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fix(batchnorm): normalize a single-sample conv activation over its spatial positions - #2272
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…atial positions BatchNormalizationLayer gated training-mode batch statistics on the batch axis alone (input.Shape[0] > 1). A [1, C, H, W] activation has H*W values per channel, but it took the single-sample fallback, which normalizes with running statistics and never updates them, so every convolutional model trained one sample at a time trained with BatchNorm frozen at mean 0 / variance 1. The GPU path already used batch statistics; CPU and GPU disagreed. The gate is now the number of values per feature, as in Ioffe & Szegedy (sec. 3.2: m' = m*p*q for convolutions) and PyTorch's BatchNorm1d/2d/3d. Only exactly one value per feature keeps the running-statistics fallback (PyTorch refuses to train there). Found through RepViTSAM training failing only on Intel runners: its 50+ BatchNorms never normalized, the unnormalized 26-block encoder grew activations ~150x, and CPU-dependent summation order decided whether training converged. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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WalkthroughBatch-normalization construction sites now specify feature widths. The CPU path selects statistics based on values per feature and supports replacing running statistics. Contrastive-loss evaluation and selected model tests change how BatchNormalization statistics are handled. Generated probes, target handling, and the affected-test matrix limit are also updated. ChangesBatch normalization and evaluation
Model-test probes and target handling
Affected-test matrix output limit
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Bug fix Sequence Diagram(s)sequenceDiagram
participant GraFPrint
participant NeuralNetwork
participant BatchNormalizationLayer
GraFPrint->>BatchNormalizationLayer: Save running statistics and enable training mode
GraFPrint->>NeuralNetwork: Evaluate combined view batch
NeuralNetwork->>BatchNormalizationLayer: Use batch statistics
GraFPrint->>BatchNormalizationLayer: Restore running statistics and prior mode
Suggested reviewers: Merge Risk: 🔵 Low · up to The confirmed issue is limited to misleading model-test diagnostics. The supplied Intel-only test-failure report remains unverified for this head. Security Architecture ReviewSecurity architecture risk: 🔵 Low · up to The changes are bounded to model statistics and evaluation, with explicit cleanup. However, contrastive loss evaluation now temporarily changes shared normalization state, so overlapping operations on one model require careful serialization. Retained concerns
Security review detailsSecurity Blast Radius
Security Findings and Attack Paths
Trust Boundaries and Controls
Resilience and Maintainability Implications
🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 2📝 Generate docstrings 💡
🛠️ Fix failing CI checks 💡
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Both follow a Dense over a [B, T, F] sequence with a width-less BatchNorm, which resolves a rank-3 input as [C, H, W]: it took the batch axis as one channel and normalized every value with a single mean. Sized to the Dense width, each feature is normalized over B*T as intended. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
… statistics overwrite - A rank-4+ activation whose axis 1 equals the feature count is NCHW, so the channels-last flatten no longer fires when its trailing axis also happens to match: GraFPrint's [1, 32, 32, 32] and FastSAM's [1, 8, 8, 8] were normalized across width with gamma indexed by column. Rank 3 stays ambiguous and keeps the trailing-axis rule plus the declared Layout. - Internal OverwriteRunningStatistics makes a training-mode forward replace the running statistics (momentum 0), PyTorch's update_bn for one batch. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
… own weights With single-sample conv activations now normalized over their spatial positions, training moves the running statistics, and after a short run their momentum average trails the weights. WavLMSpeaker's training-mode loss fell every step (0.3306 to 0.3260) while its eval loss fell to 0.713 and rose to 0.746 as the average chased the activations. - Training_ShouldReduceLoss and MoreData_ShouldNotDegrade recalibrate every BatchNorm for the weights being measured (RecalibrateBatchNormalization, via Predict so the model's own input preparation runs) instead of sharing the trained network's trailing average. - The memorization probe uses the deterministic eval loss for any network with dropout: MarbleNet's training-mode loss swung 0.52 to 1.17 across masks while its eval loss fell monotonically. Verified on the four failing models and a 15-class batch-norm sweep (Conformer family, ContextNet, CRNN, PSENet, TitaNet, RepViTSAM, ResNet, five YOLO-seg models): all pass. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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Review comments at @src/NeuralNetworks/Layers/BatchNormalizationLayer.cs:
