add LLamaReranker and tests

LLama.Unittest/Constants.cs

@@ -7,6 +7,7 @@ internal static class Constants
         public static readonly string GenerativeModelPath = "Models/Llama-3.2-1B-Instruct-Q4_0.gguf";
         public static readonly string GenerativeModelPath2 = "Models/smollm-360m-instruct-add-basics-q8_0.gguf";
         public static readonly string EmbeddingModelPath = "Models/all-MiniLM-L12-v2.Q8_0.gguf";
+        public static readonly string RerankingModelPath = "Models/jina-reranker-v1-tiny-en-FP16.gguf";
 
         public static readonly string LLavaModelPath = "Models/llava-v1.6-mistral-7b.Q3_K_XS.gguf";
         public static readonly string LLavaMmpPath = "Models/mmproj-model-f16.gguf";

LLama.Unittest/LLama.Unittest.csproj

@@ -34,8 +34,11 @@
 
     <DownloadFile SourceUrl="https://huggingface.co/HuggingFaceTB/smollm-360M-instruct-v0.2-Q8_0-GGUF/resolve/main/smollm-360m-instruct-add-basics-q8_0.gguf" DestinationFolder="Models" DestinationFileName="smollm-360m-instruct-add-basics-q8_0.gguf" SkipUnchangedFiles="true">
     </DownloadFile>
-
-	<DownloadFile SourceUrl="https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/resolve/main/llava-v1.6-mistral-7b.Q3_K_XS.gguf" DestinationFolder="Models" DestinationFileName="llava-v1.6-mistral-7b.Q3_K_XS.gguf" SkipUnchangedFiles="true">
+
+    <DownloadFile SourceUrl="https://huggingface.co/gpustack/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en-FP16.gguf" DestinationFolder="Models" DestinationFileName="jina-reranker-v1-tiny-en-FP16.gguf" SkipUnchangedFiles="true">
+    </DownloadFile>
+
+      <DownloadFile SourceUrl="https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/resolve/main/llava-v1.6-mistral-7b.Q3_K_XS.gguf" DestinationFolder="Models" DestinationFileName="llava-v1.6-mistral-7b.Q3_K_XS.gguf" SkipUnchangedFiles="true">
 	</DownloadFile>
 
 	<DownloadFile SourceUrl="https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/resolve/main/mmproj-model-f16.gguf" DestinationFolder="Models" DestinationFileName="mmproj-model-f16.gguf" SkipUnchangedFiles="true">
@@ -63,6 +66,9 @@
     <None Update="Models\Llama-3.2-1B-Instruct-Q4_0.gguf">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>
+    <None Update="Models\jina-reranker-v1-tiny-en-FP16.gguf">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
     <None Update="Models\smollm-360m-instruct-add-basics-q8_0.gguf">
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </None>

