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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tinystories.sentis filter=lfs diff=lfs merge=lfs -text

RunTinyStories.cs

@@ -0,0 +1,273 @@
+using System.Collections;
+using System.Collections.Generic;
+using UnityEngine;
+using Unity.Sentis;
+using System.IO;
+using Newtonsoft.Json;
+using System.Text;
+
+/*
+ *              Tiny Stories Inference Code
+ *              ===========================
+ *  
+ *  Put this script on the Main Camera
+ *  
+ *  In Assets/StreamingAssets put:
+ *  
+ *  tinystories.sentis
+ *  vocab.json
+ *  merges.txt
+ * 
+ *  Install package com.unity.nuget.newtonsoft-json from packagemanger
+ *  Install package com.unity.sentis
+ * 
+ */
+
+
+public class RunTinyStories : MonoBehaviour
+{
+    const BackendType backend = BackendType.GPUCompute;
+
+    //string outputString = "Once upon a time, there were three bears";
+    string outputString = "One day an alien came down from Mars. It saw a chicken";
+
+    // This is how many tokens you want. It can be adjusted.
+    const int maxTokens = 100;
+
+    //Make this smaller for more randomness
+    const float predictability = 5f;
+
+    //Special tokens
+    const int END_OF_TEXT = 50256;
+
+    Ops ops;
+    ITensorAllocator allocator;
+
+    //Store the vocabulary
+    string[] tokens;
+
+    IWorker engine;
+
+    int currentToken = 0;
+    int[] outputTokens = new int[maxTokens];
+
+    // Used for special character decoding
+    int[] whiteSpaceCharacters = new int[256];
+    int[] encodedWhiteSpace = new int[256];
+
+    bool runInference = false;
+
+
+    //stop after this many tokens
+    const int stopAfter = 100;
+
+    int totalTokens = 0;
+
+    string[] merges;
+    Dictionary<string, int> vocab;
+
+    void Start()
+    {
+        allocator = new TensorCachingAllocator();
+        ops = WorkerFactory.CreateOps(backend, allocator);
+
+        SetupWhiteSpaceShifts();
+
+        LoadVocabulary();
+
+        Model model = ModelLoader.Load(Application.streamingAssetsPath + "/tinystories.sentis");
+
+        engine = WorkerFactory.CreateWorker(backend, model);
+
+        DecodePrompt(outputString);
+
+        runInference = true;
+    }
+
+    // Update is called once per frame
+    void Update()
+    {
+        if (runInference)
+        {
+            RunInference();
+        }
+    }
+
+    void RunInference()
+    {
+        using var tokensSoFar = new TensorInt(new TensorShape(1, maxTokens), outputTokens);
+        engine.Execute(tokensSoFar);
+
+        var tokensOut = engine.PeekOutput() as TensorFloat;
+
+        using var row = ops.Slice(tokensOut, new[] { currentToken }, new[] { currentToken + 1 }, new[] { 1 }, new[] { 1 });
+        using var rowB = ops.Mul(predictability, row);
+        using var probs = ops.Softmax(rowB, 2);
+        probs.MakeReadable();
+
+        int ID = SelectRandomToken(probs.ToReadOnlyArray());
+
+        if (currentToken >= maxTokens - 1)
+        {
+            for (int i = 0; i < maxTokens - 1; i++) outputTokens[i] = outputTokens[i + 1];
+            currentToken--;
+        }
+
+        outputTokens[++currentToken] = ID;
+        totalTokens++;
+
+        if (ID == END_OF_TEXT || totalTokens >= stopAfter)
+        {
+            runInference = false;
+        }
+        else outputString += GetUnicodeText(tokens[ID]);
+
+        Debug.Log(outputString);
+
+    }
+
+
+    void DecodePrompt(string text)
+    {
+        var inputTokens = GetTokens(text);
+
+        for(int i = 0; i < inputTokens.Count; i++)
+        {
+            outputTokens[i] = inputTokens[i];
+        }
+        currentToken = inputTokens.Count - 1;
+    }
+   
+
+    void LoadVocabulary()
+    {