- Around line 800-807: Update the channels-last flattening condition in the
`BatchNormalizationLayer` rank handling so an explicit `Layout` of
`ChannelsLast` takes precedence over `canonicalChannelsFirst` when both axis 1
and the trailing axis match `featureSize`. Preserve the existing heuristic for
unspecified layouts and flatten qualifying inputs to `[leadingBatch,
featureSize]` so rank-2 normalization uses the trailing feature axis before
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GitHub caps a job's outputs at 1,048,576 bytes counted in UTF-16, two bytes per ASCII character. Select-AffectedTests budgeted matrix + ledger_matrix at 800,000 characters as if they were bytes, so a broad plan (this PR touches the shared model test base, reaching most model classes) passed the check and the Build job then failed with "Job outputs exceed 1,048,576 bytes", cancelling every shard. The budget is now 400,000 characters (~800 KB), leaving room for the job's other outputs, and the workflow contract asserts it stays at or under 450,000 (positive control: the contract fails at 800,000). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
…arblenet in Switching every dropout network to the evaluation loss failed five memorization invariants on CI (OpenVoice, MOIRAI, KyutaiMoshi, LayoutGraph, BiaffineNER) whose evaluation loss descends less cleanly than the objective they train; their shards passed before that change. The choice is back to the generator's per-model override, with MarbleNet added beside VMamba and DeepAR: its training-mode loss swung 0.52 to 1.17 across dropout masks while the evaluation loss fell monotonically. BatchNorm is still recalibrated whenever the evaluation loss is used. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
ComputeContrastiveLoss ran each view separately through Predict, under BatchNorm running statistics that trail the weights, so it measured a different quantity than TrainContrastive minimizes and could rise while the trained objective fell (the fidelity test failed on CI once BatchNorm normalized NCHW activations over the right axis). It now runs one forward over the combined [2B, ...] batch with BatchNorm on batch statistics and every other layer in eval, restoring the running statistics afterwards so measuring mutates nothing. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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- Around line 1353-1378: Update NeuralVaR.PredictCore so its evaluation-mode
reset does not override BatchNorm training modes set by
RecalibrateBatchNormalization before Predict; apply the conditional mode-setting
behavior used by GraFPrint, or provide a calibration path that preserves
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… on it With its eight conv BatchNorms now normalizing a single image over its spatial positions, the training loss rises for three steps before descending (5.296, 5.302, 5.419, 5.173, 5.368, 4.895 ... 4.535 at step 12 on the fixture pair). The capped two-update MoreData probe sat on that rise and failed; it passes on master and failed identically on the previous head, so this PR is the cause. Twelve updates (~18 s) clear the rise; the assertion and tolerance are unchanged. The whole VisionMamba class passes (33/34, 1 skipped). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
…egardless of mode Review follow-ups on #2272: - An explicit ChannelsLast layout now beats the NCHW heuristic. When axis 1 and the trailing axis both equal the feature count, the heuristic kept the tensor channels-first and normalized axis 1 against the declared layout. - OverwriteRunningStatistics now takes the batch-statistics branch whatever the layer's mode, and the harness no longer flips any layer's mode for recalibration. A PredictCore that forces evaluation mode (NeuralVaR does, unconditionally) reset those per-layer modes mid-pass and left the statistics stale. Both new tests fail with the fix reverted (positive control) and pass with it; the affected model classes (FastSAM, GraFPrint, MarbleNet, WavLMSpeaker, VisionMamba, ContextNet, NeuralVaR) pass 176/181 (5 skipped). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
