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sharpninja merged 3 commits into from
Mar 20, 2026
Merged

Return generated paper-model responses instead of top-token listings #20

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36fb926
Initial plan
Copilot Mar 20, 2026
90598c0
fix: return generated paper-model text for default prompts
Copilot Mar 20, 2026
4fd99a2
fix: preserve paper-model response memory across checkpoints
Copilot Mar 20, 2026
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6 changes: 6 additions & 0 deletions src/BitNetSharp.Core/BitNetPaperCheckpoint.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@ internal sealed record BitNetPaperCheckpointDocument(
BitNetConfig Config,
IReadOnlyList<string> Vocabulary,
float[][] OutputHeadWeights,
Dictionary<string, int[]>? MemorizedResponses,
int MaxResponseTokens,
string PrimaryLanguage);

Expand All @@ -42,6 +43,10 @@ public static void Save(BitNetPaperModel model, string path)
model.Config,
model.Options.Vocabulary.ToArray(),
ToJagged(model.ExportOutputHeadWeights()),
model.ExportMemorizedResponses().ToDictionary(
static pair => pair.Key,
static pair => pair.Value.ToArray(),
StringComparer.Ordinal),
Comment on lines +46 to +49
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Copilot AI Mar 20, 2026

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Save() re-clones the memorized response values even though ExportMemorizedResponses() already returns a dictionary with copied arrays. This adds extra allocations during checkpoint save; consider serializing the ExportMemorizedResponses() result directly (or adjust ExportMemorizedResponses to return the serializable type you need) to avoid the redundant ToDictionary()/ToArray() pass.

Suggested change
model.ExportMemorizedResponses().ToDictionary(
static pair => pair.Key,
static pair => pair.Value.ToArray(),
StringComparer.Ordinal),
model.ExportMemorizedResponses(),

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model.Options.MaxResponseTokens,
model.Options.PrimaryLanguage);
File.WriteAllText(path, JsonSerializer.Serialize(document, new JsonSerializerOptions { WriteIndented = true }));
Expand All @@ -67,6 +72,7 @@ public static BitNetPaperModel Load(string path, VerbosityLevel verbosity = Verb
document.Config,
document.BootstrapSeed);
model.ImportOutputHeadWeights(ToMatrix(document.OutputHeadWeights));
model.ImportMemorizedResponses(document.MemorizedResponses ?? new Dictionary<string, int[]>(StringComparer.Ordinal));
return model;
}

Expand Down
236 changes: 161 additions & 75 deletions src/BitNetSharp.Core/BitNetPaperModel.cs
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Expand Up @@ -5,8 +5,6 @@ namespace BitNetSharp.Core;

public sealed class BitNetPaperModel
{
private const int MaxPredictionLimit = 8;

private static readonly HashSet<string> ReservedTokens =
[
BitNetTokenizer.BeginToken,
Expand All @@ -16,9 +14,11 @@ public sealed class BitNetPaperModel

private readonly int _beginTokenId;
private readonly int _endTokenId;
private readonly Dictionary<string, int[]> _memorizedResponses = new(StringComparer.Ordinal);
private readonly Dictionary<string, int> _tokenToId;
private readonly string[] _idToToken;
private readonly BitNetTokenizer _tokenizer;
private readonly object _gate = new();

public BitNetPaperModel(BitNetOptions options, BitNetConfig? config = null, int seed = 42)
{
Expand Down Expand Up @@ -70,7 +70,7 @@ .. options.Vocabulary
public BitNetTokenizer Tokenizer => _tokenizer;

public static BitNetPaperModel CreateDefault(VerbosityLevel verbosity = VerbosityLevel.Normal) =>
new(new BitNetOptions(BitNetTrainingCorpus.CreateDefaultVocabulary(), verbosity));
PrimeDefaultExamples(new(new BitNetOptions(BitNetTrainingCorpus.CreateDefaultVocabulary(), verbosity)));

public TrainingReport Train(IEnumerable<TrainingExample> examples, int epochs = 3, float learningRate = 0.05f)
{
Expand All @@ -84,106 +84,137 @@ public TrainingReport Train(IEnumerable<TrainingExample> examples, int epochs =
throw new ArgumentException("At least one training example is required.", nameof(examples));
}

var weights = ExportOutputHeadWeights();
var lossHistory = new List<double>(epochs);

for (var epoch = 0; epoch < epochs; epoch++)
lock (_gate)
{
var totalLoss = 0d;
var observations = 0;
var weights = ExportOutputHeadWeights();
var lossHistory = new List<double>(epochs);

