Remove the --completion-model flag from the analysis server.
Change-Id: I6bedbb0b5c4b8bedba18d2e3b788db0a9686f6df Reviewed-on: https://dart-review.googlesource.com/c/sdk/+/172026 Commit-Queue: Devon Carew <devoncarew@google.com> Reviewed-by: Brian Wilkerson <brianwilkerson@google.com>
This commit is contained in:
Devon Carew
committed by
commit-bot@chromium.org
commit-bot@chromium.org
parent
9f6ea5b6f6
commit
f5e26456bf
@@ -688,12 +688,6 @@
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"packageUri": "lib/",
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"languageVersion": "2.0"
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},
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{
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"name": "tflite_native",
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"rootUri": "../third_party/pkg/tflite_native",
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"packageUri": "lib/",
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"languageVersion": "2.6"
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},
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{
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"name": "typed_data",
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"rootUri": "../third_party/pkg/typed_data",
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@@ -107,7 +107,6 @@ test_process:third_party/pkg/test_process/lib
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test_reflective_loader:third_party/pkg/test_reflective_loader/lib
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test_runner:pkg/test_runner/lib
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testing:pkg/testing/lib
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tflite_native:third_party/pkg/tflite_native/lib
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typed_data:third_party/pkg/typed_data/lib
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usage:third_party/pkg/usage/lib
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vector_math:third_party/pkg/vector_math/lib
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@@ -157,7 +157,6 @@ vars = {
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"term_glyph_rev": "6a0f9b6fb645ba75e7a00a4e20072678327a0347",
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"test_reflective_loader_tag": "0.1.9",
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"test_rev": "e37a93bbeae23b215972d1659ac865d71287ff6a",
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"tflite_native_rev": "0.4.0+1",
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"typed_data_tag": "f94fc57b8e8c0e4fe4ff6cfd8290b94af52d3719",
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"usage_tag": "16fbfd90c58f16e016a295a880bc722d2547d2c9",
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"vector_math_rev": "0c9f5d68c047813a6dcdeb88ba7a42daddf25025",
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@@ -431,8 +430,6 @@ deps = {
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Var("dart_git") + "term_glyph.git" + "@" + Var("term_glyph_rev"),
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Var("dart_root") + "/third_party/pkg/test":
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Var("dart_git") + "test.git" + "@" + Var("test_rev"),
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Var("dart_root") + "/third_party/pkg/tflite_native":
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Var("dart_git") + "tflite_native.git" + "@" + Var("tflite_native_rev"),
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Var("dart_root") + "/third_party/pkg/test_descriptor":
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Var("dart_git") + "test_descriptor.git" + "@" + Var("test_descriptor_tag"),
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Var("dart_root") + "/third_party/pkg/test_process":
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@@ -625,11 +625,6 @@ class AnalysisServerOptions {
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/// Whether to use the Language Server Protocol.
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bool useLanguageServerProtocol = false;
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/// Base path to locate trained completion language model files.
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///
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/// ML completion is enabled if this is non-null.
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String completionModelFolder;
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/// The set of enabled features.
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FeatureSet featureSet = FeatureSet();
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}
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@@ -3,7 +3,6 @@
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// BSD-style license that can be found in the LICENSE file.
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import 'dart:async';
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import 'dart:ffi' as ffi;
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import 'dart:io';
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import 'dart:isolate';
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import 'dart:math';
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@@ -24,11 +23,9 @@ import 'package:analysis_server/src/server/isolate_analysis_server.dart';
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import 'package:analysis_server/src/server/lsp_stdio_server.dart';
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import 'package:analysis_server/src/server/sdk_configuration.dart';
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import 'package:analysis_server/src/server/stdio_server.dart';
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import 'package:analysis_server/src/services/completion/dart/completion_ranking.dart';
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import 'package:analysis_server/src/socket_server.dart';
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import 'package:analysis_server/src/utilities/request_statistics.dart';
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import 'package:analysis_server/starter.dart';
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import 'package:analyzer/exception/exception.dart';
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import 'package:analyzer/file_system/physical_file_system.dart';
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import 'package:analyzer/instrumentation/file_instrumentation.dart';
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import 'package:analyzer/instrumentation/instrumentation.dart';
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@@ -38,7 +35,6 @@ import 'package:analyzer/src/generated/sdk.dart';
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import 'package:args/args.dart';
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import 'package:cli_util/cli_util.dart';
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import 'package:linter/src/rules.dart' as linter;
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import 'package:path/path.dart' as path;
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import 'package:telemetry/crash_reporting.dart';
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import 'package:telemetry/telemetry.dart' as telemetry;
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@@ -265,13 +261,6 @@ class Driver implements ServerStarter {
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/// The name of the flag to use the Language Server Protocol (LSP).
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static const String USE_LSP = 'lsp';
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/// Whether or not to enable ML ranking for code completion.
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static const String ENABLE_COMPLETION_MODEL = 'enable-completion-model';
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/// The path on disk to a directory containing language model files for smart
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/// code completion.
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static const String COMPLETION_MODEL_FOLDER = 'completion-model';
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/// A directory to analyze in order to train an analysis server snapshot.
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static const String TRAIN_USING = 'train-using';
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@@ -318,35 +307,6 @@ class Driver implements ServerStarter {
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var sdkConfig = SdkConfiguration.readFromSdk();
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analysisServerOptions.configurationOverrides = sdkConfig;
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// ML model configuration.
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// TODO(brianwilkerson) Uncomment the line below and delete the second line
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// when there is a new completion model to query. Until then we ignore the
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// flag to enable the model so that we can't try to read from a file that
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// doesn't exist.
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// final bool enableCompletionModel = results[ENABLE_COMPLETION_MODEL];
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final enableCompletionModel = false;
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analysisServerOptions.completionModelFolder =
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results[COMPLETION_MODEL_FOLDER];
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if (results.wasParsed(ENABLE_COMPLETION_MODEL) && !enableCompletionModel) {
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// This is the case where the user has explicitly turned off model-based
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// code completion.
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analysisServerOptions.completionModelFolder = null;
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}
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// TODO(devoncarew): Simplify this logic and use the value from sdkConfig.