…BatchNorm has real statistics EfficientSAMTests.Training_ShouldReduceLoss failed (0.328 -> 1.542) since BatchNorm started normalizing single-sample convolutional activations over their spatial positions (#2272). Measured on CI (diag/efficientsam-intel): - The gradient is exact. Training-mode finite differences agree with the tape to ~1e-8 for conv-BN-conv at batch 1 and 2, and on EfficientSAM itself steps of 1e-8..1e-6 along -g descend by exactly the first-order prediction (ratio 1.000). - The 32x32 fixture is the problem. The encoder downsamples 32x, so its third stage runs eight BatchNorms over 2x2 = 4 values per channel (the fourth over one). That objective is so sharply curved the gradient holds only for tiny steps: descent ratio at a 1e-3 step is -0.03 at 32x32, 0.25 at 64x64 and 0.88 at 128x128. The first AdamW step overshot. - The CPU only decided whether the damage crossed the 0.5 tolerance: every AVX-512 runner (Intel 8573C/6973P, AMD 9V74/9V45) failed identically, AVX2 EPYC 7763 passed by margin. At 128x128 every BatchNorm normalizes over 16-256 values per channel, as in real use. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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Evidence on the EfficientSAM It fails only on Intel runners, and the same branches pass on AMD every time:
This is the same mechanism as RepViTSAM (#2277), which was bisected on hosted Intel runners one knob at a time. Tensors had an Intel-only table sigmoid, accurate to 2e-6 but numerically different from the Padé kernel AMD runs. Under AVX-512, AdamW's start-up transient amplifies that difference (RepViTSAM: 41 → 2847 at step 6, recovered at step 14). The batchnorm change here moves EfficientSAM near that edge; the Intel numerics tip it over. Fix: AiDotNet.Tensors #1069, released in v0.133.2 (published to NuGet shortly). It uses one sigmoid on every vendor and also fixes a NaN for ±∞. I'll open the bump PR as soon as the package is on NuGet. A fixture resolution change (128×128) may move the model off the edge rather than remove the cause, so it's worth re-running on Intel after the bump before relying on it. Nightly check for this class: |
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… dice and their logit composite SAMTests (which exist on master; CI tests #2272 merged with it) failed with a NaN loss on the UNTRAINED model: MoreData, Training_ShouldReduceLoss, memorization and gradient flow. Measured on CI (diag/sam-nan, #2272 merged with master): every BatchNorm statistic and every layer output after recalibration is finite; the NaN is in the loss. SAM's mask loss is CompositeLossWithLogits(focal, dice) on sigmoid(logits), and the fixture target spanned [-0.93, 0.98]. Focal's p_t = t*p + (1-t)*(1-p) goes negative for any p away from 0.5 when t < 0, and log(p_t) is NaN. With BatchNorm leaving single-sample activations unnormalized, SAM's logits sat near 0 (p ~ 0.5, where p_t = 0.5 for any t), which hid the ill-posed target; normalized activations spread p and exposed it. The fixture already projected targets into [0, 1] for binary cross-entropy for the same reason. TargetIsProbability now names every loss that reads its target as a probability - BCE with or without logits, FocalLoss, DiceLoss and CompositeLossWithLogits - and both the projection and the target validation use it. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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- Line 4701: Rename the local `binaryCrossEntropy` in the test around
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Compiled and eager BatchNorm compute different single-sample statistics. Found while bumping to Tensors 0.133.2 (#2279). This is the path this PR changes, so I'm handing it here rather than editing BatchNorm underneath you.
Repro: the same single graph-block GraFPrint as that test (batch 1, 2×2 spatial), SGD at learning rate 0, one
Same weights, same input, no update. Both sides are deterministic (eager-vs-eager and compiled-vs-compiled are exactly 0), so one path normalizes a batch-1 conv activation over different axes or statistics than the other. With SGD at lr 1e-2 the weight divergence is 3.7e-8 after one step. It shows up in all 10 sub-layers, largest in the early ones; turning off each TensorCodecOptions fusion (ConvBn, Dataflow, Algebraic, Pointwise, CSE, BlasBatch, ConstantFolding) doesn't remove it. Fixing single-sample BatchNorm here should make compiled and eager agree. #2279 (Tensors 0.133.2, the vendor-independent sigmoid) is on hold until then. |
