sampler.hpp

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#pragma once
 
// SPDX-License-Identifier: Apache-2.0
// Copyright 2026 Lloyal Labs
 
 
#include "common.hpp"
#include "logits.hpp"
#include "tokenizer.hpp"
#include <cstdint>
#include <ctime>
#include <llama/llama.h>
#include <optional>
#include <stdexcept>
#include <type_traits>
#include <vector>
 
namespace lloyal::detail {
 
// ===== OPTIONAL VALUE EXTRACTION =====
 
template <class T> struct is_optional : std::false_type {};
 
template <class T> struct is_optional<std::optional<T>> : std::true_type {};
 
template <class X, class T> constexpr T as_value(const X &x, T def) {
  if constexpr (is_optional<X>::value) {
    return x.value_or(def);
  } else {
    return static_cast<T>(x);
  }
}
 
} // namespace lloyal::detail
 
namespace lloyal {
 
// ===== SAMPLING PARAMS CONCEPT =====
 
template <class P>
concept SamplingParamsLike = requires(const P &p) {
  p.temperature;
  p.top_k;
  p.top_p;
  p.typical_p;
  p.min_p;
  p.penalty_repeat;
  p.penalty_freq;
  p.penalty_present;
  p.penalty_last_n;
  p.seed;
  // Additional fields for future extensions:
  // p.mirostat, p.mirostat_tau, p.mirostat_eta
  // p.dry_multiplier, p.dry_base, p.dry_allowed_length, p.dry_penalty_last_n
  // p.xtc_probability, p.xtc_threshold
  // p.top_n_sigma
};
 
namespace sampler {
 
// ===== GREEDY SAMPLING =====
 
inline llama_token greedy(llama_context *ctx, const llama_vocab *vocab) {
  LLOYAL_LOG_DEBUG("[sampler::greedy] Sampling next token");
 
  if (!ctx) {
    LLOYAL_LOG_DEBUG("[sampler::greedy] ERROR: NULL context");
    throw std::runtime_error("sampler::greedy - NULL context");
  }
 
  if (!vocab) {
    LLOYAL_LOG_DEBUG("[sampler::greedy] ERROR: NULL vocabulary");
    throw std::runtime_error("sampler::greedy - NULL vocabulary");
  }
 
  // Get last-step logits (index -1)
  // Per llama.cpp maintainers: only works if logits=true was set for that step
  // lloyal::logits::get() handles null checking and throws descriptive errors
  const float *logits = lloyal::logits::get(ctx, -1);
 
  // Get vocabulary size
  const int n_vocab = llama_vocab_n_tokens(vocab);
  if (n_vocab <= 0) {
    LLOYAL_LOG_DEBUG("[sampler::greedy] ERROR: Invalid vocabulary size: %d",
                     n_vocab);
    throw std::runtime_error("sampler::greedy - Invalid vocabulary size");
  }
 
  // Argmax: Find token with highest probability
  int best_id = 0;
  float best_score = logits[0];
  for (int i = 1; i < n_vocab; ++i) {
    if (logits[i] > best_score) {
      best_score = logits[i];
      best_id = i;
    }
  }
 
  llama_token result = static_cast<llama_token>(best_id);
  LLOYAL_LOG_DEBUG("[sampler::greedy] Sampled token: %d (score: %.4f)", result,
                   best_score);
 
  return result;
}
 
// ===== PARAMETERIZED SAMPLING =====
 
template <SamplingParamsLike P>
inline llama_token sample_with_params(llama_context *ctx,
                                      const llama_vocab *vocab, const P &params,
                                      llama_sampler *grammarSampler = nullptr) {
  using detail::as_value;
 
  // Extract parameters with defaults (handles both T and std::optional<T>)
  uint32_t seed =
      as_value(params.seed, static_cast<uint32_t>(std::time(nullptr)));
  int32_t top_k = as_value(params.top_k, static_cast<int32_t>(40));
  float top_p = as_value(params.top_p, 0.95f);
  float min_p = as_value(params.min_p, 0.05f);
  float typical_p = as_value(params.typical_p, 1.0f);
  float temperature = as_value(params.temperature, 0.8f);
  int32_t penalty_last_n =
      as_value(params.penalty_last_n, static_cast<int32_t>(64));
  float penalty_repeat = as_value(params.penalty_repeat, 1.0f);
  float penalty_freq = as_value(params.penalty_freq, 0.0f);
  float penalty_present = as_value(params.penalty_present, 0.0f);
 