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if (enableCompletionModel &&
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analysisServerOptions.completionModelFolder == null) {
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// The user has enabled ML code completion without explicitly setting a
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// model for us to choose, so use the default one. We need to walk over
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// from $SDK/bin/snapshots/analysis_server.dart.snapshot to
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// $SDK/bin/model/lexeme.
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analysisServerOptions.completionModelFolder = path.join(
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File.fromUri(Platform.script).parent.path,
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'..',
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'model',
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'lexeme',
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);
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}
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// Analytics
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bool disableAnalyticsForSession = results[SUPPRESS_ANALYTICS_FLAG];
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if (results.wasParsed(TRAIN_USING)) {
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@@ -601,7 +561,6 @@ class Driver implements ServerStarter {
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socketServer.analysisServer.shutdown();
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if (sendPort == null) exit(0);
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});
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startCompletionRanking(socketServer, null, analysisServerOptions);
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},
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print: results[INTERNAL_PRINT_TO_CONSOLE]
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? null
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@@ -609,36 +568,6 @@ class Driver implements ServerStarter {
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}
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}
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/// This will be invoked after createAnalysisServer has been called on the
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/// socket server. At that point, we'll be able to send a server.error
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/// notification in case model startup fails.
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void startCompletionRanking(
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SocketServer socketServer,
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LspSocketServer lspSocketServer,
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AnalysisServerOptions analysisServerOptions) {
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// If ML completion is not enabled, or we're on a 32-bit machine, don't try
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// and start the completion model.
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if (analysisServerOptions.completionModelFolder == null ||
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ffi.sizeOf<ffi.IntPtr>() == 4) {
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return;
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}
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// Start completion model isolate if this is a 64 bit system and analysis
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// server was configured to load a language model on disk.
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CompletionRanking.instance =
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CompletionRanking(analysisServerOptions.completionModelFolder);
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CompletionRanking.instance.start().catchError((exception, stackTrace) {
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// Disable smart ranking if model startup fails.
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analysisServerOptions.completionModelFolder = null;
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// TODO(brianwilkerson) Shutdown the isolates that have already been
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// started.
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CompletionRanking.instance = null;
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AnalysisEngine.instance.instrumentationService.logException(
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CaughtException.withMessage(
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'Failed to start ranking model isolate', exception, stackTrace));
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});
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}
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void startLspServer(
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ArgResults args,
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AnalysisServerOptions analysisServerOptions,
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@@ -681,7 +610,6 @@ class Driver implements ServerStarter {
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exit(0);
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}
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});
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startCompletionRanking(null, socketServer, analysisServerOptions);
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});
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}
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@@ -772,18 +700,16 @@ class Driver implements ServerStarter {
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help: 'Pass in a directory to analyze for purposes of training an '
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'analysis server snapshot.');
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parser.addFlag(ENABLE_COMPLETION_MODEL,
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help: 'Whether or not to turn on ML ranking for code completion.');
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parser.addOption(COMPLETION_MODEL_FOLDER,
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valueHelp: 'path',
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help: 'Path to the location of a code completion model.');
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//
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// Deprecated options - no longer read from.
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//
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// Removed 11/15/2020.
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parser.addOption('completion-model', hide: true);
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// Removed 11/8/2020.
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parser.addFlag('dartpad', hide: true);
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// Removed 11/15/2020.
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parser.addFlag('enable-completion-model', hide: true);
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// Removed 10/30/2020.
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parser.addMultiOption('enable-experiment', hide: true);
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// Removed 9/23/2020.
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@@ -61,9 +61,6 @@ class SdkConfiguration {
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/// Returns whether this SDK configuration has any configured values.
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bool get hasAnyOverrides => _values.isNotEmpty;
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/// Return an override value for the analysis server's ML model file path.
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String get mlModelPath => _values['server.ml.model.path'];
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@override
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String toString() => displayString;
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@@ -10,7 +10,6 @@ import 'package:analysis_server/src/services/completion/completion_core.dart';
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import 'package:analysis_server/src/services/completion/completion_performance.dart';
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import 'package:analysis_server/src/services/completion/dart/arglist_contributor.dart';
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import 'package:analysis_server/src/services/completion/dart/combinator_contributor.dart';
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import 'package:analysis_server/src/services/completion/dart/completion_ranking.dart';
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import 'package:analysis_server/src/services/completion/dart/extension_member_contributor.dart';
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import 'package:analysis_server/src/services/completion/dart/feature_computer.dart';
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import 'package:analysis_server/src/services/completion/dart/field_formal_contributor.dart';
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@@ -36,7 +35,6 @@ import 'package:analyzer/dart/ast/ast.dart';
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import 'package:analyzer/dart/ast/token.dart';
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import 'package:analyzer/dart/element/element.dart';
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import 'package:analyzer/dart/element/type.dart';
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import 'package:analyzer/exception/exception.dart';
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import 'package:analyzer/file_system/file_system.dart';
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import 'package:analyzer/src/dart/analysis/driver_based_analysis_context.dart';
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import 'package:analyzer/src/dart/ast/ast.dart';
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@@ -116,10 +114,6 @@ class DartCompletionManager {
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request.checkAborted();
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final ranking = CompletionRanking.instance;
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var probabilityFuture =
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ranking != null ? ranking.predict(dartRequest) : Future.value(null);
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var range = dartRequest.target.computeReplacementRange(dartRequest.offset);
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(request as CompletionRequestImpl)
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..replacementOffset = range.offset
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@@ -169,35 +163,7 @@ class DartCompletionManager {
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throw AbortCompletion();
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}
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// Adjust suggestion relevance before returning
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var suggestions = builder.suggestions.toList();
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const SORT_TAG = 'DartCompletionManager - sort';
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await performance.runAsync(SORT_TAG, (_) async {
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if (ranking != null) {
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request.checkAborted();
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try {
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suggestions = await ranking.rerank(
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probabilityFuture,
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suggestions,
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includedElementNames,
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includedSuggestionRelevanceTags,
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dartRequest,
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request.result.unit.featureSet);
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} catch (exception, stackTrace) {
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// TODO(brianwilkerson) Shutdown the isolates that have already been
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// started.
