr bool use_dummy_work_items = false;
+
+    unsigned int idx = 0;
+    do
+    {
+        // Set kernel arguments
+        Window arg_slice = slice;
+        // CLImages created from tensor arguments. Need to be retained until enqueue
+        std::vector<cl::Image2D> cl_images;
+        for(auto id_arg : _arguments)
+        {
+            const auto arg    = id_arg.second;
+            auto       tensor = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(id_arg.first));
+            ARM_COMPUTE_ERROR_ON_NULLPTR(tensor);
+            ARM_COMPUTE_ERROR_ON_NULLPTR(tensor->info());
+            if(!slide_along_dimz)
+            {
+                // The stride_z for matrix must be zero if we do not slice
+                ARM_COMPUTE_ERROR_ON(tensor->info()->strides_in_bytes()[3] != 0);
+                arg_slice = slice_fixed_z;
+            }
+            add_tensor_argument(idx, *arg.kernel_argument_info(), tensor, arg_slice, cl_images);
+        }
+
+        // Dispatch kernel
+        enqueue(queue, *this, slice, lws_hint(), use_dummy_work_items);
+    }
+    while(skip_sliding_window && window.slide_window_slice_3D(slice));
+}
+
+} // namespace dynamic_fusion
+} // namespace experimental
+} // namespace arm_compute