  LLOYAL_LOG_DEBUG("[sampler::sample_with_params] Building sampler");
  LLOYAL_LOG_DEBUG("[sampler::sample_with_params]   temperature=%.2f, "
                   "top_k=%d, top_p=%.2f, min_p=%.2f",
                   temperature, static_cast<int>(top_k), top_p, min_p);
 
  if (!ctx) {
    throw std::runtime_error("sampler::sample_with_params - NULL context");
  }
  if (!vocab) {
    throw std::runtime_error("sampler::sample_with_params - NULL vocabulary");
  }
 
  // ROUTING DECISION: Grammar present → use grammar-aware sampling
  //                   No grammar → use lightweight chain approach
  if (grammarSampler) {
    LLOYAL_LOG_DEBUG("[sampler::sample_with_params] Grammar sampler provided, "
                     "using grammar-constrained sampling");
 
    // Get logits and build token data array
    // lloyal::logits::get() handles null checking and throws descriptive errors
    const float *logits = lloyal::logits::get(ctx, -1);
 
    const int n_vocab = llama_vocab_n_tokens(vocab);
    if (n_vocab <= 0) {
      throw std::runtime_error(
          "sampler::sample_with_params - Invalid vocabulary size");
    }
 
    // Build candidate array from logits
    std::vector<llama_token_data> candidates(n_vocab);
    for (int i = 0; i < n_vocab; i++) {
      candidates[i] =
          llama_token_data{static_cast<llama_token>(i), logits[i], 0.0f};
    }
 
    llama_token_data_array cur_p = {
        candidates.data(), static_cast<size_t>(n_vocab),
        -1,   // selected (will be set by samplers)
        false // sorted
    };
 
    // Build sampler chain (WITHOUT grammar - grammar applied separately)
    llama_sampler_chain_params chain_params =
        llama_sampler_chain_default_params();
    chain_params.no_perf = true;
    auto *chain = llama_sampler_chain_init(chain_params);
 
    // Add samplers in order (penalties → top-k → typical-p → top-p → min-p →
    // temperature → dist)
    if (penalty_repeat != 1.0f || penalty_freq != 0.0f ||
        penalty_present != 0.0f) {
      llama_sampler_chain_add(
          chain, llama_sampler_init_penalties(penalty_last_n, penalty_repeat,
                                              penalty_freq, penalty_present));
    }
    if (top_k > 0) {
      llama_sampler_chain_add(chain, llama_sampler_init_top_k(top_k));
    }
    if (typical_p < 1.0f) {
      llama_sampler_chain_add(chain, llama_sampler_init_typical(typical_p, 1));
    }
    if (top_p < 1.0f) {
      llama_sampler_chain_add(chain, llama_sampler_init_top_p(top_p, 1));
    }
    if (min_p > 0.0f) {
      llama_sampler_chain_add(chain, llama_sampler_init_min_p(min_p, 1));
    }
    llama_sampler_chain_add(chain, llama_sampler_init_temp(temperature));
    llama_sampler_chain_add(chain, llama_sampler_init_dist(seed));
 
    // Apply grammar constraint FIRST (uses persistent parser state from
    // context)
    llama_sampler_apply(grammarSampler, &cur_p);
 
    // Then apply chain
    llama_sampler_apply(chain, &cur_p);
 
    // Get selected token
    if (cur_p.selected == -1) {
      llama_sampler_free(chain);
      throw std::runtime_error(
          "No selected token during sampling - check sampling configuration");
    }
    llama_token result = cur_p.data[cur_p.selected].id;
 
    // Clean up chain (grammar sampler is managed by context, not freed here)
    llama_sampler_free(chain);
 
    LLOYAL_LOG_DEBUG("[sampler::sample_with_params] Grammar-sampled token: %d",
                     result);
    return result;
  }
 
  // NO GRAMMAR: Use lightweight chain approach
  LLOYAL_LOG_DEBUG("[sampler::sample_with_params] No grammar, using "
                   "lightweight chain approach");
 
  // Get logits
  // lloyal::logits::get() handles null checking and throws descriptive errors
  const float *logits = lloyal::logits::get(ctx, -1);
 
  const int n_vocab = llama_vocab_n_tokens(vocab);
  if (n_vocab <= 0) {
    throw std::runtime_error(
        "sampler::sample_with_params - Invalid vocabulary size");
  }
 
  // Create llama.cpp sampler chain
  llama_sampler_chain_params chain_params =
      llama_sampler_chain_default_params();
  chain_params.no_perf = true;
 
  auto *sampler_chain = llama_sampler_chain_init(chain_params);
  if (!sampler_chain) {
    throw std::runtime_error(
        "sampler::sample_with_params - Failed to create sampler chain");
  }
 
