fix(us-bf-023): remove duplicate methods in OptimizationResult#200
Merged
ooples merged 1 commit intomerge-dev2-to-masterfrom Oct 23, 2025
Conversation
- Removed duplicate DeepCopy() method definition (lines 511-528) - Removed duplicate WithParameters() method definition (lines 535-552) - Resolves CS0111 compilation errors for OptimizationResult class 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Contributor
There was a problem hiding this comment.
Pull Request Overview
This PR fixes CS0111 compilation errors in OptimizationResult.cs by removing duplicate method definitions that were causing build failures.
Key Changes:
- Removed duplicate
DeepCopy()method definition (lines 507-531) - Removed duplicate
WithParameters(Vector<T>)method definition (lines 533-556)
Tip: Customize your code reviews with copilot-instructions.md. Create the file or learn how to get started.
ooples
deleted the
fix/us-bf-023-resolve-duplicate-methods-optimizationresult
branch
ooples
pushed a commit
that referenced
this pull request
Apr 18, 2026
…ting (gaps 9+14) ## Gap 9 — autotune cache diagnostics Tensors ships AutotuneCache (Helpers.Autotune namespace) with per-kernel autotune storage. Users couldn't see whether it was active. Filed Tensors issue #200 for the WarmupCommonKernelsAsync convenience; added diagnostics surface here so users at least see the cache path + hardware fingerprint. - src/Diagnostics/AccelerationDiagnostics.cs: GetReport now emits AutotuneCache.DefaultCachePath + CurrentHardwareFingerprint. AccelerationSnapshot carries both as AutotuneCachePath / AutotuneHardwareFingerprint. ## Gap 14 — subclass Predict() routing through PredictCompiled 11 NeuralNetwork subclasses overrode Predict as literally `return Forward(input);`, bypassing the base's PredictCompiled auto-compile path. Refactored each to override PredictEager (the base's compile-lambda eager fallback) instead, keeping Forward as the implementation but routing through CompiledModelHost. After: every Predict on these 11 models goes through _compileHost.Predict, which traces → compiles → replays (and triggers disk caching via PlanCache when configured, from Gap 1). Files touched: - src/NeuralNetworks/ConvolutionalNeuralNetwork.cs - src/NeuralNetworks/EfficientNetNetwork.cs - src/NeuralNetworks/FastText.cs - src/NeuralNetworks/GloVe.cs - src/NeuralNetworks/MobileNetV2Network.cs - src/NeuralNetworks/ResNetNetwork.cs - src/NeuralNetworks/SiameseNeuralNetwork.cs - src/NeuralNetworks/UNet3D.cs - src/NeuralNetworks/VGGNetwork.cs - src/NeuralNetworks/VoxelCNN.cs - src/NeuralNetworks/Word2Vec.cs Forward methods unchanged — they still have their GPU-resident fast path (TryForwardGpuOptimized etc.) and shape-validation logic. The base's PredictCompiled treats Forward as the eager fallback but AutoTracer fires on first call regardless. ## Verify - dotnet build -f net10.0 — clean - dotnet build -f net471 — clean - dotnet test CompiledTapeTrainingStep + FusedOptimizer — 9/9 passing Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ooples
pushed a commit
that referenced
this pull request
Apr 19, 2026
…ting (gaps 9+14) ## Gap 9 — autotune cache diagnostics Tensors ships AutotuneCache (Helpers.Autotune namespace) with per-kernel autotune storage. Users couldn't see whether it was active. Filed Tensors issue #200 for the WarmupCommonKernelsAsync convenience; added diagnostics surface here so users at least see the cache path + hardware fingerprint. - src/Diagnostics/AccelerationDiagnostics.cs: GetReport now emits AutotuneCache.DefaultCachePath + CurrentHardwareFingerprint. AccelerationSnapshot carries both as AutotuneCachePath / AutotuneHardwareFingerprint. ## Gap 14 — subclass Predict() routing through PredictCompiled 11 NeuralNetwork subclasses overrode Predict as literally `return Forward(input);`, bypassing the base's PredictCompiled auto-compile path. Refactored each to override PredictEager (the base's compile-lambda eager fallback) instead, keeping Forward as the implementation but routing through CompiledModelHost. After: