[clang-tools-extra] a8b55b6 - [clangd] Use Decision Forest to score code completions.
Utkarsh Saxena via cfe-commits
cfe-commits at lists.llvm.org
Mon Sep 28 09:59:39 PDT 2020
Author: Utkarsh Saxena
Date: 2020-09-28T18:59:29+02:00
New Revision: a8b55b6939a5962d5b2bf1a57980562d6f3045e5
URL: https://github.com/llvm/llvm-project/commit/a8b55b6939a5962d5b2bf1a57980562d6f3045e5
DIFF: https://github.com/llvm/llvm-project/commit/a8b55b6939a5962d5b2bf1a57980562d6f3045e5.diff
LOG: [clangd] Use Decision Forest to score code completions.
By default clangd will score a code completion item using heuristics model.
Scoring can be done by Decision Forest model by passing `--ranking_model=decision_forest` to
clangd.
Features omitted from the model:
- `NameMatch` is excluded because the final score must be multiplicative in `NameMatch` to allow rescoring by the editor.
- `NeedsFixIts` is excluded because the generating dataset that needs 'fixits' is non-trivial.
There are multiple ways (heuristics) to combine the above two features with the prediction of the DF:
- `NeedsFixIts` is used as is with a penalty of `0.5`.
Various alternatives of combining NameMatch `N` and Decision forest Prediction `P`
- N * scale(P, 0, 1): Linearly scale the output of model to range [0, 1]
- N * a^P:
- More natural: Prediction of each Decision Tree can be considered as a multiplicative boost (like NameMatch)
- Ordering is independent of the absolute value of P. Order of two items is proportional to `a^{difference in model prediction score}`. Higher `a` gives higher weightage to model output as compared to NameMatch score.
Baseline MRR = 0.619
MRR for various combinations:
N * P = 0.6346, advantage%=2.5768
N * 1.1^P = 0.6600, advantage%=6.6853
N * **1.2**^P = 0.6669, advantage%=**7.8005**
N * **1.3**^P = 0.6668, advantage%=**7.7795**
N * **1.4**^P = 0.6659, advantage%=**7.6270**
N * 1.5^P = 0.6646, advantage%=7.4200
N * 1.6^P = 0.6636, advantage%=7.2671
N * 1.7^P = 0.6629, advantage%=7.1450
N * 2^P = 0.6612, advantage%=6.8673
N * 2.5^P = 0.6598, advantage%=6.6491
N * 3^P = 0.6590, advantage%=6.5242
N * scaled[0, 1] = 0.6465, advantage%=4.5054
Differential Revision: https://reviews.llvm.org/D88281
Added:
Modified:
clang-tools-extra/clangd/CodeComplete.cpp
clang-tools-extra/clangd/CodeComplete.h
clang-tools-extra/clangd/Quality.cpp
clang-tools-extra/clangd/Quality.h
clang-tools-extra/clangd/tool/ClangdMain.cpp
clang-tools-extra/clangd/unittests/CodeCompleteTests.cpp
Removed:
################################################################################
diff --git a/clang-tools-extra/clangd/CodeComplete.cpp b/clang-tools-extra/clangd/CodeComplete.cpp
index 4d5b2975c9ae..90e793f11564 100644
--- a/clang-tools-extra/clangd/CodeComplete.cpp
+++ b/clang-tools-extra/clangd/CodeComplete.cpp
@@ -1625,6 +1625,43 @@ class CodeCompleteFlow {
return Filter->match(C.Name);
}
+ CodeCompletion::Scores
+ evaluateCompletion(const SymbolQualitySignals &Quality,
+ const SymbolRelevanceSignals &Relevance) {
+ using RM = CodeCompleteOptions::CodeCompletionRankingModel;
+ CodeCompletion::Scores Scores;
+ switch (Opts.RankingModel) {
+ case RM::Heuristics:
+ Scores.Quality = Quality.evaluate();
+ Scores.Relevance = Relevance.evaluate();
+ Scores.Total =
+ evaluateSymbolAndRelevance(Scores.Quality, Scores.Relevance);
+ // NameMatch is in fact a multiplier on total score, so rescoring is
+ // sound.
