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fix(densenet): stop holding a forward's activations after it returns - #2408
I took a heap dump of DenseNet-121 b16 CPU inference and ran gcroot on its large float arrays. Three roots held the activations of a forward after it returned:
DenseBlockLayer and TransitionLayer fields. Every intermediate (BN out, ReLU out, conv out) was written to a field, and nothing reads those fields. The gradient tape keeps what backward needs.
DenseBlock._layerOutputs: a list of every inner layer's output, also never read.
BatchNormalizationLayer._lastInput was cached whenever no tape was recording, so eval forwards cached it too.
There was also a path through LayerForwardObserver. PredictAndValidateChain runs the first forward under this observer, which records each layer's input and output. The Tensors worker threads were created during that forward, captured the observer through their ExecutionContext, and kept the recording alive. Tensors #1610 fixes the capture. On this side, the observer now drops its recording once validation has read it.
Change
DenseBlockLayer and TransitionLayer: the intermediates are locals.
DenseBlock: the unused output list is removed.
BatchNormalizationLayer: _lastInput is cached only when ShouldCacheForBackward, and still not under a recording tape.
LayerForwardObserver.ReleaseRecording() is called after the traced-chain validation.
Traced inference and validation now release recorded layer data and temporary tensors after validation, including when validation fails.
Batch normalization and dense network layers no longer retain intermediate forward-pass values when they are not needed, helping reduce memory use during repeated predictions.
Existing pooling behavior remains unchanged.
Follow-up commit (second machine)
ebda8208c style(densenet): restore the UTF-8 BOM on DenseBlock.cs and DenseBlockLayer.cs. Master has it, and the earlier commit had stripped it. The only change is the three BOM bytes; net10.0 builds clean.
- DenseBlockLayer and TransitionLayer wrote every intermediate (BN, ReLU, conv outputs) to fields
that nothing reads; the tape keeps what backward needs. They are locals now.
- DenseBlock kept a list of every inner layer's output that nothing reads; removed.
- BatchNormalizationLayer cached its input whenever no tape was recording, so in eval mode it
held every BN input of the last forward. It now also requires ShouldCacheForBackward.
- PredictAndValidateChain's layer observer records each layer's input and output; it now drops
that recording after validating it, so a thread or task that captured the observer during the
forward cannot keep those tensors alive (ooples/AiDotNet.Tensors#1610 fixes the capture itself).
DenseNet-121, CPU, b16 inference, WS: 1904 -> 1617 MB (with Tensors #1610).
Part of #2383.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Reviewing files that changed from the base of the PR and between ebda820 and a53ba53.
Files selected for processing: 1
src/NeuralNetworks/NeuralNetworkBase.cs
Walkthrough
Forward layers now retain fewer intermediate tensor references in instance fields. Traced-chain validation releases recorded layer calls after validation, including when validation throws.
Changes
Forward tensor retention
Layer / File(s)
Summary
Layer forward state src/NeuralNetworks/Layers/BatchNormalizationLayer.cs, src/NeuralNetworks/Layers/DenseBlock.cs, src/NeuralNetworks/Layers/DenseBlockLayer.cs, src/NeuralNetworks/Layers/TransitionLayer.cs
DenseBlock no longer stores intermediate outputs. DenseBlockLayer and TransitionLayer use local variables for forward intermediates. BatchNormalizationLayer clears _lastInput when tape recording is active or backward caching is disabled.
LayerForwardObserver adds ReleaseRecording(), which clears recorded calls. PredictAndValidateChain calls it in a finally block after traced-chain validation.
ooples/AiDotNet#2397: The change reduces DenseNet inference memory retention, which is included in this issue’s performance work.
ooples/AiDotNet#2383: The change removes some retained DenseNet state and releases observer recordings, but does not establish that all issue targets are met.
ooples/AiDotNet#2391: The change implements part of the DenseNet inference memory work described in this issue.
Possibly related PRs
ooples/AiDotNet#2412: This PR continues the activation-cache changes across additional layers and models.
ooples/AiDotNet#2403: This PR changes inference-intermediate lifetime handling in overlapping DenseNet paths.
Suggested reviewers: franklinic
Merge Risk: Low · previous review ebda8
Exceptional trace-validation failures can retain activation tensors; move cleanup to cover the complete trace lifecycle before merging.
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Forward tensors take a lighter stay Local values guide the way Traces clear when checks are done Even if validation comes undone Dense paths keep their state in view Fewer references linger through
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Review comments at @src/NeuralNetworks/NeuralNetworkBase.cs:
- Around line 6327-6333: Move trace.ReleaseRecording() in NeuralNetworkBase’s
trace lifecycle to a finally that covers forward execution and
ReportArchitectureLayerDisagreement(), so recordings are cleared even when
either throws. Keep the recorded calls available through validation, and retain
cleanup on every exit.
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Folded into #2414 with git merge (all commits kept) so these changes share one 2-hour CI run. #2414 is now 98 files. Push further work for this change to #2414's branch.
…classifiers (#2413)
Part of #2391 and #2396
**Combined PR (97 files):** this PR carries two independent changes, to
save a 2-hour CI run. They touch disjoint files.
1. **Activation-cache sweep 1/3**: layer caches routed through
`SaveForBackward`.
2. **Keyed text/audio safety watermarks, pipeline embedding, and honest
classifier docs**, formerly #2415.
# Part 1: activation-cache sweep 1/3
## The defect class
Layers (and a few models) wrote per-forward activations into fields
(`_lastInput = input;`, `_lastOutput = ...`, `_xOutput = ...`, arrays
`_outs[i] = ...`, `list.Add(...)`) on every forward, in inference as
well as training. Every one of those activations then stays reachable
after the pass, so peak memory is O(sum of activations) rather than
O(max live set) (#2383). `LayerBase<T>.SaveForBackward` /
`ShouldCacheForBackward` already own the "will a manual backward read
this?" decision; ADN0065 reports the writes that bypass it.
This is sweep 1 of 3: the files in `adn0065-batch1`, minus the files in
flight elsewhere (BatchNormalizationLayer, ConvolutionalLayer,
DenseBlockLayer, GRULayer, InvertedResidualBlock, NeuralNetworkBase).
## What changed, per site
Each site was classified by reading every use of the field, not by
pattern:
- **Write-only (most sites).** Nothing ever read the field. Most of
these layers have no manual backward at all; training goes through the
tape. The field is deleted, along with its `ResetState` line, as in
#2408. Several LoRA adapters were also `Clone()`-ing the input into such
a field on every forward, so that copy is gone too.
- **Read only later in the same forward.** Now a local. Examples:
BasicBlock/BottleneckBlock intermediates, Bidirectional per-direction
outputs, the Autoformer trend/seasonal decomposition, DoRA/DVoRA
normalized direction, the LoRA-XS projection chain, NOLA reconstructed
A/B, ConvLSTM hidden/cell state (re-initialised every forward, so it was
never cross-call state), DilatedConv output, and the DirectionalGraph
and EdgeConditionalConv aggregations.
- **Read by a manual-backward helper.** Routed through
`SaveForBackward`: HRA input, LoHa input, the LoRETTA intermediates
list, DirectionalGraph combined features and gates, AnomalyDetector
input shape, AttentionLayer scores.
- **Also read by a training-only auxiliary loss.** Guarded on
`ShouldCacheForBackward || (UseAuxiliaryLoss && IsTrainingMode)`,
matching the existing `scoresNeeded` rule: AttentionLayer
cross-attention weights, CapsuleLayer routing coefficients.
- **Already gated with a ternary** (`_x = ShouldCacheForBackward ? v :
null`). Converted to `SaveForBackward(ref _x, v)`, or deleted when the
field turned out to be write-only.
- **Misses of the current analyzer.** The `*Out` fields and `list.Add`
accumulations were handled in the same files: STDiTBlock `_afterX`,
FeatureTransformer `_intermediateOutputs`, DownBlock `_resBlockOutputs`,
XLoRA `_lastExpertOutputs`.
## ADN0065 count for these files
| | ADN0065 results in batch-1 files |
|---|---|
| master (the batch list) | 235 |
| this branch (net10.0 build, `-p:ErrorLog` SARIF) | 29 |
## Sites deliberately left alone (the 29 remaining results)
None of these retains a per-pass activation. Most need an analyzer
exemption (or a design change outside this PR):
- **Persistent state across calls.** RecurrentPolicyAgent `_h/_c`
(episode RNN state). NTM `_lastHiddenState` (controller state read by
the Generate* heads after Forward). TFC
`_revinMeanTensor/_revinStdTensor` (RevIN stats, shape [B,1], consumed
by `DenormalizeForecast`). GAT/GIN/GraphSAGE `_cachedAdjacencyMatrix`
(graph structure, also set through `SetAdjacencyMatrix`).
- **Memoized or pooled buffers.** LoftQ/QLoRA `_dequantizedWeights`
(lazily dequantized weights). DenseLayer/FeedForwardLayer
`_fusedLinearScratch` and DiffusionResBlock `_preAllocatedNorm{1,2}Out`
(per-instance inference output buffers, by design). YOLOv10
`_auxHeadShapesResolved` (a bool flag).
- **Value type, nothing retained.** CRF `_lastSeqLen`.
- **Second forward output used at inference.** DeepAR `_lastSigma`, read
by `SampleQuantiles`.
- **Public accessors.** XLoRA `_lastGatingWeights`
(`GetLastGatingWeights`). AttentiveTransformer `_attentionMaskCache`
(`GetAttentionMask`, TabNet feature importance).
- **Training-forward-only hand-offs to the loss.** Tacotron2
`_lastPrePostnetMel`, which is now cleared as soon as the objective
reads it. FactorVAE `_lastKlDivergence`. CapsuleNetwork
`_lastCapsuleOutputs`, written only in `ForwardForTraining`.
- **Compiled training replay state.** DropoutLayer `_dropoutMask` is
written only in training, and `RefreshCompiledTrainingMask` refreshes it
in place across replays. Nulling it under a tape or capture would break
compiled training.
- **No training-mode signal.** SplitNeuralNetwork
`_lastTopInput/_lastTopHidden` are read by `BackwardTopModel` /
`UpdateFromGradient`, and `ISplitModel` has no mode to gate on.
- **Owned by an excluded file.** GRUNeuralNetwork `_layerOutputs`: the
`ForwardWithMemory` design lives in NeuralNetworkBase.
- **Observed by a test.** CrossAttentionLayer `_lastContext` is read by
`VisionLanguageNativeConstructionTests.Flamingo_PublicGenerationActuallySuppliesImageContextToTheLanguageGate`
after a public generation call. Nothing in the layer reads it.