LLama.Unittest/LLamaRerankerTests.cs

@@ -0,0 +1,74 @@
+using LLama.Common;
+using LLama.Extensions;
+using LLama.Native;
+using Microsoft.Extensions.AI;
+using System.Runtime.InteropServices;
+using Xunit.Abstractions;
+
+namespace LLama.Unittest;
+
+public sealed class LLamaRerankerTests
+{
+    private readonly ITestOutputHelper _testOutputHelper;
+    private readonly LLamaReranker _reranker;
+    public LLamaRerankerTests(ITestOutputHelper testOutputHelper)
+    {
+        _testOutputHelper = testOutputHelper;
+
+        var @params = new ModelParams(Constants.RerankingModelPath)
+        {
+            ContextSize = 0,
+            PoolingType = LLamaPoolingType.Rank,
+            GpuLayerCount = Constants.CIGpuLayerCount,
+
+        };
+        using var weights = LLamaWeights.LoadFromFile(@params);
+        _reranker = new LLamaReranker(weights, @params);
+    }
+
+    [Fact]
+    public async Task CompareRerankingScore()
+    {
+
+
+        var input = "what is panda?";
+        var documents = new string[] {
+            "hi",
+            "it's a bear",
+            string.Join(", ","The giant panda (Ailuropoda melanoleuca)",
+            "sometimes called a panda bear or simply panda",
+            "is a bear species endemic to China.") 
+        };
+        var scores = await _reranker.GetRelevanceScores(input, documents, normalize: false);
+
+        Assert.True(documents.Length == scores.Count);
+
+        _testOutputHelper.WriteLine($"Rerank score 0: {scores[0]:F4}");
+        _testOutputHelper.WriteLine($"Rerank score 1: {scores[1]:F4}");
+        _testOutputHelper.WriteLine($"Rerank score 2: {scores[2]:F4}");
+    }
+
+    [Fact]
+    public async Task MostRelevantDocument()
+    {
+        var input = "what is panda?";
+        var documents = new string[] {
+            "hi",
+            "it's a bear",
+            string.Join(", ","The giant panda (Ailuropoda melanoleuca)",
+            "sometimes called a panda bear or simply panda",
+            "is a bear species endemic to China.")
+        };
+        var scores = await _reranker.GetRelevanceScores(input, documents, normalize: true);
+
+        Assert.NotNull(scores);
+        Assert.True(documents.Length == scores.Count);
+
+        int maxIndex = scores.Select((score, index) => (score, index))
+                             .MaxBy(x => x.score)
+                             .index;
+
+        var maxScoreDocument = documents[maxIndex];
+        Assert.Equal(documents[2], maxScoreDocument);
+    }
+}

LLama.Unittest/Native/SafeLlamaModelHandleVocabularyTests.cs

@@ -0,0 +1,37 @@
+using System.Text;
+using System.Xml.Linq;
+using LLama.Common;
+using LLama.Extensions;
+using Microsoft.Extensions.Logging;
+
+
+namespace LLama.Unittest.Native;
+
+public class SafeLlamaModelHandleVocabularyTests
+{
+    private readonly LLamaWeights _model;
+
+    public SafeLlamaModelHandleVocabularyTests()
+    {
+        var @params = new ModelParams(Constants.RerankingModelPath)
+        {
+            ContextSize = 0,
+            PoolingType = LLama.Native.LLamaPoolingType.Rank,
+            GpuLayerCount = Constants.CIGpuLayerCount
+        };
+        _model = LLamaWeights.LoadFromFile(@params);
+    }
+
+    [Fact]
+    public void GetLLamaTokenString()
+    {
+        var bos = _model.Vocab.BOS;
+        var eos = _model.Vocab.EOS;
+
+        var bosStr = _model.Vocab.LLamaTokenToString(bos, true);
+        var eosStr = _model.Vocab.LLamaTokenToString(eos, true);
+
+        Assert.Equal("<s>", bosStr);
+        Assert.Equal("</s>", eosStr);
+    }
+}

