Refactoring for StatelessExecutor

[aropb]

Description

Hi,

To present the results of refactoring the StatelessExecutor code.

This looks strange and not optimal (the context can be many gigabytes):

Context = _weights.CreateContext(_params, logger);
Context.Dispose();

    public StatelessExecutor(LLamaWeights weights, IContextParams @params, ILogger? logger = null)
    {
        Images = [];
        _weights = weights;
        _params = @params;
        _logger = logger;
        _batch = new LLamaBatch();

        Context = _weights.CreateContext(_params, logger);
        Context.Dispose();
    }

I don't pretend to be accurate. I use this code and it works:

public class StatelessExecutor : ILLamaExecutor
{
    private readonly LLamaWeights _weights;
    private readonly IContextParams _params;
    private readonly ILogger _logger;
    private readonly LLamaBatch _batch;

    // LLava Section
    public bool IsMultiModal => false;

    /// <inheritdoc />
    public LLavaWeights ClipModel => default;

    /// <inheritdoc />
    public List<byte[]> Images { get; }

    /// <summary>
    /// The context used by the executor when running the inference.
    /// </summary>
    public LLamaContext Context { get; private set; }

    /// <summary>
    /// If true, applies the default template to the prompt as defined in the rules for <a href="https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template">llama_chat_apply_template</a> template.  
    /// </summary>
    public bool ApplyTemplate { get; init; }
    
    /// <summary>
    /// The system message to use with the prompt. Only used when <see cref="ApplyTemplate" /> is true.
    /// </summary>
    public string SystemMessage { get; init; }

    /// <summary>
    /// Create a new stateless executor which will use the given model
    /// </summary>
    /// <param name="weights"></param>
    /// <param name="params"></param>
    /// <param name="logger"></param>
    public LLamaStatelessExecutor(LLamaWeights weights, IContextParams @params, ILogger logger = null)
    {
        Images = [];
        _weights = weights;
        _params = @params;
        _logger = logger;
        _batch = new LLamaBatch();
    }

    /// <inheritdoc />
    public async IAsyncEnumerable<string> InferAsync(string text, IInferenceParams inferenceParams, [EnumeratorCancellation] CancellationToken token = default)
    {
        try
        {
            // Create an inference context which will be disposed when this method exits
            using var Context = _weights.CreateContext(_params, _logger);

            // Reset the sampling pipeline (if there is one)
            inferenceParams?.SamplingPipeline?.Reset();

            // Sanity check inference params
            inferenceParams ??= new InferenceParams();
            if (inferenceParams.TokensKeep > Context.ContextSize)
                throw new ArgumentOutOfRangeException(nameof(inferenceParams), $"TokensKeep ({inferenceParams.TokensKeep}) cannot be larger than ContextSize ({Context.ContextSize})");

            // Create decoders for the token stream
            var decoder = new StreamingTokenDecoder(Context);
            var antiprocessor = new AntipromptProcessor(inferenceParams.AntiPrompts);

            if (ApplyTemplate)
            {
                var template = new LLamaTemplate(_weights.NativeHandle) { AddAssistant = true };
                if (SystemMessage != null) template.Add("system", SystemMessage);

                template.Add("user", text);
                text = PromptTemplateTransformer.ToModelPrompt(template);
            }
            
            // Tokenize the prompt
            var tokens = Context.Tokenize(text: text, addBos: true, special: true).ToList();

            // Evaluate the prompt, in chunks smaller than the max batch size
            var n_past = 0;
            var (r, _, past) = await Context.DecodeAsync(tokens, LLamaSeqId.Zero, _batch, n_past);
            n_past = past;

            if (r != DecodeResult.Ok)
                throw new LLamaDecodeError(r);

            // Begin loop, evaluating one token at a time
            var maxTokens = inferenceParams.MaxTokens < 0 ? int.MaxValue : inferenceParams.MaxTokens;
            for(var i = 0; i < maxTokens && !token.IsCancellationRequested; i++)
            {
                // Sample with the pipeline
                var id = inferenceParams.SamplingPipeline.Sample(Context.NativeHandle, _batch.TokenCount - 1);

                // Check if this token should end generation
                if (id.IsEndOfGeneration(_weights.Vocab))
                    break;

                // Decode this token into text
                decoder.Add(id);
                var decoded = decoder.Read();
                yield return decoded;

                // Check if any of the antiprompts have been generated
                if (antiprocessor.Add(decoded))
                    break;

                tokens.Clear();
                tokens.Add(id);

                // when run out of context
                // based on this logic: https://github.com/ggerganov/llama.cpp/blob/master/examples/main/main.cpp#L497
                if (n_past + tokens.Count >= Context.ContextSize)
                {
                    var canAddBos = Context.Vocab.ShouldAddBOS;
                    var tokensKeep = inferenceParams.TokensKeep;

                    // number of tokens to keep when resetting context
                    // Ported from https://github.com/ggerganov/llama.cpp/blob/60325fa56f61c228464c9f065db3aa6a61f2156e/examples/main/main.cpp#L334
                    if (tokensKeep < 0 || tokensKeep > tokens.Count)
                    {
                        tokensKeep = tokens.Count;
                    }
                    else
                    {
                        tokensKeep += Convert.ToInt32(canAddBos);
                    }

                    var n_left = n_past - tokensKeep;
                    var n_discard = n_left / 2;

                    NativeApi.llama_kv_cache_seq_rm(Context.NativeHandle, LLamaSeqId.Zero, tokensKeep, tokensKeep + n_discard);
                    NativeApi.llama_kv_cache_seq_add(Context.NativeHandle, LLamaSeqId.Zero, tokensKeep + n_discard, n_past, -n_discard);

                    n_past -= n_discard;
                }

                // Evaluate with this new token
                _batch.Clear();
                _batch.Add(id, n_past++, LLamaSeqId.Zero, true);

                var returnCode = await Context.DecodeAsync(_batch, token);
                if (returnCode != 0)
                    throw new LLamaDecodeError(returnCode);
            }
        }
        finally
        {
            Context = null;
        }
    }
}

[sangyuxiaowu]

Yes, it is indeed possible.
#858 (comment)

[aropb]

#858 (comment)

I always use executor like this:

    public IAsyncEnumerable<GeneratedTextContent> GenerateTextAsync(string prompt, TextGenerationOptions options, CancellationToken cancellationToken = default)
    {
        LLamaStatelessExecutor executor = new(_weights, _modelParams);
        return executor.InferAsync(prompt, _inferenceParams, cancellationToken).Select(x => new GeneratedTextContent(x));
    }

I don't see the point of having a reference to a non-existent context after the constructor.

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