Debertav3.cs to 2.1.1 support

DebertaV3.cs

@@ -6,157 +6,151 @@ using UnityEngine;
 
 public sealed class DebertaV3 : MonoBehaviour
 {
-    public ModelAsset model;
-    public TextAsset vocabulary;
-    public bool multipleTrueClasses;
-    public string text = "Angela Merkel is a politician in Germany and leader of the CDU";
-    public string hypothesisTemplate = "This example is about {}";
-    public string[] classes = { "politics", "economy", "entertainment", "environment" };
-
-    IWorker engine;
-    string[] vocabularyTokens;
-
-    const int padToken = 0;
-    const int startToken = 1;
-    const int separatorToken = 2;
-    const int vocabToTokenOffset = 260;
-
-    void Start()
-    {
-        if (classes.Length == 0)
-        {
-            Debug.LogError("There need to be more than 0 classes");
-            return;
-        }
-
-        vocabularyTokens = vocabulary.text.Replace("\r", "").Split("\n");
-
-        Model baseModel = ModelLoader.Load(model);
-        Model modelWithScoring = Functional.Compile(
-            input =>
-            {
-                // The logits represent the model's predictions for entailment and non-entailment for each example in the batch.
-                // They are of shape [batch size, 2] i.e. with two values per example.
-                // To obtain a single score per example, a softmax function is applied
-                FunctionalTensor logits = baseModel.Forward(input)[0];
-
-                if (multipleTrueClasses || classes.Length == 1)
-                {
-                    // Softmax over the entailment vs. contradiction dimension for each label independently
-                    logits = Functional.Softmax(logits);
-                }
-                else
-                {
-                    // Softmax over all candidate labels
-                    logits = Functional.Softmax(logits, 0);
-                }
-
-                // The scores are stored along the first column
-                return new []{logits[.., 0]};
-            },
-            InputDef.FromModel(baseModel)
-        );
-
-        engine = WorkerFactory.CreateWorker(BackendType.GPUCompute, modelWithScoring);
-
-        string[] hypotheses = classes.Select(x => hypothesisTemplate.Replace("{}", x)).ToArray();
-        Batch batch = GetTokenizedBatch(text, hypotheses);
-        float[] scores = GetBatchScores(batch);
-
-        for (int i = 0; i < scores.Length; i++)
-        {
-            Debug.Log($"[{classes[i]}] Entailment Score: {scores[i]}");
-        }
-    }
-
-    float[] GetBatchScores(Batch batch)
-    {
-        using var inputIds = new TensorInt(new TensorShape(batch.BatchCount, batch.BatchLength), batch.BatchedTokens);
-        using var attentionMask = new TensorInt(new TensorShape(batch.BatchCount, batch.BatchLength), batch.BatchedMasks);
-
-        Dictionary<string, Tensor> inputs = new()
-        {
-            {"input_0", inputIds},
-            {"input_1", attentionMask}
-        };
-
-        engine.Execute(inputs);
-        TensorFloat scores = (TensorFloat)engine.PeekOutput("output_0");
-        scores.CompleteOperationsAndDownload();
-
-        return scores.ToReadOnlyArray();
-    }
-
-    Batch GetTokenizedBatch(string prompt, string[] hypotheses)
-    {
-        Batch batch = new Batch();
-
-        List<int> promptTokens = Tokenize(prompt);
-        promptTokens.Insert(0, startToken);
-
-        List<int>[] tokenizedHypotheses = hypotheses.Select(Tokenize).ToArray();
-        int maxTokenLength = tokenizedHypotheses.Max(x => x.Count);
-
-        // Each example in the batch follows this format:
-        // Start Prompt Separator Hypothesis Separator Padding
-
-        int[] batchedTokens = tokenizedHypotheses.SelectMany(hypothesis => promptTokens
-                .Append(separatorToken)
-                .Concat(hypothesis)
-                .Append(separatorToken)
-                .Concat(Enumerable.Repeat(padToken, maxTokenLength - hypothesis.Count)))
-            .ToArray();
-
-
-        // The attention masks have the same length as the tokens.
-        // Each attention mask contains repeating 1s for each token, except for padding tokens.
-
-        int[] batchedMasks = tokenizedHypotheses.SelectMany(hypothesis => Enumerable.Repeat(1, promptTokens.Count + 1)
-                .Concat(Enumerable.Repeat(1, hypothesis.Count + 1))
-                .Concat(Enumerable.Repeat(0, maxTokenLength - hypothesis.Count)))
-            .ToArray();
-
-        batch.BatchCount = hypotheses.Length;
-        batch.BatchLength = batchedTokens.Length / hypotheses.Length;
-        batch.BatchedTokens = batchedTokens;
-        batch.BatchedMasks = batchedMasks;
-
-        return batch;
-    }
-
-    List<int> Tokenize(string input)
-    {
-        string[] words = input.Split(null);
-
-        List<int> ids = new();
-
-        foreach (string word in words)
-        {
-            int start = 0;
-            for(int i = word.Length; i >= 0;i--)
-            {
-                string subWord = start == 0 ? "▁" + word.Substring(start, i) : word.Substring(start, i-start);