- **Not an activation.** NTM `_weightsInput` is a lazily-sized trainable
weight. It carries `[Scratch]`, which looks wrong and is reported
separately.
## Behaviour notes
- DeformableConv `_lastMask` is still written in training-mode forwards,
now through `SaveForBackward`. `Pr2054RegressionTests` reads it after
such a forward.
- LoHa no longer clones the input it caches. Its only reader,
`ComputeLoHaGradients`, is never called. HRA's `BackwardSparseFullRank`
and LoRETTA's `ComputeTensorTrainBackward` are also uncalled; their
caches now go through `SaveForBackward` anyway.
- DyLoRA now passes the forward input to `MaskOutputToRank` explicitly
instead of reading a cloned field.
- Unchanged behaviour, noted as a follow-up: AttentionLayer's
custom-activation and masked paths still cache the score matrix only
through `SaveForBackward`. Under a recording tape, the entropy auxiliary
loss sees no weights on those two paths.
## Verification
- `build.ps1 src/AiDotNet.csproj`, net10.0: 0 errors. ADN0065 counted
from the SARIF error log as above.
- `build.ps1 src/AiDotNet.csproj`, net471: 0 errors (Build succeeded).
- No local tests; CI runs them. A `git grep` of `tests/` for reflection
on the removed field names found nothing. The only reflection on fields
in this batch targets `_lastContext` (kept) and `_lastMask` (still
written in training mode).
# Part 2: keyed safety watermarks (formerly #2415)
Companion to #2407, which made the image watermarkers keyed; this PR
does the rest of the defect class in `src/Safety`, and adds a keyed
embed/detect contract that text, audio and image can share.
## What was wrong
- **Text.** `SamplingWatermarker` hashed word pairs with FNV and had no
key. `LexicalWatermarker` used synonym-pair parity with no key, and
matched substrings, so "use" was found inside "because".
`SyntacticWatermarker` cited Kirchenbauer et al. but counted
passive-voice ratios. `WatermarkDetector` took the max of these three.
None of them could embed. `TextWatermarker` had a key but no embed path,
and with no key it drew a fresh random key per instance.
`SafetyPipelineFactory` builds `new TextWatermarker<T>()`, so it could
never detect any mark.
- **Audio.** `AudioWatermarker`, `AudioSealWatermarker` and
`SpectralAudioWatermarker` were keyless flatness and periodicity
heuristics, and none could embed.
- **Classifiers.** `CLIPImageSafetyClassifier` cited Safe-CLIP and
`ViTImageSafetyClassifier` cited a ViT paper, but both ran colour rules.
`TextImageAlignmentChecker` cited CLIP and used colour plus keywords.
## What changed
### Shared keyed contract (new files, so #2407 can adopt it)
- `WatermarkKey`: key text in UTF-8, plus a published `Public` key so a
keyless instance is deterministic and never random. `RequireSecret`
turns a missing, blank or public key into an `InvalidOperationException`
(a configuration error).
- `SignPattern(salt, stream, count)` gives the bits of HMAC-SHA256(key,
salt‖stream‖block). The message layout and bit order are the same as
#2407's `ImageWatermarkerBase.KeyPattern`.
- `Seed(salt, values)` is an HMAC-derived 64-bit seed.
- `Uniform(seed, item)` is a SplitMix64 keyed coin.
- `IKeyedWatermarker.UsesPublicKey`.
- `WatermarkStatistics`:
- count z (one-proportion test) and correlation z;
- normal tail and its inverse;
- a Bonferroni-corrected z;
- the z→confidence map (0 at z=3, 1 at z=6), which is the same as
#2407's.
- `WatermarkResult` gains `ZScore`, `PValue` and `ScoredUnits`.
### Text
- **`SamplingWatermarker`** is Kirchenbauer et al. 2023.
- The green list comes from HMAC(key, previous h tokens), with γ = 0.25
and δ = 4·strength (2.0 by default).
- Detection uses z = (|s|_G − γT)/√(Tγ(1−γ)). Repeated (context, token)
pairs are scored once, as the ICLR 2024 follow-up recommends.
- Embedding goes through `BiasLogits` or
`WatermarkModel(ICausalLanguageModel)`. Incremental (KV-cached) models
stay incremental, so the result plugs into `LocalEngineChatClient`.
- Text detection re-tokenizes with the generator's
`IGenerationTokenizer`. Without one it reports no evidence.
- Documented deviation: each token is green independently with
probability γ, not an exact γ|V| permutation. The z-test and its null
distribution are unchanged.
- **`TextWatermarker`** is `SamplingWatermarker` with a z-threshold
(4.0, the paper's). Its key is a `string`, and it no longer draws a
random key.
- **`LexicalWatermarker`** and **`SyntacticWatermarker`** now embed into
finished text through the new `KeyedSubstitutionWatermarker` base.
- The keyed choice depends on the group and the previous word. Matching
is whole-word and keeps the original capitalisation.
- Detection is a heterogeneous-γ count z-test, with each (group,
previous word) pair scored once.
- Lexical uses curated synonym groups that share an onset sound, so
"a"/"an" stays correct. It cites Topkara, Topkara & Atallah 2006.
- Syntactic uses only unambiguous contractions such as don't and
they're; "it's" is left alone. It cites Atallah et al. 2001.
- **`WatermarkDetector`** takes the best of the three tests on one key,
with a Bonferroni correction so the false-positive rate stays that of a
single test.
### Audio (all keyed, all embed)
All three use the shared `AudioWatermarkerBase`: non-overlapping ~32 ms
frames, an orthonormal block DCT computed through an FFT (Makhoul), and
key-independent detection weights, so z ≈ N(0,1) on unmarked audio.
Embedding throws if the marked audio would not reach z = 6 (silent, too
short, or too weak).
- **`AudioWatermarker`**: additive spread spectrum (Kirovski & Malvar
2003) in 1–7 kHz. Amplitude is 0.2·strength·max(robust spread,
0.03·frame RMS), about 20 dB below the band. The default strength
changes from 0.3 to 0.5.
- **`SpectralAudioWatermarker`**: patchwork (Bender et al. 1996; Yeo &
Kim 2003). Magnitudes are scaled by exp(±0.3·strength), and detection
correlates log-magnitudes.
- **`AudioSealWatermarker`**: the same spread-spectrum mark, plus
`DetectSegments` for localized detection, which is AudioSeal's
objective. `Detect` takes the best of the pooled and per-segment tests,
Bonferroni-corrected. The docs state that the AudioSeal network is not
used.
### Pipeline
- `SafetyPipeline` gains `EmbedAudioWatermark`, `EmbedTextWatermark` and
`WatermarkLanguageModel`. Each uses the first registered watermarker of
its kind that holds a secret key, and refuses (configuration error) if
there is none or only public-key ones.
- Text evaluation also runs when watermark detection is configured.
Before, it was skipped unless a toxicity, PII, jailbreak, hallucination
or copyright flag was on.
### Classifiers
- `CLIPImageSafetyClassifier` and `ViTImageSafetyClassifier` are
re-documented as colour/patch heuristics. The false Safe-CLIP and ViT
citations are replaced by Kovac et al. 2003, whose skin rule they do
implement. The NeuralNetwork/Transformer tags are dropped.
- The classes are **not renamed**: in-flight test files and the scaffold
generator reference those names.
- `TextImageAlignmentChecker` gains an optional
`IMultimodalEmbedding<T>` (for example `ClipNeuralNetwork`, loaded from
ONNX encoders). With it, `EvaluateAlignment` runs CLIP zero-shot
classification: logit scale 100, a softmax over the caption and three
unsafe-concept prompts. Without it, it uses the documented heuristic
fallback.
## Not in this PR (files in flight in #2407)
- `SafetyConfig.cs` / `SafetyPipelineFactory.cs`: config keys for
text/audio and factory wiring. After #2407 merges:
- add `TextWatermarkKey` / `AudioWatermarkKey`, plus an optional
`TextWatermarkTokenizer`, next to #2407's `ImageWatermarkKey`;
- in `RegisterWatermarkModules`, construct
`TextWatermarker`/`AudioWatermarker` with those keys;
- when embedding is enabled, require a key through
`WatermarkKey.RequireSecret`.
- Generator registration for the new test family
(`TestScaffoldGenerator.cs`). The family is hand-instantiated here
instead.
## Tests
`WatermarkRoundTripTestBase<TContent>` is shared by 8 watermarker
families: KGW on token ids, `TextWatermarker` on text through a
tokenizer, Lexical, Syntactic, the composite detector, spread-spectrum
audio, patchwork audio, and localized audio. Every embed and detect call
builds a fresh watermarker from the key text. The base checks:
- embed→detect with the same key gives z ≥ 5.7 and p < 1e-8;
- 12 unmarked samples stay below z = 3, the stated false-positive rate
of 0.135%;
- across 100 foreign keys, at most 2 reach z = 3, the z-scores have mean
below 0.4 and SD in [0.6, 1.4] (calibration);
- the mark survives a mild perturbation with z ≥ 4.5: 10% of tokens
replaced, 10% of sentence contexts edited, or 30 dB noise plus 16-bit
requantization;
- embedding is deterministic and depends on the key.
Plus:
- key and statistics unit tests;
- pipeline embed and refusal tests;
- incremental-equals-full KGW generation;
- the exact green-list bias;
- AudioSeal localization on a spliced clip;
- audio SNR ≥ 35 dB, range and input immutability;
- the CLIP alignment path with a fake embedding.
Python prototypes of the audio schemes measured these numbers on 1 s, 16
kHz audio (same HMAC layout, band, amplitudes and weights):
| Signal | Scheme | SNR (dB) | Own-key z | Max \|z\| over 200 foreign
keys (SD) | z after 30 dB noise + 16-bit |
|---|---|---|---|---|---|
| Speech-like | Spread spectrum | 49.9 | 9.6 | 3.18 (0.95) | 7.1 |
| Speech-like | Patchwork | 42.7 | 12.8 | 3.62 (1.02) | 8.8 |
The KGW simulation (V = 500, T = 200, δ = 2, γ = 0.25):
- own-key z = 13–15;
- unmarked and foreign-key z within ±2.3;
- 10% tokens replaced: z = 10.6–12.
These are prototype numbers, not results from the C# tests (tests run in
CI).
## Breaking changes
- `TextWatermarker` takes `string? key` instead of `byte[]? secretKey`.
