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/*
* 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 "GpuKernelComponentGraph.h"
#include "arm_compute/dynamic_fusion/sketch/MemoryDescriptor.h"
namespace arm_compute
{
namespace experimental
{
namespace dynamic_fusion
{
namespace
{
/** Automatically create memory descriptors for all tensors in the graph
*
* @param[in] tensors @ref ITensorInfo map
* @param[in] graph @ref DependencyGraph of which the @p tensors are a part
*
* @return MemoryDescriptorMap An assignment map of @ref MemoryDescriptors for each ITensorInfo in the graph
*/
MemoryDescriptorMap assign_memory_descriptors(const std::map<ITensorInfo::Id, const ITensorInfo *> tensors, const DependencyGraph &graph)
{
MemoryDescriptorMap mem_map{};
for(auto t_id : graph.all_tensors())
{
const auto &tensor = tensors.at(t_id);
// Only global src and dst tensors to the entire component graph are "User" tensors, which are user-specified memories
if(is_in(t_id, graph.global_src_tensors()) || is_in(t_id, graph.global_dst_tensors()))
{
mem_map[t_id] = MemoryDescriptor{ MemoryType::User };
}
else
{
AuxMemoryInfo aux_mem_info{ tensor->total_size() };
mem_map[t_id] = MemoryDescriptor{ MemoryType::Auxiliary, aux_mem_info };
}
}
return mem_map;
}
} // namespace
std::vector<DependencyGraph::TensorId> GpuKernelComponentGraph::get_tensor_ids(const std::vector<const ITensorInfo *> tensors)
{
std::vector<DependencyGraph::TensorId> tensor_ids{};
std::transform(
std::begin(tensors), std::end(tensors),
std::back_inserter(tensor_ids),
[](const auto & t)
{
return t->id();
});
return tensor_ids;
}
GpuKernelComponentGraph::GpuKernelComponentGraph(GpuComponentServices *services)
: _services{ services }, _components{}, _tensors{}, _dependency_graph{}
{
}
GpuKernelComponentStream GpuKernelComponentGraph::fuse() const
{
// Obtain memory descriptor map
const auto mem_map = assign_memory_descriptors(_tensors, _dependency_graph);
/// @note Fusion constraints (for kernel components) are exactly the same as the invariants of @ref GpuKernelComponentGroup
/// Fusion can be framed as a mathematical optimization problem:
/// Given fusion constraints, find the "best" fusion patterns possible
/// "Best" is ill-defined at the moment. For now we define "best" fusion pattern as one
/// which results in the least number of fused kernels ( @ref GpuKernelComponentGroup ) at the end
/// As the first iteration, we offer a sub-optimal algorithm here which ensures all
/// constraints are met, but provides no guarantee that the fusion pattern is optimal
GpuKernelComponentStream stream{ _services, mem_map };
// Break down into linear groups of components (constraint 1), preserving topological order
const auto linear_graphs = _dependency_graph.topological_partition();
// Further divide up the linear groups based on rest of the fusion constraints (rely on component group's invariants)
for(const auto &graph : linear_graphs)
{
for(unsigned int i = 0; i < graph.size(); ++i)
{
const auto comp = _components.at(graph[i].op).get();
// Each new linear graph signals a new component group in the stream
if(i == 0)
{
stream.new_component_group();
}
// If it violates the component group's invariant / fusion constraint, breaks up the stream by inserting a new group
bool success = stream.add_component(comp);
if(!success)
{
stream.new_component_group();
success = stream.add_component(comp);
ARM_COMPUTE_ERROR_ON(!success);
}
}
}
return stream;
}
} // namespace dynamic_fusion
} // namespace experimental
} // namespace arm_compute
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