LLama/LLamaReranker.cs

@@ -0,0 +1,200 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using System.Text;
+using System.Threading;
+using System.Threading.Tasks;
+using System.Xml.Linq;
+using LLama.Abstractions;
+using LLama.Exceptions;
+using LLama.Native;
+using Microsoft.Extensions.Logging;
+
+namespace LLama;
+
+/// <summary>
+/// Get rank scores between prompt and documents 
+/// </summary>
+public sealed partial class LLamaReranker
+    : IDisposable
+{
+    /// <summary>
+    /// Dimension of embedding vectors
+    /// </summary>
+    public int EmbeddingSize => Context.EmbeddingSize;
+
+    /// <summary>
+    /// LLama Context
+    /// </summary>
+    public LLamaContext Context { get; }
+
+    /// <summary>
+    /// Create a new reranker, using the given LLamaWeights
+    /// </summary>
+    /// <param name="weights"></param>
+    /// <param name="params"></param>
+    /// <param name="logger"></param>
+    public LLamaReranker(LLamaWeights weights, IContextParams @params, ILogger? logger = null)
+    {
+        if (@params.UBatchSize != @params.BatchSize)
+            throw new ArgumentException("For non-causal models, batch size must be equal to ubatch size", nameof(@params));
+        if (weights.NativeHandle is { HasEncoder: true, HasDecoder: true })
+            throw new NotSupportedException("Computing rank in encoder-decoder models is not supported");
+        if (@params.PoolingType != LLamaPoolingType.Rank)
+            throw new NotSupportedException("Computing rank score, PoolingType must be equal to LLamaPoolingType.Rank");
+        Context = weights.CreateContext(@params, logger);
+        NativeApi.llama_set_embeddings(Context.NativeHandle, true);
+    }
+
+    /// <inheritdoc />
+    public void Dispose()
+    {
+        Context.Dispose();
+    }
+
+    /// <summary>
+    /// Retrieve relevance scores for input and documents by reranking, execute once.
+    /// </summary>
+    /// <param name="input"></param>
+    /// <param name="documents"></param>
+    /// <param name="normalize">Whether to normalize the score to the range (0, 1)</param>
+    /// <param name="cancellationToken"></param>
+    /// <returns></returns>
+    /// <exception cref="RuntimeError"></exception>
+    /// <exception cref="NotSupportedException"></exception>
+    public async Task<IReadOnlyList<float>> GetRelevanceScores(string input, IReadOnlyList<string> documents, bool normalize = false, CancellationToken cancellationToken = default)
+    {
+        List<float> scores = new List<float>(documents.Count);
+        var inputTokens = Context.Tokenize(input);
+        var batch = new LLamaBatch();
+        var clearFlag = 0;
+
+        for(var idx = 0; idx < documents.Count; idx++)
+        {
+            var docTokens = Context.Tokenize(documents[idx] ?? "");
+            LLamaToken[] tokens = [.. inputTokens, .. docTokens];
+
+            if (batch.TokenCount + tokens.Length > Context.ContextSize)
+            {
+                scores.AddRange(await CalcRelevanceScores(batch, normalize, cancellationToken));
+                batch.Clear();
+                clearFlag = idx;
+            }
+
+            for (var i = 0; i < tokens.Length; i++)
+                batch.Add(tokens[i], i, (LLamaSeqId)(idx - clearFlag), true);
+        }
+        if (batch.LogitPositionCount > 0)
+        {
+            scores.AddRange(await CalcRelevanceScores(batch, normalize, cancellationToken));
+            batch.Clear();
+        }
+
+        return scores;
+    }
+
+    /// <summary>
+    /// Retrieve relevance score for input and document by reranking
+    /// </summary>
+    /// <param name="input"></param>
+    /// <param name="document"></param>
+    /// <param name="cancellationToken"></param>
+    /// <returns></returns>
+    /// <exception cref="RuntimeError"></exception>
+    /// <exception cref="NotSupportedException"></exception>
+    public async Task<(float Score, int Tokens)> GetRelevanceScoreWithTokenCount(string input, string document, bool normalize = false, CancellationToken cancellationToken = default)
+    {
+        var inputTokens = Context.Tokenize(input);
+        var docTokens = Context.Tokenize(document);
+        LLamaToken[] tokens = [..inputTokens, ..docTokens];
+        var batch = new LLamaBatch();
+        for (var i = 0; i < tokens.Length; i++)
+            batch.Add(tokens[i], i, LLamaSeqId.Zero, true);
+
+        // clear previous kv_cache values