Its default γ changes from 0.5 to 0.25, and with no key it uses the
published key instead of a random one.
- `SamplingWatermarker`: `vocabSize` is now `int?`, the default γ is
0.25, and it adds `key`, `tokenizer`, `contextWidth` and
`scoreRepeatedNgrams`.
- `ITextWatermarker` gains `Detect`. `IAudioWatermarker` gains
`EmbedWatermark` and `Detect`.
- `AudioWatermarker` now derives from `AudioWatermarkerBase`, and its
default strength changes from 0.3 to 0.5.
- `TextWatermarkerBase.DetectWatermark` is virtual, no longer abstract;
derived classes implement `Detect`.
## After review (`ef740ba52`)
- **Test-project compile:** the three new safety test files used
`SafetyCategory` without importing `AiDotNet.Enums`. `src` imports that
namespace globally; the test project doesn't. The earlier verification
built only `src`, so this would have failed the whole test assembly in
CI. Fixed.
- **AudioSeal Bonferroni count:** `Detect` now counts only segments that
scored a frame. Before, segments shorter than a frame inflated the
correction. The `DetectSegments` doc now says such segments come back
with `ScoredUnits = 0`.
- **`WatermarkBenchmark`:** its two "watermark-like" cases expected
keyed detectors to flag text that was never embedded with a key. A keyed
detector should not flag that text, so they are now false-positive
probes. Keyed detection is covered by the round-trip tests.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **New Features**
* Added keyed text and audio watermarking, with embedding, statistical
detection results, and localized audio-segment detection.
* Added generation-time text watermarking and safety-pipeline options
for embedding text and audio watermarks.
* Text-image alignment checks can use a multimodal embedding model to
flag mismatches.
* Watermark results include z-scores, p-values, and scored-unit counts.
* **Changes**
* Text watermarking now requires a configured tokenizer when enabled.
* **Documentation**
* Clarified that image safety classifiers use fixed heuristics rather
than neural network models.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
…combined) (#2412)
**Combined PR (100 files):** to save 2-hour CI runs, this PR carries 3
independent changes, merged with `git merge` so every original commit is
kept. Each section below is the original description.
- this PR: fix: route activation caches through SaveForBackward (sweep
3/3)
- #2399 (closed, merged in here): docs(license): fill in the BSL 1.1
Parameters block
- #2420 (closed, merged in here): feat(analyzers): ADN0065 becomes an
error with a [KeepsActivation] exemption, training guards and a baseline
ratchet
# This PR: fix: route activation caches through SaveForBackward (sweep
3/3)
Part of #2391
## The class
Layers and models stored per-forward activations in fields (`_lastInput
= input;`, `_xOutput = ...`, `_cache.Add(...)`) unconditionally. So in
inference every activation stayed reachable after the forward, and peak
memory was O(sum of activations). This is the third of three sweeps of
the ADN0065 sites (`ActivationCacheGuardAnalyzer`), following #2408's
pattern.
## What changed, per site
- **Fields that nothing reads (the large majority):** removed. The value
stays a local. Most of these layers already run their backward on the
tape, so the field was write-only. This covers the 11 SSM layers (PaTH,
RWKV, RWKV-7, RG-LRU, Rebased, RetNet, Rodimus, S4D, S5, TTT,
TransNormerLLM) and
SelfAttention/Separable/SoftTree/SpatialTransformer/SpectralNorm/Spiking/SpiralConv/Split/SpyNet/SE/Subpixel/Swin
(including its `[windows, area, 3*dim]`
qkv)/TemporalMemory/TimeDistributed/TimeEmbedding/TransformerDecoder/Upsample/Upsample3D/UNetDiscriminator's
down block. It also covers TNet, GaussianSplatting, the TimeSeries
private layers (DeepANT, whose pre-activation buffer was never read;
LSTM-VAE decoder; Chronos; N-HiTS) and every tabular backbone/head cache
(AutoInt, FT-Transformer, GANDALF, Mambular, NODE, SAINT, TabDPT, TabM,
TabPFN, TabR, TabTransformer, FeatureTokenizer, GhostBatchNorm,
ContextEncoder). The list accumulators that nothing read went too.
- **Read by a manual backward:** these go through `SaveForBackward`:
SparseLinear, UNetDiscriminator's up block, and Rebased q/k/v.
- **Models and non-layer components:** `NeuralNetworkBase` is in flight
elsewhere, so I did not add the helper there. The new
`ManualBackwardCache<T>` (`src/NeuralNetworks/ManualBackwardCache.cs`)
applies the same rule as `LayerBase.SaveForBackward`: no `InferenceMode`
scope, no recording tape unless `KeepActivationCacheUnderTape`, plus the
train/eval flag where the caller has one. GraphClassificationModel,
AutoInt, DuelingNetwork and the prototype `SimpleNeuralNetwork` use it.
AutoInt now keeps the forward batch size separately, so its
learning-rate scaling does not depend on whether the caches were kept.
- **Read only by an auxiliary loss or a learning rule:** SelfAttention
attention scores (`[B,H,S,S]`), SpatialTransformer theta, SE excitation
weights, and SynapticPlasticity's STDP inputs. These are now kept only
in a training pass outside `InferenceMode` (and, for the aux-loss ones,
only with `UseAuxiliaryLoss` on). `ShouldCacheForBackward` is the wrong
guard here because it is false under a recording tape, which is exactly
when those consumers run. The ADN0065 analyzer from #2420, merged in
here, recognises this `IsTrainingMode && !InferenceMode.IsActive` guard,
so these sites are no longer reported and none of them is in the
baseline.
- **PointNet / DGCNN / PointNet++:** they wrote every layer's input and
output into `NeuralNetworkBase._layerInputs` / `_layerOutputs`. Nothing
reads those dictionaries after these models' forward:
`ForwardWithFeatures` repopulates its own, and `_layerInputs` is never
read anywhere. The writes are removed.
## ADN0065 in this batch's files (net10.0 SARIF)
| | count |
|---|---|
| master | 299 (4 of them in the excluded `NeuralNetworkBase.cs`) |
| this PR | 21 (the same 4 in `NeuralNetworkBase.cs`, plus the 17 below)
|
_The table above was measured before #2420 was merged in. With the
merged analyzer, the four aux-loss and learning-rule sites are no longer
reported, and the authoritative count is the baseline itself: 566
entries after review._
## Sites left alone, and why
- Cross-call state:
- `RVM._hiddenState` (recurrent matting memory between frames).
- `LinkPredictionModel._nodeEmbeddings` (read by `PredictEdges` after
the forward).
- `VariationalAutoencoder._pendingKlLoss` (consumed by the loss).
- `BayesianDenseLayer._samplePending` (a bool flag).
- `SpatialPoolerLayer._lastBinaryOutput` (Hebbian learning state,
`[ColumnCount]`, the same data as `LastOutput`).
- Read by public accessors:
- PointNet/DGCNN/PointNet++ `_globalFeatures` (`ExtractGlobalFeatures`).
- `SetAbstractionLayer._lastCentroidPositions` (segmentation
upsampling).
- `TabRBase._neighborIndicesCache` (`GetRetrievedNeighborIndices`).
- `RWKV7Block._incomingVFirst`: passes v_first to the time-mix within
the same forward. It is one tensor shared by every block, so it does not
grow with depth.
- `SelfAttentionLayer._sdpaOutScratch`: an intentionally reused output
buffer.
- Gated, but by a guard the analyzer does not recognise: the four
aux-loss and learning-rule sites listed above.
- `NeuralNetworkBase.cs` (4 sites): excluded, in flight elsewhere. Its
`_layerInputs` writes (2003/2034/2086) have no reader anywhere. Its
`_layerOutputs` writes are read only by CapsuleNetwork and
ResidualNeuralNetwork.
## Not covered
The tabular models, DuelingNetwork and the prototype have no train/eval
flag, and their callers do not enter `InferenceMode`. Their caches now
follow the central policy, but the memory win needs callers to predict
inside an `InferenceMode` scope.
## Verification
- `src/AiDotNet.csproj` builds on net10.0 and net471 with 0 errors.
- ADN0065 counts come from the net10.0 build's SARIF (`-p:ErrorLog`).
- No tests were run locally; CI runs them.
# From #2399: docs(license): fill in the BSL 1.1 Parameters block
Licence housekeeping decided by the maintainer on 2026-10-28. No
tracking issue.
## Summary
`LICENSE` is the Business Source License 1.1, but it had no Parameters
block and a blank Change Date, so it named no Licensor, Licensed Work,
Additional Use Grant or Change License. Without those, the licence does
not say who grants it, what it covers, or when it converts.
This fills them in with the terms published at
https://aidotnet.dev/license. The one exception is the Licensor, which
the maintainer has confirmed is CrowdTrainer LLC:
| Parameter | Value |
|---|---|
| Licensor | CrowdTrainer LLC |
| Licensed Work | AiDotNet (the "Software") |
| Additional Use Grant | You may use the Licensed Work for any
non-commercial purpose, including education, research, personal
projects, and internal evaluation. Training and inference operations are
unrestricted regardless of license status. |
| Change Date | Four years from the date of each release |
| Change License | Apache License 2.0 |
- Apart from the Licensor, the values are the website's wording, copied
word for word.
- It also adds the standard BSL "alternative licensing" line (pointing
to aidotnet.dev/license and licensing@aidotnet.dev) and the standard BSL
Notice paragraph.
- It fills in the blank `Change Date:` in the "Standard License Header"
section with the same value.
- In that section, "the LICENSE.TXT file" (twice) now reads "the LICENSE
file", because the file is `LICENSE` (commit 4426782).
- The BSL terms themselves are unchanged. This is a one-file change, so
no build was run.
## Maintainer decisions (2026-10-28) and what was done
- **Additional Use Grant: decided.** It keeps the website's wording. The
Community tier (free production use under US$1M revenue and fewer than 5
developers) stays a separate, registered licence tier, not part of the
grant.
- **Licensor and copyright holder: decided, CrowdTrainer LLC.** Places
changed:
- `LICENSE`: Licensor is CrowdTrainer LLC. The standard header's
`Copyright (C) 2026 Franklin Moormann` is now `Copyright (C) 2024-2026
CrowdTrainer LLC`.
- `NOTICE`: `Copyright 2024 Ooples Finance` is now `Copyright (C)
2024-2026 CrowdTrainer LLC`. "software developed at Ooples Finance" and
"private projects maintained by Ooples Finance" now name CrowdTrainer
LLC.
