Adding simple batch example

LLama.Examples/ExampleRunner.cs

@@ -29,6 +29,7 @@ public class ExampleRunner
         { "Semantic Kernel: Prompt", SemanticKernelPrompt.Run },
         { "Semantic Kernel: Chat", SemanticKernelChat.Run },
         { "Semantic Kernel: Store", SemanticKernelMemory.Run },
+        { "Batched Executor: Simple", BatchedExecutorSimple.Run },
         { "Batched Executor: Save/Load", BatchedExecutorSaveAndLoad.Run },
         { "Batched Executor: Fork", BatchedExecutorFork.Run },
         { "Batched Executor: Rewind", BatchedExecutorRewind.Run },

LLama.Examples/Examples/BatchedExecutorSimple.cs

@@ -0,0 +1,177 @@
+using System.Diagnostics.CodeAnalysis;
+using System.Text;
+using LLama.Batched;
+using LLama.Common;
+using LLama.Native;
+using LLama.Sampling;
+using Spectre.Console;
+
+namespace LLama.Examples.Examples;
+
+/// <summary>
+/// This demonstrates generating multiple replies to the same prompt, with a shared cache
+/// </summary>
+public class BatchedExecutorSimple
+{
+    /// <summary>
+    /// Set total length of the sequence to generate
+    /// </summary>
+    private const int TokenCount = 72;
+
+    public static async Task Run()
+    {
+        // Load model weights
+        var parameters = new ModelParams(UserSettings.GetModelPath());
+        using var model = await LLamaWeights.LoadFromFileAsync(parameters);
+
+        // Create an executor that can evaluate a batch of conversations together
+        using var executor = new BatchedExecutor(model, parameters);
+
+        // we'll need this for evaluating if we are at the end of generation
+        var modelTokens = executor.Context.NativeHandle.ModelHandle.Tokens;
+
+        // Print some info
+        var name = model.Metadata.GetValueOrDefault("general.name", "unknown model name");
+        Console.WriteLine($"Created executor with model: {name}");
+
+        var messages = new[]
+        {
+            "What's 2+2?",
+            "Where is the coldest part of Texas?",
+            "What's the capital of France?",
+            "What's a one word name for a food item with ground beef patties on a bun?",
+            "What are two toppings for a pizza?",
+            "What american football play are you calling on a 3rd and 8 from our own 25?",
+            "What liquor should I add to egg nog?",
+            "I have two sons, Bert and Ernie. What should I name my daughter?",
+            "What day comes after Friday?",
+            "What color shoes should I wear with dark blue pants?",
+        };
+
+        var conversations = new List<ConversationData>();
+        foreach (var message in messages)
+        {
+            // apply the model's prompt template to our question and system prompt
+            var template = new LLamaTemplate(model);
+            template.Add("system", "I am a helpful bot that returns short and concise answers. I include a ten word description of my reasoning when I finish.");
+            template.Add("user", message);
+            template.AddAssistant = true;
+            var templatedMessage = Encoding.UTF8.GetString(template.Apply());
+
+            // create a new conversation and prompt it. include special and bos because we are using the template
+            var conversation = executor.Create();
+            conversation.Prompt(executor.Context.Tokenize(templatedMessage, addBos: true, special: true));
+
+            conversations.Add(new ConversationData {
+                Prompt = message,
+                Conversation = conversation,
+                Sampler = new GreedySamplingPipeline(),
+                Decoder = new StreamingTokenDecoder(executor.Context)
+            });
+        }
+
+        var table = BuildTable(conversations);
+        await AnsiConsole.Live(table).StartAsync(async ctx =>
+        {
+            for (var i = 0; i < TokenCount; i++)
+            {
+                // Run inference for all conversations in the batch which have pending tokens.
+                var decodeResult = await executor.Infer();
+                if (decodeResult == DecodeResult.NoKvSlot)
+                    throw new Exception("Could not find a KV slot for the batch. Try reducing the size of the batch or increase the context.");
+                if (decodeResult == DecodeResult.Error)
+                    throw new Exception("Unknown error occurred while inferring.");
+
+                foreach (var conversationData in conversations.Where(c => c.IsComplete == false))
+                {
+                    if (conversationData.Conversation.RequiresSampling == false) continue;
+
+                    // sample a single token for the executor, passing the sample index of the conversation
+                    var token = conversationData.Sampler.Sample(
+                        executor.Context.NativeHandle,
+                        conversationData.Conversation.GetSampleIndex());
+
+                    if (modelTokens.IsEndOfGeneration(token))
+                    {
+                        conversationData.MarkComplete();
+                    }
+                    else
+                    {
+                        // it isn't the end of generation, so add this token to the decoder and then add that to our tracked data
+                        conversationData.Decoder.Add(token);
+                        conversationData.AppendAnswer(conversationData.Decoder.Read().ReplaceLineEndings(" "));
+
+                        // add the token to the conversation
+                        conversationData.Conversation.Prompt(token);
+                    }
+                }
+
+                // render the current state
+                table = BuildTable(conversations);
+                ctx.UpdateTarget(table);
+
+                if (conversations.All(c => c.IsComplete))
+                {
+                    break;
+                }
+            }
+
+            // if we ran out of tokens before completing just mark them as complete for rendering purposes.
+            foreach (var data in conversations.Where(i => i.IsComplete == false))
+            {
+                data.MarkComplete();
+            }
+
+            table = BuildTable(conversations);
+            ctx.UpdateTarget(table);
+        });
+    }
+
+    /// <summary>
+    /// Helper to build a table to display the conversations.
+    /// </summary>
+    private static Table BuildTable(List<ConversationData> conversations)
+    {
+        var table = new Table()
+            .RoundedBorder()
+            .AddColumns("Prompt", "Response");
+
+        foreach (var data in conversations)
+        {
+            table.AddRow(data.Prompt.EscapeMarkup(), data.AnswerMarkdown);
+        }
+
+        return table;
+    }
+}
+
+public class ConversationData
+{
+    public required string Prompt { get; init; } 
+    public required Conversation Conversation { get; init; } 
+    public required BaseSamplingPipeline Sampler { get; init; }
+    public required StreamingTokenDecoder Decoder { get; init; } 
+
+    public string AnswerMarkdown => IsComplete 
+        ? $"[green]{_inProgressAnswer.Message.EscapeMarkup()}{_inProgressAnswer.LatestToken.EscapeMarkup()}[/]" 
+        : $"[grey]{_inProgressAnswer.Message.EscapeMarkup()}[/][white]{_inProgressAnswer.LatestToken.EscapeMarkup()}[/]";
+
+    public bool IsComplete { get; private set; }
+
+    // we are only keeping track of the answer in two parts to render them differently.
+    private (string Message, string LatestToken) _inProgressAnswer = (string.Empty, string.Empty);
+
+    public void AppendAnswer(string newText) => _inProgressAnswer = (_inProgressAnswer.Message + _inProgressAnswer.LatestToken, newText);
+
+    public void MarkComplete()
+    {
+        IsComplete = true;
+        if (Conversation.IsDisposed == false)
+        {
+            // clean up the conversation and sampler to release more memory for inference. 
+            // real life usage would protect against these two being referenced after being disposed.
+            Conversation.Dispose();
+            Sampler.Dispose();
+        }
+    }
+}