diff options
Diffstat (limited to 'src/core/experimental/dynamic_fusion/WorkloadImpl')
10 files changed, 0 insertions, 2753 deletions
diff --git a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClFusedKernelGraph.cpp b/src/core/experimental/dynamic_fusion/WorkloadImpl/ClFusedKernelGraph.cpp deleted file mode 100644 index 4e57d66a1c..0000000000 --- a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClFusedKernelGraph.cpp +++ /dev/null @@ -1,232 +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. - */ - -#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION -#include "src/core/experimental/dynamic_fusion/WorkloadImpl/ClFusedKernelGraph.h" - -namespace arm_compute -{ -namespace experimental -{ -namespace dynamic_fusion -{ -namespace -{ -std::vector<std::pair<ClKernelFusionGroup *, ClKernelFusionGroup *>> get_combinations(const std::vector<ClKernelFusionGroup *> &sorted_fgs) -{ - ARM_COMPUTE_ERROR_ON(sorted_fgs.size() <= 1); - std::vector<std::pair<ClKernelFusionGroup *, ClKernelFusionGroup *>> combo; - for(size_t i = 0; i < sorted_fgs.size() - 1; ++i) - { - for(size_t j = i + 1; j < sorted_fgs.size(); ++j) - { - combo.push_back(std::make_pair(sorted_fgs.at(i), sorted_fgs.at(j))); - } - } - return combo; -} -} // namespace -std::vector<const ClKernel *> traverse(const ClKernelFusionGroup &group) -{ - std::vector<const ClKernel *> kernels; - const auto sorted = group.graph.topological_sort(); - for(const auto &pack : sorted.second) - { - kernels.push_back(group.fused_kernels.at(pack.op)); - } - return kernels; -} - -std::vector<const ClKernelFusionGroup *> traverse(const ClFusedKernelGraph &graph) -{ - std::vector<const ClKernelFusionGroup *> kernels; - const auto sorted = graph.fg_dependency.topological_sort(); - for(const auto &pack : sorted.second) - { - kernels.push_back(graph.fusion_groups.at(pack.op).get()); - } - return kernels; -} - -std::vector<ClKernelFusionGroup *> traverse(ClFusedKernelGraph &graph) -{ - std::vector<ClKernelFusionGroup *> kernels; - const auto sorted = graph.fg_dependency.topological_sort(); - for(const auto &pack : sorted.second) - { - kernels.push_back(graph.fusion_groups.at(pack.op).get()); - } - return kernels; -} - -std::pair<Status, ClFusedKernelGraph> init_fusion_graph(const ClKernelGraph &kernel_graph) -{ - ClFusedKernelGraph fused_kernel_graph{}; - fused_kernel_graph.original_graph = &kernel_graph; // Create a copy of the original kernel graph - fused_kernel_graph.fg_dependency = DependencyGraph(); - // Initialize all fusion groups - for(const auto &kernel : traverse(kernel_graph)) - { - fused_kernel_graph.add_fusion_group({ kernel }); - } - return { Status{}, fused_kernel_graph }; -} - -Status fuse(ClFusedKernelGraph &fused_kernel_graph) -{ - // A naive fusion algorithm that's guaranteed to find optimal pattern if there are no branches - // If there are branches, the algorithm cannot guanrantee optimality as it doesn't perform any searches - - bool fusion_found = false; - do - { - fusion_found = false; - const auto sorted_fgs = traverse(fused_kernel_graph); - if(sorted_fgs.size() <= 1) - { - // Only one or zero fusion group, thus no need to perform fusion - return Status{}; - } - auto fgs_combo = get_combinations(sorted_fgs); - for(auto fgs : fgs_combo) - { - auto fg0 = fgs.first; - auto fg1 = fgs.second; - const auto st = fused_kernel_graph.can_fuse(*fg0, *fg1); - if(bool(st)) - { - const auto st = fused_kernel_graph.fuse(*fg0, *fg1); - if(!bool(st)) - { - return st; - } - fusion_found = true; - break; - } - } - } - while(fusion_found); - return Status{}; -} -Status generate_store(ClKernelBlueprint &bp, const ClFusedKernelGraph &fused_kernel_graph, const ClKernelFusionGroup &fg) -{ - Status st{}; - for(const auto &dst_t_id : fused_kernel_graph.fg_dependency.dst_tensors(fg.id)) - { - const auto dst_t = fused_kernel_graph.original_graph->get_tensor(dst_t_id); - - /// NOTE: dst tensor must have already been added to the blueprint at this point - ArgumentID dst_id; - st = add_tensor(bp, dst_t->desc, dst_id, dst_t->id); - if(!bool(st)) - { - return st; - } - /// NOTE: the extra dst tensor is needed as the store kcomp requires 2 tensors. But this is irrelevant to the fused kernel graph - /// since both tensors share the exact same info and kernel arg descriptor - ArgumentID dst_dst_id; - st = add_tensor(bp, dst_t->desc, dst_dst_id); - if(!bool(st)) - { - return st; - } - /// NOTE: Update the merge point map to link dst_dst_id with dst_t->id instead. - /// This is required because the get_arguments() returned by the blueprint returns the dst tensor added by the store component - st = update_merge_point(bp, dst_dst_id, dst_t->id); - if(!bool(st)) - { - return st; - } - st = add_kcomp_store(bp, fg.get_root_kernel()->config().store_type, dst_id, dst_dst_id); - if(!bool(st)) - { - return st; - } - } - return st; -} - -Status generate(ClWorkload &workload, const ClWorkloadContext &ctx, const ClFusedKernelGraph &fused_kernel_graph) -{ - workload.context = ctx; - for(const auto &fg : traverse(fused_kernel_graph)) - { - ClKernelBlueprint bp{}; - for(const auto &kernel : traverse(*fg)) - { - const auto st = kernel->generate(bp); - if(!bool(st)) - { - return st; - } - } - auto st = set_tile_info(bp, fg->get_root_kernel()->config().tile_desc); - if(!bool(st)) - { - return st; - } - st = generate_store(bp, fused_kernel_graph, *fg); - if(!bool(st)) - { - return st; - } - - ClKernelCode code{}; - st = build(code, ClCodeBuilderContext{ ctx.gpu_info }, bp); - if(!bool(st)) - { - return st; - } - const auto bp_graph = get_dependency_graph(bp); - - // Get tensor info - std::vector<Id> workload_src_tensors{}; - for(const auto &src_t_id : fused_kernel_graph.fg_dependency.src_tensors(fg->id)) - { - const auto src_t = fused_kernel_graph.original_graph->get_tensor(src_t_id); - // Get corresponding kernel arg descriptor - const auto arg_desc = code.arguments.at(bp_graph.get_merge_points().at(src_t->id)); - const auto kernel_t_id = workload.add_workload_tensor(src_t->desc, src_t->memory_type, src_t->memory_info, arg_desc, src_t->id); - workload_src_tensors.push_back(kernel_t_id); - } - std::vector<Id> workload_dst_tensors{}; - for(const auto &dst_t_id : fused_kernel_graph.fg_dependency.dst_tensors(fg->id)) - { - const auto dst_t = fused_kernel_graph.original_graph->get_tensor(dst_t_id); - // Get corresponding kernel arg descriptor - const auto arg_desc = code.arguments.at(bp_graph.get_merge_points().at(dst_t->id)); - const auto kernel_t_id = workload.add_workload_tensor(dst_t->desc, dst_t->memory_type, dst_t->memory_info, arg_desc, dst_t->id); - workload_dst_tensors.push_back(kernel_t_id); - } - - workload.add_unit_workload(fg->get_root_kernel()->config().stage, code, workload_src_tensors, workload_dst_tensors); - } - - return Status{}; -} - -} // namespace dynamic_fusion -} // namespace experimental -} // namespace arm_compute -#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
\ No newline at end of file diff --git a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClFusedKernelGraph.h b/src/core/experimental/dynamic_fusion/WorkloadImpl/ClFusedKernelGraph.h deleted file mode 100644 index 2051f1b62f..0000000000 --- a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClFusedKernelGraph.h +++ /dev/null @@ -1,452 +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. - */ -#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION -#ifndef ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_CLFUSEDKERNELGRAPH_H -#define ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_CLFUSEDKERNELGRAPH_H -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/experimental/DependencyGraph.h" -#include "src/core/experimental/dynamic_fusion/ClKernelBuildingAPI.h" -#include "src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.h" -#include "support/DeepCopy.h" - -#include <vector> - -namespace arm_compute -{ -namespace experimental -{ -namespace dynamic_fusion -{ -struct ClKernelFusionGroup; - -/** A const view of a subgraph of the @ref ClKernelGraph to be fused together - * - */ -struct ClKernelFusionGroup -{ -public: - using Id = DependencyGraph::Id; - - ClKernelFusionGroup() = default; - ClKernelFusionGroup(Id id) - : id{ id }, graph{}, fused_kernels{}, tensors{} - { - } - ~ClKernelFusionGroup() = default; - - void set_id(Id i) - { - id = i; - } - - Id add_fused_kernel(const ClKernel *kernel) - { - /// PRE: Acyclicity ensured by DependencyGraph - /// PRE: Connectedness ensured by DependencyGraph - /// PRE: Single-rootedness ensured by User - std::vector<Id> src_tensors; - for(const auto t : kernel->tensors().get_const_src_tensors()) - { - auto id = graph.add_tensor(t->id); - if(tensors.find(id) == tensors.end()) - { - tensors[id] = t; - } - src_tensors.push_back(id); - } - std::vector<Id> dst_tensors; - for(const auto t : kernel->tensors().get_const_dst_tensors()) - { - auto id = graph.add_tensor(t->id); - if(tensors.find(id) == tensors.end()) - { - tensors[id] = t; - } - dst_tensors.push_back(id); - } - auto id = graph.add_operator(src_tensors, dst_tensors); - fused_kernels[id.second] = kernel; - return id.second; - } - - const ClKernel *get_root_kernel() const - { - auto root_kernels = graph.get_root_ops(); - ARM_COMPUTE_ERROR_ON(root_kernels.size() != 1); - return fused_kernels.at(root_kernels.at(0)); - } - - std::vector<const ClKernelTensor *> get_src_tensors() const - { - std::vector<const ClKernelTensor *> src_tensors; - for(auto tensor_id : graph.src_tensors()) - { - src_tensors.push_back(tensors.at(tensor_id)); - } - return src_tensors; - } - - std::vector<const