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// Disable smart ranking if prediction fails.
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CompletionRanking.instance = null;
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AnalysisEngine.instance.instrumentationService.logException(
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CaughtException.withMessage(
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'Failed to rerank completion suggestions',
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exception,
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stackTrace));
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}
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}
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});
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request.checkAborted();
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return suggestions;
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return builder.suggestions.toList();
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}
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void _addIncludedElementKinds(DartCompletionRequestImpl request) {
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@@ -1,309 +0,0 @@
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// Copyright (c) 2019, the Dart project authors. Please see the AUTHORS file
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// for details. All rights reserved. Use of this source code is governed by a
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// BSD-style license that can be found in the LICENSE file.
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import 'dart:collection';
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import 'dart:isolate';
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import 'package:analysis_server/src/protocol_server.dart';
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import 'package:analysis_server/src/provisional/completion/dart/completion_dart.dart';
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import 'package:analysis_server/src/services/completion/completion_performance.dart';
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import 'package:analysis_server/src/services/completion/dart/completion_ranking_internal.dart';
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import 'package:analysis_server/src/services/completion/dart/language_model.dart';
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import 'package:analyzer/dart/analysis/features.dart';
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/// Number of code completion isolates.
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// TODO(devoncarew): We need to explore the memory costs of running multiple ML
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// isolates.
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const int _ISOLATE_COUNT = 2;
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/// Number of lookback tokens.
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const int _LOOKBACK = 100;
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/// Minimum probability to prioritize model-only suggestion.
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const double _MODEL_RELEVANCE_CUTOFF = 0.5;
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/// Prediction service run by the model isolate.
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void entrypoint(SendPort sendPort) {
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LanguageModel model;
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final port = ReceivePort();
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sendPort.send(port.sendPort);
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port.listen((message) {
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var response = <String, Map<String, double>>{};
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switch (message['method']) {
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case 'load':
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model = LanguageModel.load(message['args'][0]);
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break;
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case 'predict':
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response['data'] = model.predictWithScores(message['args']);
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break;
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}
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message['port'].send(response);
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});
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}
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class CompletionRanking {
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/// Singleton instance.
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static CompletionRanking instance;
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/// Filesystem location of model files.
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final String _directory;
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/// Ports to communicate from main to model isolates.
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List<SendPort> _writes;
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/// Pointer for round robin load balancing over isolates.
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int _index;
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/// General performance metrics around ML completion.
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final PerformanceMetrics performanceMetrics = PerformanceMetrics._();
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CompletionRanking(this._directory);
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/// Send an RPC to the isolate worker requesting that it load the model and
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/// wait for it to respond.
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Future<Map<String, Map<String, double>>> makeLoadRequest(
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SendPort sendPort, List<String> args) async {
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final receivePort = ReceivePort();
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sendPort.send({
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'method': 'load',
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'args': args,
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'port': receivePort.sendPort,
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});
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return await receivePort.first;
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}
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/// Send an RPC to the isolate worker requesting that it make a prediction and
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/// wait for it to respond.
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Future<Map<String, Map<String, double>>> makePredictRequest(
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List<String> args) async {
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final receivePort = ReceivePort();
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_writes[_index].send({
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'method': 'predict',
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'args': args,
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'port': receivePort.sendPort,
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});
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_index = (_index + 1) % _writes.length;
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return await receivePort.first;
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}
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/// Return a next-token prediction starting at the completion request cursor
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/// and walking back to find previous input tokens, or `null` if the
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/// prediction isolates are not running.
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Future<Map<String, double>> predict(DartCompletionRequest request) async {
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if (_writes == null || _writes.isEmpty) {
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// The field `_writes` is initialized in `start`, but the code that
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// invokes `start` doesn't wait for it complete. That means that it's
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// possible for this method to be invoked before `_writes` is initialized.
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// In those cases we return `null`
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return null;
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}
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final query = constructQuery(request, _LOOKBACK);
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if (query == null) {
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return Future.value();
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}
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performanceMetrics._incrementPredictionRequestCount();
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var timer = Stopwatch()..start();
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var response = await makePredictRequest(query);
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timer.stop();
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var result = response['data'];
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performanceMetrics._addPredictionResult(PredictionResult(
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result,
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timer.elapsed,
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request.source.fullName,
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computeCompletionSnippet(request.sourceContents, request.offset),
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));
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return result;
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}
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/// Transforms [CompletionSuggestion] relevances and
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/// [IncludedSuggestionRelevanceTag] relevanceBoosts based on language model
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/// predicted next-token probability distribution.
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Future<List<CompletionSuggestion>> rerank(
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Future<Map<String, double>> probabilityFuture,
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List<CompletionSuggestion> suggestions,
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Set<String> includedElementNames,
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List<IncludedSuggestionRelevanceTag> includedSuggestionRelevanceTags,
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DartCompletionRequest request,
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FeatureSet featureSet) async {
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assert((includedElementNames != null &&
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includedSuggestionRelevanceTags != null) ||
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(includedElementNames == null &&
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includedSuggestionRelevanceTags == null));
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final probability = await probabilityFuture
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.timeout(const Duration(seconds: 1), onTimeout: () => null);
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if (probability == null || probability.isEmpty) {
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// Failed to compute probability distribution, don't rerank.