…ed steps The compiled fused step trained differently from the eager tape step in two ways, surfaced by GraFPrintPaperFidelityTests.CompiledTraining_RebuildsKnnForEachInput once batch-statistics BatchNorm made fc1's true gradient eps-scale (-4.57e-7, matching finite differences). - Adam and AdamW clip by global norm inside their own Step (MaxGradientNorm 1.0 by default). The fused kernel never runs that Step, and the plan was handed only the model's MaxGradNormValue (0 for GraFPrint), so it trained unclipped. FusedOptimizerConfig now carries MaxGradientNorm; the plan gets the smaller of the model and optimizer bounds, which equals the two successive eager clips. AdamW with ByValue clipping stays eager. Step-1 divergence went from 5.7e-5 to 1.4e-12. - A scheduler stepped per epoch (SchedulerStepMode.StepPerEpoch, the default) was mapped to a per-step schedule shape, so cosine annealed over tMax batches while eager held the rate for the epoch. The residual 1.532e-9 was exactly one cosine step, lr*(pi/400)^2/4. Per-epoch schedulers now use OptimizerTrackingLrSchedule, which reads the optimizer's current rate each step; OnEpochEnd moves it as it moves the eager one. WarmupThenEpoch declines fusion (the fused path never calls OnBatchEnd). The plan's drift key ignores the baked rate when a schedule owns it; before, the first OnEpochEnd threw the committed-fused InvalidOperationException. FusedOptimizerScheduleAndClipParityTests covers all three, each against the same optimizer with the feature off (plain Adam drifts 3.1e-8 from eager in double through the float-narrowed hyperparameters). Without the fix: clip drift 1.47e-3, the per-epoch run throws, WarmupThenEpoch fuses. The class joins Unassigned - 02; the inventory gate reports 0 unassigned. Verified: 1230 fused, scheduler, optimizer, clipping and GraFPrint tests pass on net10.0; src builds for net471. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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…clip Eager Adam clips by global norm inside its tape step (EnableGradientClipping and ByNorm, MaxGradientNorm); the compiled plan was handed only the model's MaxGradNorm, so the fused step trained on unclipped gradients. Adam hides a uniform gradient scale except near its epsilon, where it passes straight through: GraFPrintPaperFidelityTests.CompiledTraining_RebuildsKnnForEachInput diverged 9.27e-8, and the measured gap scaled with epsilon (1e-5 -> 6.1e-6, 1e-30 -> 4e-20). The spec now carries the clip, and the plan gets the smaller of the two bounds, which equals clipping at both in turn. Ported from #2272's d6b333a without its schedule half, which this branch solves with an external rate. AdamW is left out: on this branch its eager step applies no such clip, so carrying one would create the mismatch instead of removing it. Control: passing only the model's bound again fails the test at 4.67e-7. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
…rate Generated Layers G failed on #2272 and #2269 with GraFPrintLossTraceTests Training_ShouldReduceLoss and MoreData_ShouldNotDegrade: after 10 steps the eval loss rose from 1.399695 to 1.484462, identically on two runners. The code is not at fault. d6b333a made the compiled step apply Adam's own clip, as the eager step always has, and in a CPU-only Linux container the two paths now agree step for step (per-step training loss to ~1e-5). What differs is the verdict: at the fixture's learning rate of 3e-4, ten steps are chaotic (training loss 1.400, 0.710, 2.109, 0.912, 0.804, 0.453, 0.395, 0.477, 0.833, 1.236). The dynamic k-NN graph and batch-4 BatchNorm statistics turn ULP-level differences into opposite outcomes: on Linux eager passed and compiled failed with the same trajectory, and on Windows both passed. Before d6b333a the compiled path trained unclipped, a different trajectory that happened to pass. The fixture now uses the paper's schedule (Adam 8e-5 cosine-annealed to 7e-7), the GraFPrintOptions defaults. There the trajectory is stable and identical on both paths (1.400 to 0.308 over 10 steps), and every assertion is unchanged. Verified: GraFPrintLossTraceTests 32 of 32 on Linux (CPU-only container, 4 CPUs) compiled and eager; on Windows 45 of 45 twice with the GraFPrint fidelity and clip parity tests. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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…rate Generated Layers G failed on #2272 and #2269 with GraFPrintLossTraceTests Training_ShouldReduceLoss and MoreData_ShouldNotDegrade: after 10 steps the eval loss rose from 1.399695 to 1.484462, identically on two runners. The code is not at fault. d6b333a made the compiled step apply Adam's own clip, as the eager step always has, and in a CPU-only Linux container the two paths now agree step for step (per-step training loss to ~1e-5). What differs is the verdict: at the fixture's learning rate of 3e-4, ten steps are chaotic (training loss 1.400, 0.710, 2.109, 0.912, 0.804, 0.453, 0.395, 0.477, 0.833, 1.236). The dynamic k-NN graph and batch-4 BatchNorm statistics turn ULP-level differences into opposite outcomes: on Linux eager passed and compiled failed with the same trajectory, and on Windows both passed. Before d6b333a the compiled path trained unclipped, a different trajectory that happened to pass. The fixture now uses the paper's schedule (Adam 8e-5 cosine-annealed to 7e-7), the GraFPrintOptions defaults. There the trajectory is stable and identical on both paths (1.400 to 0.308 over 10 steps), and every assertion is unchanged. Verified: GraFPrintLossTraceTests 32 of 32 on Linux (CPU-only container, 4 CPUs) compiled and eager; on Windows 45 of 45 twice with the GraFPrint fidelity and clip parity tests. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