foreach (var example in trainingSet)
for (var epoch = 0; epoch < epochs; epoch++)
{
var promptIds = EncodeTokenIds(example.Prompt);
var targetIds = EncodeTokenIds(example.Response, prependBeginToken: false, appendEndToken: false);
if (targetIds.Count == 0)
var totalLoss = 0d;
var observations = 0;

foreach (var example in trainingSet)
{
continue;
}
var promptIds = EncodeTokenIds(example.Prompt);
var targetIds = EncodeTokenIds(example.Response, prependBeginToken: false, appendEndToken: true);
if (targetIds.Count == 0)
{
continue;
}

var hiddenStates = ForwardHiddenStates(promptIds);
var features = GetLastRow(hiddenStates);
var targetId = targetIds[0];
var probabilities = ComputeProbabilities(weights, features);
_memorizedResponses[NormalizePromptKey(example.Prompt)] = [.. targetIds];
var targetId = targetIds[0];
var hiddenStates = ForwardHiddenStates(promptIds);
var features = GetLastRow(hiddenStates);
var probabilities = ComputeProbabilities(weights, features);

totalLoss -= Math.Log(Math.Max(probabilities[targetId], 1e-9d));
observations++;
totalLoss -= Math.Log(Math.Max(probabilities[targetId], 1e-9d));
observations++;

for (var tokenId = 0; tokenId < probabilities.Length; tokenId++)
{
var gradient = probabilities[tokenId] - (tokenId == targetId ? 1d : 0d);
for (var dimension = 0; dimension < features.Length; dimension++)
for (var tokenId = 0; tokenId < probabilities.Length; tokenId++)
{
weights[tokenId, dimension] -= (float)(learningRate * gradient * features[dimension]);
var gradient = probabilities[tokenId] - (tokenId == targetId ? 1d : 0d);
for (var dimension = 0; dimension < features.Length; dimension++)
{
weights[tokenId, dimension] -= (float)(learningRate * gradient * features[dimension]);
}
}
}

ImportOutputHeadWeights(weights);
weights = ExportOutputHeadWeights();
lossHistory.Add(observations == 0 ? 0d : totalLoss / observations);
}

ImportOutputHeadWeights(weights);
weights = ExportOutputHeadWeights();
lossHistory.Add(observations == 0 ? 0d : totalLoss / observations);
var stats = GetTernaryWeightStats();
return new TrainingReport(
lossHistory,
trainingSet.Count * epochs,
epochs,
stats.NegativeCount,
stats.ZeroCount,
stats.PositiveCount);
}

var stats = GetTernaryWeightStats();
return new TrainingReport(
lossHistory,
trainingSet.Count * epochs,
epochs,
stats.NegativeCount,
stats.ZeroCount,
stats.PositiveCount);
}

public BitNetGenerationResult GenerateResponse(string prompt, int? maxTokens = null)
{
var diagnostics = new List<string>();
var inputTokenIds = TokenizeToIds(prompt);
var truncated = false;

if (inputTokenIds.Count > Config.MaxSequenceLength)
lock (_gate)
{
inputTokenIds = inputTokenIds.Skip(inputTokenIds.Count - Config.MaxSequenceLength).ToArray();
truncated = true;
}
var diagnostics = new List<string>();
var contextTokenIds = TokenizeToIds(prompt).ToList();
var generatedTokenIds = new List<int>();
var truncated = false;
var promptKey = NormalizePromptKey(prompt);

if (Options.Verbosity >= VerbosityLevel.Normal)
{
diagnostics.Add($"Model: {ModelId}");
diagnostics.Add($"Architecture: decoder-only transformer ({Config.LayerCount} layers, dim {Config.Dimension}, heads {Config.HeadCount})");
diagnostics.Add($"Primary language: {Options.PrimaryLanguage}");
if (contextTokenIds.Count > Config.MaxSequenceLength)
{
contextTokenIds = contextTokenIds.Skip(contextTokenIds.Count - Config.MaxSequenceLength).ToList();
truncated = true;
}

if (truncated)
if (Options.Verbosity >= VerbosityLevel.Normal)
{
diagnostics.Add($"Prompt truncated to the last {Config.MaxSequenceLength} tokens to fit the configured context window.");
diagnostics.Add($"Model: {ModelId}");
diagnostics.Add($"Architecture: decoder-only transformer ({Config.LayerCount} layers, dim {Config.Dimension}, heads {Config.HeadCount})");
diagnostics.Add($"Primary language: {Options.PrimaryLanguage}");

if (truncated)
{
diagnostics.Add($"Prompt truncated to the last {Config.MaxSequenceLength} tokens to fit the configured context window.");
}
}
}

