implement adaptive-p sampler

common/arg.cpp

@@ -1729,6 +1729,26 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_sparam());
+    add_opt(common_arg(
+        {"--adaptive-target"}, "N",
+        string_format("adaptive-p: select tokens near this probability (valid range 0.0 "
+                      "to 1.0; negative = disabled) (default: %.2f)\n"
+                      "[(more info)](https://github.com/ggml-org/llama.cpp/pull/17927)",
+                      (double)params.sampling.adaptive_target),
+        [](common_params & params, const std::string & value) {
+            params.sampling.adaptive_target = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(common_arg(
+        {"--adaptive-decay"}, "N",
+        string_format("adaptive-p: decay rate for target adaptation over time. lower values "
+                      "are more reactive, higher values are more stable.\n"
+                      "(valid range 0.0 to 0.99) (default: %.2f)",
+                      (double)params.sampling.adaptive_decay),
+        [](common_params & params, const std::string & value) {
+            params.sampling.adaptive_decay = std::stof(value);
+        }
+    ).set_sparam());
     add_opt(common_arg(
         {"--dynatemp-range"}, "N",
         string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sampling.dynatemp_range),

common/common.h

@@ -119,6 +119,7 @@ enum common_sampler_type {
     COMMON_SAMPLER_TYPE_INFILL      = 9,
     COMMON_SAMPLER_TYPE_PENALTIES   = 10,
     COMMON_SAMPLER_TYPE_TOP_N_SIGMA = 11,
+    COMMON_SAMPLER_TYPE_ADAPTIVE_P  = 12,
 };
 
 // dimensionality reduction methods, used by cvector-generator
@@ -166,32 +167,34 @@ enum common_params_sampling_config : uint64_t {
 struct common_params_sampling {
     uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
 
-    int32_t n_prev             = 64;    // number of previous tokens to remember
-    int32_t n_probs            = 0;     // if greater than 0, output the probabilities of top n_probs tokens.
-    int32_t min_keep           = 0;     // 0 = disabled, otherwise samplers should return at least min_keep tokens
-    int32_t top_k              = 40;    // <= 0 to use vocab size
-    float   top_p              = 0.95f; // 1.0 = disabled
-    float   min_p              = 0.05f; // 0.0 = disabled
-    float   xtc_probability    = 0.00f; // 0.0 = disabled
-    float   xtc_threshold      = 0.10f; // > 0.5 disables XTC
-    float   typ_p              = 1.00f; // typical_p, 1.0 = disabled
-    float   temp               = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
-    float   dynatemp_range     = 0.00f; // 0.0 = disabled
-    float   dynatemp_exponent  = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
-    int32_t penalty_last_n     = 64;    // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float   penalty_repeat     = 1.00f; // 1.0 = disabled
-    float   penalty_freq       = 0.00f; // 0.0 = disabled
-    float   penalty_present    = 0.00f; // 0.0 = disabled
-    float   dry_multiplier     = 0.0f;  // 0.0 = disabled;      DRY repetition penalty for tokens extending repetition:
-    float   dry_base           = 1.75f; // 0.0 = disabled;      multiplier * base ^ (length of sequence before token - allowed length)
-    int32_t dry_allowed_length = 2;     // tokens extending repetitions beyond this receive penalty
-    int32_t dry_penalty_last_n = -1;    // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
-    int32_t mirostat           = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
-    float   top_n_sigma        = -1.00f;// -1.0 = disabled
-    float   mirostat_tau       = 5.00f; // target entropy
-    float   mirostat_eta       = 0.10f; // learning rate
+    int32_t n_prev             = 64;     // number of previous tokens to remember
+    int32_t n_probs            = 0;      // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t min_keep           = 0;      // 0 = disabled, otherwise samplers should return at least min_keep tokens
+    int32_t top_k              = 40;     // <= 0 to use vocab size
+    float   top_p              = 0.95f;  // 1.0 = disabled
+    float   min_p              = 0.05f;  // 0.0 = disabled
+    float   xtc_probability    = 0.00f;  // 0.0 = disabled
+    float   xtc_threshold      = 0.10f;  // > 0.5 disables XTC
+    float   typ_p              = 1.00f;  // typical_p, 1.0 = disabled
+    float   temp               = 0.80f;  // <= 0.0 to sample greedily, 0.0 to not output probabilities
+    float   dynatemp_range     = 0.00f;  // 0.0 = disabled
+    float   dynatemp_exponent  = 1.00f;  // controls how entropy maps to temperature in dynamic temperature sampler
+    int32_t penalty_last_n     = 64;     // last n tokens to penalize (0 = disable penalty, -1 = context size)
+    float   penalty_repeat     = 1.00f;  // 1.0 = disabled
+    float   penalty_freq       = 0.00f;  // 0.0 = disabled
+    float   penalty_present    = 0.00f;  // 0.0 = disabled
+    float   dry_multiplier     = 0.0f;   // 0.0 = disabled;      DRY repetition penalty for tokens extending repetition:
+    float   dry_base           = 1.75f;  // 0.0 = disabled;      multiplier * base ^ (length of sequence before token - allowed length)
+    int32_t dry_allowed_length = 2;      // tokens extending repetitions beyond this receive penalty
+    int32_t dry_penalty_last_n = -1;     // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
+    float   adaptive_target    = -1.0f;  // select tokens near this probability (valid range 0.0 to 1.0; negative = disabled)
+    float   adaptive_decay     = 0.90f;  // EMA decay for adaptation; history ≈ 1/(1-decay) tokens (0.0 - 0.99)
+    int32_t mirostat           = 0;      // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
+    float   top_n_sigma        = -1.00f; // -1.0 = disabled
+    float   mirostat_tau       = 5.00f;  // target entropy
+    float   mirostat_eta       = 0.10f;  // learning rate
     bool    ignore_eos         = false;
-    bool    no_perf            = false; // disable performance metrics
+    bool    no_perf            = false;  // disable performance metrics
     bool    timing_per_token   = false;
 
