Implement Adaptive-P Sampler (#1100)

* initial implementation of adaptive-p sampler

* explicitly mark candidates unsorted + cleanup qualifiers

* cosmetic update

* reorg prototypes

* lockstep with mainline

* add _impl for _init + reorg

* add LLAMA_API to prototypes

* update sharpness to 10

* lockstep: rng seed

* delete llama_sampling member in llama_sampler_adaptive_p

* fix LLAMA_API return type

* lockstep: rng seed cont

* actually correct implementation

* lockstep: sorting behavior

* const -> constexpr for known constants

* add missing space

* fix softmax usage in adaptive p sampler

* cosmetic changes

* implement do-not-sort version of softmax

* simpify rng seed, add static to constexpr

* refactor: remove iface + use shared rng + use actually original probabilities

* adaptive-p: add dedicated rng back in

* fix initial max_logit + add float vector to adaptive p sampler context + stochastic sampling

* adaptive-p: fuse first softmax with transformation

* adaptive-p: implement binary search selection

* adaptive-p: update comment

common/sampling.h

@@ -18,7 +18,8 @@ enum class llama_sampler_type : char {
     XTC         = 'x',
     TOP_N_SIGMA = 'n',
     TYPICAL_P   = 'y',
-    TEMPERATURE = 't'
+    TEMPERATURE = 't',
+    ADAPTIVE_P  = 'w',
 };
 
 enum common_grammar_trigger_type {
@@ -66,6 +67,8 @@ typedef struct llama_sampling_params {
     float       xtc_probability       = 0.0f;               // xtc probability
     float       xtc_threshold         = 1.0f;               // xtc threshold, disabled if > 0.5
     float       top_n_sigma           = 0.0f;               // top-n-sigma
+    float       adaptive_target       = -1.0f;              // select tokens near this probability (valid range 0.0 to 1.0; <0 = disabled)
+    float       adaptive_decay        = 0.90f;              // decay rate for target adaptation over time. lower values -> faster but less stable adaptation. (valid range 0.0 to 1.0; ≤0 = no adaptation)
     bool        penalize_nl           = false;              // consider newlines as a repeatable token
     uint32_t    seed                  = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampling_context
 
@@ -80,7 +83,8 @@ typedef struct llama_sampling_params {
         llama_sampler_type::MIN_P,
         llama_sampler_type::XTC,
         llama_sampler_type::TOP_N_SIGMA,
-        llama_sampler_type::TEMPERATURE
+        llama_sampler_type::TEMPERATURE,
+        llama_sampler_type::ADAPTIVE_P,
     };
 
 
@@ -118,6 +122,8 @@ struct llama_sampling_context {
     std::vector<llama_token_data> cur;
     llama_sampler_dry* smpl;
 
+    llama_sampler_adaptive_p * adapt_p_ctx;    // adaptive p sampler
+
     size_t n_valid; // Number of correct top tokens with correct probabilities.
 
     llama_token_data_array cur_p; // current candidates