- `<Company>Ooples Finance</Company>` and `<Copyright>Ooples Finance LLC
2023</Copyright>` are now `CrowdTrainer LLC` and `Copyright (C)
2024-2026 CrowdTrainer LLC` in 7 projects:
- `src/AiDotNet.csproj`
- `AiDotNet.Privacy.HE`
- `AiDotNet.Storage.Elasticsearch`, `.Neo4j`, `.Postgres`, `.Redis` and
`.Sqlite`
- The 7 projects were edited by an anchored script. A dry run found 14
matches, as expected, and every hunk was read.
- The website, which lives in this repo under `website/`: the Licensor
on `website/src/pages/license.astro` and the footer copyright in
`website/src/components/Footer.astro`. **The live
https://aidotnet.dev/license keeps saying Ooples Finance LLC until this
PR is merged and the site is redeployed.**
- `<Authors>` is authorship and is unchanged.
- **Change Date wording:** "Four years from the date of each release" is
the website's phrase. The BSL terms already say a version converts on
the fourth anniversary of its first public distribution, so the two
agree.
## Follow-up decisions (2026-10-28) and what was done
- **Years: decided, 2024-2026.** LICENSE, NOTICE and all 7 package
`<Copyright>` values read `Copyright (C) 2024-2026 CrowdTrainer LLC`.
- **Licence issuer: decided, CrowdTrainer LLC.**
`website/src/pages/enterprise.astro` now says "a valid CrowdTrainer
LLC-issued enterprise license" and "a CrowdTrainer LLC license server".
- **Contracting entity: decided, CrowdTrainer LLC.**
`website/src/pages/federal-use.astro` now reads "CrowdTrainer LLC is a
small-business contractor (single-person LLC)."
- **No other issuer uses:** no other "Ooples" in this repo refers to the
licence issuer. What remains is the GitHub org (URLs, CODEOWNERS,
release owner) and non-licensing code comments.
- **Per-file headers (count only, not rewritten):** 30 files carry
`Copyright (c) AiDotNet. All rights reserved.`. 27 use `//`, which the
maintainer's pattern matches, and 3 are `.ps1` files with `#`. None use
the `(c) 2024` form.
- **No build was run:** the changes are licence text, NOTICE, `.csproj`
metadata (checked to parse as XML) and website strings.
## Summary by CodeRabbit
* **Documentation**
* Updated the license terms to identify the licensor and covered work,
clarify permitted non-commercial uses, and state that training and
inference are unrestricted regardless of license status.
* The license specifies a change to Apache License 2.0 four years after
each release, provides licensing contact information, and clarifies the
work’s open-source status and future licensing.
* **Chores**
* Updated company and copyright attribution across package information
and website pages to CrowdTrainer LLC.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
# From #2420: feat(analyzers): ADN0065 becomes an error with a
[KeepsActivation] exemption, training guards and a baseline ratchet
Part of #2391
## What this does
ADN0065 (`ActivationCacheGuardAnalyzer`) reports a backward-activation
cache written in a `Forward*` body without a training-pass guard. An
ungated write keeps the activation reachable after the pass, so peak
inference memory becomes O(sum of activations) instead of O(max live
set). Until now the rule was `Info`, so nothing stopped new ungated
caches. This PR makes it an `Error` with a ratchet, and tightens what it
recognises so the error has no known false positives.
### 1. `[KeepsActivation("reason")]`
`src/Attributes/KeepsActivationAttribute.cs`. A field attribute that
marks a deliberate keep, such as recurrent state carried between frames
or a value a later public call reads. The reason is required:
- The analyzer reports a blank, whitespace or `null` reason as
**ADN0068** (Error), and that field gets no exemption, so ADN0065 still
fires on it.
- The constructor throws on a blank reason.
### 2. Guards ADN0065 now recognises
- The `SaveForBackward(ref _field, value)` call is not a raw write, so
it is never reported.
- `if (ShouldCacheForBackward)` and the older
`ShouldCacheActivationsForManualBackward`, either directly or through a
local initialised from one (`bool cacheBwd = ShouldCacheForBackward; if
(cacheBwd) ...`). Only the **then**-branch counts: a write in the `else`
of a guard runs exactly when no backward is coming, so it is reported.
- The ternary form `_x = guard ? value : null`. If both branches store
something, it is still reported.
- The aux-loss / learning-rule training guard. An `&&` chain that both
requires `IsTrainingMode` and excludes `!InferenceMode.IsActive` counts
as a guard, for example `UseAuxiliaryLoss && IsTrainingMode &&
!InferenceMode.IsActive`. `ShouldCacheForBackward` is false under a
recording tape, which is exactly when those consumers run. Either half
on its own, or the two joined with `||`, is still reported.
### 3. What ADN0065 now detects
- Fields named `*Out`, in addition to `_last*`, `*Output`, `*Outputs`
and `[Scratch]`.
- `.Add(x)` into a collection field (`ICollection<T>`), which retains
the value just as an assignment does. `Tensor<T>.Add` is arithmetic and
is not counted.
- Value-typed and type-parameter-typed fields are skipped, because they
cannot pin a tensor.
- Null and `default` clears are skipped, as before.
### 4. Error plus baseline ratchet
This reuses the `ModelDefectBaselines` mechanism (ADNDEF001-003 style):
- `ModelDefectBaselines.UnguardedActivationCaches` lists the violating
fields, keyed `Containing.Type.field`.
- A field outside the set fails the build with ADN0065.
- A listed field that no longer violates is **ADNDEF004** ("baseline
entry is stale; remove its line"). So when a count falls, the build
tells you to lower the baseline, and the set can only shrink.
- The rule runs only on the `AiDotNet` assembly. Test and tool
assemblies define throwaway layers.
- The message now names the containing type, so a reported site maps
straight to its baseline key.
## Baseline: 820 fields, from origin/master `8542dd47e`
- **How it was measured.** The commit that adds the baseline records the
method: build `src/AiDotNet.csproj` for net10.0 at `8542dd47e` with this
analyzer and an empty baseline, then read the ADN0065 results from the
SARIF error log. That gave 898 write sites across 820 distinct fields,
and those 820 keys are the set. The set has 820 distinct entries and no
duplicates. I did not keep that SARIF and have not re-measured it.
- **How it is verified.** This branch changes nothing under `src/`
except the analyzer, the baseline and the new attribute, so `src` is
otherwise identical to `8542dd47e`. A 0-error `src` build at this head
therefore proves the set is exact: an unlisted violating field fails as
ADN0065, and a listed field that no longer violates fails as ADNDEF004.
**That local build has not completed.** The machine ran low on memory
and the build was stopped. CI's build of this PR is the verification. A
later commit narrows `IsGuarded` to the then-branch, which can only add
violations. If it adds any, CI lists them as ADN0065, and they get added
to the set.
**Interaction with the activation-cache sweeps #2412, #2413 and #2416.**
Each of those PRs gates fields that are listed here. Whichever lands
second has to delete the lines for the fields it fixed. ADNDEF004 names
each of those lines, so this is mechanical.
## Tests
(`tests/AiDotNet.Tests/Generators/ActivationCacheGuardAnalyzerTests.cs`)
and the mutation each catches
| Test | Analyzer mutation that makes it fail |
|---|---|
| `UnguardedField_IsReportedAsError` | `IsBackwardCacheField` returns
false, or the `ReportDiagnostic` call is removed |
| `KeepsActivationWithReason_SuppressesTheField` | the
`KeepsActivation(field) is { } keep && HasReason(keep)` skip in
`AnalyzeMethod` is removed |
| `KeepsActivationWithoutReason_IsReportedAndDoesNotExempt` (`""`, `"
"`, `null`) | `IsNullOrWhiteSpace` is removed from `HasReason` (ADN0065
disappears), or the `SymbolKind.Field` action is removed (ADN0068
disappears) |
| `SaveForBackward_IsNotReported` | any field passed by `ref` to an
invocation is treated as a write |
| `ShouldCacheForBackwardIf_IsNotReported` (direct, and through a local)
| `IsGuarded` returns false, or the local-initializer walk is removed |
| `WriteInElseBranchOfGuard_IsReported` | the `child !=
ifStatement.Statement` test is removed from `IsGuarded` |
| `ShouldCacheForBackwardTernary_IsNotReported` | the `IsGuardedValue`
check is removed |
| `TernaryThatStoresOnBothBranches_IsReported` | the
`IsNullLike(conditional.WhenFalse)` requirement is removed |
| `AuxiliaryLossTrainingGuard_IsNotReported` (4 forms) | the
`requiresTraining && excludesInference` return in `IsGuardCondition` is
removed |
| `IncompleteTrainingGuard_IsReported` (3 forms) | `IsTrainingMode`
alone is accepted, or conjuncts are collected through `\|\|` |
| `OutSuffixField_IsReportedUnlessGuarded` | the `EndsWith("Out")` name
test is removed |
| `AddIntoListField_IsReportedUnlessGuarded` | the
`InvocationExpressionSyntax` (`Add`) branch is removed |
| `TensorArithmeticAddOnCacheField_IsNotReported` | the `IsCollection`
filter on `Add` is removed |
| `ValueTypedCacheNamedField_IsNotReported` | the `IsValueType` /
`TypeParameter` filter is removed |
| `BaselinedField_IsNotReported_ButANewFieldIs` | the
`_baseline.Contains(key)` skip is removed. It also catches an over-eager
ratchet: recording the key only after that skip, or dropping the
`!violating.ContainsKey` filter, makes the baselined field look stale |
| `StaleBaselineEntry_IsReported` | the compilation-end action is
removed |
| `OtherAssemblies_AreNotAnalyzed` | the `AssemblyName` check is removed
|
I reasoned these mutations from the code. I did not run them: tests run
in CI only, and no test host was run locally.
## Follow-up (not in this PR)
A field kept only so that a test can read it after the forward is not a
deliberate keep. CrossAttention `_lastContext` is one example. These
fields should be **removed**, or the test should get what it needs
through the API. They should not be exempted with `[KeepsActivation]`,
and the attribute's remarks say so. This PR touches no layer files or
`NeuralNetworkBase.cs`, because other PRs own them.
## Verification
- `src/AiDotNet.Generators` builds with 0 errors, and this change adds
no warnings.
- **Not verified locally:**
- `src/AiDotNet.csproj`. The net10.0 build was stopped for low machine
memory, and the other TFMs were not attempted.
- The test project: tests run in CI only.
- No baselined type is declared in a file that contains `#if`, so I do
not expect stale entries on the legacy TFMs (net471, net462,
netstandard2.0). No build has confirmed that.