     uint64_t user_sampling_config = 0; // bitfield to track user-specified samplers

common/sampling.cpp

@@ -167,11 +167,11 @@ std::string common_params_sampling::print() const {
             "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
             "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n"
             "\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, top_n_sigma = %.3f, temp = %.3f\n"
-            "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
+            "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f, adaptive_target = %.3f, adaptive_decay = %.3f",
             penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
             dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n,
             top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, top_n_sigma, temp,
-            mirostat, mirostat_eta, mirostat_tau);
+            mirostat, mirostat_eta, mirostat_tau, adaptive_target, adaptive_decay);
 
     return std::string(result);
 }
@@ -255,6 +255,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
     }
 
     if (params.mirostat == 0) {
+
+        bool use_adaptive_p = false; // see below
+
         for (const auto & cnstr : params.samplers) {
             switch (cnstr) {
                 case COMMON_SAMPLER_TYPE_DRY:
@@ -264,43 +267,54 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
                         for (const auto & str : params.dry_sequence_breakers) {
                             c_breakers.push_back(str.c_str());
                         }
-
-                        samplers.push_back(llama_sampler_init_dry    (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+                        samplers.push_back(llama_sampler_init_dry(vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                     }
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_K:
-                    samplers.push_back(llama_sampler_init_top_k      (params.top_k));
+                    samplers.push_back(llama_sampler_init_top_k(params.top_k));
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_P:
-                    samplers.push_back(llama_sampler_init_top_p      (params.top_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_top_p(params.top_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
                     samplers.push_back(llama_sampler_init_top_n_sigma(params.top_n_sigma));
                     break;
                 case COMMON_SAMPLER_TYPE_MIN_P:
-                    samplers.push_back(llama_sampler_init_min_p      (params.min_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_min_p(params.min_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_XTC:
-                    samplers.push_back(llama_sampler_init_xtc        (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
+                    samplers.push_back(llama_sampler_init_xtc(params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
                     break;
                 case COMMON_SAMPLER_TYPE_TYPICAL_P:
-                    samplers.push_back(llama_sampler_init_typical    (params.typ_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_typical(params.typ_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                    samplers.push_back(llama_sampler_init_temp_ext   (params.temp, params.dynatemp_range, params.dynatemp_exponent));
+                    samplers.push_back(llama_sampler_init_temp_ext(params.temp, params.dynatemp_range, params.dynatemp_exponent));
                     break;
                 case COMMON_SAMPLER_TYPE_INFILL:
-                    samplers.push_back(llama_sampler_init_infill     (vocab));
+                    samplers.push_back(llama_sampler_init_infill(vocab));
                     break;
                 case COMMON_SAMPLER_TYPE_PENALTIES:
-                    samplers.push_back(llama_sampler_init_penalties  (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    samplers.push_back(llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    break;
+                case COMMON_SAMPLER_TYPE_ADAPTIVE_P:
+                    // the `adaptive-p` sampler is like `dist` and `mirostat` in that it selects
+                    // a single token, so we will add `dist` at the end of the chain by default,
+                    // unless the user specifically included `adaptive-p`. we set this flag here
+                    // so we know to add the sampler at the very end.
+                    use_adaptive_p = true;
                     break;
                 default:
                     GGML_ASSERT(false && "unknown sampler type");
             }
         }
-
-        samplers.push_back(llama_sampler_init_dist(params.seed));
+        if (use_adaptive_p) {
+            // only if user explicitly included adaptive-p sampler
+            samplers.push_back(llama_sampler_init_adaptive_p(params.adaptive_target, params.adaptive_decay, params.seed));
+        } else {
+            // default: sample from distribution
+            samplers.push_back(llama_sampler_init_dist(params.seed));
+        }
     } else if (params.mirostat == 1) {
         samplers.push_back(llama_sampler_init_temp(params.temp));
         samplers.push_back(llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
@@ -625,6 +639,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_XTC:         return 'x';
         case COMMON_SAMPLER_TYPE_INFILL:      return 'i';
         case COMMON_SAMPLER_TYPE_PENALTIES:   return 'e';
+        case COMMON_SAMPLER_TYPE_ADAPTIVE_P:  return 'a';
         default : return '?';
     }
 }
@@ -641,6 +656,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_XTC:         return "xtc";
         case COMMON_SAMPLER_TYPE_INFILL:      return "infill";
         case COMMON_SAMPLER_TYPE_PENALTIES:   return "penalties";
+        case COMMON_SAMPLER_TYPE_ADAPTIVE_P:  return "adaptive_p";
         default : return "";
     }
 }
@@ -657,6 +673,7 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "xtc",         COMMON_SAMPLER_TYPE_XTC },
         { "infill",      COMMON_SAMPLER_TYPE_INFILL },
         { "penalties",   COMMON_SAMPLER_TYPE_PENALTIES },
+        { "adaptive_p",  COMMON_SAMPLER_TYPE_ADAPTIVE_P },
     };
 