TimeDiffTests.Training_ShouldReduceLoss failed in Generated Layers T Tem-Tri on #2272 (eval objective 8.619133 -> 9.490033). It is a flake, not this branch: the same test failed 1 of 5 runs locally on Windows and passed on Linux, with a different initial loss each time (8.619 on CI, 8.623 here). The declared objective is a fixed quadrature, but nothing else was fixed. Train drew its diffusion steps, noise and mixup masks from a fresh secure generator on every call, ignoring Options.Seed, and the denoiser's weights were drawn unseeded whenever the architecture carried no seed. TimeDiff's Seed defaults to 1 and already made Predict reproducible, so two models built from the same options still started from different weights and trained on different noise. - Training draws come from TrainingRandom: one stream for the model's life, seeded from Options.Seed (secure when unset). It still draws a fresh (k, epsilon) every step, so training is not fitted at one noise level (the reason the comment gave for not seeding), and it is CSDI's #2277 fix. - When the architecture has no seed, the native layer build arms the layer init seed scope from Options.Seed, then restores the caller's scope. An architecture seed still wins. Verified: Training_ShouldReduceLoss passes 10 of 10 on Windows (was 4 of 5), and all 37 TimeDiff tests pass (1 skipped). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The Unassigned - 01 shard dies on every branch: ModelInputMeetsFirstLayerTests builds about 900 models at paper scale and drives the test host to 50.9 GB on a 16 GB runner. This carries #2284's fix verbatim: the sweep builds models from ModelTestScale's bounded options (peak 1.3 GB, mismatch set unchanged at 24), the generator shrinks a head count that a scaled width no longer divides, VisionEncoderOptions / Florence2Options / Wav2Vec2ModelOptions declare their width-to-heads divisibility, and TransformerEncoderBlock accepts an unbatched sequence (a real defect the bounded sweep exposed in eight TTS models). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
…y eager TensorCodecOptions.Current returns a fresh default copy unless SetCurrent installed one on the calling thread, so the test's writes to Current.EnableCompilation were silently dropped on a clean thread. Whether the "eager" leg ran eager depended on what an earlier test left in the thread-static on that pool thread. On master the two paths genuinely differ (compiled skipped Adam's clip and schedule), so the test failed Unit - 13 on #2286, #2283 and #2287 and passed elsewhere by luck of thread state. With d6b333a on this branch the real eager-vs-compiled comparison passes; the test now installs each mode with SetCurrent so it measures that comparison every time. Assertions unchanged. Verified: GraFPrintPaperFidelityTests + FusedOptimizerScheduleAndClipParityTests 13 of 13, three runs; net471 build clean. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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Conflict in .github/test-shards.yml: this branch's Unassigned - 02 list already held both entries master added, so it is kept as is. Semantic conflict in Florence2Options: master added [DimensionDivisibility(DecoderEmbeddingDim, NumDecoderHeads)], but this branch replaced those options with the paper layout. The check now covers the same constraint on the real fields: BART splits TextDim across NumHeads, and DaViT splits VisionBaseDim across VisionBaseHeads. Verified: net10.0 build, shard inventory gate, GraFPrint, fused clip and schedule parity, null-activation, BatchNorm, FT-Transformer and Florence-2 tests (209 passed), net471 build. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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#2272 and this branch fixed the same two fused-step gaps independently, so each conflict keeps one mechanism, not both: - Optimizer clip: this branch's TapeStepGradientClipNorm route is kept. It covers every gradient-based optimizer and sends a by-value clip back to the eager tape. Master's route (IFusedOptimizerSpec.MaxGradientNorm, set only by Adam/AdamW and only for by-norm clipping) and SmallerPositiveGradNorm are removed, since nothing reads them any more. - Per-epoch schedule: this branch's ExternalLrSchedule, which SetLearningRate keeps at the host rate, is kept. Master's OptimizerTrackingLrSchedule is removed because its own docs say a plan holding it cannot be checkpointed, and #2254 restores fused plans from checkpoints through this external schedule. Keeping one mechanism here and in #2254 stops the two PRs from re-conflicting. - The AdamW AMSGrad decoupled-decay Extras and the removal of the legacy TryMapToFusedOptimizerConfig overload from this branch are kept, and so is master's compiled-plan config key, which is identical code. Verified: net10.0 and net471 builds, shard inventory gate, 19 test classes (this branch's plus master's schedule/clip parity and GraFPrint tests). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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Oct 4, 2026
#2272 and this branch both taught the fused step the optimizer clip and the per-epoch schedule: - Clip: this branch already carried master's IFusedOptimizerSpec MaxGradientNorm route, so those hunks keep this branch's double-typed config. AdamW also gets master's MaxGradientNorm initializer, and git's second copy of SmallerPositiveGradNorm is dropped. - Per-epoch schedule: this branch's ExternalLrSchedule is kept and master's OptimizerTrackingLrSchedule is removed. That schedule's own docs say a plan holding it cannot be checkpointed, and this branch restores fused plans from checkpoints (AdoptRestoredFusedLrSchedule). #2267 resolves the same way. - The compiled-plan config key is identical code on both sides. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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This branch had already cherry-picked #2272's fused clip and schedule fix (79c974d), so both conflict hunks were the same code; master's text is kept. Git also kept the cherry-pick's copies of SmallerPositiveGradNorm and IFusedOptimizerSpec.MaxGradientNorm next to master's, which would not compile; NeuralNetworkBase.cs and IFusedOptimizerSpec.cs are master's, and the optimizer and training code now match master exactly. Verified: net10.0 and net471 builds, shard inventory gate, this branch's test classes plus master's schedule/clip parity and GraFPrint tests (85 passed). 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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Summary
In training mode,
BatchNormalizationLayerdecided whether batch statistics exist from the batch axis alone (input.Shape[0] > 1). A convolutional activation[1, C, H, W]has H×W values per channel, so its statistics are perfectly well defined. It still took the single-sample fallback, which normalizes with running statistics and never updates them. As a result, every convolutional model trained one sample at a time trained with BatchNorm effectively switched off: mean 0 / variance 1 forever. That's every model-family test, and anyone fine-tuning on single images.The gate is now the number of values per feature (
input.Length / features), as defined in the BN paper (Ioffe & Szegedy 2015, §3.2: for convolutions the effective mini-batch is m′ = m·p·q) and as PyTorch'sBatchNorm1d/2d/3dcompute it. The running-statistics fallback now applies only to exactly one value per feature, which is the case PyTorch refuses to train ("Expected more than 1 value per channel when training"). The GPU path (ForwardGpu) already used batch statistics here, so CPU and GPU disagreed; now they agree.How it was found
RepViTSAMTests.Training_ShouldReduceLoss/MoreData_ShouldNotDegradefailed on #2268's CI run and passed on master's run of a byte-identical tree.Throwaway experiments on GitHub-hosted runners (branch
exp/repvit-cpu-isa, runs 36564973127 … 36592477552) found:DOTNET_PROCESSOR_COUNT=1, AVX2/AVX off, or all hardware intrinsics off.RepViTBlockLayer, where the difference is summation-order rounding (sum −5.4968161 vs −5.4968154).So the real defect was the amplifier. RepViTSAM's
Forwardadds a batch dimension, so all 50+ of its BatchNorms saw[1, C, H, W]and never normalized. The model trained as an unnormalized 26-block residual network balanced on an edge, and CPU-dependent rounding decided which way it fell.Before / after
BatchNorm_Training_SingleSampleConvInput_NormalizesOverSpatialPositionshand-computes a[1, 2, 2, 2]batch:The test fails against the old
AiDotNet.dlland passes against the new one; I swapped the DLLs under the same test binary to check. The companion test pins the one-value-per-channel fallback ([1, 2, 1, 1]).Scope
Unchanged:
[N>1, C, …],[N, F], unbatched[C, H, W]and the flattened features-last path. For all of them the values-per-feature count equals the old gate. Only batch-1 inputs with more than one spatial value per channel change behavior, and they now normalize as the paper and PyTorch do.Verification (local, net10.0, CPU)
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