ClKernelTensor *> get_dst_tensors() const - { - std::vector<const ClKernelTensor *> dst_tensors; - for(auto tensor_id : graph.dst_tensors()) - { - dst_tensors.push_back(tensors.at(tensor_id)); - } - return dst_tensors; - } - - friend bool operator==(const ClKernelFusionGroup &fg0, const ClKernelFusionGroup &fg1) - { - return fg0.id == fg1.id && fg0.graph == fg1.graph && fg0.fused_kernels == fg1.fused_kernels && fg0.tensors == fg1.tensors; - } - - Id id{}; - DependencyGraph graph{}; // A subgraph of the original ClKernelGraph - std::map<Id, const ClKernel *> fused_kernels{}; - std::map<Id, const ClKernelTensor *> tensors{}; -}; - -std::vector<const ClKernel *> traverse(const ClKernelFusionGroup &group); - -struct ClFusedKernelGraph -{ -public: - using Id = DependencyGraph::Id; - - using KernelFusionGroupMap = std::map<Id, utils::memory::deep_unique_ptr<ClKernelFusionGroup>>; - - ClFusedKernelGraph() = default; - ~ClFusedKernelGraph() = default; - ClFusedKernelGraph(const ClFusedKernelGraph &graph) = default; - ClFusedKernelGraph &operator=(const ClFusedKernelGraph &graph) = default; - ClFusedKernelGraph(ClFusedKernelGraph &&graph) = default; - ClFusedKernelGraph &operator=(ClFusedKernelGraph &&graph) = default; - - friend bool operator==(const ClFusedKernelGraph &graph0, const ClFusedKernelGraph &graph1) - { - /// NOTE: fg_dependency may change based on the order of fusion, and thus is omitted in the comparison. - /// The fusion groups can already guarantee the equivalence of fusion - /// In the future we may want to enforce a stronger equivalence by implementing topological comparison between @ref DependencyGraph s - return graph0.original_graph == graph1.original_graph && graph0.fusion_groups == graph1.fusion_groups; - } - - Id add_fusion_group(const std::vector<const ClKernel *> &fused_kernels) - { - auto fg = utils::memory::make_deep_unique<ClKernelFusionGroup, ClKernelFusionGroup>(); - for(const auto k : fused_kernels) - { - fg->add_fused_kernel(k); - } - const auto src_tensors = fg->get_src_tensors(); - const auto dst_tensors = fg->get_dst_tensors(); - std::vector<Id> inputs{}; - std::transform(std::begin(src_tensors), std::end(src_tensors), std::back_inserter(inputs), [this](auto kernel) - { - return fg_dependency.add_tensor(kernel->id); - }); - std::vector<Id> outputs{}; - std::transform(std::begin(dst_tensors), std::end(dst_tensors), std::back_inserter(outputs), [this](auto kernel) - { - return fg_dependency.add_tensor(kernel->id); - }); - const auto id = fg_dependency.add_operator(inputs, outputs); - fg->set_id(id.second); - fusion_groups[id.second] = std::move(fg); - return id.second; - } - - Status fuse(ClKernelFusionGroup &fg0, ClKernelFusionGroup &fg1) - { - /// PRE: Already checked by can_fuse, and thus all the INVs and ASSUMPTIONS still hold - ClKernelFusionGroup *fg_src{}; - ClKernelFusionGroup *fg_dst{}; - // Find fg_src (parent / root) and fg_dst (child / non-root) - if(is_in(fg1.id, fg_dependency.dst_ops(fg0.id))) - { - fg_src = &fg0; - fg_dst = &fg1; - } - else if(is_in(fg0.id, fg_dependency.dst_ops(fg1.id))) - { - fg_src = &fg1; - fg_dst = &fg0; - } - else - { - return Status{ ErrorCode::RUNTIME_ERROR, "Invalid fusion: Not directly connected fusion groups cannot be fused together" }; - } - - for(const auto &t : fg_dependency.src_tensors(fg_dst->id)) - { - if(!is_in(t, fg_dependency.dst_tensors(fg_src->id))) - { - // Link any incoming tensors of fg_dst, that ARE NOT in between fg_src and fg_dst, to fg_src - - // Before: - // fg_src - // | - // .. t1 - // | | - // -> fg_dst <- - // - // After: - // fg_src <---t1 - // - const auto st = link_src_tensors(fg_src->id, { t }); - if(!bool(st)) - { - return st; - } - } - else - { - const auto dst_fgs = fg_dependency.dst_ops_from_tensor(t); - if(dst_fgs.size() == 1U && dst_fgs.at(0) == fg_dst->id) - { - // Remove any incoming tensors of fg_dst, that ARE in between fg_src and fg_dst - // AND that are not connected to any other outgoing fgs (Note that they cannot connect to any other incoming fgs as all tensors can have at most 1 incoming fg (ASSUMPTION 3)) - - // Before: - // fg_src - // | - // t0 - // | - // -> fg_dst - // - // After: - // fg_src - // - const auto st = remove_fg_tensor(t); - if(!bool(st)) - { - return st; - } - } - else - { - // If the tensors ARE in between fg_src and fg_dst - // BUT have any other outgoing fgs than fg_dst, then we leave it as a dst tensor to the fused fg_src - - // Before: - // fg_src - // | - // t0 - // | - // |----------- - // | | - // -> fg_dst -> fg_other - // - // After: - // fg_src - // | - // t0 - // | - // -> fg_other - // - - // Note that this may seem like a case we shouldn't fuse. But actually all it means is that t0 is an - // intermediate tensor between the fused fg_src and fg_dst, but only that we also STORE it to memory - // so that any unfused fg's (fg_other in this case) can read it. - // So all this means that we not only can STORE the tensors at the "end" of a fusion group, - // but also any other tensors that are not source tensors. And all tensors that are STORED (exported), - // can be termed "dst tensors" to a fusion group - void(); - } - } - } - - for(const auto &t : fg_dependency.dst_tensors(fg_dst->id)) - { - // Link any outgoing tensors of fg_dst to fg_src - - // Before: - // fg_src - // | - // .. - // | - // -> fg_dst - // | - // |-------- - // | | - // |-> t0 |-> t1 - // - // After: - // fg_src - // | - // |-------- - // | | - // |-> t0 |-> t1 - // - const auto st = link_dst_tensors(fg_src->id, { t }); - if(!bool(st)) - { - return st; - } - } - - // Merge fg_dst's graph into fg_src's graph - for(const auto kernel : traverse(*fg_dst)) - { - fg_src->add_fused_kernel(kernel); - } - - const auto st = remove_fg(fg_dst->id); - return st; - } - Status can_fuse(const ClKernelFusionGroup &fg0, const ClKernelFusionGroup &fg1) const - { - /// ASSUMPTION0: All tensors have 0 or 1 incoming kernel - /// ASSUMPTION1: All kernels have exactly 1 dst tensor (Temporary, can be lifted once we start supporting multi-dst kernels) - /// Note that this does not apply to fusion groups - /// ASSUMPTION2: Simple kernels' tile infos can be overriden (share with) that of the root kernel's - /// ASSUMPTION3: Extension of ASSUMPTION0: All tensors have 0 or 1 incoming fusion group - /// INV0: All Fusion groups have a single root - /// INV1: All Fusion groups have no cycles or loops within themselves <- guaranteed by the underlying ClKernelGraph having no cycles or loops; enforced by DependencyGraph - /// INV2: The ClKernelFusionGroup itself has no cycles or loops <- enforced by DependencyGraph - /// INV3: All non-roots are Simple kernels - /// INV4: All non roots' dst tensors have the same shape as that of the root kernel - /// INV5: All kernels within a fusion group have the same UnitWorkloadStage - const ClKernelFusionGroup *fg_src {}; - const ClKernelFusionGroup *fg_dst{}; - - // Check 0: Ensure fg0 and fg1 are "directly connected": one of them is a direct parent of the other - // This guarantess INV0 - // This also finds fg_src (parent / root) and fg_dst (child / non-root) - if(is_in(fg1.id, fg_dependency.dst_ops(fg0.id))) - { - fg_src = &fg0; - fg_dst = &fg1; - } - else if(is_in(fg0.id, fg_dependency.dst_ops(fg1.id))) - { - fg_src = &fg1; - fg_dst = &fg0; - } - else - { - return Status{ ErrorCode::RUNTIME_ERROR, "Invalid fusion: Not directly connected fusion groups cannot be fused together" }; - } - - // Find unconnected tensors between fg_src and fg_dst - std::vector<Id> unconnected_tensors{}; - for(const auto &t : fg_dependency.dst_tensors(fg_src->id)) - { - if(!is_in(t, fg_dependency.src_tensors(fg_dst->id))) - { - unconnected_tensors.push_back(t); - } - } - - // Check 1: Any unconnected tensor cannot be an ancestor of fg_dst - // This guarantees INV2: That is, the fused graph does not have any cycles or loops between different fusion groups - for(const auto &t : unconnected_tensors) - { - if(fg_dependency.path_exists_from_tensor_to_op(t, fg_dst->id)) - { - return Status{ ErrorCode::RUNTIME_ERROR, "Invalid fusion: the fusion would result in cycles or loops" }; - } - } - - // Check 2: All non-root fgs are simple. Ensure INV3 - if(fg_dst->get_root_kernel()->complexity() != Complexity::Simple) - { - return Status{ ErrorCode::RUNTIME_ERROR, "Invalid fusion: only root kernel can be a complex kernel" }; - } - - // Check 3: All non roots' dst tensors have the same shape as that of the root kernel. Ensure INV4 - const auto root_kernel_dst_tensors = fg_dependency.dst_tensors(fg_src->id); - ARM_COMPUTE_ERROR_ON(root_kernel_dst_tensors.size() != 1); // (ASSUMPTION 1: All kernels have exactly 1 dst tensor) - const auto root_kernel_dst_tensor_info = original_graph->get_tensor(root_kernel_dst_tensors[0])->desc; - - for(const auto &t : fg_dependency.dst_tensors(fg_dst->id)) - { - const auto t_info = original_graph->get_tensor(t)->desc; - if(detail::have_different_dimensions(root_kernel_dst_tensor_info->tensor_shape(), t_info->tensor_shape(), 0)) - { - return Status{ ErrorCode::RUNTIME_ERROR, "Invalid fusion: all non roots' dst tensors should have the same shape as that of the root kernel" }; - } - } - - // Check 4: All kernels within a fg have the same UnitWorkloadStage. Ensure INV5 - if(!