+ Scores.ExcludingName = Relevance.NameMatch
+ ? Scores.Total / Relevance.NameMatch
+ : Scores.Quality;
+ return Scores;
+
+ case RM::DecisionForest:
+ Scores.Quality = 0;
+ Scores.Relevance = 0;
+ // Exponentiating DecisionForest prediction makes the score of each tree a
+ // multiplciative boost (like NameMatch). This allows us to weigh the
+ // prediciton score and NameMatch appropriately.
+ Scores.ExcludingName = pow(Opts.DecisionForestBase,
+ evaluateDecisionForest(Quality, Relevance));
+ // NeedsFixIts is not part of the DecisionForest as generating training
+ // data that needs fixits is not-feasible.
+ if (Relevance.NeedsFixIts)
+ Scores.ExcludingName *= 0.5;
+ // NameMatch should be a multiplier on total score to support rescoring.
+ Scores.Total = Relevance.NameMatch * Scores.ExcludingName;
+ return Scores;
+ }
+ llvm_unreachable("Unhandled CodeCompletion ranking model.");
+ }
+
// Scores a candidate and adds it to the TopN structure.
void addCandidate(TopN<ScoredBundle, ScoredBundleGreater> &Candidates,
CompletionCandidate::Bundle Bundle) {
@@ -1632,6 +1669,7 @@ class CodeCompleteFlow {
SymbolRelevanceSignals Relevance;
Relevance.Context = CCContextKind;
Relevance.Name = Bundle.front().Name;
+ Relevance.FilterLength = HeuristicPrefix.Name.size();
Relevance.Query = SymbolRelevanceSignals::CodeComplete;
Relevance.FileProximityMatch = FileProximity.getPointer();
if (ScopeProximity)
@@ -1680,15 +1718,7 @@ class CodeCompleteFlow {
}
}
- CodeCompletion::Scores Scores;
- Scores.Quality = Quality.evaluate();
- Scores.Relevance = Relevance.evaluate();
- Scores.Total = evaluateSymbolAndRelevance(Scores.Quality, Scores.Relevance);
- // NameMatch is in fact a multiplier on total score, so rescoring is sound.
- Scores.ExcludingName = Relevance.NameMatch
- ? Scores.Total / Relevance.NameMatch
- : Scores.Quality;
-
+ CodeCompletion::Scores Scores = evaluateCompletion(Quality, Relevance);
if (Opts.RecordCCResult)
Opts.RecordCCResult(toCodeCompletion(Bundle), Quality, Relevance,
Scores.Total);
diff --git a/clang-tools-extra/clangd/CodeComplete.h b/clang-tools-extra/clangd/CodeComplete.h
index beffabd19f3b..82a2656f172e 100644
--- a/clang-tools-extra/clangd/CodeComplete.h
+++ b/clang-tools-extra/clangd/CodeComplete.h
@@ -147,6 +147,22 @@ struct CodeCompleteOptions {
std::function<void(const CodeCompletion &, const SymbolQualitySignals &,
const SymbolRelevanceSignals &, float Score)>
RecordCCResult;
+
+ /// Model to use for ranking code completion candidates.
+ enum CodeCompletionRankingModel {
+ Heuristics,
+ DecisionForest,
+ } RankingModel = Heuristics;
+
+ /// Weight for combining NameMatch and Prediction of DecisionForest.
+ /// CompletionScore is NameMatch * pow(Base, Prediction).
+ /// The optimal value of Base largely depends on the semantics of the model
+ /// and prediction score (e.g. algorithm used during training, number of
+ /// trees, etc.). Usually if the range of Prediciton is [-20, 20] then a Base
+ /// in [1.2, 1.7] works fine.
+ /// Semantics: E.g. the completion score reduces by 50% if the Prediciton
+ /// score is reduced by 2.6 points for Base = 1.3.