## Merge order with the other activation-cache sweeps
The ADN0065 baseline in this PR is exact for this tree. The combined
build reported 255 stale entries, for fields sweep 3 fixed, and 2 new
ones (`ConvolutionalLayer<T>._lastInput`/`_lastOutput` in
`ForwardTraced`, reported once the else-branch guard fix landed). Both
are corrected in `81867831b`: 820 → 567 entries.
#2413 (sweep 1) and #2416 (sweep 2) fix fields that are still listed
here. Whichever lands after this PR has to delete those entries. The
build names each one as `ADNDEF004 '<Type>.<field>' ... no longer
violates`, so it is mechanical.
## After review (`72414674c`)
- **Test-project compile:** the `WriteInElseBranchOfGuard_IsReported`
test had a raw newline inside a regular string literal (CS1010). That
would have broken the test assembly; it now uses `
`.
- **Negated guards:** ADN0065 no longer accepts a negated guard as a
guard. `if (!ShouldCacheForBackward)`, `if
(!this.ShouldCacheForBackward)`, `== false` and `!(IsTrainingMode &&
ShouldCacheForBackward)` all store exactly when no backward is coming,
and they are now reported.
- An `||` alongside the guard still counts as guarded, because the
aux-loss gates in #2413 use that form.
- New tests: `NegatedGuard_IsReported` (4 cases) and
`GuardComparedWithTrue_IsNotReported`. The generators project builds
with 0 errors.
- **`ConvolutionalLayer`:** a stray `[Scratch]`, left by a deleted
field, had attached to `_optimizedVerifiedForShape` and made it
ADN0065-eligible. The attribute and its baseline key are removed,
leaving 566 entries.
- **Known false positive:** an early-return guard, `if
(!ShouldCacheForBackward) return y; _last = x;`, is still reported. No
site in `src` uses that shape today.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **New Features**
* TabDPT and TabPFN models now provide parameter retrieval and
restoration methods.
* **Behavior Changes**
* Attention and transformation details are retained only during eligible
training runs.
* Synaptic-plasticity learning state is no longer updated during
inference or non-training runs.
* **Refactor**
* Forward-state handling has been streamlined across neural-network,
tabular, point-cloud, and time-series models; core forward calculations
and outputs remain unchanged.
* **Documentation**
* Updated license terms to identify CrowdTrainer LLC, clarify permitted
non-commercial use, and specify a change to Apache License 2.0 four
years after release.
* **Chores**
* Updated company and copyright attribution across package information
and website pages.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
## Prediction boundaries
AutoInt regression/classification predictions now enter `InferenceMode`,
releasing manual-backward activation caches during public prediction
calls. Classification training calls the forward/softmax path directly
so it retains training caches.
Dueling DQN action selection, action-value queries, lazy initialization,
and Double DQN target evaluation now use its inference prediction path.
The current-state training forward still retains the caches required by
`UpdateWeights`.
Regression coverage checks output parity, released caches, restored
scope after exceptions, training cache retention, and Dueling parameter
updates for terminal and nonterminal replay samples.
Validation: the net10.0 core build succeeded (analyzers/documentation
disabled for local memory limits); fifteen focused tests passed. Full CI
remains pending behind #2400. AutoInt training now computes and applies
tape gradients to the embeddings, attention stack, MLP and head; the
cache regression exercises the real training path.
## AutoInt training
Both legacy AutoInt training APIs now compute a differentiable batch
objective and apply SGD to the complete parameter set. Numerical and
categorical embeddings, reshapes and attention stay on the tape.
Attention splits heads independently. Gradient buffers are scratch state
rather than published parameters, and six obsolete activation-cache
baseline entries are removed.
Fifteen focused tests pass, including finite differences for every
parameter for classification/regression with and without categorical
features, categorical-only training, independent attention heads, and
Dueling terminal/nonterminal replay updates. The local net10.0 build
passed with nullable flow analysis, analyzers and XML generation
disabled for memory limits. Full CI is queued serially after #2400.
Parameter vectors for InteractingLayer no longer contain gradient
buffers; vectors saved with those erroneous entries have a different
length.
Categorical embeddings use the tape-aware gather operation rather than a
batch-by-cardinality one-hot matrix. Published interaction weights
remain available after prediction, with an explicit KeepsActivation
rationale and regression coverage. Parameter updates invalidate
persistent GPU tensors.
The training path now delegates to
TapeTrainingStepper.EagerSgdObjectiveStep, preserving the central
tape/arena lifecycle and satisfying NoNewHandWrittenTapeTrainingLoop.
The local build and all 18 focused cases pass, including all three
training-loop ratchet checks and the six whole-model finite-difference
configurations. This fixes the single failure from the
opencl-foundations run on the previous head (185 other cases passed
there).
---------
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: t <t@e.com>
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: ooples <3075685+ooples@users.noreply.github.com>
… every deep agent's learning rates (+4 combined) (#2414)
**Combined PR (209 files):** to save 2-hour CI runs, this PR carries 5
independent changes, merged with `git merge` so every original commit is
kept. Each section below is the original description.
- this PR: fix(rl): implement the TRPO, CQL, MuZero and Rainbow options
and wire every deep agent's learning rates
- #2410 (closed, merged in here): docs(metalearning): state what the
remaining options actually do (companion to #2406)
- #2398 (closed, merged in here): fix(census): models that own their
objective through training hooks are not probed as canonical
- #2408 (closed, merged in here): fix(densenet): stop holding a
forward's activations after it returns
- #2416 (closed, merged in here): fix: route activation caches through
SaveForBackward (sweep 2/3)
# This PR: fix(rl): implement the TRPO, CQL, MuZero and Rainbow options
and wire every deep agent's learning rates
Part of #2393
Refs #2090
## What this fixes
Reinforcement-learning agents stored options that no update read, so
several agents were not the algorithm their names and docs claim. A
sweep of every agent under `src/ReinforcementLearning/Agents` found 61
option properties that their agent file never references. On top of
that, every deep agent built its networks without an optimizer. Each
network therefore trained at the network's built-in rate (AMSGrad,
5e-4), so no learning-rate option reached any network, even the ones the
agent file did reference.
### Agents now implemented to their papers
- **TRPO** (Schulman et al. 2015). `Train()` used to compute advantages
and return without updating anything. It now:
- fits the value function with `ValueIterations` passes at
`ValueLearningRate`;
- takes a natural-gradient step: conjugate gradient
(`ConjugateGradientIterations`) on Fisher-vector products with
`Damping`, using the analytic Fisher J^T M J of Appendix C.1;
- scales that step so ½·sᵀFs = `MaxKL`;
- accepts it through a backtracking line search (`LineSearchSteps`,
`LineSearchBacktrackCoeff`). A candidate passes only if its surrogate
improvement is at least `LineSearchAcceptRatio` of the predicted
improvement and its exact KL is at most `MaxKL`. If no candidate passes,
the policy is unchanged.
- **CQL** (Kumar et al. 2020). The old code regressed Q on random
actions toward Q − `CQLAlpha`. It now:
- minimises the CQL(H) objective with the importance-sampled log-sum-exp
over uniform and policy actions (Appendix F);
- when `CQLLagrange` is set, runs dual ascent on α' against
`CQLTargetActionGap`;
- trains the actor with the reparameterised SAC loss;
- tunes the entropy temperature toward `TargetEntropy` (−dim A when
unset) at `AlphaLearningRate`, replacing the hard-coded −ActionSize and
0.0003;
- runs `GradientSteps` steps per call;
- uses `QLossFunction` for the Bellman term.
- **MuZero** (Schrittwieser et al. 2020):
- root priors are mixed with Dir(`RootDirichletAlpha`) noise by
`RootExplorationFraction` while acting;
- replay is prioritized: windows are drawn with probability ∝
p^`PriorityAlpha`, where p = |v(o₀) − z₀|, and weighted by 1/(N·P(i))
through the existing step mask, so the fused plan's graph is unchanged.
- **Rainbow** (Hessel et al. 2018, Fortunato et al. 2018):
- the noisy layer starts at σ₀/√p with σ₀ = `NoisyNetSigma`;
- the trunk is built from `SharedLayers`;
- the network trains at `LearningRate`.
### Learning-rate and loss options now reach their networks
A new base helper, `CreateNetworkOptimizer`, builds each network with
Adam at its own configured rate:
- A2C, A3C, PPO: policy and value rates;
- SAC, CQL, IQL: policy, Q and value rates;
- DDPG, TD3, MADDPG: actor and critic rates;
- DQN, DoubleDQN, REINFORCE, Rainbow: `LearningRate`.
Other options in the same class:
- PPO clips gradients to `MaxGradNorm`.
- A2C, A3C and PPO add the `EntropyCoefficient` bonus.
- TD3 uses `CriticLossFunction` and IQL uses `QLossFunction`.
- Modified policy iteration stops each partial evaluation early once a
sweep changes no value by more than `Theta`.
- SAC's `TargetEntropy ?? -dim(A)` never fell back, because `T?` on an
unconstrained generic is not `Nullable<T>` for float or double.
`TargetEntropy` on SAC and CQL is now `double?`: null is −dim(A), and
any other value, zero included, is the target (see below).
### Bugs fixed on the way
- A2C, A3C and PPO sliced the continuous log-std half with length
`actSize * 2`. `TensorSlice` takes a length, so this read past the end
of the tensor; the continuous path could not train.
## Tests
`RLAgentOptionsHonoredTests`. Each test is listed with the mutation that
makes it fail.
| Test | Fails if… |
|---|---|
| `TRPO_PolicyStepStaysInsideMaxKL_AndShrinksWithIt` | MaxKL is ignored
(fixed step), the line search skips the KL check, or the reported KL is
not the real one |
| `TRPO_ValueRegressionFollowsValueLearningRateAndIterations` | the
value network is built without `ValueLearningRate`, or `ValueIterations`
is not looped |
| `CQL_LagrangeMultiplierMovesAgainstTheTargetActionGap` |
`CQLTargetActionGap` or `CQLLagrange` is ignored, or α' is not stepped
at `QLearningRate` |
| `CQL_EntropyTemperatureFollowsTargetEntropyAndAlphaLearningRate` |
`TargetEntropy` is replaced by −dim A, or `AlphaLearningRate` is
hard-coded |
| `Rainbow_NoisyNetSigmaSetsTheInitialNoiseScale` | `NoisyNetSigma` is
not passed to the noisy layer |
| `MuZero_RootNoiseFollowsDirichletAlphaAndExplorationFraction` | α or
the fraction is ignored |
| `MuZero_TrainingRewritesReplayPrioritiesFromValueError` | priorities
are never updated |
| `A2C_ValueNetworkTrainsAtValueLearningRate` | the value network trains
at its default rate |
## Build
`src/AiDotNet.csproj` builds with 0 errors on net10.0 and net471, and
`tests/AiDotNet.Tests` compiles on net10.0. No new warnings in the
touched files. These tests have not been run locally (machine rule); CI
runs them.