+        var jsonText = File.ReadAllText(Application.streamingAssetsPath + "/vocab.json");
+        vocab = Newtonsoft.Json.JsonConvert.DeserializeObject<Dictionary<string, int>>(jsonText);
+        tokens = new string[vocab.Count];
+        foreach (var item in vocab)
+        {
+            tokens[item.Value] = item.Key;
+        }
+
+        merges = File.ReadAllLines(Application.streamingAssetsPath + "/merges.txt");
+    }
+
+
+    int SelectRandomToken(float[] probs)
+    {
+        float p = UnityEngine.Random.Range(0, 1f);
+        float t = 0;
+        for (int i = 0; i < probs.Length; i++)
+        {
+            t += probs[i];
+            if (p < t)
+            {
+                return i;
+            }
+        }
+        return probs.Length - 1;
+    }
+
+    // Translates encoded special characters to Unicode
+    string GetUnicodeText(string text)
+    {
+        var bytes = Encoding.GetEncoding("ISO-8859-1").GetBytes(ShiftCharacterDown(text));
+        return Encoding.UTF8.GetString(bytes);
+    }
+    string GetASCIIText(string newText)
+    {
+        var bytes = Encoding.UTF8.GetBytes(newText);
+        return ShiftCharacterUp(Encoding.GetEncoding("ISO-8859-1").GetString(bytes));
+    }
+
+    string ShiftCharacterDown(string text)
+    {
+        string outText = "";
+        foreach (char letter in text)
+        {
+            outText += ((int)letter <= 256) ? letter :
+                (char)whiteSpaceCharacters[(int)(letter - 256)];
+        }
+        return outText;
+    }
+
+    string ShiftCharacterUp(string text)
+    {
+        string outText = "";
+        foreach (char letter in text)
+        {
+            outText += (char)encodedWhiteSpace[(int)letter];
+        }
+        return outText;
+    }
+
+    void SetupWhiteSpaceShifts()
+    {
+        for (int i = 0, n = 0; i < 256; i++)
+        {
+            encodedWhiteSpace[i] = i;
+            if (IsWhiteSpace((char)i))
+            {
+                encodedWhiteSpace[i] = n + 256;
+                whiteSpaceCharacters[n++] = i;
+            }
+        }
+    }
+
+    bool IsWhiteSpace(char c)
+    {
+        return !(('!' <= c && c <= '~') || ('�' <= c && c <= '�') || ('�' <= c && c <= '�'));
+    }
+
+    List<int> GetTokens(string text)
+    {
+        text = GetASCIIText(text);
+
+        // Start with a list of single characters
+        var inputTokens = new List<string>();
+        foreach(var letter in text)
+        {
+            inputTokens.Add(letter.ToString());
+        }
+
+        ApplyMerges(inputTokens);
+
+        //Find the ids of the words in the vocab
+        var ids = new List<int>();
+        foreach(var token in inputTokens)
+        {
+            if (vocab.TryGetValue(token, out int id))
+            {
+                ids.Add(id);
+            }
+        }
+
+        return ids;
+    }
+
+    void ApplyMerges(List<string> inputTokens)
+    {
+        foreach(var merge in merges)
+        {
+            string[] pair = merge.Split(' ');
+            int n = 0;
+            while (n >= 0)
+            {
+                n = inputTokens.IndexOf(pair[0], n);
+                if (n != -1 && n < inputTokens.Count - 1 && inputTokens[n + 1] == pair[1])
+                {
+                    inputTokens[n] += inputTokens[n + 1];
+                    inputTokens.RemoveAt(n + 1);
+                }
+                if (n != -1) n++;
+            }
+        }
+    }
+
+    private void OnDestroy()
+    {
+        engine?.Dispose();
+        ops?.Dispose();
+        allocator?.Dispose();
+    }
+}

tinystories.sentis

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d8ed28a03db24da6fa58cc2bde739ecfb83b731ca47c263d17cdfec22e4b1698
+size 478881707