+ float DecisionForestBase = 1.3f;
};
// Semi-structured representation of a code-complete suggestion for our C++ API.
diff --git a/clang-tools-extra/clangd/Quality.cpp b/clang-tools-extra/clangd/Quality.cpp
index bf0c0957084c..37f1cf62821a 100644
--- a/clang-tools-extra/clangd/Quality.cpp
+++ b/clang-tools-extra/clangd/Quality.cpp
@@ -8,6 +8,7 @@
#include "Quality.h"
#include "AST.h"
+#include "CompletionModel.h"
#include "FileDistance.h"
#include "SourceCode.h"
#include "URI.h"
@@ -486,6 +487,34 @@ float evaluateSymbolAndRelevance(float SymbolQuality, float SymbolRelevance) {
return SymbolQuality * SymbolRelevance;
}
+float evaluateDecisionForest(const SymbolQualitySignals &Quality,
+ const SymbolRelevanceSignals &Relevance) {
+ Example E;
+ E.setIsDeprecated(Quality.Deprecated);
+ E.setIsReservedName(Quality.ReservedName);
+ E.setIsImplementationDetail(Quality.ImplementationDetail);
+ E.setNumReferences(Quality.References);
+ E.setSymbolCategory(Quality.Category);
+
+ SymbolRelevanceSignals::DerivedSignals Derived =
+ Relevance.calculateDerivedSignals();
+ E.setIsNameInContext(Derived.NameMatchesContext);
+ E.setIsForbidden(Relevance.Forbidden);
+ E.setIsInBaseClass(Relevance.InBaseClass);
+ E.setFileProximityDistance(Derived.FileProximityDistance);
+ E.setSemaFileProximityScore(Relevance.SemaFileProximityScore);
+ E.setSymbolScopeDistance(Derived.ScopeProximityDistance);
+ E.setSemaSaysInScope(Relevance.SemaSaysInScope);
+ E.setScope(Relevance.Scope);
+ E.setContextKind(Relevance.Context);
+ E.setIsInstanceMember(Relevance.IsInstanceMember);
+ E.setHadContextType(Relevance.HadContextType);
+ E.setHadSymbolType(Relevance.HadSymbolType);
+ E.setTypeMatchesPreferred(Relevance.TypeMatchesPreferred);
+ E.setFilterLength(Relevance.FilterLength);
+ return Evaluate(E);
+}
+
// Produces an integer that sorts in the same order as F.
// That is: a < b <==> encodeFloat(a) < encodeFloat(b).
static uint32_t encodeFloat(float F) {
diff --git a/clang-tools-extra/clangd/Quality.h b/clang-tools-extra/clangd/Quality.h
index 04c6ce211ca9..694653e1a714 100644
--- a/clang-tools-extra/clangd/Quality.h
+++ b/clang-tools-extra/clangd/Quality.h
@@ -77,6 +77,7 @@ struct SymbolQualitySignals {
void merge(const CodeCompletionResult &SemaCCResult);
void merge(const Symbol &IndexResult);
+ // FIXME(usx): Rename to evaluateHeuristics().
// Condense these signals down to a single number, higher is better.
float evaluate() const;
};
@@ -136,6 +137,10 @@ struct SymbolRelevanceSignals {
// Whether the item matches the type expected in the completion context.
bool TypeMatchesPreferred = false;
+ /// Length of the unqualified partial name of Symbol typed in
+ /// CompletionPrefix.
+ unsigned FilterLength = 0;
+
/// Set of derived signals computed by calculateDerivedSignals(). Must not be
/// set explicitly.
struct DerivedSignals {
@@ -161,6 +166,8 @@ llvm::raw_ostream &operator<<(llvm::raw_ostream &,
/// Combine symbol quality and relevance into a single score.
float evaluateSymbolAndRelevance(float SymbolQuality, float SymbolRelevance);
+float evaluateDecisionForest(const SymbolQualitySignals &Quality,
+ const SymbolRelevanceSignals &Relevance);
/// TopN<T> is a lossy container that preserves only the "best" N elements.
