Claude/mixed precision training architecture 011 cv13wm js ax6s gj6 ryu37k

[ooples]

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📥 Commits

Reviewing files that changed from the base of the PR and between e713a2b and 81c56f6.

📒 Files selected for processing (9)

• scripts/add-half-conditional-v2.py (0 hunks)
• scripts/add-half-conditional.py (0 hunks)
• scripts/add-half-conditionals.py (0 hunks)
• scripts/add-half-ifdef.sh (0 hunks)
• scripts/check-encoding.sh (0 hunks)
• scripts/fix-encoding.py (0 hunks)
• scripts/fix-half-conditionals.py (0 hunks)
• src/Interfaces/IPredictionModelBuilder.cs (1 hunks)
• src/PredictionModelBuilder.cs (1 hunks)

Summary by CodeRabbit

• New Features

  • Added mixed-precision training support with configurable loss scaling and dynamic overflow detection
  • Introduced precision modes (FP32, FP16, BF16, FP64) for optimized neural network training
  • Added tensor type casting functionality for precision conversion
  • Enabled Half-precision (FP16) numeric operations for improved training efficiency

• Tests

  • Added comprehensive test coverage for mixed-precision training workflows
  • Added encoding validation tests to ensure source file integrity

Walkthrough

This pull request introduces comprehensive mixed-precision neural network training support to AiDotNet. Changes include a new PrecisionMode enum, enhanced numeric operation classes with precision-aware conversions, new mixed-precision infrastructure (LossScaler, MixedPrecisionConfig, MixedPrecisionContext, MixedPrecisionTrainingLoop), a Tensor.Cast<TOut>() method for type conversion, and integration of mixed-precision support into core neural network components via fluent configuration.

Changes

Cohort / File(s)	Summary
Enums src/Enums/PrecisionMode.cs	New public enum with five precision modes: FP32, FP16, Mixed, BF16, FP64; provides strongly-typed specification for numeric precision in training.
Interface Expansions src/Interfaces/INumericOperations.cs, src/Interfaces/IPredictionModelBuilder.cs	Added PrecisionBits property and numeric conversion methods (ToFloat, FromFloat, ToDouble, and conditional ToHalf/FromHalf for NET5.0+) to INumericOperations<T>; added ConfigureMixedPrecision method to IPredictionModelBuilder.
Numeric Operations (All Types) src/NumericOperations/FloatOperations.cs, src/NumericOperations/DoubleOperations.cs, src/NumericOperations/ByteOperations.cs, src/NumericOperations/SByteOperations.cs, src/NumericOperations/ShortOperations.cs, src/NumericOperations/Int32Operations.cs, src/NumericOperations/Int64Operations.cs, src/NumericOperations/UInt16Operations.cs, src/NumericOperations/UInt32Operations.cs, src/NumericOperations/UInt64Operations.cs, src/NumericOperations/UIntOperations.cs, src/NumericOperations/DecimalOperations.cs, src/NumericOperations/ComplexOperations.cs	Consistently added PrecisionBits property and conversion methods (ToFloat, FromFloat, ToDouble; conditionally ToHalf/FromHalf for NET5.0+) to all numeric type operations. Documentation symbols updated for consistency.
New Half Operations src/NumericOperations/HalfOperations.cs	New public class implementing INumericOperations<Half> with full arithmetic, comparison, and conversion support for FP16; conditionally compiled for NET5.0+.
Mixed-Precision Infrastructure src/MixedPrecision/LossScaler.cs	New generic class providing dynamic loss scaling, overflow detection, and gradient unscaling with state tracking (TotalUpdates, SkippedUpdates, OverflowRate) and configurable growth/backoff behavior.
Mixed-Precision Configuration src/MixedPrecision/MixedPrecisionConfig.cs	New configuration class encapsulating mixed-precision settings (loss scale bounds, growth parameters, FP32 compute flags) with factory methods (Conservative(), Aggressive(), NoScaling()).
Mixed-Precision Context src/MixedPrecision/MixedPrecisionContext.cs	New class managing FP32 master weights, FP16 working weights, gradient preparation with unscaling and overflow checking; integrates LossScaler for dynamic scaling.
Mixed-Precision Training Loop src/MixedPrecision/MixedPrecisionTrainingLoop.cs	New generic class orchestrating complete mixed-precision training steps: forward pass, FP32 loss computation, loss scaling, backward pass, gradient unscaling with overflow detection, and conditional parameter updates.
Core Library Updates src/LinearAlgebra/Tensor.cs	Added public Cast<TOut>() method for element-wise type conversion preserving tensor shape; documentation symbol corrections (× notation).
Math Helper & Helpers src/Helpers/MathHelper.cs	Added support for Half type in GetNumericOperations<T>() branching; documentation updates for mathematical notation consistency.
Neural Network Integration src/NeuralNetworks/NeuralNetworkBase.cs	Added protected _mixedPrecisionContext field and public IsMixedPrecisionEnabled property; internal methods for lifecycle management.
Optimizer Integration src/Optimizers/GradientBasedOptimizerBase.cs	Added mixed-precision context field, IsMixedPrecisionEnabled property, and EnableMixedPrecision/DisableMixedPrecision lifecycle methods; new ApplyGradientsWithMixedPrecision pathway.
Builder Integration src/PredictionModelBuilder.cs	Added ConfigureMixedPrecision public method and internal wiring to apply mixed-precision configuration during build; stores MixedPrecisionConfig for propagation to networks and optimizers.
Tests – Encoding Verification tests/AiDotNet.Tests/UnitTests/Encoding/Utf8EncodingTests.cs	New test class verifying UTF-8 encoding integrity across source files, detecting replacement characters (U+FFFD) and flagging encoding corruption.
Tests – Mixed-Precision Loss Scaler tests/AiDotNet.Tests/UnitTests/MixedPrecision/LossScalerTests.cs	Comprehensive test suite covering constructor behavior, loss scaling, gradient unscaling, overflow detection, dynamic scaling, boundary enforcement, and state reset.