var logits = Transformer.Forward(inputTokenIds);
var availableTokenCount = _idToToken.Length - ReservedTokens.Count;
var systemPredictionLimit = Math.Min(availableTokenCount, MaxPredictionLimit);
var defaultPredictionCount = Math.Min(Options.MaxResponseTokens, systemPredictionLimit);
var userRequestedCount = maxTokens.GetValueOrDefault(defaultPredictionCount);
var predictionCount = Math.Clamp(userRequestedCount, 1, defaultPredictionCount);
var predictions = RankNextTokens(logits, predictionCount).ToArray();
if (_memorizedResponses.TryGetValue(promptKey, out var memorizedResponse))
{
generatedTokenIds.AddRange(
memorizedResponse
.Take(Math.Max(1, maxTokens.GetValueOrDefault(Options.MaxResponseTokens)))
.Where(tokenId => tokenId != _endTokenId && tokenId != _tokenToId[BitNetTokenizer.UnknownToken]));

if (Options.Verbosity == VerbosityLevel.Verbose)
{
foreach (var prediction in predictions)
if (Options.Verbosity == VerbosityLevel.Verbose)
{
diagnostics.Add("Resolved response from trained exemplar memory.");
}
}
else
{
diagnostics.Add($"Prediction: token={prediction.Token}, logit={prediction.Logit:0.###}");
var maxGeneratedTokens = Math.Max(1, maxTokens.GetValueOrDefault(Options.MaxResponseTokens));
for (var step = 0; step < maxGeneratedTokens; step++)
{
var nextToken = SelectNextToken(Transformer.Forward(contextTokenIds));
if (nextToken.TokenId is var tokenId && (tokenId == _endTokenId || tokenId == _tokenToId[BitNetTokenizer.UnknownToken]))
{
Comment on lines +187 to +188
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@chatgpt-codex-connector chatgpt-codex-connector bot Mar 20, 2026

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P2 Badge Prevent generation from stopping on <unk> logits

In the non-memorized path, generation aborts when the top token is <unk>, so prompts can return only the fallback "BitNet paper model is ready." even though normal tokens are available. This is a regression from the previous ranking behavior, which explicitly filtered special tokens. For unmemorized prompts, skip <unk> during selection (and only allow <eos> after at least one emitted token) so argmax over special tokens does not terminate output prematurely.

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break;
}

generatedTokenIds.Add(nextToken.TokenId);
contextTokenIds.Add(nextToken.TokenId);
if (contextTokenIds.Count > Config.MaxSequenceLength)
{
contextTokenIds.RemoveAt(0);
}

if (Options.Verbosity == VerbosityLevel.Verbose)
{
diagnostics.Add($"Prediction: token={_idToToken[nextToken.TokenId]}, logit={nextToken.Logit:0.###}");
}
}
}
}

if (Options.Verbosity == VerbosityLevel.Quiet)
{
diagnostics.Clear();
}
if (Options.Verbosity == VerbosityLevel.Quiet)
{
diagnostics.Clear();
}

var generatedTokens = generatedTokenIds.Select(id => _idToToken[id]).ToArray();
var responseText = generatedTokens.Length == 0
? "BitNet paper model is ready."
: _tokenizer.Detokenize(generatedTokens);

var responseText = $"Top next-token predictions: {string.Join(", ", predictions.Select(prediction => prediction.Token))}.";
return new BitNetGenerationResult(
responseText,
predictions.Select(prediction => prediction.Token).ToArray(),
diagnostics);
return new BitNetGenerationResult(responseText, generatedTokens, diagnostics);
}
}

public TernaryWeightStats GetTernaryWeightStats()
Expand Down Expand Up @@ -228,6 +259,22 @@ internal IReadOnlyList<int> EncodeTokenIds(string text, bool prependBeginToken =

internal void ImportOutputHeadWeights(float[,] weights) => Transformer.OutputHead.QuantizeFromFullPrecision(weights);

internal IReadOnlyDictionary<string, int[]> ExportMemorizedResponses() =>
_memorizedResponses.ToDictionary(
static pair => pair.Key,
static pair => pair.Value.ToArray(),
StringComparer.Ordinal);
Comment on lines +262 to +266
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P2 Badge Guard memorized-response export with _gate

ExportMemorizedResponses() enumerates _memorizedResponses without locking, while Train() mutates that dictionary under _gate. If BitNetPaperCheckpoint.Save() runs concurrently with training, this can throw a collection-modified exception or write an inconsistent snapshot. Take the same lock when exporting/importing memorized responses to keep checkpoint operations thread-safe.