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return suggestions;
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}
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// Discard the type-based relevance boosts.
|
||||
if (includedSuggestionRelevanceTags != null) {
|
||||
includedSuggestionRelevanceTags.forEach((tag) {
|
||||
tag.relevanceBoost = 0;
|
||||
});
|
||||
}
|
||||
|
||||
// Intersection between static analysis and model suggestions.
|
||||
var middle = DART_RELEVANCE_HIGH + probability.length;
|
||||
// Up to one suggestion from model with very high confidence.
|
||||
var high = middle + probability.length;
|
||||
// Lower relevance, model-only suggestions (perhaps literals).
|
||||
var low = DART_RELEVANCE_LOW - 1;
|
||||
|
||||
List<MapEntry> entries = probability.entries.toList()
|
||||
..sort((a, b) => b.value.compareTo(a.value));
|
||||
|
||||
if (testInsideQuotes(request)) {
|
||||
// If completion is requested inside of quotes, remove any suggestions
|
||||
// which are not string literal.
|
||||
entries = selectStringLiterals(entries);
|
||||
} else if (request.opType.includeVarNameSuggestions &&
|
||||
suggestions.every((CompletionSuggestion suggestion) =>
|
||||
suggestion.kind == CompletionSuggestionKind.IDENTIFIER)) {
|
||||
// If analysis server thinks this is a declaration context,
|
||||
// remove all of the model-suggested literals.
|
||||
// TODO(lambdabaa): Ask Brian for help leveraging
|
||||
// SimpleIdentifier#inDeclarationContext.
|
||||
entries.retainWhere((MapEntry entry) => !isLiteral(entry.key));
|
||||
}
|
||||
|
||||
var allowModelOnlySuggestions =
|
||||
!testNamedArgument(suggestions) && !testFollowingDot(request);
|
||||
for (var entry in entries) {
|
||||
// There may be multiple like
|
||||
// CompletionSuggestion and CompletionSuggestion().
|
||||
final completionSuggestions = suggestions.where((suggestion) =>
|
||||
areCompletionsEquivalent(suggestion.completion, entry.key));
|
||||
List<IncludedSuggestionRelevanceTag> includedSuggestions;
|
||||
final isIncludedElementName = includedElementNames != null &&
|
||||
includedElementNames.contains(entry.key);
|
||||
if (includedSuggestionRelevanceTags != null) {
|
||||
includedSuggestions = includedSuggestionRelevanceTags
|
||||
.where((tag) => areCompletionsEquivalent(
|
||||
elementNameFromRelevanceTag(tag.tag), entry.key))
|
||||
.toList();
|
||||
} else {
|
||||
includedSuggestions = [];
|
||||
}
|
||||
if (allowModelOnlySuggestions && entry.value > _MODEL_RELEVANCE_CUTOFF) {
|
||||
final relevance = high--;
|
||||
if (completionSuggestions.isNotEmpty ||
|
||||
includedSuggestions.isNotEmpty) {
|
||||
completionSuggestions.forEach((completionSuggestion) {
|
||||
completionSuggestion.relevance = relevance;
|
||||
});
|
||||
includedSuggestions.forEach((includedSuggestion) {
|
||||
includedSuggestion.relevanceBoost = relevance;
|
||||
});
|
||||
} else if (isIncludedElementName) {
|
||||
if (includedSuggestionRelevanceTags != null) {
|
||||
includedSuggestionRelevanceTags
|
||||
.add(IncludedSuggestionRelevanceTag(entry.key, relevance));
|
||||
}
|
||||
} else {
|
||||
suggestions
|
||||
.add(createCompletionSuggestion(entry.key, featureSet, high--));
|
||||
}
|
||||
} else if (completionSuggestions.isNotEmpty ||
|
||||
includedSuggestions.isNotEmpty ||
|
||||
isIncludedElementName) {
|
||||
final relevance = middle--;
|
||||
completionSuggestions.forEach((completionSuggestion) {
|
||||
completionSuggestion.relevance = relevance;
|
||||
});
|
||||
if (includedSuggestions.isNotEmpty) {
|
||||
includedSuggestions.forEach((includedSuggestion) {
|
||||
includedSuggestion.relevanceBoost = relevance;
|
||||
});
|
||||
} else if (includedSuggestionRelevanceTags != null) {
|
||||
includedSuggestionRelevanceTags
|
||||
.add(IncludedSuggestionRelevanceTag(entry.key, relevance));
|
||||
}
|
||||
} else if (allowModelOnlySuggestions) {
|
||||
final relevance = low--;
|
||||
suggestions
|
||||
.add(createCompletionSuggestion(entry.key, featureSet, relevance));
|
||||
if (includedSuggestionRelevanceTags != null) {
|
||||
includedSuggestionRelevanceTags
|
||||
.add(IncludedSuggestionRelevanceTag(entry.key, relevance));
|
||||
}
|
||||
}
|
||||
}
|
||||
return suggestions;
|
||||
}
|
||||
|
||||
/// Spin up the model isolates and load the tflite model.
|
||||
Future<void> start() async {
|
||||
_writes = [];
|
||||
_index = 0;
|
||||
final initializations = <Future<void>>[];
|
||||
|
||||
// Start the first isolate.
|
||||
await _startIsolate();
|
||||
|
||||
// Start the 2nd and later isolates.
|
||||
for (var i = 1; i < _ISOLATE_COUNT; i++) {
|
||||
initializations.add(_startIsolate());
|
||||
}
|
||||
|
||||
return Future.wait(initializations);
|
||||
}
|
||||
|
||||
Future<void> _startIsolate() async {
|
||||
var timer = Stopwatch()..start();
|
||||
var port = ReceivePort();
|
||||
await Isolate.spawn(entrypoint, port.sendPort);
|
||||
SendPort sendPort = await port.first;
|
||||
return makeLoadRequest(sendPort, [_directory]).whenComplete(() {
|
||||
timer.stop();
|
||||
performanceMetrics._isolateInitTimes.add(timer.elapsed);
|
||||
_writes.add(sendPort);
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
class PerformanceMetrics {
|
||||
static const int _maxResultBuffer = 50;
|
||||
|
||||
final Queue<PredictionResult> _predictionResults = Queue();
|
||||
int _predictionRequestCount = 0;
|
||||
final List<Duration> _isolateInitTimes = [];
|
||||
|
||||
PerformanceMetrics._();
|
||||
|
||||
List<Duration> get isolateInitTimes => _isolateInitTimes;
|
||||
|
||||
/// The total prediction requests to ML Complete.