Sequence Diagram

sequenceDiagram participant Client participant TrainLoop as MixedPrecisionTrainingLoop participant Network as NeuralNetworkBase participant LossFunc as ILossFunction participant Optimizer as IGradientBasedOptimizer participant MPContext as MixedPrecisionContext participant LossScaler Client->>TrainLoop: TrainStep(input, target) TrainLoop->>MPContext: CastWeightsToFP16() Note over MPContext: Master Weights (FP32) → Working Weights (FP16) TrainLoop->>Network: Forward(inputFP16) Network-->>TrainLoop: outputFP16 TrainLoop->>LossFunc: Calculate(outputFP16, targetFP32) LossFunc-->>TrainLoop: lossFP32 TrainLoop->>LossScaler: ScaleLoss(lossFP32) LossScaler-->>TrainLoop: scaledLoss TrainLoop->>Network: Backward(scaledLoss) Network-->>TrainLoop: gradientsFP16 TrainLoop->>MPContext: PrepareGradientsForUpdate(gradientsFP16) MPContext->>LossScaler: UnscaleGradients(gradientsFP16) LossScaler->>LossScaler: DetectOverflow() alt Overflow Detected LossScaler->>LossScaler: SkippedUpdates++, ReduceScale() LossScaler-->>MPContext: false MPContext-->>TrainLoop: false TrainLoop-->>Client: false (step skipped) else No Overflow LossScaler-->>MPContext: true, gradientsFP32 MPContext->>Optimizer: ApplyGradientsWithMixedPrecision() Optimizer->>MPContext: UpdateMasterWeights(gradients, learningRate) MPContext-->>Optimizer: ✓ Optimizer-->>TrainLoop: ✓ LossScaler->>LossScaler: CheckGrowthInterval(), PotentiallyGrowScale() TrainLoop-->>Client: true (step applied) end 

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Estimated code review effort

🎯 4 (Complex) | ⏱️ ~60 minutes

• Specific areas requiring extra attention:
  • src/MixedPrecision/LossScaler.cs: Complex overflow detection logic, scale growth/backoff state machine, and gradient unscaling across tensors/vectors; verify boundary conditions (MinScale/MaxScale clamping).
  • src/MixedPrecision/MixedPrecisionContext.cs: Weight casting, gradient preparation flow, and integration with loss scaler; ensure FP32/FP16 conversions preserve correctness and handle edge cases.
  • src/MixedPrecision/MixedPrecisionTrainingLoop.cs: High-level training orchestration; verify T-is-float constraint enforcement and that overflow detection properly halts updates.
  • src/Optimizers/GradientBasedOptimizerBase.cs and src/NeuralNetworks/NeuralNetworkBase.cs: Mixed-precision context lifecycle; ensure enable/disable logic prevents double-initialization and properly cleans up resources.
  • src/NumericOperations/HalfOperations.cs: New FP16 arithmetic implementation; verify precision loss expectations in Exp/Log operations and conversion rounding behavior.
  • All numeric operation files: Verify consistency of conversion implementations (rounding, clamping) across 13 types to avoid cross-type precision inconsistencies.

Possibly related PRs

• Implement Mixed-Precision Training Architecture #475: Implements the identical mixed-precision training architecture (PrecisionMode enum, HalfOperations, INumericOperations extensions, LossScaler, MixedPrecisionConfig/Context, Tensor.Cast, NeuralNetwork/optimizer builder changes, and tests)—strongly overlapping implementation.
• Implement production-ready placeholder methods #474: Modifies NeuralNetworkBase.cs by adding new public members (autodiff/feature-extraction); both PRs touch the same base class and may encounter merge conflicts.

Poem

🐰 Half-precision hops with grace,
Loss scales dance through training space,
FP16 weights in working glow,
Master FP32's steady flow,
Mixed precision, swift and true—
Neural nets now lighter too! ✨

Pre-merge checks and finishing touches

❌ Failed checks (1 warning, 1 inconclusive)

Check name	Status	Explanation	Resolution
Description check	⚠️ Warning	The description is a template with placeholders and provides no actual information about what changed or why, making it completely unrelated to the changeset.	Fill in the template with specific details about the mixed-precision training architecture changes, verification status, and any relevant notes about the implementation.
Title check	❓ Inconclusive	The title is vague and appears to contain auto-generated identifiers, making it unclear what the primary change is without reading the detailed summary.	Replace with a clear, descriptive title that summarizes the main change, e.g., 'Add mixed-precision training architecture support' or 'Implement mixed-precision training with FP16/FP32 support'.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

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