(fg_src->get_root_kernel()->config().stage == fg_dst->get_root_kernel()->config().stage)) - { - return Status{ ErrorCode::RUNTIME_ERROR, "Invalid fusion: all kernels within a fusion group should have the same UnitWorkloadStage" }; - } - - return Status{}; - } - - const ClKernelGraph *original_graph{}; - DependencyGraph fg_dependency{}; - KernelFusionGroupMap fusion_groups{}; - // Note: no need to store tensors pointers in the ClFusedKernelGraph, as they are stored in side the individual fusion groups. - -private: - Status link_src_tensors(Id fg, const std::vector<Id> &src_tensors) - { - for(auto t : src_tensors) - { - fg_dependency.link_input(fg, t); - } - return Status{}; - } - Status link_dst_tensors(Id fg, const std::vector<Id> &dst_tensors) - { - for(auto t : dst_tensors) - { - fg_dependency.link_output(fg, t); - } - return Status{}; - } - Status remove_fg(Id fg) - { - fg_dependency.remove_operator(fg); - fusion_groups.erase(fg); - return Status{}; - } - Status remove_fg_tensor(Id tensor) - { - fg_dependency.remove_tensor(tensor); - return Status{}; - } -}; - -std::vector<const ClKernelFusionGroup *> traverse(const ClFusedKernelGraph &graph); -std::vector<ClKernelFusionGroup *> traverse(ClFusedKernelGraph &graph); - -std::pair<Status, ClFusedKernelGraph> init_fusion_graph(const ClKernelGraph &kernel_graph); - -Status fuse(ClFusedKernelGraph &fused_kernel_graph); - -Status generate_store(ClKernelBlueprint &bp, const ClFusedKernelGraph &fused_kernel_graph, const ClKernelFusionGroup &fg); - -Status generate(ClWorkload &workload, const ClWorkloadContext &ctx, const ClFusedKernelGraph &fused_kernel_graph); - -} // namespace dynamic_fusion -} // namespace experimental -} // namespace arm_compute -#endif //ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_CLFUSEDKERNELGRAPH_H -#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
\ No newline at end of file diff --git a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelDescriptors.h b/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelDescriptors.h deleted file mode 100644 index f10e97e3e9..0000000000 --- a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelDescriptors.h +++ /dev/null @@ -1,121 +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. - */ -#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION -#ifndef ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_CLKERNELDESCRIPTORS_H -#define ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_CLKERNELDESCRIPTORS_H - -#include "arm_compute/core/experimental/OperatorGraph.h" - -namespace arm_compute -{ -namespace experimental -{ -namespace dynamic_fusion -{ -struct ClDirectConv2dKernelDescriptor -{ - friend bool operator==(const ClDirectConv2dKernelDescriptor &desc0, const ClDirectConv2dKernelDescriptor &desc1) - { - return desc0.conv2d == desc1.conv2d; - } - Conv2dDescriptor conv2d{}; -}; - -struct ClElementwiseKernelDescriptor -{ - friend bool operator==(const ClElementwiseKernelDescriptor &desc0, const ClElementwiseKernelDescriptor &desc1) - { - return desc0.eltwise == desc1.eltwise; - } - ElementwiseDescriptor eltwise{}; -}; - -struct ClFloorKernelDescriptor -{ - friend bool operator==(const ClFloorKernelDescriptor &desc0, const ClFloorKernelDescriptor &desc1) - { - return desc0.floor == desc1.floor; - } - FloorDescriptor floor{}; -}; - -struct ClActivationKernelDescriptor -{ - friend bool operator==(const ClActivationKernelDescriptor &, const ClActivationKernelDescriptor &) - { - return true; - } -}; - -enum class ClippingStrategy -{ - TOP_LEFT, - TOP_RIGHT, - BOTTOM_LEFT, - BOTTOM_RIGHT, -}; -/** Component: Store */ -struct TileDescriptor -{ - Size2D tile_dims{}; - Size2D boundaries{}; - ClippingStrategy clipping{ ClippingStrategy::TOP_LEFT }; - - TileDescriptor() - { - } - - TileDescriptor(Size2D dims, const Size2D &bound, const ClippingStrategy &clip) - : tile_dims(dims), boundaries(bound), clipping(clip) - { - } - - bool empty() const - { - return (tile_dims.area() == 0) || (boundaries.area() == 0); - } - friend bool operator==(const TileDescriptor &tile0, const TileDescriptor &tile1) - { - return tile0.tile_dims == tile1.tile_dims && tile0.boundaries == tile1.boundaries && tile0.clipping == tile1.clipping; - } -}; -enum class StoreType -{ - VStore, - VStorePartial, - StoreRow, - ConvertStoreRow, - StoreBlock, - ConvertStoreBlock, - StoreRowPartial, - StoreBlockPartial, - StoreBlockBoundaryAware, - StoreVectorSelect, - TStoreIndirectWidthSelect -}; -} // namespace dynamic_fusion -} // namespace experimental -} // namespace arm_compute -#endif //ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_CLKERNELDESCRIPTORS_H -#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
\ No newline at end of file diff --git a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.cpp b/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.cpp deleted file mode 100644 index cab51a2ce6..0000000000 --- a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.cpp +++ /dev/null @@ -1,271 +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. - */ -#ifdef 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 ClElementwiseKernel::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_op(bp, desc, lhs_id, rhs_id, dst_id); - return Status{}; -} - -Status ClElementwiseKernel::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 ClElementwiseKernel::operator==(const ClKernel &other) const -{ - const auto converted = *utils::cast::polymorphic_downcast<const ClElementwiseKernel *>(&other); - return config() == other.config() && tensors() == other.tensors() && desc == converted.desc; -} - -Status ClFloorKernel::generate(ClKernelBlueprint &bp) const -{ - const auto src = _tensors.get_const_tensor(TensorType::ACL_SRC_0); - const auto dst = _tensors.get_const_tensor(TensorType::ACL_DST_0); - ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); - ArgumentID src_id; - add_tensor(bp, src->desc, src_id, src->id); - ArgumentID dst_id; - add_tensor(bp, dst->desc, dst_id, dst->id); - - add_kcomp_floor(bp, desc, src_id, dst_id); - return Status{}; -} - -Status ClFloorKernel::validate(const ITensorInfo *src, const ITensorInfo *dst) -{ - // 1. Check validity - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); - - // Matching data type - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); - - // Matching data layout - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst); - - // All tensor infos are initialized - ARM_COMPUTE_RETURN_ERROR_ON(src->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(src); - - // dst shape is correct - ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(src->tensor_shape(), dst->tensor_shape(), 0), "Wrong shape for dst"); - - // 2. Check support level - - // Data type - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32, DataType::F16); - - // Data layout - ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(src, DataLayout::NHWC); - - return Status{}; -} - -bool ClFloorKernel::operator==(const ClKernel &other) const -{ - const auto converted = *utils::cast::polymorphic_downcast<const ClFloorKernel *>(&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 -#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
\ No newline at end of file diff --git a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.h b/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.h deleted file mode 100644 index c3580cfaca..0000000000 --- a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.h +++ /dev/null @@ -1,259 +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. - */ -#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION -#ifndef ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_CLKERNELGRAPH_H -#define ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_CLKERNELGRAPH_H - -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/experimental/ClWorkload.h" -#include "arm_compute/core/experimental/DependencyGraph.h" -#include "src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelDescriptors.h" -#include "src/core/experimental/dynamic_fusion/WorkloadImpl/ITensorDescPack.h" -#include "support/DeepCopy.h" - -namespace arm_compute -{ -namespace experimental -{ -namespace dynamic_fusion -{ -struct ClKernelGraph; -class ClKernelBlueprint; - -enum class Complexity -{ - Simple, - Complex -}; - -/** Configurations for ClKernel - * - */ -struct ClKernelConfig -{ - UnitWorkloadStage stage{}; - TileDescriptor tile_desc{}; - StoreType store_type{}; - friend bool operator==(const ClKernelConfig &config0, const ClKernelConfig &config1) - { - return config0.stage == config1.stage && config0.tile_desc == config1.tile_desc && config0.store_type == config1.store_type; - } -}; - -struct ClKernelTensor -{ -public: - using Id = DependencyGraph::Id; - ClKernelTensor() = default; - ClKernelTensor(Id id, ITensorInfo *desc, MemoryType memory_type, const AuxMemoryInfo &memory_info) - : id{ id }, desc{ desc }, memory_type{ memory_type }, memory_info{ memory_info } - { - } - bool operator==(const ClKernelTensor &other) const - { - return desc == other.desc; - } - - Id id{}; - ITensorInfo *desc{}; - MemoryType memory_type{}; - AuxMemoryInfo memory_info{}; -}; - -struct ClKernel -{ -public: - using Id = DependencyGraph::Id; - ClKernel() = default; - virtual ~ClKernel() = default; - ClKernel(const ClKernel &kernel) = default; - ClKernel &operator=(const ClKernel &kernel) = default; - ClKernel(ClKernel &&kernel) = default; - ClKernel &operator=(ClKernel &&kernel) = default; - ClKernel(const ClKernelGraph *graph, Id id, const ClKernelConfig &config, const ITensorDescPack<ClKernelTensor> &tensors) - : _graph{ graph }, _id{ id }, _config{ config }, _tensors{ tensors } - { - } - virtual bool operator==(const ClKernel &other) const = 0; - virtual Complexity complexity() const = 0; - virtual Status generate(ClKernelBlueprint &bp) const = 0; - Id id() const - { - return _id; - } - ITensorDescPack<ClKernelTensor> tensors() const - { - return _tensors; - } - ClKernelConfig config() const - { - return _config; - } - -protected: - const ClKernelGraph *_graph {}; - Id _id{}; - ClKernelConfig _config{}; - ITensorDescPack<ClKernelTensor> _tensors{}; -}; - -struct ClDirectConv2dKernel : public ClKernel -{ -public: - Complexity complexity() const override - { - return Complexity::Complex; - } - ClDirectConv2dKernel() = default; - ~ClDirectConv2dKernel() override = default; - ClDirectConv2dKernel(const ClKernelGraph *graph, Id id, const ClKernelConfig config, const ClDirectConv2dKernelDescriptor &desc, const ITensorDescPack<ClKernelTensor> tensors) - : ClKernel{ graph, id, config, tensors }, desc{ desc } - { - } - static Status validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const ClDirectConv2dKernelDescriptor &conv2d_desc); - bool operator==(const ClKernel &other) const override; - Status generate(ClKernelBlueprint &bp) const override; - - ClDirectConv2dKernelDescriptor desc{}; -}; - -struct ClElementwiseKernel : public ClKernel -{ -public: - Complexity complexity() const override - { - return Complexity::Simple; - } - ClElementwiseKernel() = default; - ~ClElementwiseKernel() override = default; - ClElementwiseKernel(const ClKernelGraph *graph, Id id, const ClKernelConfig &config, const ClElementwiseKernelDescriptor &desc, const ITensorDescPack<ClKernelTensor> tensors) - : ClKernel{ graph, id, config, tensors }, desc{ desc } - { - } - static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst); - bool operator==(const ClKernel &other) const override; - Status generate(ClKernelBlueprint &bp) const override; - - ClElementwiseKernelDescriptor desc{}; -}; - -struct ClFloorKernel : public ClKernel -{ -public: - Complexity complexity() const override - { - return Complexity::Simple; - } - ClFloorKernel() = default; - ~ClFloorKernel() override = default; - ClFloorKernel(const ClKernelGraph *graph, Id id, const ClKernelConfig &config, const ClFloorKernelDescriptor &desc, const ITensorDescPack<ClKernelTensor> tensors) - : ClKernel{ graph, id, config, tensors }, desc{ desc } - { - } - static Status validate(const ITensorInfo *src, const ITensorInfo *dst); - bool operator==(const ClKernel &other) const override; - Status generate(ClKernelBlueprint &bp) const override; - - ClFloorKernelDescriptor desc{}; -}; - -struct ClKernelGraph -{ -public: - using Id = DependencyGraph::Id; - using KernelMap = std::map<Id, utils::memory::deep_unique_ptr<ClKernel>>; - using KernelTensorMap = std::map<Id, utils::memory::deep_unique_ptr<ClKernelTensor>>; - - ClKernelGraph() = default; - ~ClKernelGraph() = default; - - friend bool operator==(const ClKernelGraph &graph0, const ClKernelGraph &graph1) - { - return graph0.graph == graph1.graph && graph0.kernels == graph1.kernels && graph0.tensors == graph1.tensors; - } - - Status add_kernel_tensor(ITensorInfo *desc, MemoryType memory_type, const AuxMemoryInfo &memory_info, Id &tensor_id, Id merge_point = DependencyGraph::empty_id()) - { - tensor_id = graph.add_tensor(merge_point); - if(tensors.find(tensor_id) == tensors.end()) - { - tensors[tensor_id] = utils::memory::make_deep_unique<ClKernelTensor, ClKernelTensor>(tensor_id, desc, memory_type, memory_info); - } - return Status{}; - } - - template <typename ContentT, typename KernelDescT> - Status add_kernel(const ClKernelConfig &config, const KernelDescT &desc, const ITensorDescPack<ClKernelTensor> &tensors, Id &kernel_id) - { - const auto src_tensors = tensors.get_const_src_tensors(); - const auto dst_tensors = tensors.get_const_dst_tensors(); - std::vector<Id> src_tensor_ids{}; - std::vector<Id> dst_tensor_ids{}; - for(const auto &t : src_tensors) - { - src_tensor_ids.push_back(t->id); - } - for(const auto &t : dst_tensors) - { - dst_tensor_ids.push_back(t->id); - } - kernel_id = graph.add_operator(src_tensor_ids, dst_tensor_ids).second; - auto k = utils::memory::make_deep_unique<ClKernel, ContentT>(this, kernel_id, config, desc, tensors); - kernels[kernel_id] = std::move(k); - return Status{}; - } - - ClKernel *get_kernel(Id id) - { - return kernels.at(id).get(); - } - const ClKernel *get_kernel(Id id) const - { - return kernels.at(id).get(); - } - - ClKernelTensor *get_tensor(Id id) - { - return tensors.at(id).get(); - } - const ClKernelTensor *get_tensor(Id id) const - { - return tensors.at(id).get(); - } - - DependencyGraph graph{}; - KernelMap kernels{}; - KernelTensorMap tensors{}; -}; -using Id = DependencyGraph::Id; - -std::vector<const ClKernel *> traverse(const ClKernelGraph &graph); -std::vector<ClKernel *> traverse(ClKernelGraph &graph); - -} // namespace dynamic_fusion -} // namespace experimental -} // namespace arm_compute -#endif //ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_CLKERNELGRAPH_H -#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
\ No newline at end of file diff --git a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClWorkload.cpp b/src/core/experimental/dynamic_fusion/WorkloadImpl/ClWorkload.cpp deleted file mode 100644 index dcada4f64b..0000000000 --- a/src/core/experimental/dynamic_fusion/WorkloadImpl/ClWorkload.cpp +++ /dev/null @@ -1,72 +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. - */ -#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION -#include "arm_compute/core/experimental/ClWorkload.h" -#include "src/core/experimental/dynamic_fusion/WorkloadImpl/ClFusedKernelGraph.h" -#include "src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.h" -#include "src/core/experimental/dynamic_fusion/WorkloadImpl/OperatorGraphImpl.h" - -namespace arm_compute -{ -namespace experimental -{ -namespace dynamic_fusion -{ -Status build(ClWorkload &workload, const OperatorGraph &op_graph, const ClWorkloadContext &ctx) -{ - workload.context = ctx; - ClKernelGraph kernel_graph; - workload.status = validate(op_graph); - ARM_COMPUTE_RETURN_ON_ERROR(workload.status); - workload.status = translate(kernel_graph, *op_graph.impl()); - ARM_COMPUTE_RETURN_ON_ERROR(workload.status); - ClFusedKernelGraph fused_k_graph; - std::tie(workload.status, fused_k_graph) = init_fusion_graph(kernel_graph); - ARM_COMPUTE_RETURN_ON_ERROR(workload.status); - workload.status = fuse(fused_k_graph); - ARM_COMPUTE_RETURN_ON_ERROR(workload.status); - workload.status = generate(workload, ctx, fused_k_graph); - ARM_COMPUTE_RETURN_ON_ERROR(workload.status); - - // Get operator tensor id to workload tensor id map - const auto op_tensor_to_kernel_tensor = fused_k_graph.original_graph->graph.get_merge_points(); - const auto kernel_tensor_to_workload_tensor = workload.graph.get_merge_points(); - for(const auto op_t : op_graph.impl()->graph.src_tensors()) - { - const auto kernel_t = op_tensor_to_kernel_tensor.at(op_t); - const auto workload_t = kernel_tensor_to_workload_tensor.at(kernel_t); - workload.op_tensor_id_lut[workload_t] = op_t; - } - for(const auto op_t : op_graph.impl()->graph.dst_tensors()) - { - const auto kernel_t = op_tensor_to_kernel_tensor.at(op_t); - const auto workload_t = kernel_tensor_to_workload_tensor.at(kernel_t); - workload.op_tensor_id_lut[workload_t] = op_t; - } - return workload.status; -} -} // namespace dynamic_fusion -} // namespace experimental -} // namespace arm_compute -#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
\ No newline at end of file diff --git a/src/core/experimental/dynamic_fusion/WorkloadImpl/DependencyGraph.cpp b/src/core/experimental/dynamic_fusion/WorkloadImpl/DependencyGraph.cpp deleted file mode 100644 index 7350255ebe..0000000000 --- a/src/core/experimental/dynamic_fusion/WorkloadImpl/DependencyGraph.cpp +++ /dev/null @@ -1,430 +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. - */ -#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION -#include "arm_compute/core/experimental/DependencyGraph.h" - -#include <algorithm> -#include <deque> -#include <set> - -namespace arm_compute -{ -namespace experimental -{ -namespace dynamic_fusion -{ -DependencyGraph::DependencyGraph(const AdjList &adj_src_tensors, const AdjList &adj_dst_tensors, const AdjList &adj_src_ops, const AdjList &adj_dst_ops, std::map<Id, Id> merge_points) - : _adj_src_tensors{ adj_src_tensors }, _adj_dst_tensors{ adj_dst_tensors }, _adj_src_ops{ adj_src_ops }, _adj_dst_ops{ adj_dst_ops }, _merge_to_internal{ merge_points }, _operator_id{}, _tensor_id{} -{ -} -DependencyGraph::DependencyGraph(const std::vector<Id> &imported_tensors) - : _adj_src_tensors{}, _adj_dst_tensors{}, _adj_src_ops{}, _adj_dst_ops{}, _merge_to_internal{}, _operator_id{}, _tensor_id{} -{ - for(auto t : imported_tensors) - { - _adj_src_ops[t] = {}; - _adj_dst_ops[t] = {}; - } -} - -Status DependencyGraph::update_merge_point(Id t_id, Id merge_point) -{ - if(_merge_to_internal.find(merge_point) == _merge_to_internal.end()) - { - return Status{ ErrorCode::RUNTIME_ERROR, "Merge point does not exist" }; - } - _merge_to_internal[merge_point] = t_id; - return Status{}; -} - -DependencyGraph::Id DependencyGraph::add_tensor(Id merge_tensor) -{ - Id new_tensor{ empty_id() }; - if(merge_tensor != empty_id()) - { - if(_merge_to_internal.find(merge_tensor) != _merge_to_internal.end()) - { - new_tensor = _merge_to_internal[merge_tensor]; - } - else - { - new_tensor = insert_new_tensor(); - _merge_to_internal[merge_tensor] = new_tensor; - } - } - else - { - new_tensor = insert_new_tensor(); - } - return new_tensor; -} - -void DependencyGraph::remove_tensor(Id tensor) -{ - for(auto src_op : _adj_src_ops.at(tensor)) - { - auto &dst_tensors = _adj_dst_tensors.at(src_op); - dst_tensors.erase( - std::remove(std::begin(dst_tensors), std::end(dst_tensors), tensor), - std::end(dst_tensors)); - } - for(auto dst_op : _adj_dst_ops.at(tensor)) - { - auto &src_tensors = _adj_src_tensors.at(dst_op); - src_tensors.erase( - std::remove(std::begin(src_tensors), std::end(src_tensors), tensor), - std::end(src_tensors)); - } - _adj_src_ops.erase(tensor); - _adj_dst_ops.erase(tensor); -} - -std::pair<Status, DependencyGraph::Id> DependencyGraph::add_operator(const std::vector<Id> &inputs, const std::vector<Id> &outputs) -{ - Id new_op = insert_new_op(); - for(Id tensor : inputs) - { - link_input(new_op, tensor); - } - for(Id tensor : outputs) - { - link_output(new_op, tensor); - } - - // Use topological sort in order to detect possible loops / cycles. - // NOTE: This is unscalable. We'll need to have a better way of detecting loops or relax this invariant during operation, and add a validate method instead - return std::pair<Status, DependencyGraph::Id>(topological_sort().first, new_op); -} - -void DependencyGraph::remove_operator(Id op) -{ - for(auto src_tensor : _adj_src_tensors.at(op)) - { - auto &dst_ops = _adj_dst_ops.at(src_tensor); - dst_ops.erase( - std::remove(std::begin(dst_ops), std::end(dst_ops), op), - std::end(dst_ops)); - } - for(auto dst_tensor : _adj_dst_tensors.at(op)) - { - auto &src_ops = _adj_src_ops.at(dst_tensor); - src_ops.erase( - std::remove(std::begin(src_ops), std::end(src_ops), op), - std::end(src_ops)); - } - _adj_src_tensors.erase(op); - _adj_dst_tensors.erase(op); -} - -std::map<DependencyGraph::Id, DependencyGraph::Id> DependencyGraph::get_merge_points() const -{ - return _merge_to_internal; -} - -std::vector<DependencyGraph::Id> DependencyGraph::get_root_ops() const -{ - std::vector<Id> ops{}; - const auto op_list = all_ops(); - - for(auto op : op_list) - { - if(src_ops(op).empty()) - { - ops.emplace_back(op); - } - } - return ops; -} - -std::vector<DependencyGraph::Id> DependencyGraph::get_dst_ops() const -{ - std::vector<Id> ops{}; - const auto op_list = all_ops(); - - for(auto op : op_list) - { - if(dst_ops(op).empty()) - { - ops.emplace_back(op); - } - } - return ops; -} - -std::vector<DependencyGraph::Id> DependencyGraph::src_tensors(Id op) const -{ - ARM_COMPUTE_ERROR_ON(!operator_exists(op)); - return _adj_src_tensors.at(op); -} - -std::vector<DependencyGraph::Id> DependencyGraph::dst_tensors(Id op) const -{ - ARM_COMPUTE_ERROR_ON(!operator_exists(op)); - return _adj_dst_tensors.at(op); -} - -std::vector<DependencyGraph::Id> DependencyGraph::src_tensors() const -{ - std::vector<Id> tensors; - for(auto tensor_src_ops : _adj_src_ops) - { - if(tensor_src_ops.second.empty()) - tensors.push_back(tensor_src_ops.first); - } - return tensors; -} - -std::vector<DependencyGraph::Id> DependencyGraph::dst_tensors() const -{ - std::vector<Id> tensors; - for(auto tensor_dst_ops : _adj_dst_ops) - { - if(tensor_dst_ops.second.empty()) - tensors.push_back(tensor_dst_ops.first); - } - return tensors; -} - -std::vector<DependencyGraph::Id> DependencyGraph::src_ops_from_tensor(Id tensor) const -{ - return _adj_src_ops.at(tensor); -} -std::vector<DependencyGraph::Id> DependencyGraph::dst_ops_from_tensor(Id tensor) const -{ - return _adj_dst_ops.at(tensor); -} - -std::vector<DependencyGraph::Id> DependencyGraph::all_ops() const -{ - std::vector<Id> ops{}; - std::transform(std::begin(_adj_src_tensors), std::end(_adj_src_tensors), std::back_inserter(ops), [](const auto & it) - { - return it.first; - }); - return ops; -} - -bool DependencyGraph::path_exists_from_tensor_to_op(Id src_tensor, Id dst_op) const -{ - for(auto child_op : dst_ops_from_tensor(src_tensor)) - { - if(path_exists_from_op_to_op(child_op, dst_op)) - { - return true; - } - } - return false; -} - -bool DependencyGraph::path_exists_from_op_to_op(Id src_op, Id dst_op) const -{ - if(src_op == dst_op) - { - return true; - } - if(is_in(src_op, get_dst_ops())) - { - return false; - } - for(auto child_tensor : dst_tensors(src_op)) - { - if(path_exists_from_tensor_to_op(child_tensor, dst_op)) - { - return true; - } - } - return false; -} - -std::vector<DependencyGraph::Id> DependencyGraph::all_tensors() const -{ - std::vector<Id> tensors{}; - std::transform(std::begin(_adj_src_ops), std::end(_adj_src_ops), std::back_inserter(tensors), [](const auto & it) - { - return it.first; - }); - return tensors; -} - -unsigned int DependencyGraph::number_of_ops() const -{ - return _adj_src_tensors.size(); -} - -unsigned int DependencyGraph::number_of_tensors() const -{ - return _adj_src_ops.size(); -} - -DependencyGraph::Id DependencyGraph::insert_new_tensor() -{ - Id new_tensor = _tensor_id.alloc(); - _adj_src_ops[new_tensor] = {}; - _adj_dst_ops[new_tensor] = {}; - return new_tensor; -} -DependencyGraph::Id DependencyGraph::insert_new_op() -{ - Id new_op = _operator_id.alloc(); - _adj_src_tensors[new_op] = {}; - _adj_dst_tensors[new_op] = {}; - return new_op; -} -void DependencyGraph::link_input(Id op, Id in_tensor) -{ - ARM_COMPUTE_ERROR_ON(!operator_exists(op)); - ARM_COMPUTE_ERROR_ON(!tensor_exists(in_tensor)); - ARM_COMPUTE_ERROR_ON(are_connected(op, in_tensor)); - _adj_src_tensors[op].push_back(in_tensor); - _adj_dst_ops[in_tensor].push_back(op); -} -void DependencyGraph::link_output(Id op, Id out_tensor) -{ - ARM_COMPUTE_ERROR_ON(!operator_exists(op)); - ARM_COMPUTE_ERROR_ON(!tensor_exists(out_tensor)); - ARM_COMPUTE_ERROR_ON(are_connected(op, out_tensor)); - _adj_dst_tensors[op].push_back(out_tensor); - _adj_src_ops[out_tensor].push_back(op); -} -bool DependencyGraph::tensor_exists(Id tensor) const -{ - return _adj_src_ops.find(tensor) != _adj_src_ops.end() && _adj_dst_ops.find(tensor) != _adj_dst_ops.end(); -} -bool DependencyGraph::operator_exists(Id op) const -{ - return _adj_src_tensors.find(op) != _adj_src_tensors.end() && _adj_dst_tensors.find(op) != _adj_dst_tensors.end(); -} - -bool DependencyGraph::is_src_tensor(Id tensor) const -{ - if(!tensor_exists(tensor)) - { - return false; - } - return _adj_src_ops.at(tensor).empty(); -} - -bool DependencyGraph::is_dst_tensor(Id tensor) const -{ - if(!tensor_exists(tensor)) - { - return false; - } - return _adj_dst_ops.at(tensor).empty(); -} -bool DependencyGraph::is_src_tensor_of(Id op, Id tensor) const -{ - if(!operator_exists(op) || !tensor_exists(tensor)) - { - return false; - } - const auto op_inputs = src_tensors(op); - return std::find(op_inputs.begin(), op_inputs.end(), tensor) != op_inputs.end(); -} -bool DependencyGraph::is_dst_tensor_of(Id op, Id tensor) const -{ - if(!operator_exists(op) || !tensor_exists(tensor)) - { - return false; - } - const auto op_outputs = dst_tensors(op); - return std::find(op_outputs.begin(), op_outputs.end(), tensor) != op_outputs.end(); -} -bool DependencyGraph::are_connected(Id op, Id tensor) const -{ - return is_src_tensor_of(op, tensor) || is_dst_tensor_of(op, tensor); -} -std::vector<DependencyGraph::Id> DependencyGraph::src_ops(Id op) const -{ - ARM_COMPUTE_ERROR_ON(!operator_exists(op)); - std::vector<Id> ops{}; - for(Id src_tensor : src_tensors(op)) - { - ops.insert(ops.end(), std::begin(_adj_src_ops.at(src_tensor)), std::end(_adj_src_ops.at(src_tensor))); - } - return ops; -} - -std::vector<DependencyGraph::Id> DependencyGraph::dst_ops(Id op) const -{ - ARM_COMPUTE_ERROR_ON(!operator_exists(op)); - std::vector<Id> ops{}; - for(Id dst_tensor : _adj_dst_tensors.at(op)) - { - ops.insert(ops.end(), std::begin(_adj_dst_ops.at(dst_tensor)), std::end(_adj_dst_ops.at(dst_tensor))); - } - return ops; -} - -std::pair<Status, std::vector<DependencyGraph::OpPack>> DependencyGraph::topological_sort() const -{ - // Incident degree (number of source operators to an op) - std::map<Id, unsigned int> in_degree{}; - std::set<Id> visited_ops{}; - std::deque<Id> zero_in_degree_ops{}; - std::vector<OpPack> sorted_op_packs{}; - for(auto op : all_ops()) - { - const auto degree = src_ops(op).size(); - in_degree[op] = degree; - if(degree == 0) - { - zero_in_degree_ops.push_back(op); - visited_ops.insert(op); - } - } - - while(!zero_in_degree_ops.empty()) - { - const Id op = zero_in_degree_ops.front(); - zero_in_degree_ops.pop_front(); - sorted_op_packs.push_back(OpPack{ op, src_tensors(op), dst_tensors(op) }); - - for(const auto next_op : dst_ops(op)) - { - if(in_degree[next_op] > 0) - { - in_degree[next_op]--; - } - if(in_degree[next_op] == 0 && visited_ops.find(next_op) == visited_ops.end()) - { - zero_in_degree_ops.push_back(next_op); - visited_ops.insert(op); - } - } - } - - // If there are remaining ops with in_degree > 0, then it's indication that there are cycles in the graph - Status st{}; - if(sorted_op_packs.size() != number_of_ops()) - { - st = Status{ ErrorCode::RUNTIME_ERROR, "Cycles or loops are not allowed in a DependencyGraph" }; - } - return std::make_pair(st, sorted_op_packs); -} - -} // namespace dynamic_fusion -} // namespace experimental -} // namespace arm_compute -#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