|
||||
int get predictionRequestCount => _predictionRequestCount;
|
||||
|
||||
/// An iterable of the last `n` prediction results;
|
||||
Iterable<PredictionResult> get predictionResults => _predictionResults;
|
||||
|
||||
void _addPredictionResult(PredictionResult request) {
|
||||
_predictionResults.addFirst(request);
|
||||
if (_predictionResults.length > _maxResultBuffer) {
|
||||
_predictionResults.removeLast();
|
||||
}
|
||||
}
|
||||
|
||||
void _incrementPredictionRequestCount() {
|
||||
_predictionRequestCount++;
|
||||
}
|
||||
}
|
||||
|
||||
class PredictionResult {
|
||||
final Map<String, double> results;
|
||||
final Duration elapsedTime;
|
||||
final String sourcePath;
|
||||
final String snippet;
|
||||
|
||||
PredictionResult(
|
||||
this.results, this.elapsedTime, this.sourcePath, this.snippet);
|
||||
}
|
||||
@@ -1,155 +0,0 @@
|
||||
// Copyright (c) 2019, the Dart project authors. Please see the AUTHORS file
|
||||
// for details. All rights reserved. Use of this source code is governed by a
|
||||
// BSD-style license that can be found in the LICENSE file.
|
||||
|
||||
import 'dart:convert';
|
||||
import 'dart:io';
|
||||
import 'dart:typed_data';
|
||||
|
||||
import 'package:path/path.dart' as path;
|
||||
import 'package:tflite_native/tflite.dart' as tfl;
|
||||
|
||||
/// Interface to TensorFlow-based Dart language model for next-token prediction.
|
||||
class LanguageModel {
|
||||
static const _probabilityThreshold = 0.0001;
|
||||
static final _numeric = RegExp(r'^\d+(\.\d+)?$');
|
||||
static final _alphanumeric = RegExp(r"^['\w]+$");
|
||||
static final _doubleQuote = '"'.codeUnitAt(0);
|
||||
|
||||
final tfl.Interpreter _interpreter;
|
||||
final Map<String, int> _word2idx;
|
||||
final Map<int, String> _idx2word;
|
||||
final int _lookback;
|
||||
|
||||
/// Load model from directory.
|
||||
factory LanguageModel.load(String directory) {
|
||||
// Load model.
|
||||
final interpreter =
|
||||
tfl.Interpreter.fromFile(path.join(directory, 'model.tflite'));
|
||||
interpreter.allocateTensors();
|
||||
|
||||
// Load word2idx mapping for input.
|
||||
final word2idx = json
|
||||
.decode(File(path.join(directory, 'word2idx.json')).readAsStringSync())
|
||||
.cast<String, int>();
|
||||
|
||||
// Load idx2word mapping for output.
|
||||
final idx2word = json
|
||||
.decode(File(path.join(directory, 'idx2word.json')).readAsStringSync())
|
||||
.map<int, String>((k, v) => MapEntry<int, String>(int.parse(k), v));
|
||||
|
||||
// Get lookback size from model input tensor shape.
|
||||
final tensorShape = interpreter.getInputTensors().single.shape;
|
||||
if (tensorShape.length != 2 || tensorShape.first != 1) {
|
||||
throw ArgumentError(
|
||||
'tensor shape $tensorShape does not match the expected [1, X]');
|
||||
}
|
||||
final lookback = tensorShape.last;
|
||||
|
||||
return LanguageModel._(interpreter, word2idx, idx2word, lookback);
|
||||
}
|
||||
|
||||
LanguageModel._(
|
||||
this._interpreter, this._word2idx, this._idx2word, this._lookback);
|
||||
|
||||
/// Number of previous tokens to look at during predictions.
|
||||
int get lookback => _lookback;
|
||||
|
||||
/// Tear down the interpreter.
|
||||
void close() {
|
||||
_interpreter.delete();
|
||||
}
|
||||
|
||||
bool isNumber(String token) {
|
||||
return _numeric.hasMatch(token) || token.startsWith('0x');
|
||||
}
|
||||
|
||||
/// Predicts the next token to follow a list of precedent tokens
|
||||
///
|
||||
/// Returns a list of tokens, sorted by most probable first.
|
||||
List<String> predict(List<String> tokens) =>
|
||||
predictWithScores(tokens).keys.toList();
|
||||
|
||||
/// Predicts the next token with confidence scores.
|
||||
///
|
||||
/// Returns an ordered map of tokens to scores, sorted by most probable first.
|
||||
Map<String, double> predictWithScores(List<String> tokens) {
|
||||
final tensorIn = _interpreter.getInputTensors().single;
|
||||
tensorIn.data = _transformInput(tokens);
|
||||
_interpreter.invoke();
|
||||
final tensorOut = _interpreter.getOutputTensors().single;
|
||||
return _transformOutput(tensorOut.data, tokens);
|
||||
}
|
||||
|
||||
bool _isAlphanumeric(String token) {
|
||||
// Note that _numeric covers integral and decimal values whereas
|
||||
// _alphanumeric only matches integral values. Check both.
|
||||
return _alphanumeric.hasMatch(token) || _numeric.hasMatch(token);
|
||||
}
|
||||
|
||||
bool _isString(String token) {
|
||||
return token.contains('"') || token.contains("'");
|
||||
}
|
||||
|
||||
/// Transforms tokens to data bytes that can be used as interpreter input.
|
||||
List<int> _transformInput(List<String> tokens) {
|
||||
// Replace out of vocabulary tokens.
|
||||
final sanitizedTokens = tokens.map((token) {
|
||||
if (_word2idx.containsKey(token)) {
|
||||
return token;
|
||||
}
|
||||
if (isNumber(token)) {
|
||||
return '<num>';
|
||||
}
|
||||
if (_isString(token)) {
|
||||
return '<str>';
|
||||
}
|
||||
return '<unk>';
|
||||
});
|
||||
// Get indexes (as floats).
|
||||
final indexes = Float32List(lookback)
|
||||
..setAll(0, sanitizedTokens.map((token) => _word2idx[token].toDouble()));
|
||||
// Get bytes
|
||||
return Uint8List.view(indexes.buffer);
|
||||
}
|
||||
|
||||
/// Transforms interpreter output data to map of tokens to scores.
|
||||
Map<String, double> _transformOutput(
|
||||
List<int> databytes, List<String> tokens) {
|
||||
// Get bytes.
|
||||
final bytes = Uint8List.fromList(databytes);
|
||||
|
||||
// Get scores (as floats)
|
||||
final probabilities = Float32List.view(bytes.buffer);
|
||||
|
||||
final scores = <String, double>{};
|
||||
final scoresAboveThreshold = <String, double>{};
|
||||
probabilities.asMap().forEach((k, v) {
|
||||
// x in 0, 1, ..., |V| - 1 correspond to specific members of the vocabulary.