## Options documentation to update after #2405 merges
The options files are in flight in #2405. Apart from the declaration
changes listed under "Maintainer decisions applied", this PR does not
edit them. After #2405 merges:
- `CQLOptions.QLearningRate`: it also drives the α' multiplier.
- `RainbowDQNOptions.NoisyNetSigma` is σ₀ (initial σ = σ₀/√fan-in).
`SharedLayers` is the trunk. `ValueStreamLayers` and
`AdvantageStreamLayers` are still unread (see below).
- `MuZeroOptions.PriorityAlpha`: priority = |v(o₀) − z₀|.
## Not in this PR (listed for decision)
- Dreamer: `KLScale`, `ContinueScale`, `BatchLength`,
`DeterministicSize`, `StochasticSize`, `RewardScale`.
- World Models: `VAEBeta`, `RNNLayers`, `Temperature`,
`ControllerLayers`, `VAEEpochs`, `RNNEpochs`, `ControllerGenerations`,
`ControllerPopulationSize`, `RolloutLength`.
- Decision Transformer: `NumHeads`, `DropoutRate`, `BufferSize`.
These options need agent rewrites (an RSSM; VAE + MDN-RNN + CMA-ES; a
causal transformer) and are tracked separately.
These items are also outside this PR:
- **Optimizers on the other agents:** MADDPG, QMIX and Decision
Transformer still store an unread Vector `Optimizer` here. Their
`OptimizerFactory` change is in #2416.
- **MuZero's policy target:** the policy head is trained by MSE toward
the one-hot action taken, and its raw outputs are the PUCT priors. The
paper trains with cross-entropy toward the MCTS visit distribution and
uses softmax priors. Root noise therefore mixes unnormalised scores with
a Dirichlet sample. This belongs with the paper rewrites above, not this
options PR.
- **Untested here:** PPO's `MaxGradNorm` clipping (now on by default at
0.5, as in the PPO reference settings), the A2C/A3C/PPO entropy bonus,
the `actSize * 2` slice fix, stopping modified policy evaluation at
`Theta`, and TD3's `CriticLossFunction` / IQL's `QLossFunction`.
## After review (`924f138de`)
- **MuZero:** importance weights are now divided by the batch maximum
(Schaul et al. 2016, section 3.4). Before, a window drawn at
`PriorityFloor` could carry a weight near 1e6.
- **CQL temperature optimizer:** the log α optimizer is built on the
first temperature update. A zero `AlphaLearningRate` with
`AutoTuneTemperature = false` no longer throws.
- **CQL docs:** the temperature-update doc now states the step the code
takes, −(log π + H̄) on log α, as the reference implementations do.
- **BOMs:** restored on `LayerForwardObserver.cs` and
`TransitionLayer.cs`.
- **Behaviour change:** Rainbow's default shared trunk is now 128 units,
because `SharedLayers` defaults to `{128}`. It was 64.
## Maintainer decisions applied
These change public option declarations in files that #2405 also edits
(doc comments only there). Expect small, mechanical conflicts in those
files, on these lines only.
- **`Optimizer` on MuZero, Rainbow, A3C, Dreamer and World Models is
replaced by `OptimizerFactory`.** The old property was typed
`IOptimizer<T, Vector<T>, Vector<T>>`, which no network can take, so it
was stored and never used. The replacement is a
`Func<IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>>?`, following
`DiffusionModelOptions.OptimizerFactory`.
- A3C, Rainbow, Dreamer and World Models invoke the factory once for
every network they build, including each A3C worker's local networks.
One optimizer instance per network is required because Adam's step
counter drives bias correction; if the instance were shared, the counter
would advance on every network's step.
- MuZero invokes it once per agent. Its representation, dynamics and
prediction networks train with one loss in one fused step, so a single
optimizer stepped once per joint step is the paper's setup.
- When the factory is null, each agent keeps its current behaviour:
- A3C trains the policy at `PolicyLearningRate` and the value at
`ValueLearningRate`.
- Rainbow and MuZero use Adam at the agent's learning rate.
- Dreamer and World Models use each network's own default.
- The constructor `optimizer` parameter on A3C, Rainbow and Dreamer is
removed in favour of the option. Nothing passed it.
- A3C builds each worker's local networks once and reuses them, so a
worker keeps its optimizer state across `TrainAsync` calls.
- The unused Vector optimizers these agents built in their constructors
are gone.
- **`TRPOOptions.Optimizer` and the `TRPOAgent` `optimizer` argument are
deleted.** The policy step is the natural-gradient step and the value
function trains at `ValueLearningRate`, so no optimizer was ever
consulted.
- **`ValueLossCoefficient` is deleted** from A2C, A3C and PPO.
- The policy and value are separate networks with separate optimizers,
so the coefficient only weighted a reported number.
- The reported loss is now the sum of the two losses each network
minimised.
- The `RandomSearchRLAutoML` search spaces no longer sample it.
- **`TargetEntropy` on SAC and CQL is `double?`.** Null means −dim(A),
and any value, zero included, is the target. The zero-means-unset
workaround is removed.
- `MuZeroOptions.UseValuePrefix` is unchanged until #2405 merges.
| New test | Fails if… |
|---|---|
| `A3C_TrainsWithTheConfiguredOptimizer` | A3C ignores
`OptimizerFactory` (either network falls back to its own Adam) |
| `Rainbow_TrainsWithTheConfiguredOptimizer` | Rainbow's online network
is built without the factory's optimizer |
| `Dreamer_TrainsWithTheConfiguredOptimizer` | any Dreamer network is
built without it |
| `WorldModels_TrainsWithTheConfiguredOptimizer` | any World Models
network is built without it |
| `MuZero_TrainsWithTheConfiguredOptimizer` | MuZero's joint step uses
its own Adam instead of the factory's |
|
`A3C_GivesEveryNetworkItsOwnOptimizer_IncludingEachWorkersLocalNetworks`
| the factory result is shared between networks, a worker's networks
skip the factory, or a worker's networks are rebuilt on each call |
| `Rainbow_GivesEveryNetworkItsOwnOptimizer` | the online and target
networks share one instance |
| `Dreamer_GivesEveryNetworkItsOwnOptimizer` | any two of the six
networks share one instance |
| `WorldModels_GivesEveryNetworkItsOwnOptimizer` | any two of the three
networks share one instance |
| `MuZero_BuildsOneJointOptimizerPerAgent_EvenFromSharedOptions` | the
factory is invoked per step instead of per agent, or two agents built
from the same options share one instance |
| `TRPO_OffersNoOptimizer_BecauseItsPolicyStepIsTheNaturalGradient` |
`TRPOOptions.Optimizer` or the constructor argument comes back |
|
`ActorCriticOptions_OfferNoValueLossCoefficient_BecausePolicyAndValueTrainSeparately`
| `ValueLossCoefficient` comes back on A2C, A3C or PPO |
| `TargetEntropy_NullMeansMinusActionDimension_AndZeroIsHonoured` | null
stops meaning −dim(A), or zero is again treated as unset |
The `…TrainsWithTheConfiguredOptimizer` tests train the agent twice,
with factories returning Adam at 1e-3 and at 1e-9, and require the
faster run to move the network weights more than 1000× further. If the
factory is ignored, both runs train at the same default rate and move
about equally.
The `…OwnOptimizer` tests use a counting factory. They require the
factory to have been called exactly once per network, and every
network's `TrainingOptimizer` to be a distinct instance that the factory
returned.
Dreamer, World Models and Decision Transformer rewrites are not in this
PR; they get their own PRs.
Build at b592b5e (this branch merged with #2410, #2398 and #2408):
`src/AiDotNet.csproj` has 0 errors on net10.0 and net471 (checked before
the merge), and `tests/AiDotNet.Tests` compiles on net10.0 with 0 errors
at the merge commit. No new warnings in the touched files. These tests
have not been run locally (machine rule); CI runs them.
# From #2410: docs(metalearning): state what the remaining options
actually do (companion to #2406)
Companion to #2406. That PR fixes the MetaLearning options files it
already touches. This PR applies the same review corrections to the
other 46 files, which keeps #2406 under the 100-file limit. Docs only.
**What was wrong (verified against the algorithms):**
- **`MetaOptimizer` / `InnerOptimizer`.** None of the 104 algorithms
reads them. They are passed to `MetaLearnerBase`, which only resets
them; `ApplyOuterUpdate` and the custom updates are plain SGD. Yet the
docs said "null uses the algorithm's default", and some said "uses
built-in Adam/SGD optimizer". They now say the optimizer is not used yet
and the update is built in. The three files that already said "not used"
(AWGIM, VERSA, SimpleShot) and Reptile's accurate note are unchanged.
Wiring the optimizers is a behavioral change for a separate PR.
- **`LossFunction`.** The docs said null uses the meta-model's default
loss, but the algorithms fall back to different losses. Each doc is now
generated from its algorithm's `options.LossFunction ?? ...` line.
- **Defaults.** Where a class default differs from
`IMetaLearnerOptions`, mostly `AdaptationSteps` 0 or 1 behind
`<inheritdoc cref=...>` plus PMF's outer rate, the inherited "Default:
5" is replaced with the real value. That covers 21 files.
The interface-level `Clone` correction (collaborators are shared, not
deep-copied) is in #2406, because the interface file is there.
Verified: core builds on net10.0 with no XML-doc warnings or errors in
the changed files. The text came from a scripted rewrite with a dry-run
report; a rescan finds no remaining inherited-default mismatches.
Refs #2397
# From #2398: fix(census): models that own their objective through
training hooks are not probed as canonical
Part of #2390 (A-1 in the v1 flaw plan, epic #2397). Fixes the causes
behind #2387 (census), #2389 (Docs Wiki) and the comparator half of
#2388 (Cross-ISA).
## Summary
### Census: 5 failures (#2387)
The nightly Model Performance Census had been red since 2026-10-19 with
5 failures. All five come from
`NeuralNetworkModelTestBase.ModelPerformanceCensus()`. It calls
`ForwardPreparedForTraining` on public fixture input, but only for
models whose objective it treats as the base class's canonical tape
objective.
