| //===- InlineModelFeatureMaps.h - common model runner defs ------*- C++ -*-===// |
| // |
| // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. |
| // See https://llvm.org/LICENSE.txt for license information. |
| // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception |
| // |
| //===----------------------------------------------------------------------===// |
| // |
| |
| #ifndef LLVM_ANALYSIS_INLINEMODELFEATUREMAPS_H |
| #define LLVM_ANALYSIS_INLINEMODELFEATUREMAPS_H |
| |
| #include "llvm/Analysis/TensorSpec.h" |
| |
| #include <array> |
| #include <string> |
| #include <vector> |
| |
| namespace llvm { |
| |
| // List of cost features. A "cost" feature is a summand of the heuristic-based |
| // inline cost, and we define them separately to preserve the original heuristic |
| // behavior. |
| #define INLINE_COST_FEATURE_ITERATOR(M) \ |
| M(int64_t, {1}, sroa_savings, \ |
| "Savings from SROA (scalar replacement of aggregates)") \ |
| M(int64_t, {1}, sroa_losses, \ |
| "Losses from SROA (scalar replacement of aggregates)") \ |
| M(int64_t, {1}, load_elimination, "Cost of load elimination in the call") \ |
| M(int64_t, {1}, call_penalty, \ |
| "Accumulation of penalty applied to call sites when inlining") \ |
| M(int64_t, {1}, call_argument_setup, \ |
| "Accumulation of call argument setup costs") \ |
| M(int64_t, {1}, load_relative_intrinsic, \ |
| "Accumulation of costs of loading relative intrinsics") \ |
| M(int64_t, {1}, lowered_call_arg_setup, \ |
| "Accumulation of cost of lowered call argument setups") \ |
| M(int64_t, {1}, indirect_call_penalty, \ |
| "Accumulation of costs for indirect calls") \ |
| M(int64_t, {1}, jump_table_penalty, "Accumulation of costs for jump tables") \ |
| M(int64_t, {1}, case_cluster_penalty, \ |
| "Accumulation of costs for case clusters") \ |
| M(int64_t, {1}, switch_penalty, \ |
| "Accumulation of costs for switch statements") \ |
| M(int64_t, {1}, unsimplified_common_instructions, \ |
| "Costs from unsimplified common instructions") \ |
| M(int64_t, {1}, num_loops, "Number of loops in the caller") \ |
| M(int64_t, {1}, dead_blocks, "Number of dead blocks in the caller") \ |
| M(int64_t, {1}, simplified_instructions, \ |
| "Number of simplified instructions") \ |
| M(int64_t, {1}, constant_args, \ |
| "Number of constant arguments in the call site") \ |
| M(int64_t, {1}, constant_offset_ptr_args, \ |
| "Number of constant offset pointer args in the call site") \ |
| M(int64_t, {1}, callsite_cost, "Estimated cost of the call site") \ |
| M(int64_t, {1}, cold_cc_penalty, "Penalty for a cold calling convention") \ |
| M(int64_t, {1}, last_call_to_static_bonus, \ |
| "Bonus for being the last call to static") \ |
| M(int64_t, {1}, is_multiple_blocks, \ |
| "Boolean; is the Callee multiple blocks") \ |
| M(int64_t, {1}, nested_inlines, \ |
| "Would the default inliner perfom nested inlining") \ |
| M(int64_t, {1}, nested_inline_cost_estimate, \ |
| "Estimate of the accumulated cost of nested inlines") \ |
| M(int64_t, {1}, threshold, "Threshold for the heuristic inliner") |
| |
| // clang-format off |
| enum class InlineCostFeatureIndex : size_t { |
| #define POPULATE_INDICES(DTYPE, SHAPE, NAME, DOC) NAME, |
| INLINE_COST_FEATURE_ITERATOR(POPULATE_INDICES) |
| #undef POPULATE_INDICES |
| |
| NumberOfFeatures |
| }; |
| // clang-format on |
| |
| using InlineCostFeatures = |
| std::array<int, |
| static_cast<size_t>(InlineCostFeatureIndex::NumberOfFeatures)>; |
