Integrate Dynamic Fusion patches

* Add public interfaces:
    * OperatorGraph: Describe a workload that could contain fused kernels
    * IWorkload: Generic interface for workloads built from OperatorGraph
    * ClWorkload: OpenCL workloads built from OperatorGraph
    * ClCompositeOperator: Runtime async operator to execute a ClWorkload
    * DependencyGraph (will likely be deprecated in later iterations)

* Add example
    * cl_fused_conv2d_elementwise_add.cpp to explain how to use the new
      interfaces

* Add internal translation layer

* Refactor ClKernelBuildingAPI
    * Remove non-tile based gemm native kernel component
    * Minor interface changes

* Add integration tests

Resolves COMPMID-5161

Signed-off-by: SiCong Li <sicong.li@arm.com>
Change-Id: Ib987ed79289ab0bcbd3130d54f5793408d9f1240
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7510
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 219 insertions, 0 deletions

diff --git a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.cpp b/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.cpp
new file mode 100644
index 0000000000..8aaf0946bb
--- /dev/null
+++ b/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.cpp
@@ -0,0 +1,219 @@
+/*
+ * 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 ENABLE_EXPERIMENTAL_DYNAMIC_FUSION
+#error "This experimental feature must be enabled with -DENABLE_EXPERIMENTAL_DYNAMIC_FUSION"
+#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+
+#include "src/core/CL/CLValidate.h"
+#include "src/core/experimental/dynamic_fusion/ClKernelBuildingAPI.h"
+#include "src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.h"
+
+#include "support/Cast.h"
+
+namespace arm_compute
+{
+namespace experimental
+{
+namespace dynamic_fusion
+{
+Status ClDirectConv2dKernel::generate(ClKernelBlueprint &bp) const
+{
+    const auto input  = _tensors.get_const_tensor(TensorType::ACL_SRC_0);
+    const auto weight = _tensors.get_const_tensor(TensorType::ACL_SRC_1);
+    const auto bias   = _tensors.get_const_tensor(TensorType::ACL_SRC_2);
+    const auto dst    = _tensors.get_const_tensor(TensorType::ACL_DST_0);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weight, dst);
+    ArgumentID input_id;
+    add_tensor(bp, input->desc, input_id, input->id);
+    ArgumentID weight_id;
+    add_tensor(bp, weight->desc, weight_id, weight->id);
+    ArgumentID bias_id = g_arg_placeholder;
+    if(bias != nullptr)
+    {
+        add_tensor(bp, bias->desc, bias_id, bias->id);
+    }
+    ArgumentID dst_id;
+    add_tensor(bp, dst->desc, dst_id, dst->id);
+
+    add_kcomp_direct_conv2d(bp, desc, input_id, weight_id, bias_id, dst_id);
+    return Status{};
+}
+Status ClDirectConv2dKernel::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const ClDirectConv2dKernelDescriptor &conv2d_desc)
+{
+    // 1. Check validity
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
+    // Matching data type
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
+    }
+
+    // Matching data layout
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst);
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, biases);
+    }
+
+    // All tensor infos are initialized
+    ARM_COMPUTE_RETURN_ERROR_ON(src->tensor_shape().total_size() == 0);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->tensor_shape().total_size() == 0);
+    ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0);
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->tensor_shape().total_size() == 0);
+    }
+    // Device requirements are met
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
+    // weights shape is correct
+    const DataLayout data_layout = src->data_layout();
+    const int        channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(channel_idx) != src->dimension(channel_idx), "Weights feature map dimension should match the respective src's one");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->num_dimensions() > 4, "Weights can be at most 4 dimensional");
+
+    // dst shape is correct
+    PadStrideInfo legacy_pad_stride(conv2d_desc.conv2d.stride.x(), conv2d_desc.conv2d.stride.y(), conv2d_desc.conv2d.pad.left, conv2d_desc.conv2d.pad.right, conv2d_desc.conv2d.pad.top,
+                                    conv2d_desc.conv2d.pad.bottom, DimensionRoundingType{});