**Four were a stale gate.** The census decided "canonical" by checking
whether `Train` was overridden. #2295 (2026-10-18) gave the base class
ownership of `Train`. Since then, models own their objective through the
hooks `Train` dispatches to (`TryTrainDeclaredObjectives` →
`DefineTrainingObjectives`, else `TryApplyLearningRule`). They no longer
override `Train`, so the census probed a surface their training never
uses:
| Model | Owns its objective through | Census probe |
| --- | --- | --- |
| GOT-OCR2 | `DefineTrainingObjectives` | fed fixture features into the
Qwen token-index layer |
| WGAN-GP | `TryApplyLearningRule` (critic/generator alternation) | ran
the critic's layers on the generator's input |
| CLAPModel | `TryApplyLearningRule` (contrastive pair) | index out of
range in BatchNorm |
| OpenSora | `TryApplyLearningRule` (latent diffusion) | 144 channels
into a 48-channel conv |
The gate now also treats an override of `DefineTrainingObjectives` or
`TryApplyLearningRule` as owning the objective. It looks the hooks up by
reflection and throws if one is renamed, so the check cannot silently
pass.
**EfficientConformer was a real bug, in `NeuralNetworkBase`'s lazy shape
walk, not in the model.** The walk keeps the last rank-2+ shape as a
fallback for layers that reject a rank-1 running shape. Dense and
LayerNorm declare rank-1 shapes ([features] → [width]) but run along the
last axis of a [time, features] sequence, so the fallback stayed at the
shape from before a projection. The stage-1 `TemporalFrameSplicingLayer`
resolved from [32, 64] (the stage-0 splicer's output) where its real
input is [32, 32]. It declared [16, 128] while computing [16, 64], and
the first training forward failed its shape check. `Predict` never
verifies declared shapes, so inference looked healthy. When a layer's
own input is exactly the fallback's feature axis, the walk now carries
the layer's output width into the fallback (`TryApplyFeatureAxisWidth`).
### Docs Wiki (#2389)
The `<example>` samples in AudioVAE, AutoencoderKL,
TemporalInterpolationVAE, CCDM, Florence2 and GOTOCR2 used an input they
never declared, so the generated wiki pages failed their compile gate.
Each sample now declares its input at the source.
### Cross-ISA nightly (#2388, comparator half)
The partition's expected-test list used exact names, while discovery
used a substring filter that also matched the `_Accuracy` /
`_ClusterQuality` variants. Every partition therefore reported
"incomplete results". The expected list is now built from the tests
discovery actually found in the partition. The other half of #2388 is
real AVX2-off training divergence (DiffSeg, UniTS, MegaTTS) plus a VGG
timeout. Its kernel-side cause is in AiDotNet.Tensors, being fixed in
ooples/AiDotNet.Tensors#1578: production paths ran AVX2 intrinsics with
no ISA guard. That fix shipped: this branch already moves Tensors,
OneDNN and CLBlast to 0.144.2 (and Evolution to 0.7.0), so CI here
re-checks that half.
## Verification
- **Census on this branch before the EfficientConformer fix** (run
40690852308): 962/962 fixtures ran, and the only non-`ok` record was
EfficientConformer, with exactly the splicer error above. WGAN-GP,
GOT-OCR2, CLAPModel and OpenSora are `ok`.
- **EfficientConformer**, census-faithful probe (a console app with
InternalsVisibleTo, float, the census call order), in four modes: plain,
under a `TensorArena`, with parameters read before the first forward
(which triggers the walk), and arena plus parameters. In every mode the
splicers declare [32, 64], [16, 64] and [8, 88], and
`ForwardPreparedForTraining` returns [1, 8, 4]. Before the fix the same
probe reproduced the census error ([16, 128] declared, [16, 64]
computed).
- **New tests** in `LazyShapeWalkFeatureAxisTests`: the splicer
declarations after the walk and after `Predict`, and the training
forward after `Predict`. Mutation (predicted from the pre-fix probe, not
executed, since tests run only in CI here): without the
`TryApplyFeatureAxisWidth` branch the stage-1 splicer goes back to [16,
128], which fails the declaration asserts and the training forward.
- **Docs Wiki**, dispatched on this branch: success.
- `src/AiDotNet.csproj` (net10.0) and `AiDotNetTests` (net10.0) build
with 0 errors. Tests run in CI.
- A census re-run on the final head is dispatched; its result will be
added in a comment.
## Adversarial review
- **Does the census-gate fix hide a real defect?** No, for these four.
Their `Train` never routes public input through `ForwardForTraining`:
base `Train` calls their hook first and returns. The census still
measures their real training phases.
- **Reflection lookup.** `Type.GetMethod(..., Instance | NonPublic)`
returns inherited `protected` members, so for a model without an
override it finds the base declaration, and the `DeclaringType`
comparison works. A renamed hook returns null, and that now throws.
- **Can the width carry fire where it shouldn't?** It only fires when a
rank-1 layer's declared input equals the fallback's last axis. After a
global pool or flatten ([H, W, C] → [C]), a following Dense(C → k) also
matches, and carries the fallback to [H, W, k]. That's no worse than
before: the walk already treats any rank-2 → rank-1 layer as a
degenerate report and keeps the earlier sequence shape, so after a pool
the fallback was already the stale [H, W, C]. The fallback is only
consulted when a later layer rejects the rank-1 running shape, which
layers after a pool + Dense don't do.
- **Unexplained:** before the fix, the census's float run hit the walk
through `Predict` while the double fixture did not. The probe shows the
walk is reached whenever parameters are read before the first forward.
Why the float census path reads them earlier than the double one wasn't
traced; the fix covers both.
# From #2408: fix(densenet): stop holding a forward's activations after
it returns
Part of #2383 (DenseNet-121 memory). Pairs with
ooples/AiDotNet.Tensors#1610 (T-3, ooples/AiDotNet.Tensors#1573).
## What held the memory
I took a heap dump of DenseNet-121 b16 CPU inference and ran `gcroot` on
its large float arrays. Three roots held the activations of a forward
after it returned:
1. **`DenseBlockLayer` and `TransitionLayer` fields.** Every
intermediate (BN out, ReLU out, conv out) was written to a field, and
nothing reads those fields. The gradient tape keeps what backward needs.
2. **`DenseBlock._layerOutputs`**: a list of every inner layer's output,
also never read.
3. **`BatchNormalizationLayer._lastInput`** was cached whenever no tape
was recording, so eval forwards cached it too.
There was also a path through `LayerForwardObserver`.
`PredictAndValidateChain` runs the first forward under this observer,
which records each layer's input and output. The Tensors worker threads
were created during that forward, captured the observer through their
ExecutionContext, and kept the recording alive. Tensors #1610 fixes the
capture. On this side, the observer now drops its recording once
validation has read it.
## Change
- `DenseBlockLayer` and `TransitionLayer`: the intermediates are locals.
- `DenseBlock`: the unused output list is removed.
- `BatchNormalizationLayer`: `_lastInput` is cached only when
`ShouldCacheForBackward`, and still not under a recording tape.
- `LayerForwardObserver.ReleaseRecording()` is called after the
traced-chain validation.
## Verification
- DenseNet, BatchNormalization, transition, dense-block,
lazy-spatial-layer and advanced-layers tests: **382 passed, 1 skipped, 0
failed** (net10.0, against Tensors #1610).
- **DenseNet-121, CPU, b16, inference only.** AiDotNet.PyTorchParity
harness, `MemoryMbPeak` (process WS), 2 runs each:
| | WS (MB) |
|---|---|
| master + Tensors main | 2090 |
| + Tensors #1610 | 1904 |
| + this PR | 1617 |
- The CUDA b16 number (the ≤ 2 GB target in
ooples/AiDotNet.Tensors#1573) is next; I'll add it here.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **New Features**
* Reinforcement learning agents now support per-network optimizer
configuration, with improved policy updates, entropy tuning, prioritized
replay, and exploration controls.
* MuZero supports configurable visit-temperature sampling; SAC and CQL
accept explicit zero target entropy.
* **Bug Fixes**
* Improved neural-network shape handling and reduced retention of
intermediate tensors.
* Modified policy evaluation can stop early when updates fall below its
threshold.
* **Documentation**
* Clarified meta-learning option behavior and added complete input
examples for several model guides.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
## Follow-up commit (second machine)
- `ebda8208c` style(densenet): restore the UTF-8 BOM on `DenseBlock.cs`
and `DenseBlockLayer.cs`. Master has it, and the earlier commit had
stripped it. The only change is the three BOM bytes; `net10.0` builds
clean.
# Added by the merge of #2416 (`b9217a928c`)
Folding #2416 in left the RL agents with one consistent set of changes,
plus three paper fixes that only made sense once both sides were
together:
- **QMIX** steps the paper's RMSProp (lr 5e-4, decay 0.99, eps 1e-5)
unless an `OptimizerFactory` is given.
- **MADDPG** defaults to the paper's optimizer settings (Lowe et al.
2017) unless an `OptimizerFactory` is given.
- **Rainbow's dueling head.** `DuelingCombinationLayer` gains hidden
value and advantage streams and a per-atom distributional head. Before,
V cancelled inside the per-atom softmax, so the value stream learned
nothing. `DeserializationHelper` rebuilds the layer from its metadata.
The new test has its shard entry.
# From #2416: fix: route activation caches through SaveForBackward
(sweep 2/3)
Part of #2391
## The defect class
Layers wrote per-forward activations into fields (`_lastInput = input;`,
`_lastOutput = ...`, `_xOutput = ...`, arrays `_outs[i] = ...`,
`List.Add(...)`) with no regard for whether a backward would ever read
them. In inference (eval mode, `InferenceMode`, under a recording tape,
graph capture) every one of those activations stays reachable after the
forward, so peak memory is the sum of all activations instead of the
live set. `LayerBase<T>.ShouldCacheForBackward` / `SaveForBackward(ref
field, value)` already own that decision; ADN0065
(`ActivationCacheGuardAnalyzer`) reports the sites. This PR is sweep 2
of 3 over those sites (the 86-file batch `adn0065-batch2`, of which 82
files needed a change; `InvertedResidualBlock` is excluded because it is
in flight elsewhere).
## What changed, per site
Each site was read individually rather than replaced mechanically.