\ No newline at end of file diff --git a/src/core/experimental/dynamic_fusion/WorkloadImpl/ITensorDescPack.h b/src/core/experimental/dynamic_fusion/WorkloadImpl/ITensorDescPack.h deleted file mode 100644 index a4e4eaa3bb..0000000000 --- a/src/core/experimental/dynamic_fusion/WorkloadImpl/ITensorDescPack.h +++ /dev/null @@ -1,241 +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. - */ -#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION -#ifndef ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_ITENSORDESCPACK_H -#define ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_ITENSORDESCPACK_H - -#include <cstddef> -#include <unordered_map> -#include <vector> - -namespace arm_compute -{ -namespace experimental -{ -namespace dynamic_fusion -{ -template <typename TDesc> -class ITensorDescPack -{ -public: - struct PackElement - { - PackElement() = default; - ~PackElement() = default; - PackElement(const PackElement &) = default; - PackElement &operator=(const PackElement &) = default; - PackElement(PackElement &&) = default; - PackElement &operator=(PackElement &&) = default; - PackElement(int id, TDesc *tensor) - : id(id), tensor(tensor), ctensor(nullptr) - { - } - PackElement(int id, const TDesc *ctensor) - : id(id), tensor(nullptr), ctensor(ctensor) - { - } - - int id{ -1 }; - TDesc *tensor{ nullptr }; - const TDesc *ctensor{ nullptr }; - - friend bool operator==(const PackElement &elem0, const PackElement &elem1) - { - const bool same_ctensor = (elem0.tensor == nullptr && elem1.tensor == nullptr && elem0.ctensor != nullptr && elem1.ctensor != nullptr && *elem0.ctensor == *elem1.ctensor); - const bool same_tensor = (elem0.ctensor == nullptr && elem1.ctensor == nullptr && elem0.tensor != nullptr && elem1.tensor != nullptr && *elem0.tensor == *elem1.tensor); - - return elem0.id == elem1.id && (same_ctensor || same_tensor); - } - }; - -public: - /** Default Constructor */ - ITensorDescPack() = default; - ~ITensorDescPack() = default; - ITensorDescPack<TDesc>(const ITensorDescPack<TDesc> &other) = default; - ITensorDescPack<TDesc> &operator=(const ITensorDescPack<TDesc> &other) = default; - ITensorDescPack<TDesc>(ITensorDescPack<TDesc> &&other) = default; - ITensorDescPack<TDesc> &operator=(ITensorDescPack<TDesc> &&other) = default; - /** Initializer list Constructor */ - ITensorDescPack(std::initializer_list<PackElement> l) - : _pack{} - { - for(auto &e : l) - { - _pack[e.id] = e; - } - } - /** Add tensor to the pack - * - * @param[in] id ID/type of the tensor to add - * @param[in] tensor Tensor to add - */ - void add_tensor(int id, TDesc *tensor) - { - _pack[id] = PackElement(id, tensor); - } - - /** Add const tensor to the pack - * - * @param[in] id ID/type of the tensor to add - * @param[in] tensor Tensor to add - */ - void add_const_tensor(int id, const TDesc *tensor) - { - _pack[id] = PackElement(id, tensor); - } - /** Get tensor of a given id from the pac - * - * @param[in] id ID of tensor to extract - * - * @return The pointer to the tensor if exist and is non-const else nullptr - */ - TDesc *get_tensor(int id) - { - auto it = _pack.find(id); - return it != _pack.end() ? it->second.tensor : nullptr; - } - /** Get constant tensor of a given id - * - * @param[in] id ID of tensor to extract - * - * @return The pointer to the tensor if exist and is const else nullptr - */ - const TDesc *get_const_tensor(int id) const - { - auto it = _pack.find(id); - if(it != _pack.end()) - { - return it->second.ctensor != nullptr ? it->second.ctensor : it->second.tensor; - } - return nullptr; - } - /** Remove the tensor stored with the given id - * - * @param[in] id ID of tensor to remove - */ - void remove_tensor(int id) - { - _pack.erase(id); - } - /** Pack size accessor - * - * @return Number of tensors registered to the pack - */ - size_t size() const - { - return _pack.size(); - } - /** Checks if pack is empty - * - * @return True if empty else false - */ - bool empty() const - { - return _pack.empty(); - } - - /** Get the ACL_SRC_* tensors - * - * @return std::vector<TDesc *> - */ - std::vector<TDesc *> get_src_tensors() - { - std::vector<TDesc *> src_tensors{}; - for(int id = static_cast<int>(TensorType::ACL_SRC); id <= static_cast<int>(TensorType::ACL_SRC_END); ++id) - { - auto tensor = get_tensor(id); - if(tensor != nullptr) - { - src_tensors.push_back(tensor); - } - } - return src_tensors; - } - /** Get the const ACL_SRC_* tensors - * - * @return std::vector<const TDesc *> - */ - std::vector<const TDesc *> get_const_src_tensors() const - { - std::vector<const TDesc *> src_tensors{}; - for(int id = static_cast<int>(TensorType::ACL_SRC); id <= static_cast<int>(TensorType::ACL_SRC_END); ++id) - { - auto tensor = get_const_tensor(id); - if(tensor != nullptr) - { - src_tensors.push_back(tensor); - } - } - return src_tensors; - } - /** Get the ACL_DST_* tensors - * - * @return std::vector<TDesc *> - */ - std::vector<TDesc *> get_dst_tensors() - { - std::vector<TDesc *> dst_tensors{}; - for(int id = static_cast<int>(TensorType::ACL_DST); id <= static_cast<int>(TensorType::ACL_DST_END); ++id) - { - auto tensor = get_tensor(id); - if(tensor != nullptr) - { - dst_tensors.push_back(tensor); - } - } - return dst_tensors; - } - /** Get the const ACL_DST_* tensors - * - * @return std::vector<const TDesc *> - */ - std::vector<const TDesc *> get_const_dst_tensors() const - { - std::vector<const TDesc *> dst_tensors{}; - for(int id = static_cast<int>(TensorType::ACL_DST); id <= static_cast<int>(TensorType::ACL_DST_END); ++id) - { - auto tensor = get_const_tensor(id); - if(tensor != nullptr) - { - dst_tensors.push_back(tensor); - } - } - return dst_tensors; - } - - friend bool operator==(const ITensorDescPack<TDesc> &pack0, const ITensorDescPack<TDesc> &pack1) - { - return pack0._pack == pack1._pack; - } - -private: - std::unordered_map<int, PackElement> _pack{}; /**< Container with the packed tensors */ -}; - -} // namespace dynamic_fusion -} // namespace experimental -} // namespace arm_compute -#endif //ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_ITENSORDESCPACK_H -#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
\ No newline at end of file diff --git a/src/core/experimental/dynamic_fusion/WorkloadImpl/OperatorGraphImpl.cpp b/src/core/experimental/dynamic_fusion/WorkloadImpl/OperatorGraphImpl.cpp deleted file mode 100644 index 663b89e235..0000000000 --- a/src/core/experimental/dynamic_fusion/WorkloadImpl/OperatorGraphImpl.cpp +++ /dev/null @@ -1,423 +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. - */ -#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/runtime/CL/CLScheduler.h" - -#include "src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelGraph.h" -#include "src/core/experimental/dynamic_fusion/WorkloadImpl/OperatorGraphImpl.h" - -namespace arm_compute -{ -namespace experimental -{ -namespace dynamic_fusion -{ -namespace -{ -Status add_kernel_tensor(ClKernelGraph &k_graph, const OperatorGraph::Implementation &op_graph, const OpTensorContent &op_tensor, MemoryType memory_type, AuxMemoryInfo memory_info, - DependencyGraph::Id &id) -{ - ARM_COMPUTE_UNUSED(op_graph); - return k_graph.add_kernel_tensor(op_tensor.desc, memory_type, memory_info, id, op_tensor.id); -} - -Status add_kernel_tensor(ClKernelGraph &k_graph, const OperatorGraph::Implementation &op_graph, const OpTensorContent &op_tensor, DependencyGraph::Id &id) -{ - // For a tensor t - // 1. If t is a src tensor of the entire op graph, then it's Core. - // (Optimisation opportunity, if we guanrantee that all translate methods are called in topological order, we can always assign t to Core. - // Because even if the op is non-root (which would mean t should be an Aux tensor), the src tensors would be already be determined by the ancestor ops (topological order), and thus would not be overriden by it) - // 2. If t is a dst tensor of the entire op graph, then it's Core. - // 3. Aux tensor with Persistent and Prepare lifetime is manually specified - // 4. All other ts not captured by the above are assigned Aux, with lifetime of Temporary. - // kernel_graph.add_kernel_tensor(input->desc, ); - bool is_src_tensor_of_graph = is_in(op_tensor.id, op_graph.graph.src_tensors()); - bool is_dst_tensor_of_graph = is_in(op_tensor.id, op_graph.graph.dst_tensors()); - MemoryType memory_type; - AuxMemoryInfo memory_info; - if(is_src_tensor_of_graph || is_dst_tensor_of_graph) - { - memory_type = MemoryType::Core; - } - else - { - memory_type = MemoryType::Auxiliary; - memory_info.lifetime = AuxMemoryLifetime::Temporary; - memory_info.size = op_tensor.desc->total_size(); - } - return add_kernel_tensor(k_graph, op_graph, op_tensor, memory_type, memory_info, id); -} - -/** Get the suitable kernel size for using direct convolution method with NHWC data layout. - * - * @note Duplicate of the function with the same name in src/gpu/cl/operators/ClConv2d.cpp - * - * @note Direct convolution should be executed when the kernel has the spatial dimensions greater than or equal to the value returned by this function - * - * @param[in] gpu_target GPU target - * - * @return the suitable kernel size for using direct convolution method with NHWC data layout - */ -size_t get_direct_conv_kernel_threshold_nhwc(arm_compute::GPUTarget gpu_target) -{ - switch(gpu_target) - { - case arm_compute::GPUTarget::G76: - case arm_compute::GPUTarget::G77: - case arm_compute::GPUTarget::G78: - return 5; - case arm_compute::GPUTarget::G71: - case arm_compute::GPUTarget::G72: - case arm_compute::GPUTarget::MIDGARD: - case arm_compute::GPUTarget::BIFROST: - return 7; - default: - return 5; - } -} -} // namespace - -bool operator==(const OpTensor &t0, const OpTensor &t1) -{ - return std::make_tuple(t0.id()) == std::make_tuple(t1.id()); -} -bool operator==(const Conv2dDescriptor &conv2d0, const Conv2dDescriptor &conv2d1) -{ - return std::make_tuple(conv2d0.stride, conv2d0.dilation) == std::make_tuple(conv2d1.stride, conv2d1.dilation); -} - -bool operator==(const ElementwiseDescriptor &ed0, const ElementwiseDescriptor &ed1) -{ - return ed0.op == ed1.op; // Compare Arithmatic Operations of two ElementwiseDescriptor objects -} - -bool operator==(const FloorDescriptor &, const FloorDescriptor &) -{ - return