+        Context.NativeHandle.KvCacheClear();
+
+        // Check if we should cancel the work, just before doing anything expensive (encode/decode)
+        cancellationToken.ThrowIfCancellationRequested();
+
+        // Run model
+        switch (Context.NativeHandle.ModelHandle.HasEncoder, Context.NativeHandle.ModelHandle.HasDecoder)
+        {
+            case (true, false):
+                {
+                    var result = await Context.EncodeAsync(batch, cancellationToken);
+                    if (result != EncodeResult.Ok)
+                        throw new RuntimeError($"Failed to encode: {result}");
+                    break;
+                }
+
+            case (false, true):
+                {
+                    var result = await Context.DecodeAsync(batch, cancellationToken);
+                    if (result != DecodeResult.Ok)
+                        throw new RuntimeError($"Failed to decode: {result}");
+                    break;
+                }
+
+            default:
+                throw new NotSupportedException("Unsupported model type");
+        }
+
+        var score = Context.NativeHandle.GetEmbeddingsSeq(LLamaSeqId.Zero)[0];
+
+        Context.NativeHandle.KvCacheClear();
+
+        return (normalize ? Sigmoid(score) : score, tokens.Length);
+    }
+
+    private async Task<IReadOnlyList<float>> CalcRelevanceScores(LLamaBatch batch, bool normalize = false, CancellationToken cancellationToken = default)
+    {
+        var (logicCap, _) = batch.GetLogitPositions()[batch.LogitPositionCount - 1];
+        var seqNum = logicCap.Value + 1;
+        List<float> scores = new List<float>(seqNum);
+        // clear previous kv_cache values
+        Context.NativeHandle.KvCacheClear();
+
+        // Check if we should cancel the work, just before doing anything expensive (encode/decode)
+        cancellationToken.ThrowIfCancellationRequested();
+
+        // Run model
+        switch (Context.NativeHandle.ModelHandle.HasEncoder, Context.NativeHandle.ModelHandle.HasDecoder)
+        {
+            case (true, false):
+                {
+                    var result = await Context.EncodeAsync(batch, cancellationToken);
+                    if (result != EncodeResult.Ok)
+                        throw new RuntimeError($"Failed to encode: {result}");
+                    break;
+                }
+
+            case (false, true):
+                {
+                    var result = await Context.DecodeAsync(batch, cancellationToken);
+                    if (result != DecodeResult.Ok)
+                        throw new RuntimeError($"Failed to decode: {result}");
+                    break;
+                }
+
+            default:
+                throw new NotSupportedException("Unsupported model type");
+        }
+
+        for (var seq = 0; seq < seqNum; seq++)
+        {
+            var score = Context.NativeHandle.GetEmbeddingsSeq((LLamaSeqId)seq)[0];
+            scores.Add(normalize ? Sigmoid(score) : score);
+        }
+
+        Context.NativeHandle.KvCacheClear();
+
+        return scores;
+    }
+
+    private float Sigmoid(float x)
+    {
+        return (float)(1 / (1 + Math.Exp(-x)));
+    }
+}

LLama/Native/SafeLlamaModelHandle.cs

@@ -651,7 +651,18 @@ internal Vocabulary(SafeLlamaModelHandle model)
                 _model = model;
             }
 
-            private string? LLamaTokenToString(LLamaToken? token, bool isSpecialToken)
+            private static LLamaToken? Normalize(LLamaToken token)
+            {
+                return token == -1 ? null : token;
+            }
+
+            /// <summary>
+            /// Translate LLamaToken to String
+            /// </summary>
+            /// <param name="token"></param>
+            /// <param name="isSpecialToken"></param>
+            /// <returns></returns>
+            public string? LLamaTokenToString(LLamaToken? token, bool isSpecialToken)
             {
                 if (!token.HasValue)
                     return null;
@@ -676,11 +687,6 @@ internal Vocabulary(SafeLlamaModelHandle model)
                 return Encoding.UTF8.GetStringFromSpan(slice);
             }
 
-            private static LLamaToken? Normalize(LLamaToken token)
-            {
-                return token == -1 ? null : token;
-            }
-
             /// <summary>
             /// Total number of tokens in this vocabulary
             /// </summary>