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(),
+                                                       misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, legacy_pad_stride));
+
+    // biases shape is correct
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->dimension(0) != weights->dimension(3),
+                                        "Biases size and number of dst feature maps should match");
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->num_dimensions() > 1,
+                                        "Biases should be one dimensional");
+    }
+
+    // 2. Check support level
+    // Data type
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
+    // Data layout
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(src, DataLayout::NHWC);
+
+    return Status{};
+}
+
+bool ClDirectConv2dKernel::operator==(const ClKernel &other) const
+{
+    const auto converted = *utils::cast::polymorphic_downcast<const ClDirectConv2dKernel *>(&other);
+    return config() == other.config() && tensors() == other.tensors() && desc == converted.desc;
+}
+
+Status ClAddKernel::generate(ClKernelBlueprint &bp) const
+{
+    const auto lhs = _tensors.get_const_tensor(TensorType::ACL_SRC_0);
+    const auto rhs = _tensors.get_const_tensor(TensorType::ACL_SRC_1);
+    const auto dst = _tensors.get_const_tensor(TensorType::ACL_DST_0);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
+    ArgumentID lhs_id;
+    add_tensor(bp, lhs->desc, lhs_id, lhs->id);
+    ArgumentID rhs_id;
+    add_tensor(bp, rhs->desc, rhs_id, rhs->id);
+    ArgumentID dst_id;
+    add_tensor(bp, dst->desc, dst_id, dst->id);
+
+    add_kcomp_eltwise_add(bp, desc, lhs_id, rhs_id, dst_id);
+    return Status{};
+}
+
+Status ClAddKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst)
+{
+    // 1. Check validity
+    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
+
+    // Matching data type
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
+
+    // Matching data layout
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(lhs, rhs);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(lhs, dst);
+
+    // All tensor infos are initialized
+    ARM_COMPUTE_RETURN_ERROR_ON(lhs->tensor_shape().total_size() == 0);
+    ARM_COMPUTE_RETURN_ERROR_ON(rhs->tensor_shape().total_size() == 0);
+    ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0);
+
+    // Device requirements are met
+    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(lhs);
+
+    const bool in_place      = (lhs == dst) || (rhs == dst);
+    const bool src0_in_place = in_place && (lhs == dst);
+
+    // dst shape is correct
+    const TensorShape out_shape = TensorShape::broadcast_shape(lhs->tensor_shape(), rhs->tensor_shape());
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), "Wrong shape for dst");
+    if(in_place)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, src0_in_place ? lhs->tensor_shape() : rhs->tensor_shape(), 0),
+                                        "Wrong shape for dst, cannot do in_place calculation");
+    }
+
+    // 2. Check support level
+
+    // Data type
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16);
+
+    // Data layout
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(lhs, DataLayout::NHWC);
+
+    return Status{};
+}
+
+bool ClAddKernel::operator==(const ClKernel &other) const
+{
+    const auto converted = *utils::cast::polymorphic_downcast<const ClAddKernel *>(&other);
+    return config() == other.config() && tensors() == other.tensors() && desc == converted.desc;
+}
+
+std::vector<const ClKernel *> traverse(const ClKernelGraph &graph)
+{
+    std::vector<const ClKernel *> kernels;
+    const auto                    sorted = graph.graph.topological_sort();
+    for(const auto &pack : sorted.second)
+    {
+        kernels.push_back(graph.kernels.at(pack.op).get());
+    }
+    return kernels;
+}
+std::vector<ClKernel *> traverse(ClKernelGraph &graph)
+{
+    std::vector<ClKernel *> kernels;
+    const auto              sorted = graph.graph.topological_sort();
+    for(const auto &pack : sorted.second)
+    {
+        kernels.push_back(graph.kernels.at(pack.op).get());
+    }
+    return kernels;
+}
+} // namespace dynamic_fusion
+} // namespace experimental
+} // namespace arm_compute
\ No newline at end of file