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fix(timeseries): honour Options.Seed in the deep time-series models; stop N-BEATS blocks sharing initial weights - #2285
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…and stop N-BEATS blocks sharing …initial weights Every deep time-series model seeded its random streams with hard-coded literals (42, 1042, 12345, and 42 + i * 1000 for sub-layers) and ignored Options.Seed. Two runs with different seeds were therefore the same run: seed-to-seed variance could not be measured, and an ensemble over seeds was one model repeated. TimeSeriesModelBase.SeedOr(fallback) returns the historical literal when Options.Seed is unset, so unseeded results reproduce exactly as before. When Options.Seed is set it returns a SplitMix64 derivation of (seed, stream), masked to 30 bits so sub-layer offsets cannot overflow. Every model-level and sub-layer seed in DeepAR (LSTM cells, Gaussian/Student-t/spline heads), N-BEATS, N-HiTS, Informer (encoder, distilling, decoder), Autoformer, Chronos, TFT, DLinear, NLinear, TiDE, DeepANT, LSTM-VAE and the base SPSA fallback now goes through it. N-BEATS built every block with seed 42, so all blocks started from identical weights. Blocks now take a seed and get SeedOr(42) + i. This changes N-BEATS's default initial weights (block 0 is unchanged). Tests (TimeSeriesSeedTests): same seed -> identical initial weights, different seeds -> different, no seed -> reproducible, for DeepAR/N-BEATS/N-HiTS/TFT/Informer/Autoformer/TiDE; DLinear/NLinear (fixed 1/L init by design) differ in trained weights; N-BEATS blocks differ. Falsified: SeedOr ignoring the seed turns 9 red; a shared N-BEATS block seed turns NBeatsBlocks_DoNotShareInitialWeights red. All 732 TimeSeries tests pass (net10.0). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_017otuSGr3GdmvPoYLbiaWR2
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WalkthroughTime-series models now use configured seeds for initialization and training random generators, while retaining fallback seeds when no seed is set. Integration tests check reproducibility for matching seeds and variation across different seeds. ChangesTime-Series Seed Handling
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Bug fix Suggested reviewers: Merge Risk: 🔵 Low · up to Cloned models may not honor a configured seed. The impact is limited to cloning paths, but preserving the seed should be addressed before relying on reproducible clones. Security Architecture ReviewSecurity architecture risk: 🔵 Low · up to The inspected changes affect numerical reproducibility rather than permissions or isolation. Some default initial weights change, and reproducibility after recovery may depend on retaining the original seed configuration. No introduced security weakness was established. Retained concerns Security review detailsSecurity Blast Radius
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Review comments at @src/TimeSeries/AnomalyDetection/DeepANT.cs:
- Line 110: Update the copy constructors for DeepANTOptions<T>,
LSTMVAEOptions<T>, and ChronosOptions<T> to copy the inherited ModelOptions.Seed
from the source options, preserving the configured seed when CreateInstance
clones these options.
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src/TimeSeries/AnomalyDetection/DeepANT.cssrc/TimeSeries/AnomalyDetection/LSTMVAE.cssrc/TimeSeries/AutoformerModel.cssrc/TimeSeries/ChronosFoundationModel.cssrc/TimeSeries/DLinearModel.cssrc/TimeSeries/DeepARModel.cssrc/TimeSeries/InformerModel.cssrc/TimeSeries/NBEATSBlock.cssrc/TimeSeries/NBEATSModel.cssrc/TimeSeries/NHiTSModel.cssrc/TimeSeries/NLinearModel.cssrc/TimeSeries/TemporalFusionTransformer.cssrc/TimeSeries/TiDEModel.cssrc/TimeSeries/TimeSeriesModelBase.cstests/AiDotNet.Tests/IntegrationTests/TimeSeries/TimeSeriesSeedTests.cs
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…ions are copied CreateInstance clones a model's options through their copy constructors. DeepANTOptions, LSTMVAEOptions and ChronosOptions copied their own fields but not the inherited Seed, so a clone of a seeded DeepANT initialised from SeedOr(42) instead. They were the only 3 of 67 TimeSeriesRegressionOptions subclasses that neither chain to the base nor copy Seed by hand. They now chain to TimeSeriesRegressionOptions(other), which copies Seed and the other inherited settings, rather than adding one more hand-copied line. TimeSeriesSeedTests.CopiedOptions_KeepTheSeed covers all three. Verified: 57 time-series seed, DeepANT, LSTM-VAE and Chronos tests pass on net10.0. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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…er-state Both branches solved the per-epoch fused learning rate (LrSchedule.External) and the optimizer's own clip independently. Resolution keeps master's mechanisms and this branch's unique work: - Clip: master's TapeStepGradientClipNorm (any gradient-based optimizer, declines ByValue, fused L2) replaces this branch's spec MaxGradientNorm / SmallerPositiveGradNorm. - Per-epoch schedule: master's TryGetFusedLrSchedule verbatim; this branch keeps AdoptRestoredFusedLrSchedule, which rebinds the external rate after a checkpoint import. - Double hyperparameters kept, and extended to master's new MultiSlotFusedStep and WganGpFusedStep overloads (master passed the config's fields to float parameters). - The legacy out-parameter TryMapToFusedOptimizerConfig goes, as on master; master's callers use the config. Synthetic generators take master's version (ours only widened locals). - Nadam: both sides fixed the t+1 momentum correction; one declaration kept per method. AMSGrad: kept the paper-variant comment, which matches SecondMomentCorrection. - Removed auto-merge duplicates the compiler caught (biasCorrectionMNext x3, a repeated eagerOptimizer: argument) and one it could not (a second StepPerEpoch block). - Tests: master's AMSGrad PyTorch-variant and Nadam two-step tests, plus this branch's paper-default AMSGrad step-1 test. Also fixes a regression master's explicit-regularization change introduced: a proximal optimizer applies its regularizer inside its own step, but the network step also added it to the gradient (L1 applied twice) and refused the fused proximal kernel for it. GradientBasedOptimizerBase.AppliesRegularizationInStep (true for ProximalGradientDescent) now excludes it. FusedOptimizerParityTests ProximalGradientDescentL1 failed without it. 229 optimizer, fused, schedule, checkpoint and GraFPrint tests pass; net471 builds. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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…ross models, FastSpeech2 back in the PR gate (#2292) Closes #2157 Closes #2151 Closes #2290 Closes #2093 Four issue fixes, combined into one CI run: 90 files. ## #2157: latent diffusion trained the denoiser on raw input `LatentDiffusionModelBase` passed the training sample to the scheduler unchanged. A caller who passed images therefore trained the denoiser on pixels, while `Generate` runs it on latents. On master, training a latent model on an image does not run at all: it throws a shape mismatch. - `PrepareTrainingSample` now encodes an image-shaped sample (`[B, VAE.InputChannels, H, W]` or `[C, H, W]`) with the frozen first stage, as Rombach et al. 2022 §3.3 does (posterior sample, scaled). It matches what `EncodeToLatent` does for inference. - A sample that is already a latent is used as is. - Models that override the hook (UpscaleAVideo, DiffusionAutoML) keep their own. - **Tests:** an image reaches the denoiser as `[1, LatentChannels, H/f, W/f]`, and a latent passes unchanged. The first test fails on master. ## #2151: text conditioners had no layers before a forward Fixed on master by `1aa1d15bd`, which did not close the issue and added no test. This PR adds `TextConditionerEagerLayersTests`: all eight conditioners must report layers straight after construction. Removing CLIP's eager `InitializeLayers()` makes it fail. ## #2290: models ignored `Options.Seed` (much wider than reported) Building every financial model twice with the same `Options.Seed` showed that only **9 of 90** neural finance models reproduced their initial weights. The cause: layer initialisation draws its seeds from `LayerInitializationSeedScope`, which `NeuralNetworkBase`'s constructor resets from `Architecture.RandomSeed` alone. - **`NeuralNetworkBase.Options` now applies `Options.Seed`** (`ApplyOptionsSeed`). Constructors assign their options before building layers, so the construction scope restarts from the seed. An explicit architecture seed still wins. This change alone fixed 81 models. - **Two layers had unseeded random sources:** - `MambaBlock` drew its dt init from `CreateSecureRandom()`, which affected Mamba and TimeMachine. - `RWKV7Block` initialised four LoRA matrices with a bare `OrthogonalInitializationStrategy`, which affected RWKVForecaster. Both now draw from the layer's own seeded stream. - **GraphAttentionPortfolio and SignatureInformedTransformer** now assign `Options` before building their layers. - **Breaking change, by decision (delete rather than obsolete):** the 24 options classes that declared `RandomSeed` beside the inherited `ModelOptions.Seed` lose it. - Every reader now uses `Seed`: 53 sites in src, 105 in tests, and one sample. - `ObjectDetectionOptions` and `TimeSeriesIsolationForestOptions` keep their historical default by starting `Seed` at 42. - **Tests:** - `FinanceModelSeedTests`: the same seed gives identical initial weights for every neural finance model. On the eight transformers named in the issue, different seeds differ and an architecture seed wins. That is 111 cases, and 94 of them fail on the unfixed code. - The finance integration suite passes 1016/1016. - The finance test factory now picks a constructor that takes options when a test configures them. The parameterless constructor had made the portfolio models look unseeded. ## #2093: FastSpeech2 training was invisible to CI The reported divergence (9.66 → 15.63) **no longer reproduces**. A fixed (input, target) pair trains from 11.88 to 1.30 over 40 steps, and clean master gives the identical trajectory. It was fixed on master before this branch, most likely by the 2026-09-06 paper-optimizer rework. What remained was how it had been hidden: - **FastSpeech2 leaves the `HeavyTimeout` list.** Its 33 generated tests run in 2 minutes, the slowest in 24 s against a 120 s gate. The tag had been suppressing a correctness failure, not a timeout. - **Its training probes ran one step, which cannot show a trajectory.** `Training_ShouldReduceLoss` and the MoreData probe now run 12 steps, past a measured step-4 bump. The strictly decreasing memorization probe keeps 2. FastSpeech (v1) is unchanged, because it was not measured. ## Not included #2138 (35 models declaring a category without its interface) needs its own PR. #2290's caller migration used most of the 100-file budget. ## Verification (local, `AIDOTNET_DISABLE_GPU=1`) - **Builds:** net10.0 src and tests; the net8.0 + net471 compat build of the whole solution; ParameterSweepWorker; Serving.Tests. All were re-run after merging master (80 commits, including #2285's time-series seeding), with no conflicts and zero errors. - **Regression tests fail on the unfixed code:** #2157's image test, #2151's CLIP case, and #2290's 94 seed cases. - **Post-merge test run:** the system stopped it for low memory after 178 tests had passed with 0 failures, so CI is the complete run. 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Latent diffusion training supports image samples, which are encoded into VAE latents, and pre-encoded latent samples. * TimeGrad now provides autoregressive forecasts with uncertainty estimates. Diffusion-TS now offers context-conditioned forecasts, including quantile and interval predictions in native mode. * **Bug Fixes** * Configured seeds are applied more consistently across model initialization, training, evaluation, and sampling to improve reproducibility. * **Compatibility** * Randomization options now use `Seed` instead of `RandomSeed`; update existing configurations and samples. * Diffusion-TS options and forecasting interfaces have changed; previous decomposition controls and `Forward` are no longer available. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: Franklin Moormann <franklin@ivorycloud.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: t <t@e.com>
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Problem
Every deep time-series model seeded its random streams with hard-coded literals (
42,1042,12345,42 + i * 1000for sub-layers) and ignoredOptions.Seed. Runs with different seeds were the same run, so seed-to-seed variance could not be measured and an ensemble over seeds was one model repeated.Found while building a seeded model A/B harness in OoplesFinanceAdminClient (#253 there):
Options.Seedhad no effect on DeepAR, N-BEATS, N-HiTS, Informer, Autoformer, TFT, Chronos, DLinear, NLinear, TiDE, DeepANT or LSTM-VAE.Second defect: N-BEATS built every block with seed 42, so all blocks started from identical weights.
Fix
TimeSeriesModelBase.SeedOr(fallback): returns the historical literal whenOptions.Seedis unset (unseeded results reproduce exactly as before), otherwise a SplitMix64 derivation of (seed, stream), masked to 30 bits so sub-layer offsets cannot overflow.NBEATSBlocktakes an optional seed; the model passesSeedOr(42) + i. This changes N-BEATS's default initial weights for blocks after the first.Tests
TimeSeriesSeedTests(new):Falsified:
SeedOrignoring the seed → 9 red; a shared N-BEATS block seed →NBeatsBlocks_DoNotShareInitialWeightsred.All 732 tests under
.TimeSeries.pass (net10.0, local).🤖 Generated with Claude Code
https://claude.ai/code/session_017otuSGr3GdmvPoYLbiaWR2
Summary by CodeRabbit