- **Field never read anywhere (only written, or cleared in
`ResetState`).** This was the large majority, including essentially
every cache in `Layers/SSM`. The field is deleted and the value stays a
local, the same approach as #2408 (DenseBlockLayer). Where a value
existed only to feed the cache (for example, Longhorn's per-step state
list, HGRN/MinGRU/MinLSTM's `[initialState, output]` concatenations,
DeltaFormer's `[B,H,S,S]` attention weights, MemoryWrite's write-value
matmul, LogVariance's unused `ReduceMean`, the LSTM per-step `SetSlice`
copies), the computation is removed as well.
- **Field read by a manual backward or backward helper.** The write goes
through `SaveForBackward(ref _field, local)`, and the forward uses the
local: GraphIsomorphism, GraphSAGE, PNA, MoE (`_lastInput`,
`_lastPreActivation`), Multiply (`_lastInputs`, read by `BackwardGpu`),
RBM (CD update), MEGA (`_lastEmaInput`), and Kimi (`_lastKVGate*`).
Several of those helpers have no remaining callers, so the conservative
route keeps them compiling.
- **Field read by an auxiliary loss.** The write is retained only while
that loss can be asked for:
- Highway: `SaveForBackward`, the same semantics as its existing
`cacheBwd` gate.
- MemoryRead and MemoryWrite: `IsTrainingMode && UseAuxiliaryLoss`.
- MultiHeadAttention: `_lastHeadOutputs` gated on `UseAuxiliaryLoss &&
IsTrainingMode`.
- **LSTM.** `_lastHiddenState` / `_lastCellState` are kept only during a
`ForwardFromState` call, which matches the fused path. The unread
`_cachedHiddenStates` / `_cachedCellStates` and `_lastInput` are
removed.
- **Also removed:** dead `out _maxIndices` buffers in GlobalPooling and
MaxPool3D, and MaxPooling's `_lastOutputShape`.
## ADN0065 in these 86 files
| | count |
|---|---|
| master (`adn0065-batch2.txt`) | 457 |
| this branch (SARIF from the net10.0 build) | 26 |
## Sites left alone, and why (26 remaining reports)
These are not backward caches, so routing them through `SaveForBackward`
would break a reader. They need an analyzer exemption (an attribute or a
recognised pattern) instead.
- **Memoized table:** `PositionalEncodingLayer.encodings` (x5). Built
once and reused by every forward, as the `SaveForBackward` docs
themselves call out.
- **Persistent recurrent / streaming state:**
- `ReservoirLayer._reservoirState`: echo-state reservoir.
- `MambaBlock._lastHiddenStates`: opt-in through
`RequireHiddenStateOutput` for chunked autoregressive inference; read by
`GetHiddenState()`.
- `LSTMLayer._initialHidden` / `_initialCell` / `_stateStepping` (x4)
and `_lastHiddenState` / `_lastCellState` (x2): `ForwardFromState` state
passing.
- **Outputs consumed by another component in inference or under the
tape:**
- `SAM2HieraImageEncoderLayer._lastStride4` / `_lastStride8`:
high-resolution skips read by `SAM2NativePipelineLayer`.
- `RepParameterizationLayer._lastMean` / `_lastLogVar`: read by the
VAE's KL term under the tape.
- **Interpretability accessors read after an inference forward:**
- `GatedFeatureLearningUnitLayer._gateCache`: `GetGateValues` /
`GetFeatureImportance`.
- `ObliviousDecisionTreeLayer._featureSelectionsCache`:
parameter-derived, not batch-sized.
- **Auxiliary-loss state that must survive a tape training step:**
- `GraphConvolutionalLayer._lastNodeFeatures` (already
`IsTrainingMode`-gated).
- `MixtureOfExpertsLayer._lastRoutingWeights`: `[B, experts]`,
load-balancing loss and diagnostics.
- `MemoryReadLayer` / `MemoryWriteLayer._lastAttentionScores` and
`MultiHeadAttentionLayer._lastHeadOutputs`: now training-and-aux-only.
- **Lazily initialised parameters:**
`ExtendedLSTMLayer._outputNormGamma` / `_outputNormBeta` (an analyzer
false positive).
## Verification
- `src/AiDotNet.csproj` builds with 0 errors on net10.0 and on net471.
- The ADN0065 counts above come from the net10.0 build's SARIF.
- No tests were run locally (CI runs them). No test was changed.
## RL agents: `Optimizer` option becomes `OptimizerFactory`
(`67eace97c`)
DQN, MADDPG, QMIX and Decision Transformer now take `OptimizerFactory`
(`Func<IGradientBasedOptimizer<T, Tensor<T>, Tensor<T>>>?`) in place of
an `Optimizer` option that no code read.
- **DQN** calls the factory once for the Q-network and once for its
target. Before, both networks shared one optimizer instance.
- **MADDPG** calls it once for each of an agent's four networks: actor,
target actor, critic and target critic.
- **Decision Transformer** calls it once, for its only network.
- **QMIX** calls it once per agent. The agent networks and the mixing
network train on one TD loss in one fused step, the same reasoning as
MuZero in #2414.
- **When the factory is null**, DQN uses Adam at its configured learning
rate; MADDPG uses Adam at its actor/critic rates (both default to the
paper's 0.01); Decision Transformer builds its AdamW recipe with the
configured warmup and clipping; QMIX's joint step uses the paper's
RMSprop recipe at its configured learning rate (default 5e-4). Explicit
factories take precedence.
- **QMIX test:** it also asserts that each agent's joint step holds the
optimizer the factory returned, not a fallback SGD (`ba50ee24a`).
- **Removed:** the unread `_optimizer` fields and the unused `optimizer`
constructor parameters on MADDPG, QMIX and Decision Transformer.
The tests in `RLOptimizerFactoryTests` fail in these cases:
- two networks share an optimizer instance;
- a network is built without calling the factory;
- QMIX calls the factory on every step instead of once per agent;
- two QMIX agents built from the same options share an instance.
**Conflict with #2414:** both PRs change the network-construction lines
in `DQNAgent.cs` and `MADDPGAgent.cs`. Whichever merges second should
resolve each line to `OptimizerFactory?.Invoke() ??
CreateNetworkOptimizer(<rate>)`, which keeps both changes.
**Verification:** the `src` net10.0 build passed with 0 errors. The
local net471 and test-project builds were stopped for low memory, so CI
is their first build. I dispatched CI on this branch.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **Performance**
* Reduced memory retained between neural network layer operations by
limiting storage of intermediate forward-pass results. Forward
computations remain in place.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Package validation repair (277a8bc): the compatibility job rejected
40 member removals already listed in PublicAPI.Unshipped.txt, plus
Dreamer's replacement of generated parameter registration with its
native network inventory. Exact released-baseline exceptions now cover
net10.0, net8.0 and net471; package validation and cross-framework
checks remain enabled. Migration details:
docs/reinforcement-learning/options-api-migration.md. The SDK comparison
against the actual compiled CI net10.0 assembly fails with the old
exceptions and passes with the updated exceptions; deleting one
exception restores CP0002. Full hosted validation of the corrected
commit: https://github.com/ooples/AiDotNet/actions/runs/41546448223
(pending).
## Failed-shard repairs (aaa713c)
- Attention metadata queries preserve lazy weights while initializing
child registration. Both failing CvAdapterLiveParameterTests cases now
pass locally using the actual CI assembly with the equivalent metadata
guard applied for verification.
- MuZero's joint objective evaluates replay windows as batch rows at
each unroll step. This preserves all replay windows, masks, importance
weights, unroll steps and loss terms while avoiding one copied network
graph per sampled window.
- The unchanged Policy_ShouldFollowTheReward invariant passes locally in
28.96 seconds with two logical processors. After five seeded training
steps, all 206,864 parameters remain within the existing 1e-5 tolerance
of the previous implementation (maximum difference 1.181e-6); batched
eager and fused updates differ by at most 8.027e-7. The modified MuZero
source compiles locally with its source generators against the actual CI
dependencies.
Full hosted validation is pending:
https://github.com/ooples/AiDotNet/actions/runs/41632190301 .
Assertions, training configurations, iterations and CI timeouts were not
relaxed.
## Completed-run audit: third failed shard (5ea1fa7)
The completed run also contained three failures in Unassigned - 01.
Generated layer factories bypassed the constructor-specific
nested-attention restoration, so the common post-construction hook now
restores encoder self-attention and decoder self/cross-attention
configuration. The unchanged encoder, decoder and legacy-default
round-trip cases all pass locally against the compiled changed helper
hooked into the CI assembly for verification.
The remaining geometry test expected an unresolved downstream layer even
though feature-axis propagation now correctly resolves its input to
width 8. Its expectation now requires readiness and the correct width
before forward, retaining the output shape and post-forward width
checks. The separate invalid-geometry rejection test is unchanged and
passes.
All three failed shards' observed causes are addressed; full hosted
validation is still pending on the latest head:
https://github.com/ooples/AiDotNet/actions/runs/41633795607 .
---------
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: t <t@e.com>
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Part of #2383 (DenseNet-121 memory). Pairs with ooples/AiDotNet.Tensors#1610 (T-3, ooples/AiDotNet.Tensors#1573).
What held the memory
I took a heap dump of DenseNet-121 b16 CPU inference and ran
gcrooton its large float arrays. Three roots held the activations of a forward after it returned:DenseBlockLayerandTransitionLayerfields. Every intermediate (BN out, ReLU out, conv out) was written to a field, and nothing reads those fields. The gradient tape keeps what backward needs.DenseBlock._layerOutputs: a list of every inner layer's output, also never read.BatchNormalizationLayer._lastInputwas cached whenever no tape was recording, so eval forwards cached it too.There was also a path through
LayerForwardObserver.PredictAndValidateChainruns the first forward under this observer, which records each layer's input and output. The Tensors worker threads were created during that forward, captured the observer through their ExecutionContext, and kept the recording alive. Tensors #1610 fixes the capture. On this side, the observer now drops its recording once validation has read it.Change
DenseBlockLayerandTransitionLayer: the intermediates are locals.DenseBlock: the unused output list is removed.BatchNormalizationLayer:_lastInputis cached only whenShouldCacheForBackward, and still not under a recording tape.LayerForwardObserver.ReleaseRecording()is called after the traced-chain validation.Verification
MemoryMbPeak(process WS), 2 runs each:🤖 Generated with Claude Code
Summary by CodeRabbit
Follow-up commit (second machine)
ebda8208cstyle(densenet): restore the UTF-8 BOM onDenseBlock.csandDenseBlockLayer.cs. Master has it, and the earlier commit had stripped it. The only change is the three BOM bytes;net10.0builds clean.