template <typename T, typename Compare = std::greater<T>> class TopN {
public:
diff --git a/clang-tools-extra/clangd/tool/ClangdMain.cpp b/clang-tools-extra/clangd/tool/ClangdMain.cpp
index 9660f1bd76f7..8e5d6cb97a32 100644
--- a/clang-tools-extra/clangd/tool/ClangdMain.cpp
+++ b/clang-tools-extra/clangd/tool/ClangdMain.cpp
@@ -167,6 +167,26 @@ opt<CodeCompleteOptions::CodeCompletionParse> CodeCompletionParse{
Hidden,
};
+opt<CodeCompleteOptions::CodeCompletionRankingModel> RankingModel{
+ "ranking-model",
+ cat(Features),
+ desc("Model to use to rank code-completion items"),
+ values(clEnumValN(CodeCompleteOptions::Heuristics, "heuristics",
+ "Use hueristics to rank code completion items"),
+ clEnumValN(CodeCompleteOptions::DecisionForest, "decision_forest",
+ "Use Decision Forest model to rank completion items")),
+ init(CodeCompleteOptions().RankingModel),
+ Hidden,
+};
+
+opt<bool> DecisionForestBase{
+ "decision-forest-base",
+ cat(Features),
+ desc("Base for exponentiating the prediction from DecisionForest."),
+ init(CodeCompleteOptions().DecisionForestBase),
+ Hidden,
+};
+
// FIXME: also support "plain" style where signatures are always omitted.
enum CompletionStyleFlag { Detailed, Bundled };
opt<CompletionStyleFlag> CompletionStyle{
@@ -739,6 +759,8 @@ clangd accepts flags on the commandline, and in the CLANGD_FLAGS environment var
CCOpts.EnableFunctionArgSnippets = EnableFunctionArgSnippets;
CCOpts.AllScopes = AllScopesCompletion;
CCOpts.RunParser = CodeCompletionParse;
+ CCOpts.RankingModel = RankingModel;
+ CCOpts.DecisionForestBase = DecisionForestBase;
RealThreadsafeFS TFS;
std::vector<std::unique_ptr<config::Provider>> ProviderStack;
diff --git a/clang-tools-extra/clangd/unittests/CodeCompleteTests.cpp b/clang-tools-extra/clangd/unittests/CodeCompleteTests.cpp
index 460976d64f9f..de73bc66a178 100644
--- a/clang-tools-extra/clangd/unittests/CodeCompleteTests.cpp
+++ b/clang-tools-extra/clangd/unittests/CodeCompleteTests.cpp
@@ -10,7 +10,6 @@
#include "ClangdServer.h"
#include "CodeComplete.h"
#include "Compiler.h"
-#include "CompletionModel.h"
#include "Matchers.h"
#include "Protocol.h"
#include "Quality.h"
@@ -163,14 +162,38 @@ Symbol withReferences(int N, Symbol S) {
return S;
}
-TEST(DecisionForestRuntime, SanityTest) {
- using Example = clangd::Example;
- using clangd::Evaluate;
- Example E1;
- E1.setContextKind(ContextKind::CCC_ArrowMemberAccess);
- Example E2;
- E2.setContextKind(ContextKind::CCC_SymbolOrNewName);
- EXPECT_GT(Evaluate(E1), Evaluate(E2));
+TEST(DecisionForestRankingModel, NameMatchSanityTest) {
+ clangd::CodeCompleteOptions Opts;
+ Opts.RankingModel = CodeCompleteOptions::DecisionForest;
+ auto Results = completions(
+ R"cpp(
+struct MemberAccess {
+ int ABG();
+ int AlphaBetaGamma();
+};
+int func() { MemberAccess().ABG^ }
+)cpp",
+ /*IndexSymbols=*/{}, Opts);
+ EXPECT_THAT(Results.Completions,
+ ElementsAre(Named("ABG"), Named("AlphaBetaGamma")));
+}
+
+TEST(DecisionForestRankingModel, ReferencesAffectRanking) {
+ clangd::CodeCompleteOptions Opts;
+ Opts.RankingModel = CodeCompleteOptions::DecisionForest;
+ constexpr int NumReferences = 100000;
+ EXPECT_THAT(
+ completions("int main() { clang^ }",
+ {ns("clangA"), withReferences(NumReferences, func("clangD"))},
+ Opts)
+ .Completions,
+ ElementsAre(Named("clangD"), Named("clangA")));
+ EXPECT_THAT(
+ completions("int main() { clang^ }",
+ {withReferences(NumReferences, ns("clangA")), func("clangD")},
+ Opts)
+ .Completions,
+ ElementsAre(Named("clangA"), Named("clangD")));
}
TEST(CompletionTest, Limit) {
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