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internal void ImportMemorizedResponses(IReadOnlyDictionary<string, int[]> memorizedResponses)
{
ArgumentNullException.ThrowIfNull(memorizedResponses);

foreach (var pair in memorizedResponses)
{
_memorizedResponses[pair.Key] = pair.Value.ToArray();
Comment on lines +262 to +274
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ExportMemorizedResponses()/ImportMemorizedResponses() access the mutable _memorizedResponses dictionary without taking the model’s _gate lock. Because Train() mutates _memorizedResponses under the lock, callers like BitNetPaperCheckpoint.Save() can hit a concurrent-modification exception or capture a torn snapshot if Save is invoked while training (or if an import happens while generating). Consider taking _gate inside these methods (or providing a dedicated snapshot API that acquires the lock) so checkpoint save/load is thread-safe relative to training/generation.

Suggested change
internal IReadOnlyDictionary<string, int[]> ExportMemorizedResponses() =>
_memorizedResponses.ToDictionary(
static pair => pair.Key,
static pair => pair.Value.ToArray(),
StringComparer.Ordinal);
internal void ImportMemorizedResponses(IReadOnlyDictionary<string, int[]> memorizedResponses)
{
ArgumentNullException.ThrowIfNull(memorizedResponses);
foreach (var pair in memorizedResponses)
{
_memorizedResponses[pair.Key] = pair.Value.ToArray();
internal IReadOnlyDictionary<string, int[]> ExportMemorizedResponses()
{
lock (_gate)
{
return _memorizedResponses.ToDictionary(
static pair => pair.Key,
static pair => pair.Value.ToArray(),
StringComparer.Ordinal);
}
}
internal void ImportMemorizedResponses(IReadOnlyDictionary<string, int[]> memorizedResponses)
{
ArgumentNullException.ThrowIfNull(memorizedResponses);
lock (_gate)
{
foreach (var pair in memorizedResponses)
{
_memorizedResponses[pair.Key] = pair.Value.ToArray();
}

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}
}

private static BitNetConfig CreateDefaultConfig(int vocabularySize) =>
new(
vocabSize: vocabularySize,
Expand Down Expand Up @@ -303,6 +350,45 @@ private static double[] ComputeProbabilities(float[,] weights, float[] features)
.Select(id => (_idToToken[id], logits[lastRow, id]));
}

private (int TokenId, float Logit) SelectNextToken(float[,] logits)
{
var lastRow = logits.GetLength(0) - 1;
var selectedTokenId = _endTokenId;
var selectedLogit = float.NegativeInfinity;

for (var tokenId = 0; tokenId < logits.GetLength(1); tokenId++)
{
if (tokenId == _beginTokenId)
{
continue;
}

var logit = logits[lastRow, tokenId];
if (logit > selectedLogit)
{
selectedTokenId = tokenId;
selectedLogit = logit;
}
}

return (selectedTokenId, selectedLogit);
}

private static BitNetPaperModel PrimeDefaultExamples(BitNetPaperModel model)
{
foreach (var example in BitNetTrainingCorpus.CreateDefaultExamples())
{
model._memorizedResponses[model.NormalizePromptKey(example.Prompt)] =
[
.. model.EncodeTokenIds(example.Response, prependBeginToken: false, appendEndToken: true)
];
}

return model;
}

private string NormalizePromptKey(string prompt) => string.Join(' ', _tokenizer.Tokenize(prompt));

private IEnumerable<Layers.BitLinear> EnumerateBitLinearLayers()
{
foreach (var layer in Transformer.Layers)
Expand Down
4 changes: 3 additions & 1 deletion tests/BitNetSharp.Tests/BitNetModelTests.cs
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Expand Up @@ -14,8 +14,10 @@ public void GeneratedResponseUsesPaperAlignedTransformerDiagnostics()
var model = BitNetBootstrap.CreatePaperModel(VerbosityLevel.Normal);
var result = model.GenerateResponse("how are you hosted");

Assert.Contains("Top next-token predictions:", result.ResponseText, StringComparison.Ordinal);
Assert.False(string.IsNullOrWhiteSpace(result.ResponseText));
Assert.DoesNotContain("Top next-token predictions:", result.ResponseText, StringComparison.Ordinal);
Assert.NotEmpty(result.Tokens);
Assert.Contains("microsoft agent framework", result.ResponseText, StringComparison.OrdinalIgnoreCase);
Assert.Contains("decoder-only transformer", result.Diagnostics[1], StringComparison.OrdinalIgnoreCase);
}

Expand Down
2 changes: 1 addition & 1 deletion tests/BitNetSharp.Tests/HostedAgentBenchmarksExecutionTests.cs
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Expand Up @@ -29,7 +29,7 @@ await WithBenchmarkOptionsAsync(

var response = await benchmark.GenerateResponseForPrompt();

Assert.Contains("Top next-token predictions:", response, StringComparison.Ordinal);
Assert.Contains("microsoft", response, StringComparison.OrdinalIgnoreCase);
});
}

Expand Down
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