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Removed `SamplingApi`. it has been marked as Obsolete for a while, replaced by instance methods on `LLamaTokenDataArray` (#552)

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Martin Evans 2024-02-28 19:30:53 +00:00 committed by GitHub
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LLama/Native/SamplingApi.cs
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using System;
#pragma warning disable IDE1006 // Naming Styles
namespace LLama.Native
{
/// <summary>
/// Direct translation of the llama.cpp sampling API
/// </summary>
public class SamplingApi
{
/// <summary>
/// Apply grammar rules to candidate tokens
/// </summary>
/// <param name="ctx"></param>
/// <param name="candidates"></param>
/// <param name="grammar"></param>
[Obsolete("use LLamaTokenDataArray ApplyGrammar method")]
public static void llama_sample_grammar(SafeLLamaContextHandle ctx, LLamaTokenDataArray candidates, SafeLLamaGrammarHandle grammar)
{
candidates.ApplyGrammar(ctx, grammar);
}
/// <summary>
/// Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
/// </summary>
/// <param name="ctx"></param>
/// <param name="candidates">Pointer to LLamaTokenDataArray</param>
[Obsolete("use LLamaTokenDataArray Softmax method")]
public static void llama_sample_softmax(SafeLLamaContextHandle ctx, LLamaTokenDataArray candidates)
{
candidates.Softmax(ctx);
}
/// <summary>
/// Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
/// </summary>
/// <param name="ctx"></param>
/// <param name="candidates">Pointer to LLamaTokenDataArray</param>
/// <param name="k"></param>
/// <param name="min_keep"></param>
[Obsolete("use LLamaTokenDataArray TopK method")]
public static void llama_sample_top_k(SafeLLamaContextHandle ctx, LLamaTokenDataArray candidates, int k, ulong min_keep)
{
candidates.TopK(ctx, k, min_keep);
}
/// <summary>
/// Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
/// </summary>
/// <param name="ctx"></param>
/// <param name="candidates">Pointer to LLamaTokenDataArray</param>
/// <param name="p"></param>
/// <param name="min_keep"></param>
[Obsolete("use LLamaTokenDataArray TopP method")]
public static void llama_sample_top_p(SafeLLamaContextHandle ctx, LLamaTokenDataArray candidates, float p, ulong min_keep)
{
candidates.TopP(ctx, p, min_keep);
}
/// <summary>
/// Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
/// </summary>
/// <param name="ctx"></param>
/// <param name="candidates">Pointer to LLamaTokenDataArray</param>
/// <param name="z"></param>
/// <param name="min_keep"></param>
[Obsolete("use LLamaTokenDataArray TailFree method")]
public static void llama_sample_tail_free(SafeLLamaContextHandle ctx, LLamaTokenDataArray candidates, float z, ulong min_keep)
{
candidates.TailFree(ctx, z, min_keep);
}
/// <summary>
/// Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
/// </summary>
/// <param name="ctx"></param>
/// <param name="candidates">Pointer to LLamaTokenDataArray</param>
/// <param name="p"></param>
/// <param name="min_keep"></param>
[Obsolete("use LLamaTokenDataArray LocallyTypical method")]
public static void llama_sample_typical(SafeLLamaContextHandle ctx, LLamaTokenDataArray candidates, float p, ulong min_keep)
{
candidates.LocallyTypical(ctx, p, min_keep);
}
/// <summary>
/// Sample with temperature.
/// As temperature increases, the prediction becomes diverse but also vulnerable to hallucinations -- generating tokens that are sensible but not factual
/// </summary>
/// <param name="ctx"></param>
/// <param name="candidates"></param>
/// <param name="temp"></param>
[Obsolete("use LLamaTokenDataArray Temperature() method")]
public static void llama_sample_temperature(SafeLLamaContextHandle ctx, LLamaTokenDataArray candidates, float temp)
{
candidates.Temperature(ctx, temp);
}
/// <summary>
/// Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
/// </summary>
/// <param name="ctx"></param>
/// <param name="candidates">A vector of `LLamaTokenData` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.</param>
/// <param name="tau">The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.</param>
/// <param name="eta">The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.</param>
/// <param name="m">The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.</param>
/// <param name="mu">Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.</param>
/// <returns></returns>
[Obsolete("use LLamaTokenDataArray SampleTokenMirostat() method")]
public static LLamaToken llama_sample_token_mirostat(SafeLLamaContextHandle ctx, LLamaTokenDataArray candidates, float tau, float eta, int m, ref float mu)
{
return candidates.SampleTokenMirostat(ctx, tau, eta, m, ref mu);
}
/// <summary>
/// Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
/// </summary>
/// <param name="ctx"></param>
/// <param name="candidates">A vector of `LLamaTokenData` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.</param>
/// <param name="tau">The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.</param>
/// <param name="eta">The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.</param>
/// <param name="mu">Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.</param>
/// <returns></returns>
[Obsolete("use LLamaTokenDataArray SampleTokenMirostat2() method")]
public static LLamaToken llama_sample_token_mirostat_v2(SafeLLamaContextHandle ctx, LLamaTokenDataArray candidates, float tau, float eta, ref float mu)
{
return candidates.SampleTokenMirostat2(ctx, tau, eta, ref mu);
}
/// <summary>
/// Selects the token with the highest probability.
/// </summary>
/// <param name="ctx"></param>
/// <param name="candidates">Pointer to LLamaTokenDataArray</param>
/// <returns></returns>
[Obsolete("Use LLamaTokenDataArray SampleTokenGreedy() method")]
public static LLamaToken llama_sample_token_greedy(SafeLLamaContextHandle ctx, LLamaTokenDataArray candidates)
{
return candidates.SampleTokenGreedy(ctx);
}
/// <summary>
/// Randomly selects a token from the candidates based on their probabilities.
/// </summary>
/// <param name="ctx"></param>
/// <param name="candidates">Pointer to LLamaTokenDataArray</param>
/// <returns></returns>
[Obsolete("use LLamaTokenDataArray SampleToken() method")]
public static LLamaToken llama_sample_token(SafeLLamaContextHandle ctx, LLamaTokenDataArray candidates)
{
return candidates.SampleToken(ctx);
}
}
}