  LLOYAL_LOG_DEBUG("[sampler::sample_with_params] Sampler chain created, "
                   "adding samplers...");
 
  // 1. Repetition penalties (if enabled)
  if (penalty_repeat != 1.0f || penalty_freq != 0.0f ||
      penalty_present != 0.0f) {
    LLOYAL_LOG_DEBUG("[sampler::sample_with_params]   + penalties "
                     "(repeat=%.2f, freq=%.2f, present=%.2f, last_n=%d)",
                     penalty_repeat, penalty_freq, penalty_present,
                     penalty_last_n);
    llama_sampler_chain_add(sampler_chain, llama_sampler_init_penalties(
                                               penalty_last_n, penalty_repeat,
                                               penalty_freq, penalty_present));
  }
 
  // 2. Top-K sampling (if enabled)
  if (top_k > 0) {
    LLOYAL_LOG_DEBUG("[sampler::sample_with_params]   + top_k (%d)",
                     static_cast<int>(top_k));
    llama_sampler_chain_add(sampler_chain, llama_sampler_init_top_k(top_k));
  }
 
  // 3. Typical-P sampling (if enabled)
  if (typical_p < 1.0f) {
    LLOYAL_LOG_DEBUG("[sampler::sample_with_params]   + typical_p (%.2f)",
                     typical_p);
    llama_sampler_chain_add(sampler_chain,
                            llama_sampler_init_typical(typical_p, 1));
  }
 
  // 4. Top-P sampling (if enabled)
  if (top_p < 1.0f) {
    LLOYAL_LOG_DEBUG("[sampler::sample_with_params]   + top_p (%.2f)", top_p);
    llama_sampler_chain_add(sampler_chain, llama_sampler_init_top_p(top_p, 1));
  }
 
  // 5. Min-P sampling (if enabled)
  if (min_p > 0.0f) {
    LLOYAL_LOG_DEBUG("[sampler::sample_with_params]   + min_p (%.2f)", min_p);
    llama_sampler_chain_add(sampler_chain, llama_sampler_init_min_p(min_p, 1));
  }
 
  // 6. Temperature scaling
  LLOYAL_LOG_DEBUG("[sampler::sample_with_params]   + temperature (%.2f)",
                   temperature);
  llama_sampler_chain_add(sampler_chain, llama_sampler_init_temp(temperature));
 
  // 7. Final distribution sampler
  LLOYAL_LOG_DEBUG("[sampler::sample_with_params]   + dist (seed=%u)", seed);
  llama_sampler_chain_add(sampler_chain, llama_sampler_init_dist(seed));
 
  // Sample from the chain
  llama_token result = llama_sampler_sample(sampler_chain, ctx, -1);
 
  // Free the sampler chain
  llama_sampler_free(sampler_chain);
 
  LLOYAL_LOG_DEBUG("[sampler::sample_with_params] Sampled token: %d "
                   "(temp=%.2f, top_k=%d, top_p=%.2f, min_p=%.2f)",
                   result, temperature, static_cast<int>(top_k), top_p, min_p);
 
  return result;
}
 
// ===== MODEL-ACCEPTING CONVENIENCE OVERLOADS =====
//
// These overloads accept llama_model* and handle vocab extraction internally.
// They delegate to the vocab-accepting primitives above.
//
// Benefits:
// - Eliminate boilerplate (vocab extraction) in calling code
// - Reduce code duplication across projects
// - Backwards compatible - existing code unchanged
 
inline llama_token greedy(llama_context *ctx, const llama_model *model) {
  if (!model) {
    LLOYAL_LOG_DEBUG("[sampler::greedy] ERROR: model is null");
    throw std::runtime_error("sampler::greedy - NULL model");
  }
 
  const llama_vocab *vocab = lloyal::tokenizer::get_vocab(model);
  if (!vocab) {
    LLOYAL_LOG_DEBUG("[sampler::greedy] ERROR: get_vocab returned null");
    throw std::runtime_error(
        "sampler::greedy - Failed to get vocab from model");
  }
 
  return greedy(ctx, vocab);
}
 
template <SamplingParamsLike P>
inline llama_token sample_with_params(llama_context *ctx,
                                      const llama_model *model, const P &params,
                                      llama_sampler *grammarSampler = nullptr) {
  if (!model) {
    LLOYAL_LOG_DEBUG("[sampler::sample_with_params] ERROR: model is null");
    throw std::runtime_error("sampler::sample_with_params - NULL model");
  }
 