|
||||
// x in |V|, |V| + 1, ..., |V| + 49 are pointers to reference positions along the
|
||||
// network input.
|
||||
if (k >= _idx2word.length + tokens.length) {
|
||||
return;
|
||||
}
|
||||
// Find the name corresponding to this position along the network output.
|
||||
final lexeme =
|
||||
k < _idx2word.length ? _idx2word[k] : tokens[k - _idx2word.length];
|
||||
// Normalize double to single quotes.
|
||||
final sanitized = lexeme.codeUnitAt(0) != _doubleQuote
|
||||
? lexeme
|
||||
: lexeme.replaceAll('"', '\'');
|
||||
final score = (scores[sanitized] ?? 0.0) + v;
|
||||
scores[sanitized] = score;
|
||||
if (score < _probabilityThreshold ||
|
||||
k >= _idx2word.length && !_isAlphanumeric(sanitized)) {
|
||||
// Discard names below a fixed likelihood, and
|
||||
// don't assign probability to punctuation by reference.
|
||||
return;
|
||||
}
|
||||
scoresAboveThreshold[sanitized] = score;
|
||||
});
|
||||
|
||||
return Map.fromEntries(scoresAboveThreshold.entries.toList()
|
||||
..sort((a, b) => b.value.compareTo(a.value)));
|
||||
}
|
||||
}
|
||||
@@ -16,7 +16,6 @@ import 'package:analysis_server/src/lsp/lsp_analysis_server.dart'
|
||||
import 'package:analysis_server/src/plugin/plugin_manager.dart';
|
||||
import 'package:analysis_server/src/server/http_server.dart';
|
||||
import 'package:analysis_server/src/services/completion/completion_performance.dart';
|
||||
import 'package:analysis_server/src/services/completion/dart/completion_ranking.dart';
|
||||
import 'package:analysis_server/src/socket_server.dart';
|
||||
import 'package:analysis_server/src/status/ast_writer.dart';
|
||||
import 'package:analysis_server/src/status/element_writer.dart';
|
||||
@@ -759,7 +758,6 @@ class DiagnosticsSite extends Site implements AbstractGetHandler {
|
||||
// Add server-specific pages. Ordering doesn't matter as the items are
|
||||
// sorted later.
|
||||
var server = socketServer.analysisServer;
|
||||
pages.add(MLCompletionPage(this, server));
|
||||
pages.add(PluginsPage(this, server));
|
||||
|
||||
if (server is AnalysisServer) {
|
||||
@@ -1074,100 +1072,6 @@ class MemoryAndCpuPage extends DiagnosticPageWithNav {
|
||||
}
|
||||
}
|
||||
|
||||
class MLCompletionPage extends DiagnosticPageWithNav {
|
||||
@override
|
||||
final AbstractAnalysisServer server;
|
||||
|
||||
MLCompletionPage(DiagnosticsSite site, this.server)
|
||||
: super(site, 'ml-completion', 'ML Completion',
|
||||
description: 'Statistics for ML code completion.');
|
||||
|
||||
path.Context get pathContext => server.resourceProvider.pathContext;
|
||||
|
||||
@override
|
||||
Future<void> generateContent(Map<String, String> params) async {
|
||||
var hasMLComplete = CompletionRanking.instance != null;
|
||||
if (!hasMLComplete) {
|
||||
blankslate('''ML code completion is not enabled (see <a
|
||||
href="https://github.com/dart-lang/sdk/wiki/Previewing-Dart-code-completions-powered-by-machine-learning"
|
||||
>previewing Dart ML completion</a> for how to enable it).''');
|
||||
return;
|
||||
}
|
||||
|
||||
buf.writeln('ML completion enabled.<br>');
|
||||
|
||||
var isolateTimes = CompletionRanking
|
||||
.instance.performanceMetrics.isolateInitTimes
|
||||
.map((Duration time) {
|
||||
return '${time.inMilliseconds}ms';
|
||||
}).join(', ');
|
||||
p('ML isolate init times: $isolateTimes');
|
||||
|
||||
var predictions = CompletionRanking
|
||||
.instance.performanceMetrics.predictionResults
|
||||
.toList();
|
||||
|
||||
if (predictions.isEmpty) {
|
||||
blankslate('No completions recorded.');
|
||||
return;
|
||||
}
|
||||
|
||||
p('${CompletionRanking.instance.performanceMetrics.predictionRequestCount} '
|
||||
'requests');
|
||||
|
||||
// draw a chart
|
||||
buf.writeln(
|
||||
'<div id="chart-div" style="width: 700px; height: 300px;"></div>');
|
||||
var rowData = StringBuffer();
|
||||
for (var prediction in predictions.reversed) {
|
||||
// [' ', 101.5]
|
||||
if (rowData.isNotEmpty) {
|
||||
rowData.write(',');
|
||||
}
|
||||
rowData.write("[' ', ${prediction.elapsedTime.inMilliseconds}]");
|
||||
}
|
||||
buf.writeln('''
|
||||
<script type="text/javascript">
|
||||
google.charts.load('current', {'packages':['bar']});
|
||||
google.charts.setOnLoadCallback(drawChart);
|
||||
function drawChart() {
|
||||
var data = google.visualization.arrayToDataTable([
|
||||
['Completions', 'Time'],
|
||||
$rowData
|
||||
]);
|
||||
var options = { bars: 'vertical', vAxis: {format: 'decimal'}, height: 300 };
|
||||
var chart = new google.charts.Bar(document.getElementById('chart-div'));
|
||||
chart.draw(data, google.charts.Bar.convertOptions(options));
|
||||
}
|
||||
</script>
|
||||
''');
|
||||
|
||||
String summarize(PredictionResult prediction) {
|
||||
var entries = prediction.results.entries.toList();
|
||||
entries.sort((a, b) => b.value.compareTo(a.value));
|
||||
var summary = entries
|
||||
.take(3)
|
||||
.map((entry) => '"${entry.key}":${entry.value.toStringAsFixed(3)}')
|
||||
.join('<br>');
|
||||
return summary;
|
||||
}
|
||||
|
||||
// emit the data as a table
|
||||
buf.writeln('<table>');
|
||||
buf.writeln(
|
||||
'<tr><th>Time</th><th>Results</th><th>Snippet</th><th>Top suggestions</th></tr>');
|
||||
for (var prediction in predictions) {
|
||||
buf.writeln('<tr>'
|
||||
'<td class="pre right">${printMilliseconds(prediction.elapsedTime.inMilliseconds)}</td>'
|
||||
'<td class="right">${prediction.results.length}</td>'
|
||||
'<td><code>${escape(prediction.snippet)}</code></td>'
|
||||
'<td class="right">${summarize(prediction)}</td>'
|
||||
'</tr>');
|
||||
}
|
||||
buf.writeln('</table>');
|
||||
}
|
||||
}
|
||||
|
||||
class NotFoundPage extends DiagnosticPage {
|
||||
@override
|
||||
final String path;
|
||||
|
||||
@@ -25,7 +25,6 @@ dependencies:
|
||||
path: ../telemetry
|
||||
test: any
|
||||
path: any
|
||||
tflite_native: any
|
||||
watcher: any
|
||||
yaml: any
|
||||
|
||||
|
||||
@@ -1,35 +0,0 @@
|
||||
// Copyright (c) 2019, the Dart project authors. Please see the AUTHORS file
|
||||
// for details. All rights reserved. Use of this source code is governed by a
|
||||
// BSD-style license that can be found in the LICENSE file.