every Predict on these 11 models goes through _compileHost.Predict, which traces → compiles → replays (and triggers disk caching via PlanCache when configured, from Gap 1). Files touched: - src/NeuralNetworks/ConvolutionalNeuralNetwork.cs - src/NeuralNetworks/EfficientNetNetwork.cs - src/NeuralNetworks/FastText.cs - src/NeuralNetworks/GloVe.cs - src/NeuralNetworks/MobileNetV2Network.cs - src/NeuralNetworks/ResNetNetwork.cs - src/NeuralNetworks/SiameseNeuralNetwork.cs - src/NeuralNetworks/UNet3D.cs - src/NeuralNetworks/VGGNetwork.cs - src/NeuralNetworks/VoxelCNN.cs - src/NeuralNetworks/Word2Vec.cs Forward methods unchanged — they still have their GPU-resident fast path (TryForwardGpuOptimized etc.) and shape-validation logic. The base's PredictCompiled treats Forward as the eager fallback but AutoTracer fires on first call regardless. ## Verify - dotnet build -f net10.0 — clean - dotnet build -f net471 — clean - dotnet test CompiledTapeTrainingStep + FusedOptimizer — 9/9 passing Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ooples
added a commit
that referenced
this pull request
Apr 20, 2026
…loses #1015) (#1155) * chore(deps): bump AiDotNet.Tensors 0.38.0 → 0.42.3 Also bumps AiDotNet.Native.OneDNN in lockstep. Picks up the recent perf work (backward-op primitive fast paths, net471 SIMD gap fix, memory planning / tile scheduling / operator reordering, plan serialization + stitching audit fixes) and is the baseline for the dead-JIT-scaffolding cleanup in issue #1015. Both net10.0 and net471 build with 0 errors. No source changes needed — the Tensors API is backward-compatible across 0.38 → 0.42. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore: remove dead src/InferenceOptimization/ tree and TapeLayerBridge The src/InferenceOptimization/ directory (16 .cs files + Core/IR/Kernels/Passes subdirs + README + ARCHITECTURE) was orphaned JIT IR/optimization-pass scaffolding from a compilation model that no longer exists. Zero production code in src/ referenced it — only tests and benchmarks did. The AiDotNet.Tensors compilation system (AutoTracer + CompiledInferencePlan + CompiledTrainingPlan, auto-enabled) is the replacement. Removed: - src/InferenceOptimization/ (full tree, 36 .cs files) - tests/AiDotNet.Tests/InferenceOptimization/ + IntegrationTests/InferenceOptimization/ - AiDotNetBenchmarkTests/InferenceOptimization/ - The InferenceOptimization PR #768 regression-tests region in MergedPRBugFixTests.cs - global using MemoryLayout / QuantizationParams aliases in src/Helpers/UsingsHelper.cs and tests/AiDotNet.Tests/GlobalUsings.cs (nothing else in either project uses either of those short names) - src/NeuralNetworks/SyntheticData/TapeLayerBridge.cs — public method ExportMLPGeneratorGraph had zero callers; xmldoc falsely claimed WGAN-GP use but WGANGP.cs:450-456 already uses GradientTape<T> directly - SyntheticTabularGeneratorBase.ExportMLPGeneratorGraph wrapper (dead) - SyntheticTabularGeneratorBase.SupportsJitCompilation + ExportComputationGraph stubs (both threw NotSupportedException) Scrubbed TapeLayerBridge mentions from MedSynthGenerator, MisGANGenerator, TimeGANGenerator private helper xmldoc (the helpers themselves remain — they're still used by in-progress gradient-penalty code or kept for analysis). Cosmetic: the "// InferenceOptimization Operations" and "// Fused Operations for InferenceOptimization" comment labels in src/Enums/OperationType.cs are replaced with generic labels. The enum values themselves are public API and left in place. LIVE and untouched (different systems that share a prefix): - src/Inference/InferenceOptimizer.cs (KV-cache, speculative decoding) - src/Configuration/InferenceOptimizationConfig.cs (quantization config) Build: net10.0 + net471 both green, 0 errors. Tests build green. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * refactor: remove dead AiDotNet-side ExportComputationGraph scaffolding The removed AiDotNet JitCompiler left behind a graveyard of stub methods and now-unreachable graph-building helpers. All of them either threw NotSupportedException or built ComputationNode<T> trees for a compiler that no longer exists. Deleted: - ExportComputationGraph method on: LayerBase, NeuralNetworkBase, ClassifierBase, MultiLabelClassifierBase, RegressionBase, NonLinearRegressionBase, DecisionTreeRegressionBase, DecisionTreeAsyncRegressionBase, DiffusionModelBase, VAEModelBase, NoisePredictorBase, AutoMLModelBase, ShardedModelBase, CausalModelBase, OnlineLearningModelBase, TimeSeriesModelBase, ReinforcementLearningAgentBase, SurvivalModelBase, NBEATSBlock, AiModelResult, MCDropoutLayer, BayesianDenseLayer. - ConvertLayerToGraph helper on NeuralNetworkBase. - SupportsJitCompilation property on LayerBase + same accompanying bases (kept on IActivationFunction / IVectorActivationFunction interfaces and their implementations since those are still consumed by LoRA / SE / Hyperbolic layer fallback paths). - Layer-internal graph helpers: LayerBase.ApplyActivationToGraph, CanActivationBeJitted, SparseLinearLayer.ApplyActivationToComputationNode, SqueezeAndExcitationLayer.ApplyActivationToGraphNode, SpyNetLayer.{BuildComputationGraph,BuildPyramidGraph,CreateGridFromFlowGraph, CreateIdentityGrid,CreateScaleTensor}, DeformableConvolutionalLayer.BuildComputationGraph. - NonLinearRegressionBase kernel-graph helpers: ComputeLinearKernel, ComputeRBFKernel, ComputeSigmoidKernel, ComputePolynomialKernel, ComputeLaplacianKernel, CreateFilledTensorLike. - DecisionTreeRegressionBase / DecisionTreeAsyncRegressionBase ExportNodeAsComputationGraph + GetMaxFeatureIndexFromTree helpers. - NBEATSBlock.ApplyBasisExpansionGraph helper. - TestScaffoldGenerator emitting the SupportsJitCompilation / ExportComputationGraph stubs into generated test fixtures. Stale xmldoc bullets "JIT compilation support via ExportComputationGraph()" removed from GraphAttentionLayer, GraphIsomorphismLayer, GraphSAGELayer, PrincipalNeighbourhoodAggregationLayer. Tests: removed obsolete *_JitRemoved_SupportsJitIsFalse* assertions from BaseClassesIntegrationTests and the LoRA/KNN/LWR/RotaryPE/QuantizedAttention SupportsJitCompilation checks. Removed MixedPrecisionIntegrationTests' TestLayer override of SupportsJitCompilation / ExportComputationGraph. Compilation is transparent via AiDotNet.Tensors' AutoTracer (auto-enabled, hot paths compile to CompiledInferencePlan after the 2nd call). Opt out via TensorCodecOptions.Current.EnableCompilation or the still-supported AiModelBuilder.ConfigureJitCompilation() builder API (which projects config onto the live TensorCodecOptions under the hood). Build: net10.0 + net471 + tests all green, 0 errors. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore(deps): bump AiDotNet.Tensors 0.42.3 → 0.46.0 Newer Tensors release while this PR was in flight. Brings the work after 0.42.x (0.43, 0.44, 0.45, 0.46). Backward-compatible API — no source changes required. Build: net10.0 + net471 both green, 0 errors. Auto-compile regression tests (CompileForwardTests + CompiledModelHostTests, 14 total): all pass on both TFMs. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix: address PR #1155 review comments on NBEATSBlock Three review comments from CodeRabbit, all on src/TimeSeries/NBEATSBlock.cs: 1. (Major) ParameterCount / GetParameters / SetParameters ignored the trainable V_b / V_f bases in generic mode — only fc weights + biases were exported/imported. That made parameter round-trips drop learned basis state. Fixed: include the bases in all three APIs when _useInterpretableBasis == false, and also re-register them in ReRegisterParameters so the SetParameters tensor swap doesn't drop them from the trainable registry. 