| |
| constexpr bool isHeuristicInlineCostFeature(InlineCostFeatureIndex Feature) { |
| return Feature != InlineCostFeatureIndex::sroa_savings && |
| Feature != InlineCostFeatureIndex::is_multiple_blocks && |
| Feature != InlineCostFeatureIndex::dead_blocks && |
| Feature != InlineCostFeatureIndex::simplified_instructions && |
| Feature != InlineCostFeatureIndex::constant_args && |
| Feature != InlineCostFeatureIndex::constant_offset_ptr_args && |
| Feature != InlineCostFeatureIndex::nested_inlines && |
| Feature != InlineCostFeatureIndex::nested_inline_cost_estimate && |
| Feature != InlineCostFeatureIndex::threshold; |
| } |
| |
| // List of features. Each feature is defined through a triple: |
| // - the name of an enum member, which will be the feature index |
| // - a textual name, used for Tensorflow model binding (so it needs to match the |
| // names used by the Tensorflow model) |
| // - a documentation description. Currently, that is not used anywhere |
| // programmatically, and serves as workaround to inability of inserting comments |
| // in macros. |
| #define INLINE_FEATURE_ITERATOR(M) \ |
| M(int64_t, {1}, callee_basic_block_count, \ |
| "number of basic blocks of the callee") \ |
| M(int64_t, {1}, callsite_height, \ |
| "position of the call site in the original call graph - measured from " \ |
| "the farthest SCC") \ |
| M(int64_t, {1}, node_count, \ |
| "total current number of defined functions in the module") \ |
| M(int64_t, {1}, nr_ctant_params, \ |
| "number of parameters in the call site that are constants") \ |
| M(int64_t, {1}, cost_estimate, "total cost estimate (threshold - free)") \ |
| M(int64_t, {1}, edge_count, "total number of calls in the module") \ |
| M(int64_t, {1}, caller_users, \ |
| "number of module-internal users of the caller, +1 if the caller is " \ |
| "exposed externally") \ |
| M(int64_t, {1}, caller_conditionally_executed_blocks, \ |
| "number of blocks reached from a conditional instruction, in the caller") \ |
| M(int64_t, {1}, caller_basic_block_count, \ |
| "number of basic blocks in the caller") \ |
| M(int64_t, {1}, callee_conditionally_executed_blocks, \ |
| "number of blocks reached from a conditional instruction, in the callee") \ |
| M(int64_t, {1}, callee_users, \ |
| "number of module-internal users of the callee, +1 if the callee is " \ |
| "exposed externally") |
| |
| // clang-format off |
| enum class FeatureIndex : size_t { |
| #define POPULATE_INDICES(DTYPE, SHAPE, NAME, COMMENT) NAME, |
| // InlineCost features - these must come first |
| INLINE_COST_FEATURE_ITERATOR(POPULATE_INDICES) |
| |
| // Non-cost features |
| INLINE_FEATURE_ITERATOR(POPULATE_INDICES) |
| #undef POPULATE_INDICES |
| |
| NumberOfFeatures |
| }; |
| // clang-format on |
| |
| constexpr FeatureIndex |
| inlineCostFeatureToMlFeature(InlineCostFeatureIndex Feature) { |
| return static_cast<FeatureIndex>(static_cast<size_t>(Feature)); |
| } |
| |
| constexpr size_t NumberOfFeatures = |
| static_cast<size_t>(FeatureIndex::NumberOfFeatures); |
| |
| extern const std::vector<TensorSpec> FeatureMap; |
| |
| extern const char *const DecisionName; |
| extern const TensorSpec InlineDecisionSpec; |
| extern const char *const DefaultDecisionName; |
| extern const TensorSpec DefaultDecisionSpec; |
| extern const char *const RewardName; |
| |
| using InlineFeatures = std::vector<int64_t>; |
| |
| } // namespace llvm |
| #endif // LLVM_ANALYSIS_INLINEMODELFEATUREMAPS_H |