-                int index = Array.IndexOf(vocabularyTokens, subWord);
-                if (index >= 0)
-                {
-                    ids.Add(index + vocabToTokenOffset);
-                    if (i == word.Length) break;
-                    start = i;
-                    i = word.Length + 1;
-                }
-            }
-        }
-
-        return ids;
-    }
-
-    void OnDestroy() => engine?.Dispose();
-
-    struct Batch
-    {
-        public int BatchCount;
-        public int BatchLength;
-        public int[] BatchedTokens;
-        public int[] BatchedMasks;
-    }
+	public ModelAsset model;
+	public TextAsset vocabulary;
+	public bool multipleTrueClasses;
+	public string text = "Angela Merkel is a politician in Germany and leader of the CDU";
+	public string hypothesisTemplate = "This example is about {}";
+	public string[] classes = { "politics", "economy", "entertainment", "environment" };
+
+	Worker engine;
+	string[] vocabularyTokens;
+
+	const int padToken = 0;
+	const int startToken = 1;
+	const int separatorToken = 2;
+	const int vocabToTokenOffset = 260;
+
+	void Start()
+	{
+		if (classes.Length == 0)
+		{
+			Debug.LogError("There need to be more than 0 classes");
+			return;
+		}
+
+		vocabularyTokens = vocabulary.text.Replace("\r", "").Split("\n");
+
+		Model baseModel = ModelLoader.Load(model);
+        
+		// Create the engine with the base model using the updated constructor
+		engine = new Worker(baseModel, BackendType.GPUCompute);
+
+		string[] hypotheses = classes.Select(x => hypothesisTemplate.Replace("{}", x)).ToArray();
+		Batch batch = GetTokenizedBatch(text, hypotheses);
+		float[] scores = GetBatchScores(batch);
+
+		for (int i = 0; i < scores.Length; i++)
+		{
+			Debug.Log($"[{classes[i]}] Entailment Score: {scores[i]}");
+		}
+	}
+
+	float[] GetBatchScores(Batch batch)
+	{
+		using var inputIds = new Tensor<int>(new TensorShape(batch.BatchCount, batch.BatchLength), batch.BatchedTokens);
+		using var attentionMask = new Tensor<int>(new TensorShape(batch.BatchCount, batch.BatchLength), batch.BatchedMasks);
+
+		// Schedule the execution with the inputs as array
+		engine.Schedule(new Tensor[] { inputIds, attentionMask });
+        
+		// Get the output tensor
+		var output = engine.PeekOutput(0);
+		var scores = new float[batch.BatchCount];
+
+		// Get the raw data from tensor using the new method
+		if (output is Tensor<float> floatOutput)
+		{
+			var shape = floatOutput.shape;
+			var data = floatOutput.DownloadToArray();
+
+			// Apply softmax manually
+			for (int i = 0; i < batch.BatchCount; i++)
+			{
+				float val1 = data[i * 2];
+				float val2 = data[i * 2 + 1];
+				float maxVal = Math.Max(val1, val2);
+                
+				float exp1 = (float)Math.Exp(val1 - maxVal);
+				float exp2 = (float)Math.Exp(val2 - maxVal);
+				float sum = exp1 + exp2;
+                
+				scores[i] = exp1 / sum;  // Normalized probability for the first class
+			}
+		}
+        
+		return scores;
+	}
+
+	Batch GetTokenizedBatch(string prompt, string[] hypotheses)
+	{
+		Batch batch = new Batch();
+
+		List<int> promptTokens = Tokenize(prompt);
+		promptTokens.Insert(0, startToken);
+
+		List<int>[] tokenizedHypotheses = hypotheses.Select(Tokenize).ToArray();
+		int maxTokenLength = tokenizedHypotheses.Max(x => x.Count);
+
+		// Each example in the batch follows this format:
+		// Start Prompt Separator Hypothesis Separator Padding
+
+		int[] batchedTokens = tokenizedHypotheses.SelectMany(hypothesis => promptTokens
+			.Append(separatorToken)
+			.Concat(hypothesis)
+			.Append(separatorToken)
+			.Concat(Enumerable.Repeat(padToken, maxTokenLength - hypothesis.Count)))
+			.ToArray();
+
+		// The attention masks have the same length as the tokens.
+		// Each attention mask contains repeating 1s for each token, except for padding tokens.
+
+		int[] batchedMasks = tokenizedHypotheses.SelectMany(hypothesis => Enumerable.Repeat(1, promptTokens.Count + 1)
+			.Concat(Enumerable.Repeat(1, hypothesis.Count + 1))
+			.Concat(Enumerable.Repeat(0, maxTokenLength - hypothesis.Count)))
+			.ToArray();
+
+		batch.BatchCount = hypotheses.Length;
+		batch.BatchLength = batchedTokens.Length / hypotheses.Length;
+		batch.BatchedTokens = batchedTokens;
+		batch.BatchedMasks = batchedMasks;
+
+		return batch;
+	}
+
+	List<int> Tokenize(string input)
+	{
+		string[] words = input.Split(null);
+
+		List<int> ids = new();
+
+		foreach (string word in words)
+		{
+			int start = 0;
+			for(int i = word.Length; i >= 0; i--)
+			{
+				string subWord = start == 0 ? "▁" + word.Substring(start, i) : word.Substring(start, i-start);
+				int index = Array.IndexOf(vocabularyTokens, subWord);
+				if (index >= 0)
+				{
+					ids.Add(index + vocabToTokenOffset);
+					if (i == word.Length) break;
+					start = i;
+					i = word.Length + 1;
+				}
+			}
+		}
+
+		return ids;
+	}
+
+	void OnDestroy() => engine?.Dispose();
+
+	struct Batch
+	{
+		public int BatchCount;
+		public int BatchLength;
+		public int[] BatchedTokens;
+		public int[] BatchedMasks;
+	}
 }