2. (Major) Constructor validated lookbackWindow / forecastHorizon / hiddenLayerSize / numHiddenLayers but accepted thetaSizeBackcast, thetaSizeForecast, and polynomialDegree without validation. Invalid values deferred failure to tensor allocation/math paths where diagnosis is much harder. Added explicit checks: both theta sizes must be positive; polynomialDegree must be non-negative when useInterpretableBasis is true. 3. (Critical) UpdateParameters(T learningRate) was an empty override that silently ignored update calls — the kind of silent no-op that becomes an accuracy regression you can only find by bisecting. Replaced with a fail-fast InvalidOperationException pointing callers at the tape-based training path (CompiledTapeTrainingStep), so misuse is caught at the training boundary instead of producing silently-undertrained models. Build: net10.0 + net471 + tests all green, 0 errors. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix: two more PR #1155 review comments on NBEATSBlock 1. (Major) Interpretable theta sizes weren't validated against polynomialDegree + 1. ComputeBasisTensor + ApplyBasisExpansion both cap usable rows at polynomialDegree + 1, so oversized thetaSizeBackcast / thetaSizeForecast silently allocated dead trainable weights that couldn't influence the output. Added explicit checks for interpretable mode. 2. (Critical) ForwardInternal's generic branch in ApplyBasisExpansion returned theta directly instead of multiplying by the learned V_b / V_f basis tensors. With the Phase 1 fix that made those bases round-trip through GetParameters/SetParameters, PredictSingle would diverge from both Forward() (which uses _basisBackcast/_basisForecast via matmul) and from loaded-model state. Changed ApplyBasisExpansion to take a basis tensor argument and multiply by it in the generic branch, matching training and tape paths per Oreshkin et al. 2020 §3.2. Build: net10.0 clean, 0 errors. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * refactor: audit pass — remove dead SupportsJitCompilation properties + empty IJitCompilable regions Sweep #1 of the end-to-end JIT audit. The IJitCompilable interface no longer exists (removed with the JitCompiler). Every remaining reference was either a stale region marker with no content, or a vestigial SupportsJitCompilation property that nothing reads. Removed: - SupportsJitCompilation property on 11 model base classes (AutoML, CausalInference, Classification, MultiLabelClassification, Diffusion, NoisePredictor, VAE, DistributedTraining/ShardedModelBase, NeuralNetworkBase, OnlineLearning, Survival). Kept on IActivationFunction / IVectorActivationFunction implementations since LoRA / SqueezeAndExcitation / HyperbolicLinear / SparseLinear still consult those for activation-graph fallback (live code path). - 21 empty #region IJitCompilable Override markers across the SyntheticData generators (AutoDiffTab, CausalGAN, CopulaGAN, CTABGANPlus, CTGAN, DPCTGAN, FinDiff, GOGGLE, MedSynth, MisGAN, OCTGAN, PATEGAN, REaLTabFormer, TabDDPM, TabFlow, TableGAN, TabLLMGen, TabSyn, TabTransformerGen, TimeGAN, TVAE). - 4 empty #region IJitCompilable Implementation Override markers in Regression (AdaBoostR2, ExtremelyRandomizedTrees, GradientBoosting, RandomForest) and TransferLearning (TransferRandomForest). - ExpressionTree.BuildComputationGraph (private method with only recursive self-calls; nothing external called it after JitCompiler removal). - VectorModel.VectorToTensor (private method inside #region IJitCompilable Implementation; only defined, never referenced inside or outside the file). - SuperNet.ExportOperationGraph + SuperNet.Forward (both dead, only existed to satisfy the removed IJitCompilable interface). Build: net10.0 + net471 both green, 0 errors. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore(jit-audit): remove dead BuildComputationGraph chain + IJitCompilable xmldoc Audit A: continues the dead JIT-scaffolding sweep from 010bc09 / fb9ebb2. After removal in prior commits of the JitCompiler / InferenceOptimization IR / ExportComputationGraph surface, these eight layer files still carried a closed chain of private BuildComputationGraph() methods that only called each other (zero external callers), plus xmldoc remarks referencing IJitCompilable on interfaces that were removed. All of it is dead. - 8 neural-network layers: remove private BuildComputationGraph chain (DenseBlock, InvertedResidual, RRDB, RRDBNetGenerator, ResidualDense, SqueezeAndExcitation, Transition, UNetDiscriminator) - 5 KnowledgeDistillation teacher-model xmldocs: strip IJitCompilable references; note Tensors' AutoTracer handles auto-compile transparently - DeepReinforcementLearningAgentBase: same xmldoc fix + point to TensorCodecOptions.Current.EnableCompilation for opt-out - InterfaceGuard / IFullModel: scrub IJitCompilable from the remarks list Build: net10.0 + net471 clean (0 errors). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore(jit-audit): remove orphan IJitCompilable stubs from tests & docs After the production-side IJitCompilable removal in prior commits, test mocks, testconsole examples, the benchmark harness, and the golden-standard pattern doc still carried SupportsJitCompilation + ExportComputationGraph stubs for a removed interface — purely orphan code that compiled only because no interface required those members. This sweep removes all of them (29 test files + 3 testconsole examples + 1 benchmark helper + GOLDEN_STANDARD_PATTERNS.md). The only remaining SupportsJitCompilation references are on IActivationFunction / IVectorActivationFunction, which are part of the live ComputationNode graph-mode autodiff path (distinct from the removed JIT compiler). Build: net10.0 + net471 clean across src/, tests/, testconsole/, and AiDotNetBenchmarkTests/. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore(jit-audit): remove stale JitCompiler/InferenceOptimization filters from CI Deep Audit A found 5 dangling references to deleted test namespaces in CI and test-sharding configs. These filters targeted tests that were deleted in the JIT/InferenceOptimization sweep — now no-ops that confuse CI dashboards. - .github/workflows/sonarcloud.yml: - Unit-06 shard: drop UnitTests.JitCompiler (deleted); rename "JIT/KD/..." -> "KD/..." - Exclusion filter: drop UnitTests.JitCompiler (no longer emits tests) - Drop "Other - InferenceOptimization" shard entirely (namespace deleted) - scripts/run-tests-sharded.ps1: - Drop "JitCompiler" from unitNamespaceRoots - Rename Unit-07 shard "Interpretability/JIT/KD" -> "Interpretability/KD" - Drop shard 13 "InferenceOptimization" (namespace deleted); renumber PromptEngineering -> 13 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(tensors-parity): acceleration diagnostics + SymbolicShape dynamic-batch compile (gaps 2+5) Part of the Tensors-0.46.0 parity work (closes 2 of 14 gaps; see Tensors issues #197 + #198 for the two gaps that need upstream Tensors features). ## Gap 5 — acceleration diagnostics (PlatformDetector + NativeLibraryDetector) Tensors already ships PlatformDetector (SIMD level, cache sizes, GPU support flags) and NativeLibraryDetector (OpenBLAS / CLBlast / cuDNN / MKL availability). AiDotNet was ignoring both — users had no visibility into which acceleration path was actually engaged. - src/Diagnostics/AccelerationDiagnostics.cs — new facade wrapping both detectors. GetReport() returns a human-readable summary; GetSnapshot() returns a structured AccelerationSnapshot for programmatic assertions. - AiModelBuilder.ReportAccelerationStatus(Action<string>? logger) — opt-in builder method. Runs after ApplyGpuConfiguration so the snapshot reflects the engine state the built model will actually see. - AiModelResult.AccelerationSnapshot — new property on every AiModelResult. 7 construction sites updated via AttachDiagnostics() helper. ## Gap 2 — SymbolicShape for dynamic batch/seq-len compile keys CompiledModelHost keyed the compile cache on concrete shape via GetOrCompileInference(shape, forward). Every batch-size change forced a fresh trace+compile — wasteful for real inference traffic where request batches vary. Tensors exposes SymbolicShape.BatchDynamic / BatchAndSeqDynamic / AllDynamic + a 3-arg GetOrCompileInference overload for exactly this case. - src/NeuralNetworks/CompiledModelHost.cs: new SymbolicShapeMode enum (Static / BatchDynamic / BatchAndSeqDynamic / AllDynamic). Default = BatchDynamic (matches PyTorch torch.compile(dynamic=True) default). - Predict() builds a SymbolicShape from mode + concrete shape and calls the 3-arg overload, falling back to the 2-arg concrete overload when rank is too small (e.g. 1-D scalar input with BatchDynamic requested). ## Verify dotnet build -f net10.0 — clean. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(tensors-parity): disk-backed plan caching via CompiledPlanLoader (gap 1) PyTorch-parity equivalent: torch.jit.save + torch.jit.load. Before this change AiDotNet recompiled every forward-pass plan on every process start — wasteful for production serving where warm inference latency matters. Tensors 0.46.0 already ships everything needed: ICompiledPlan.SaveAsync, CompiledPlanLoader.LoadInferenceAsync, PlanCompatibilityInfo for hardware- fingerprint gating. We just weren't wiring it. ## Public API await new PredictionModelBuilder<float, Tensor<float>, Tensor<float>>() .ConfigureModel(myNet) .ConfigurePlanCaching(@"C:\PlanCache") // NEW .BuildAsync(); Plans are saved under modelTypeName_T_v{structureVersion}_s{shapeHash}.plan. Per-(model, T, version, shape) — one directory can host multiple models without collision. Loads that fail PlanCompatibilityInfo fall through to a fresh compile silently. ## Files - src/NeuralNetworks/PlanCache.cs: new. Static Current, directory-based storage, atomic writes (tmp + rename). Shape hash = SHA256 of int32[]. - src/NeuralNetworks/CompiledModelHost.cs: - ctor now accepts optional modelIdentity — null = disk caching off - new fields: _diskCheckedShapes (one load attempt per shape-version), _preloadedPlans (in-memory cache of disk-loaded plans) - Predict(): before GetOrCompileInference, call TryUseDiskCachedPlan. If hit, skip compile entirely. - After fresh compile, fire-and-forget save via Task.Run so Predict doesn't block on IO. - src/NeuralNetworks/NeuralNetworkBase.cs: _compileHost is now assigned in the ctor so GetType().FullName reflects the concrete subclass — different model types don't collide on disk keys. - src/Diffusion/NoisePredictors/NoisePredictorBase.cs: same change. - src/AiModelBuilder.cs + src/Interfaces/IAiModelBuilder.cs: new ConfigurePlanCaching(directory) fluent method. ## Verify dotnet build -f net10.0 — clean. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(tensors-parity): add FusedLinear in FeedForward + Tensors op profiling (gaps 6/7/12) ## Gap 12 — Fused GEMM+Bias+Activation (already mostly wired; completing) Audit confirmed the major linear layers (DenseLayer, FullyConnectedLayer, and several others) already dispatch through Engine.FusedLinear / FusedLinearGpu for CPU + GPU paths. The notable miss was FeedForwardLayer.Forward, which was doing separate TensorMatMul + Reshape + TensorBroadcastAdd + ApplyActivation calls (4 kernel launches per forward). Refactored to use Engine.FusedLinear(input, weights, biases, fusedActivation) with the standard pre-activation tape-safe fallback for training. - src/NeuralNetworks/Layers/FeedForwardLayer.cs: Forward() rewritten. Mirror of DenseLayer's fused-inference path. ## Gap 7 — Tensors per-op profiling (orthogonal to existing AiDotNet ProfilerSession) AiDotNet already has its own ProfilerSession / ProfileReport / AiModelResult. ProfilingReport surfacing HIGHER-level workflow timings (Welford stats, hierarchical call trees, reservoir percentiles, memory tracking — richer than Tensors' simpler per-op profiler). Tensors has no parity with that feature set, so we keep it. What was missing: visibility into LOWER-level tensor-op kernel timings. Tensors ships PerformanceProfiler.Instance which wraps every engine op in an IDisposable scope — useful for finding which kernel (MatMul, Softmax, LayerNorm) is the actual bottleneck. - src/Diagnostics/ProfilingReport.cs: new. TensorsOperationProfile wraps PerformanceProfiler output. FormatSummary formats top-N ops. - src/AiModelBuilder.cs + src/Interfaces/IAiModelBuilder.cs: new EnableTensorsOpProfiling() fluent method. - src/Models/Results/AiModelResult.Diagnostics.cs: new TensorsOperationProfile property. Sits alongside existing ProfilingReport (not replacing it). ## Verify dotnet build -f net10.0 — clean. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(tensors-parity): diagnostics surface + subclass PredictEager routing (gaps 9+14) ## Gap 9 — autotune cache diagnostics Tensors ships AutotuneCache (Helpers.Autotune namespace) with per-kernel autotune storage. Users couldn't see whether it was active. Filed Tensors issue #200 for the WarmupCommonKernelsAsync convenience; added diagnostics surface here so users at least see the cache path + hardware fingerprint. - src/Diagnostics/AccelerationDiagnostics.cs: GetReport now emits AutotuneCache.DefaultCachePath + CurrentHardwareFingerprint. AccelerationSnapshot carries both as AutotuneCachePath / AutotuneHardwareFingerprint. ## Gap 14 — subclass Predict() routing through PredictCompiled 11 NeuralNetwork subclasses overrode Predict as literally `return Forward(input);`, bypassing the base's PredictCompiled auto-compile path. Refactored each to override PredictEager (the base's compile-lambda eager fallback) instead, keeping Forward as the implementation but routing through CompiledModelHost. After: every Predict on these 11 models goes through _compileHost.Predict, which traces → compiles → replays (and triggers disk caching via PlanCache when configured, from Gap 1). Files touched: - src/NeuralNetworks/ConvolutionalNeuralNetwork.cs - src/NeuralNetworks/EfficientNetNetwork.cs - src/NeuralNetworks/FastText.cs - src/NeuralNetworks/GloVe.cs - src/NeuralNetworks/MobileNetV2Network.cs - src/NeuralNetworks/ResNetNetwork.cs - src/NeuralNetworks/SiameseNeuralNetwork.cs - src/NeuralNetworks/UNet3D.cs - src/NeuralNetworks/VGGNetwork.cs - src/NeuralNetworks/VoxelCNN.cs - src/NeuralNetworks/Word2Vec.cs Forward methods unchanged — they still have their GPU-resident fast path (TryForwardGpuOptimized etc.) and shape-validation logic. The base's PredictCompiled treats Forward as the eager fallback but AutoTracer fires on first call regardless. ## Verify - dotnet build -f net10.0 — clean - dotnet build -f net471 — clean - dotnet test CompiledTapeTrainingStep + FusedOptimizer — 9/9 passing Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix: address 5 CodeRabbit review comments on PR #1155 - NBEATSBlock ctor: extract CreateInputShape / CreateOutputShape static factories that validate lookbackWindow / forecastHorizon BEFORE the base(...) call. Invalid values now surface as ArgumentException with the right nameof(...) tag instead of a downstream LayerBase<T> shape error. - InterfaceGuard: class visibility reduced from public to internal to match the AiDotNet facade pattern. InternalsVisibleTo on src/AiDotNet.csproj already grants access to AiDotNetTests / AiDotNetTestConsole / AiDotNet.Serving / AiDotNetBenchmarkTests, so the 58 existing test call sites still compile. Doc remark added explaining the visibility choice. - PretrainedTeacherModel + TransformerTeacherModel: reworded "auto-compiles via Tensors' AutoTracer" remarks. The wrapper only invokes the delegate; whether auto-compile actually happens depends entirely on what's inside the delegate. Removed the unconditional guarantee and added a note that external paths (ONNX, REST, etc.) won't pick up engine optimizations. - SelfTeacherModel.GetLogits: rewrote XML-doc so summary/returns/exception match the throw-only behavior (method has no underlying model to run and always throws InvalidOperationException). Previous summary said "Gets logits from the underlying model" which was misleading. Verify: dotnet build net10.0 + net471 — 0 errors. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: franklinic <franklin@ivorycloud.com> Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Summary
Removes duplicate method definitions in OptimizationResult that were causing CS0111 compilation errors.
Changes:
DeepCopy()method definition (kept first occurrence)WithParameters(Vector<T>)method definition (kept first occurrence)Build Status:
User Story: US-BF-023
🤖 Generated with Claude Code