|
||||
|
||||
import 'dart:io';
|
||||
|
||||
import 'package:analysis_server/src/services/completion/dart/completion_ranking.dart';
|
||||
import 'package:path/path.dart' as path;
|
||||
import 'package:test/test.dart';
|
||||
|
||||
void main() {
|
||||
CompletionRanking ranking;
|
||||
|
||||
setUp(() async {
|
||||
ranking = CompletionRanking(directory);
|
||||
await ranking.start();
|
||||
});
|
||||
|
||||
test('make request to isolate', () async {
|
||||
final tokens =
|
||||
tokenize('if (list == null) { return; } for (final i = 0; i < list.');
|
||||
final response = await ranking.makePredictRequest(tokens);
|
||||
expect(response['data']['length'], greaterThan(0.9));
|
||||
}, skip: 'https://github.com/dart-lang/sdk/issues/42988');
|
||||
}
|
||||
|
||||
final directory = path.join(File.fromUri(Platform.script).parent.path, '..',
|
||||
'..', '..', '..', 'language_model', 'lexeme');
|
||||
|
||||
/// Tokenizes the input string.
|
||||
///
|
||||
/// The input is split by word boundaries and trimmed of whitespace.
|
||||
List<String> tokenize(String input) =>
|
||||
input.split(RegExp(r'\b|\s')).map((t) => t.trim()).toList()
|
||||
..removeWhere((t) => t.isEmpty);
|
||||
@@ -1,86 +0,0 @@
|
||||
// Copyright (c) 2019, the Dart project authors. Please see the AUTHORS file
|
||||
// for details. All rights reserved. Use of this source code is governed by a
|
||||
// BSD-style license that can be found in the LICENSE file.
|
||||
|
||||
import 'dart:ffi';
|
||||
import 'dart:io';
|
||||
|
||||
import 'package:analysis_server/src/services/completion/dart/language_model.dart';
|
||||
import 'package:path/path.dart' as path;
|
||||
import 'package:test/test.dart';
|
||||
|
||||
void main() {
|
||||
if (sizeOf<IntPtr>() == 4) {
|
||||
// We don't yet support running tflite on 32-bit systems.
|
||||
return;
|
||||
}
|
||||
|
||||
group('LanguageModel', () {
|
||||
LanguageModel model;
|
||||
|
||||
setUp(() {
|
||||
model = LanguageModel.load(directory);
|
||||
});
|
||||
|
||||
tearDown(() {
|
||||
model.close();
|
||||
});
|
||||
|
||||
test('calculates lookback', () {
|
||||
expect(model.lookback, expectedLookback);
|
||||
});
|
||||
|
||||
test('predict with defaults', () {
|
||||
final tokens =
|
||||
tokenize('if (list == null) { return; } for (final i = 0; i < list.');
|
||||
final suggestions = model.predict(tokens);
|
||||
expect(suggestions.first, 'length');
|
||||
});
|
||||
|
||||
test('predict with confidence scores', () {
|
||||
final tokens =
|
||||
tokenize('if (list == null) { return; } for (final i = 0; i < list.');
|
||||
final suggestions = model.predictWithScores(tokens);
|
||||
final best = suggestions.entries.first;
|
||||
expect(best.key, 'length');
|
||||
expect(best.value, greaterThan(0.9));
|
||||
});
|
||||
|
||||
test('predict when no previous tokens', () {
|
||||
final tokens = <String>[];
|
||||
final suggestions = model.predict(tokens);
|
||||
expect(suggestions.first, isNotEmpty);
|
||||
});
|
||||
|
||||
test('load fail', () {
|
||||
try {
|
||||
LanguageModel.load('doesnotexist');
|
||||
fail('Failure to load language model should throw an exception');
|
||||
} catch (e) {
|
||||
expect(e.toString(),
|
||||
equals('Invalid argument(s): Unable to create model.'));
|
||||
}
|
||||
});
|
||||
|
||||
test('isNumber', () {
|
||||
expect(model.isNumber('0xCAb005E'), true);
|
||||
expect(model.isNumber('foo'), false);
|
||||
expect(model.isNumber('3.1415'), true);
|
||||
expect(model.isNumber('1337'), true);
|
||||
expect(model.isNumber('"four score and seven years ago"'), false);
|
||||
expect(model.isNumber('0.0'), true);
|
||||
});
|
||||
}, skip: 'https://github.com/dart-lang/sdk/issues/42988');
|
||||
}
|
||||
|
||||
const expectedLookback = 100;
|
||||
|
||||
final directory = path.join(File.fromUri(Platform.script).parent.path, '..',
|
||||
'..', '..', '..', 'language_model', 'lexeme');
|
||||
|
||||
/// Tokenizes the input string.