std::make_tuple() == std::make_tuple(); // Currently two Floor ops are always the same -} - -bool Conv2dContent::operator==(const OperatorContent &other) const -{ - const auto converted = *utils::cast::polymorphic_downcast<const Conv2dContent *>(&other); - return desc == converted.desc; -} - -bool ElementwiseContent::operator==(const OperatorContent &other) const -{ - const auto converted = *utils::cast::polymorphic_downcast<const ElementwiseContent *>(&other); - return desc == converted.desc; -} - -bool FloorContent::operator==(const OperatorContent &other) const -{ - const auto converted = *utils::cast::polymorphic_downcast<const FloorContent *>(&other); - return desc == converted.desc; -} - -ConvolutionMethod Conv2dContent::select_conv_method(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const Conv2dDescriptor &conv2d_desc, const GPUTarget gpu_target) -{ - // Modified from ClConv2d::get_convolution_method - - ARM_COMPUTE_ERROR_ON_NULLPTR(src); - ARM_COMPUTE_ERROR_ON_NULLPTR(dst); - ARM_COMPUTE_ERROR_ON_NULLPTR(weights); - - const PadStrideInfo legacy_pad_stride(conv2d_desc.stride.x(), conv2d_desc.stride.y(), conv2d_desc.pad.left, conv2d_desc.pad.right, conv2d_desc.pad.top, conv2d_desc.pad.bottom, DimensionRoundingType{}); - const Size2D dilation = conv2d_desc.dilation; - - const size_t idx_w = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH); - const size_t idx_h = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT); - const size_t idx_c = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::CHANNEL); - - /* Input spatial dims, kernel size, IFM/OFM, conv info*/ - using ConvolutionConfiguration = std::tuple<Size2D, Size2D, Size2D, PadStrideInfo, DataLayout>; - using ConfigurationMethod = std::pair<ConvolutionConfiguration, ConvolutionMethod>; - - const std::vector<ConfigurationMethod> known_configs = - { - // Alexnet - ConfigurationMethod(ConvolutionConfiguration(Size2D(27U, 27U), Size2D(5U, 5U), Size2D(48U, 128U), PadStrideInfo(1U, 1U, 2U, 2U), DataLayout::NCHW), ConvolutionMethod::DIRECT), - // VGG16 / VGG19 - ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 64U), PadStrideInfo(1U, 1U, 1U, 1U), DataLayout::NCHW), ConvolutionMethod::DIRECT), - // Mobilenet 224 - ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NCHW), ConvolutionMethod::GEMM), - // Mobilenet 160 - ConfigurationMethod(ConvolutionConfiguration(Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NCHW), ConvolutionMethod::GEMM), - // Mobilenet 224 - ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NHWC), ConvolutionMethod::GEMM), - // Mobilenet 160 - ConfigurationMethod(ConvolutionConfiguration(Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), DataLayout::NHWC), ConvolutionMethod::GEMM), - }; - - const auto find_config = [&](ConfigurationMethod c) - { - const ConvolutionConfiguration config = c.first; - const PadStrideInfo info = std::get<3>(config); - const DataLayout data_layout = std::get<4>(config); - - return std::get<0>(config) == Size2D(src->dimension(idx_w), src->dimension(idx_h)) && std::get<1>(config) == Size2D(weights->dimension(idx_w), weights->dimension(idx_h)) - && std::get<2>(config) == Size2D(weights->dimension(idx_c), weights->dimension(3)) && info.pad_top() == legacy_pad_stride.pad_top() && info.pad_right() == legacy_pad_stride.pad_right() - && info.pad_bottom() == legacy_pad_stride.pad_bottom() && info.pad_left() == legacy_pad_stride.pad_left() && info.stride() == legacy_pad_stride.stride() && (data_layout == src->data_layout()); - }; - - std::vector<ConfigurationMethod>::const_iterator found; - if((found = std::find_if(known_configs.begin(), known_configs.end(), find_config)) != known_configs.end()) - { - return (*found).second; - } - - if(dilation != Size2D(1U, 1U)) - { - return ConvolutionMethod::GEMM; - } - else - { - if(src->data_layout() == DataLayout::NCHW) - { - ARM_COMPUTE_ERROR("NCHW not supported"); - } - else - { - const bool is_direct_valid = bool(ClDirectConv2dKernel::validate(src, weights, nullptr, dst, ClDirectConv2dKernelDescriptor{ conv2d_desc })); - const size_t kernel_sz_direct_conv_thr = get_direct_conv_kernel_threshold_nhwc(gpu_target); - - // SRGAN case - if((src->dimension(idx_h) > 720U) && (dst->dimension(idx_h) > 720U) && (weights->dimension(idx_h) == 9) && (conv2d_desc.pad.top < 3) - && is_direct_valid) - { - return ConvolutionMethod::DIRECT; - } - - // Floating-point case: GeMM/Direct - if(is_data_type_float(src->data_type())) - { - // Get dst shape - TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, legacy_pad_stride); - const bool is_large_kernel_sz = (weights->dimension(idx_w) >= kernel_sz_direct_conv_thr) && (weights->dimension(idx_h) >= kernel_sz_direct_conv_thr); - const bool is_ifm_ge_16 = src->dimension(idx_c) >= 16; - const bool is_ofm_lte_8 = weights->dimension(3U) <= 8; - const bool workload_gte_8192 = (output_shape[0] * output_shape[1] * output_shape[2]) / 16 >= 8192; - const bool is_ifm_gt_ofm = src->dimension(idx_c) > weights->dimension(3U); - - // Direct convolution case - if(is_direct_valid) - { - if((gpu_target == arm_compute::GPUTarget::G71 || gpu_target == arm_compute::GPUTarget::G72 || gpu_target == arm_compute::GPUTarget::MIDGARD)) - { - if(is_large_kernel_sz && is_ifm_ge_16 && is_ifm_gt_ofm) - { - return ConvolutionMethod::DIRECT; - } - } - else - { - if((is_large_kernel_sz && workload_gte_8192 && is_ifm_ge_16) || (is_ofm_lte_8 && is_ifm_ge_16)) - { - return ConvolutionMethod::DIRECT; - } - } - } - - // Default case - return ConvolutionMethod::GEMM; - } - - // Generic case for quantized. Only GeMM - return ConvolutionMethod::GEMM; - } - } - return ConvolutionMethod::DIRECT; -} - -Status Conv2dContent::translate(ClKernelGraph &kernel_graph) 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 dst = _tensors.get_const_tensor(TensorType::ACL_DST_0); - const auto method = forced_method_enabled ? forced_method : Conv2dContent::select_conv_method(input->desc, weight->desc, dst->desc, desc, CLScheduler::get().target()); - switch(method) - { - case ConvolutionMethod::DIRECT: - { - return translate_direct_conv2d(kernel_graph); - } - default: - { - ARM_COMPUTE_RETURN_ERROR_MSG("Not implemented"); - } - } - return Status{}; -} -Status Conv2dContent::translate_direct_conv2d(ClKernelGraph &kernel_graph) 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); - - ITensorDescPack<ClKernelTensor> tensors; - - DependencyGraph::Id input_id; - auto st = add_kernel_tensor(kernel_graph, *_graph, *input, input_id); - ARM_COMPUTE_RETURN_ON_ERROR(st); - tensors.add_const_tensor(ACL_SRC_0, kernel_graph.get_tensor(input_id)); - - DependencyGraph::Id weight_id; - st = add_kernel_tensor(kernel_graph, *_graph, *weight, weight_id); - ARM_COMPUTE_RETURN_ON_ERROR(st); - tensors.add_const_tensor(ACL_SRC_1, kernel_graph.get_tensor(weight_id)); - - if(bias != nullptr) - { - DependencyGraph::Id bias_id; - st = add_kernel_tensor(kernel_graph, *_graph, *bias, bias_id); - ARM_COMPUTE_RETURN_ON_ERROR(st); - tensors.add_const_tensor(ACL_SRC_2, kernel_graph.get_tensor(bias_id)); - } - - DependencyGraph::Id dst_id; - st = add_kernel_tensor(kernel_graph, *_graph, *dst, dst_id); - ARM_COMPUTE_RETURN_ON_ERROR(st); - tensors.add_const_tensor(ACL_DST_0, kernel_graph.get_tensor(dst_id)); - - DependencyGraph::Id direct_conv2d_id; - const auto kernel_desc = ClDirectConv2dKernelDescriptor{ desc }; - - st = ClDirectConv2dKernel::validate(input->desc, weight->desc, bias == nullptr ? nullptr : bias->desc, dst->desc, kernel_desc); - ARM_COMPUTE_RETURN_ON_ERROR(st); - - ClKernelConfig config{ UnitWorkloadStage{ UnitWorkloadStage::Stage::Run }, TileDescriptor{}, StoreType::TStoreIndirectWidthSelect }; - st = kernel_graph.add_kernel<ClDirectConv2dKernel>(config, kernel_desc, tensors, direct_conv2d_id); - ARM_COMPUTE_RETURN_ON_ERROR(st); - ARM_COMPUTE_UNUSED(direct_conv2d_id); - - return Status{}; -} - -Status ElementwiseContent::translate(ClKernelGraph &kernel_graph) 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); - - ITensorDescPack<ClKernelTensor> tensors; - - DependencyGraph::Id lhs_id; - auto st = add_kernel_tensor(kernel_graph, *_graph, *lhs, lhs_id); - ARM_COMPUTE_RETURN_ON_ERROR(st); - tensors.add_const_tensor(ACL_SRC_0, kernel_graph.get_tensor(lhs_id)); - - DependencyGraph::Id rhs_id; - st = add_kernel_tensor(kernel_graph, *_graph, *rhs, rhs_id); - ARM_COMPUTE_RETURN_ON_ERROR(st); - tensors.add_const_tensor(ACL_SRC_1, kernel_graph.get_tensor(rhs_id)); - - DependencyGraph::Id dst_id; - st = add_kernel_tensor(kernel_graph, *_graph, *dst, dst_id); - ARM_COMPUTE_RETURN_ON_ERROR(st); - tensors.add_const_tensor(ACL_DST_0, kernel_graph.get_tensor(dst_id)); - - DependencyGraph::Id add_id; - ClKernelConfig config{ UnitWorkloadStage{ UnitWorkloadStage::Stage::Run }, TileDescriptor{}, StoreType::TStoreIndirectWidthSelect }; - - st = ClElementwiseKernel::validate(lhs->desc, rhs->desc, dst->desc); - ARM_COMPUTE_RETURN_ON_ERROR(st); - - st = kernel_graph.add_kernel<ClElementwiseKernel>(config, ClElementwiseKernelDescriptor{ desc }, tensors, add_id); - ARM_COMPUTE_RETURN_ON_ERROR(st); - ARM_COMPUTE_UNUSED(add_id); - - return Status{}; -} - -Status FloorContent::translate(ClKernelGraph &kernel_graph) const -{ - const auto src = _tensors.get_const_tensor(TensorType::ACL_SRC_0); - const auto dst = _tensors.get_const_tensor(TensorType::ACL_DST_0); - ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); - - ITensorDescPack<ClKernelTensor> tensors; - - DependencyGraph::Id src_id; - auto st = add_kernel_tensor(kernel_graph, *_graph, *src, src_id); - ARM_COMPUTE_RETURN_ON_ERROR(st); - tensors.add_const_tensor(ACL_SRC_0, kernel_graph.get_tensor(src_id)); - - DependencyGraph::Id dst_id; - st = add_kernel_tensor(kernel_graph, *_graph, *dst, dst_id); - ARM_COMPUTE_RETURN_ON_ERROR(st); - tensors.add_const_tensor(ACL_DST_0, kernel_graph.get_tensor(dst_id)); - - DependencyGraph::Id add_id; - ClKernelConfig config{ UnitWorkloadStage{ UnitWorkloadStage::Stage::Run }, TileDescriptor{}, StoreType::TStoreIndirectWidthSelect }; - - st = ClFloorKernel::validate(src->desc, dst->desc); - ARM_COMPUTE_RETURN_ON_ERROR(st); - - st = kernel_graph.add_kernel<ClFloorKernel>(config, ClFloorKernelDescriptor{ desc }, tensors, add_id); - ARM_COMPUTE_RETURN_ON_ERROR(st); - - return Status{}; -} - -std::vector<const OperatorContent *> traverse(const OperatorGraph::Implementation &graph) -{ - std::vector<const OperatorContent *> ops; - const auto sorted = graph.graph.topological_sort(); - for(const auto &pack : sorted.second) - { - ops.push_back(graph.operators.at(pack.op).get()); - } - return ops; -} - -std::vector<OperatorContent *> traverse(OperatorGraph::Implementation &graph) -{ - std::vector<OperatorContent *> ops; - const auto sorted = graph.graph.topological_sort(); - for(const auto &pack : sorted.second) - { - ops.push_back(graph.operators.at(pack.op).get()); - } - return ops; -} - -Status translate(ClKernelGraph &kernel_graph, const OperatorGraph::Implementation &op_graph) -{ - for(const auto &op : traverse(op_graph)) - { - const auto st = op->translate(kernel_graph); - ARM_COMPUTE_RETURN_ON_ERROR(st); - } - return Status{}; -} - -} // namespace dynamic_fusion -} // namespace experimental -} // namespace arm_compute -#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */ diff --git a/src/core/experimental/dynamic_fusion/WorkloadImpl/OperatorGraphImpl.h b/src/core/experimental/dynamic_fusion/WorkloadImpl/OperatorGraphImpl.h deleted file mode 100644 index b303cdb9fc..0000000000 --- a/src/core/experimental/dynamic_fusion/WorkloadImpl/OperatorGraphImpl.h +++ /dev/null @@ -1,252 +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. - */ -#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION -#ifndef ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_OPERATORGRAPHIMPL -#define ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_OPERATORGRAPHIMPL - -#include "arm_compute/core/experimental/ClWorkload.h" -#include "src/core/experimental/dynamic_fusion/WorkloadImpl/ITensorDescPack.h" - -#include "support/Cast.h" -#include "support/DeepCopy.h" - -#include <map> -#include <tuple> -#include <type_traits> - -namespace arm_compute -{ -namespace experimental -{ -namespace dynamic_fusion -{ -enum class OperatorComplexity -{ - Complex = 0, - Simple -}; - -struct ClKernelGraph; -struct OpTensorContent -{ -public: - using Id = DependencyGraph::Id; - OpTensorContent() = default; - OpTensorContent(Id id) - : id{ id }, desc{} - { - } - OpTensorContent(Id id, ITensorInfo *desc) - : id{ id }, desc{ desc } - { - } - ~OpTensorContent() = default; - OpTensorContent(const OpTensorContent &) = default; - OpTensorContent &operator=(const OpTensorContent &) = default; - OpTensorContent(OpTensorContent &&) = default; - OpTensorContent &operator=(OpTensorContent &&) = default; - bool operator==(const OpTensorContent &other) const - { - return desc == other.desc; - } - - const ITensorInfo *get_tensor_info() const - { - return desc; - } - ITensorInfo *get_tensor_info() - { - return desc; - } - - Id id{}; - ITensorInfo *desc{}; -}; - -struct OperatorContent -{ -public: - using Id = DependencyGraph::Id; - OperatorContent() = default; - OperatorContent(const OperatorGraph::Implementation *graph, Id id, const ITensorDescPack<OpTensorContent> &tensors) - : _graph{ graph }, _id{ id }, _tensors{ tensors } - { - } - OperatorContent(const OperatorContent &op) = default; - OperatorContent &operator=(const OperatorContent &op) = default; - OperatorContent(OperatorContent &&op) = default; - OperatorContent &operator=(OperatorContent &&op) = default; - virtual ~OperatorContent() = default; - virtual OperatorComplexity complexity() const = 0; - virtual bool operator==(const OperatorContent &other) const = 0; - virtual Status translate(ClKernelGraph &kernel_graph) const = 0; - -protected: - const OperatorGraph::Implementation *_graph {}; - Id _id{}; - ITensorDescPack<OpTensorContent> _tensors{}; -}; - -struct Conv2dContent : public OperatorContent -{ -public: - Conv2dContent() = default; - Conv2dContent(const OperatorGraph::Implementation *graph, Id id, const Conv2dDescriptor &desc, const ITensorDescPack<OpTensorContent> &tensors) - : OperatorContent(graph, id, tensors), desc(desc), forced_method(), forced_method_enabled(false) - { - } - // Temporary. Do not need to pass ConvolutionMethod - Conv2dContent(const OperatorGraph::Implementation *graph, Id id, const Conv2dDescriptor &desc, const ITensorDescPack<OpTensorContent> &tensors, ConvolutionMethod method) - : OperatorContent(graph, id, tensors), desc(desc), forced_method(method), forced_method_enabled(true) - { - } - ~Conv2dContent() = default; - Conv2dContent(const Conv2dContent &) = default; - Conv2dContent &operator=(const Conv2dContent &) = default; - Conv2dContent(Conv2dContent &&) = default; - Conv2dContent &operator=(Conv2dContent &&) = default; - bool operator==(const OperatorContent &other) const override; - OperatorComplexity complexity() const override - { - return OperatorComplexity::Complex; - } - void set_method(ConvolutionMethod method) - { - forced_method_enabled = true; - forced_method = method; - } - - Status translate(ClKernelGraph &kernel_graph) const override; - /** Replicate heuristics of @ref ClConv2d::get_convolution_method(), except that non-supported data types and data layouts are removed from the heuristics - * - * @param src - * @param weights - * @param dst - * @param conv2d_desc - * @param gpu_target - * @return ConvolutionMethod - */ - static ConvolutionMethod select_conv_method(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const Conv2dDescriptor &conv2d_desc, const GPUTarget gpu_target); - - Conv2dDescriptor desc{}; - ConvolutionMethod forced_method{ ConvolutionMethod::GEMM_CONV2D }; - bool forced_method_enabled{ false }; - -private: - Status translate_direct_conv2d(ClKernelGraph &kernel_graph) const; -}; - -class ElementwiseContent : public OperatorContent -{ -public: - ElementwiseContent() = default; - ElementwiseContent(const OperatorGraph::Implementation *graph, Id id, const ElementwiseDescriptor &desc, const ITensorDescPack<OpTensorContent> &tensors) - : OperatorContent(graph, id, tensors), desc(desc) - { - } - ~ElementwiseContent() = default; - ElementwiseContent(const ElementwiseContent &) = default; - ElementwiseContent &operator=(const ElementwiseContent &) = default; - ElementwiseContent(ElementwiseContent &&) = default; - ElementwiseContent &operator=(ElementwiseContent &&) = default; - bool operator==(const OperatorContent &other) const override; - OperatorComplexity complexity() const override - { - return OperatorComplexity::Simple; - } - Status translate(ClKernelGraph &kernel_graph) const override; - -private: - ElementwiseDescriptor desc{}; -}; - -class FloorContent : public OperatorContent -{ -public: - FloorContent() = default; - FloorContent(const OperatorGraph::Implementation *graph, Id id, const FloorDescriptor &desc, const ITensorDescPack<OpTensorContent> &tensors) - : OperatorContent(graph, id, tensors), desc(desc) - { - } - ~FloorContent() = default; - FloorContent(const FloorContent &) = default; - FloorContent &operator=(const FloorContent &) = default; - FloorContent(FloorContent &&) = default; - FloorContent &operator=(FloorContent &&) = default; - bool operator==(const OperatorContent &other) const override; - OperatorComplexity complexity() const override - { - return OperatorComplexity::Simple; - } - Status translate(ClKernelGraph &kernel_graph) const override; - -private: - FloorDescriptor desc{}; -}; - -struct OperatorGraph::Implementation -{ -public: - template <typename ContentT, typename... Args> - void add_node(Operator::Id id, Args &&... args) - { - operators[id] = utils::memory::make_deep_unique<OperatorContent, ContentT>(this, id, std::forward<Args>(args)...); - } - - template <typename... Args> - void add_tensor(OpTensor::Id id, Args &&... args) - { - tensors[id] = utils::memory::make_deep_unique<OpTensorContent, OpTensorContent>(id, std::forward<Args>(args)...); - } - - using Dependency = DependencyGraph; - using OperatorMap = std::map<Operator::Id, utils::memory::deep_unique_ptr<OperatorContent>>; - using OpTensorMap = std::map<OpTensor::Id, utils::memory::deep_unique_ptr<OpTensorContent>>; - - Implementation() = default; - ~Implementation() = default; - - friend bool operator==(const OperatorGraph::Implementation &graph0, const OperatorGraph::Implementation &graph1) - { - return graph0.graph == graph1.graph && graph0.operators == graph1.operators && graph0.tensors == graph1.tensors; - } - - Dependency graph{}; - OperatorMap operators{}; - OpTensorMap tensors{}; - Status status{}; -}; - -std::vector<const OperatorContent *> traverse(const OperatorGraph::Implementation &graph); - -std::vector<OperatorContent *> traverse(OperatorGraph::Implementation &graph); - -Status translate(ClKernelGraph &kernel_graph, const OperatorGraph::Implementation &op_graph); - -} // namespace dynamic_fusion -} // namespace experimental -} // namespace arm_compute - -#endif //ARM_COMPUTE_EXPERIMENTAL_DYNAMICFUSION_OPERATORGRAPHIMPL -#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
\ No newline at end of file |