  const llama_vocab *vocab = lloyal::tokenizer::get_vocab(model);
  if (!vocab) {
    LLOYAL_LOG_DEBUG("[sampler::sample_with_params] ERROR: get_vocab "
                     "returned null");
    throw std::runtime_error(
        "sampler::sample_with_params - Failed to get vocab from model");
  }
 
  return sample_with_params(ctx, vocab, params, grammarSampler);
}
 
// ===== PERSISTENT SAMPLER CHAIN PRIMITIVES =====
//
// These functions manage persistent sampler chains for use cases like
// branch-based tree search where the same chain is reused across multiple samples.
// Unlike sample_with_params() which creates/destroys a chain per call,
// these allow chain reuse for better performance.
 
template <SamplingParamsLike P>
inline llama_sampler* create_chain(const P& params) {
  using detail::as_value;
 
  // Extract parameters with defaults
  int32_t penalty_last_n = as_value(params.penalty_last_n, static_cast<int32_t>(64));
  float penalty_repeat = as_value(params.penalty_repeat, 1.0f);
  float penalty_freq = as_value(params.penalty_freq, 0.0f);
  float penalty_present = as_value(params.penalty_present, 0.0f);
  int32_t top_k = as_value(params.top_k, static_cast<int32_t>(40));
  float top_p = as_value(params.top_p, 0.95f);
  float min_p = as_value(params.min_p, 0.05f);
  float typical_p = as_value(params.typical_p, 1.0f);
  float temperature = as_value(params.temperature, 0.8f);
  uint32_t seed = as_value(params.seed, static_cast<uint32_t>(std::time(nullptr)));
 
  llama_sampler_chain_params chain_params = llama_sampler_chain_default_params();
  chain_params.no_perf = true;
  llama_sampler* chain = llama_sampler_chain_init(chain_params);
 
  if (!chain) {
    throw std::runtime_error("sampler::create_chain - Failed to create sampler chain");
  }
 
  // Add samplers in standard order: penalties → filters → temperature/greedy → dist
  if (penalty_repeat != 1.0f || penalty_freq != 0.0f || penalty_present != 0.0f) {
    llama_sampler_chain_add(chain,
        llama_sampler_init_penalties(penalty_last_n, penalty_repeat, penalty_freq, penalty_present));
  }
  if (top_k > 0) {
    llama_sampler_chain_add(chain, llama_sampler_init_top_k(top_k));
  }
  if (typical_p < 1.0f) {
    llama_sampler_chain_add(chain, llama_sampler_init_typical(typical_p, 1));
  }
  if (top_p < 1.0f) {
    llama_sampler_chain_add(chain, llama_sampler_init_top_p(top_p, 1));
  }
  if (min_p > 0.0f) {
    llama_sampler_chain_add(chain, llama_sampler_init_min_p(min_p, 1));
  }
 
  // Temperature handling: temp <= 0 means greedy (deterministic argmax)
  if (temperature <= 0.0f) {
    llama_sampler_chain_add(chain, llama_sampler_init_greedy());
  } else {
    llama_sampler_chain_add(chain, llama_sampler_init_temp(temperature));
    llama_sampler_chain_add(chain, llama_sampler_init_dist(seed));
  }
 
  return chain;
}
 
inline llama_sampler* clone_chain(llama_sampler* chain) {
  if (!chain) {
    return nullptr;
  }
  return llama_sampler_clone(chain);
}
 
inline void reseed_chain(llama_sampler* chain, uint32_t new_seed) {
  if (!chain) {
    return;
  }
 
  int n = llama_sampler_chain_n(chain);
  if (n == 0) {
    return;
  }
 
  // Remove last sampler (dist by convention)
  llama_sampler* old_dist = llama_sampler_chain_remove(chain, n - 1);
  if (old_dist) {
    llama_sampler_free(old_dist);
  }
 
  // Add new dist with new seed
  llama_sampler_chain_add(chain, llama_sampler_init_dist(new_seed));
}
 
inline void free_chain(llama_sampler* chain) {
  if (chain) {
    llama_sampler_free(chain);
  }
}
 
inline void apply(llama_sampler* chain, llama_token_data_array* cur_p) {
  if (chain && cur_p) {
    llama_sampler_apply(chain, cur_p);
  }
}
 
inline void accept(llama_sampler* chain, llama_token token) {
  if (chain) {
    llama_sampler_accept(chain, token);
  }
}
 
} // namespace sampler
} // namespace lloyal