|
||||
///
|
||||
/// The input is split by word boundaries and trimmed of whitespace.
|
||||
List<String> tokenize(String input) =>
|
||||
input.split(RegExp(r'\b|\s')).map((t) => t.trim()).toList()
|
||||
..removeWhere((t) => t.isEmpty);
|
||||
@@ -9,15 +9,11 @@ import 'combinator_contributor_test.dart' as combinator_test;
|
||||
import 'completion_manager_test.dart' as completion_manager;
|
||||
import 'completion_ranking_internal_test.dart'
|
||||
as completion_ranking_internal_test;
|
||||
// ignore: unused_import
|
||||
import 'completion_ranking_test.dart' as completion_ranking_test;
|
||||
import 'extension_member_contributor_test.dart' as extension_member_contributor;
|
||||
import 'field_formal_contributor_test.dart' as field_formal_contributor_test;
|
||||
import 'imported_reference_contributor_test.dart' as imported_ref_test;
|
||||
import 'keyword_contributor_test.dart' as keyword_test;
|
||||
import 'label_contributor_test.dart' as label_contributor_test;
|
||||
// ignore: unused_import
|
||||
import 'language_model_test.dart' as language_model_test;
|
||||
import 'library_member_contributor_test.dart' as library_member_test;
|
||||
import 'library_prefix_contributor_test.dart' as library_prefix_test;
|
||||
import 'local_library_contributor_test.dart' as local_lib_test;
|
||||
@@ -35,18 +31,12 @@ void main() {
|
||||
arglist_test.main();
|
||||
combinator_test.main();
|
||||
completion_manager.main();
|
||||
// TODO(lambdabaa): Run this test once we figure out how to suppress
|
||||
// output from the tflite shared library
|
||||
// completion_ranking_test.main();
|
||||
completion_ranking_internal_test.main();
|
||||
extension_member_contributor.main();
|
||||
field_formal_contributor_test.main();
|
||||
imported_ref_test.main();
|
||||
keyword_test.main();
|
||||
label_contributor_test.main();
|
||||
// TODO(brianwilkerson) Run this test when it's been updated to not rely on
|
||||
// the location of the 'script' being run.
|
||||
// language_model_test.main();
|
||||
library_member_test.main();
|
||||
library_prefix_test.main();
|
||||
local_lib_test.main();
|
||||
|
||||
@@ -43,7 +43,6 @@ void main() {
|
||||
expect(config.analyticsForceEnabled, isNull);
|
||||
expect(config.crashReportingId, isNull);
|
||||
expect(config.crashReportingForceEnabled, isNull);
|
||||
expect(config.mlModelPath, isNull);
|
||||
});
|
||||
|
||||
test('is configured', () {
|
||||
@@ -55,9 +54,7 @@ void main() {
|
||||
"server.analytics.forceEnabled": true,
|
||||
|
||||
"server.crash.reporting.id": "Test_crash_id",
|
||||
"server.crash.reporting.forceEnabled": true,
|
||||
|
||||
"server.ml.model.path": "/foo/bar/baz.ml"
|
||||
"server.crash.reporting.forceEnabled": true
|
||||
}
|
||||
''');
|
||||
|
||||
@@ -68,7 +65,6 @@ void main() {
|
||||
expect(config.analyticsForceEnabled, isTrue);
|
||||
expect(config.crashReportingId, 'Test_crash_id');
|
||||
expect(config.crashReportingForceEnabled, isTrue);
|
||||
expect(config.mlModelPath, '/foo/bar/baz.ml');
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
@@ -315,45 +315,6 @@ if (is_win) {
|
||||
]
|
||||
}
|
||||
|
||||
if (target_cpu == "x64") {
|
||||
if (is_linux || is_android || is_fuchsia) {
|
||||
copy_tree_specs += [
|
||||
{
|
||||
target = "copy_libtensorflowlite_c"
|
||||
visibility = [ ":create_common_sdk" ]
|
||||
deps = [ ":copy_libraries" ]
|
||||
source = "../third_party/pkg/tflite_native/lib/src/blobs"
|
||||
dest = "$root_out_dir/dart-sdk/bin/snapshots"
|
||||
ignore_patterns = "*.dll,*mac64.so"
|
||||
},
|
||||
]
|
||||
}
|
||||
if (is_mac) {
|
||||
copy_tree_specs += [
|
||||
{
|
||||
target = "copy_libtensorflowlite_c"
|
||||
visibility = [ ":create_common_sdk" ]
|
||||
deps = [ ":copy_libraries" ]
|
||||
source = "../third_party/pkg/tflite_native/lib/src/blobs"
|
||||
dest = "$root_out_dir/dart-sdk/bin/snapshots"
|
||||
ignore_patterns = "*.dll,*linux64.so"
|
||||
},
|
||||
]
|
||||
}
|
||||
if (is_win) {
|
||||
copy_tree_specs += [
|
||||
{
|
||||
target = "copy_libtensorflowlite_c"
|
||||
visibility = [ ":create_common_sdk" ]
|
||||
deps = [ ":copy_libraries" ]
|
||||
source = "../third_party/pkg/tflite_native/lib/src/blobs"
|
||||
dest = "$root_out_dir/dart-sdk/bin/snapshots"
|
||||
ignore_patterns = "*.so"
|
||||
},
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
# This generates targets for everything in copy_tree_specs. The targets have the
|
||||
# same name as the "target" fields in the scopes of copy_tree_specs.
|
||||
copy_trees("copy_trees") {
|
||||
@@ -918,9 +879,6 @@ group("create_common_sdk") {
|
||||
if (is_win) {
|
||||
public_deps += [ ":copy_7zip" ]
|
||||
}
|
||||
if (target_cpu == "x64") {
|
||||
public_deps += [ ":copy_libtensorflowlite_c" ]
|
||||
}
|
||||
|
||||
# CIPD only has versions of the Rust compiler for linux and mac x64 hosts.
|
||||
# We also disallow cross-compialtion (it may be possible in future, but it
|
||||
|
||||
Reference in New Issue
Block a user