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Summary: This implementation uses a pre-trained model which is statically compiled into a native function. RFC: http://lists.llvm.org/pipermail/llvm-dev/2020-April/140763.html Reviewers: davidxl, jdoerfert, dblaikie Subscribers: mgorny, eraman, hiraditya, arphaman, llvm-commits Tags: #llvm Differential Revision: https://reviews.llvm.org/D81515
71 lines
3.4 KiB
C++
71 lines
3.4 KiB
C++
//===- InlineModelFeatureMaps.h - common model runner defs ------*- C++ -*-===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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#ifndef LLVM_ANALYSIS_INLINEMODELFEATUREMAPS_H
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#define LLVM_ANALYSIS_INLINEMODELFEATUREMAPS_H
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#include <array>
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#include <string>
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#include <vector>
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namespace llvm {
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// List of features. Each feature is defined through a triple:
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// - the name of an enum member, which will be the feature index
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// - a textual name, used for Tensorflow model binding (so it needs to match the
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// names used by the Tensorflow model)
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// - a documentation description. Currently, that is not used anywhere
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// programmatically, and serves as workaround to inability of inserting comments
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// in macros.
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#define INLINE_FEATURE_ITERATOR(M) \
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M(CalleeBasicBlockCount, "callee_basic_block_count", \
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"number of basic blocks of the callee") \
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M(CallSiteHeight, "callsite_height", \
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"position of the call site in the original call graph - measured from " \
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"the farthest SCC") \
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M(NodeCount, "node_count", \
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"total current number of defined functions in the module") \
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M(NrCtantParams, "nr_ctant_params", \
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"number of parameters in the call site that are constants") \
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M(CostEstimate, "cost_estimate", "total cost estimate (threshold - free)") \
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M(EdgeCount, "edge_count", \
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"number of module-internal users of the caller, +1 if the caller is " \
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"exposed externally") \
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M(CallerUsers, "caller_users", \
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"number of blocks reached from a conditional instruction, in the caller") \
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M(CallerConditionallyExecutedBlocks, "caller_conditionally_executed_blocks", \
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"number of blocks reached from a conditional instruction, in the caller") \
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M(CallerBasicBlockCount, "caller_basic_block_count", \
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"number of basic blocks in the caller") \
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M(CalleeConditionallyExecutedBlocks, "callee_conditionally_executed_blocks", \
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"number of blocks reached from a conditional instruction, in the callee") \
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M(CalleeUsers, "callee_users", \
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"number of blocks reached from a conditional instruction, in the callee")
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enum class FeatureIndex : size_t {
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#define POPULATE_INDICES(INDEX_NAME, NAME, COMMENT) INDEX_NAME,
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INLINE_FEATURE_ITERATOR(POPULATE_INDICES)
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#undef POPULATE_INDICES
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NumberOfFeatures
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};
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constexpr size_t NumberOfFeatures =
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static_cast<size_t>(FeatureIndex::NumberOfFeatures);
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extern const std::array<std::string, NumberOfFeatures> FeatureNameMap;
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extern const char *const DecisionName;
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extern const char *const DefaultDecisionName;
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extern const char *const RewardName;
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using InlineFeatures = std::vector<int64_t>;
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} // namespace llvm
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#endif // LLVM_ANALYSIS_INLINEMODELFEATUREMAPS_H
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