     // since samplers names are written multiple ways
@@ -672,6 +689,7 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "typ",         COMMON_SAMPLER_TYPE_TYPICAL_P },
         { "min-p",       COMMON_SAMPLER_TYPE_MIN_P },
         { "temp",        COMMON_SAMPLER_TYPE_TEMPERATURE },
+        { "adaptive-p",  COMMON_SAMPLER_TYPE_ADAPTIVE_P },
     };
 
     std::vector<common_sampler_type> samplers;
@@ -708,6 +726,7 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC),         COMMON_SAMPLER_TYPE_XTC },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL),      COMMON_SAMPLER_TYPE_INFILL },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES),   COMMON_SAMPLER_TYPE_PENALTIES },
+        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_ADAPTIVE_P),  COMMON_SAMPLER_TYPE_ADAPTIVE_P },
     };
 
     std::vector<common_sampler_type> samplers;

include/llama.h

@@ -1395,6 +1395,33 @@ extern "C" {
                           const char ** seq_breakers,
                               size_t    num_breakers);
 
+    /// adaptive-p: select tokens near a configurable target probability over time.
+    ///
+    /// the adaptive-p sampler transforms the token probability distribution to favor tokens
+    /// that fall near a user-configurable probability target.
+    ///
+    /// internally, the sampler maintains an exponential moving average of the *ORIGINAL*
+    /// probabilities of selected tokens at each sampling step. it uses this EMA to compute an
+    /// adapted target probability at each sampling step, thus maintaining the desired target
+    /// probability over time.
+    ///
+    /// adaptive-p selects a token ID rather than just mutating candidates, so it must be last
+    /// in the sampler chain (like mirostat, dist, greedy).
+    ///
+    /// only mild truncation before this sampler is recommended. we suggest applying min-p
+    /// before adaptive-p as the only other active sampler in the chain.
+    ///
+    /// @param target select tokens near this probability (valid range 0.0 to 1.0; negative = disabled)
+    /// @param decay  EMA decay for adaptation; history ≈ 1/(1-decay) tokens (valid range 0.0 - 0.99)
+    /// @param seed   RNG seed
+    ///
+    /// ref: https://github.com/ggml-org/llama.cpp/pull/17927
+    ///
+    LLAMA_API struct llama_sampler * llama_sampler_init_adaptive_p(
+                               float   target,
+                               float   decay,
+                            uint32_t   seed);
+
     LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias(
                              int32_t   n_vocab,
                              int32_t   n_logit_bias,