Implement Adaptive-P Sampler

common/common.cpp

@@ -925,6 +925,16 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         }
         return true;
     }
+    if (arg == "--adaptive-target") {
+        CHECK_ARG
+        sparams.adaptive_target = std::stof(argv[i]);
+        return true;
+    }
+    if (arg == "--adaptive-decay") {
+        CHECK_ARG
+        sparams.adaptive_decay = std::stof(argv[i]);
+        return true;
+    }
     if (arg == "--spec-replace") {
         CHECK_ARG
         std::string target = argv[i];
@@ -2201,6 +2211,8 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
     options.push_back({ "*",           "       --xtc-probability p",    "xtc probability (default: %.1f, 0.0 = disabled)", (double)sparams.xtc_probability });
     options.push_back({ "*",           "       --xtc-threshold t",      "xtc threshold (default: %.1f, >0.5 = disabled)", (double)sparams.xtc_threshold});
     options.push_back({ "*",           "       --top-n-sigma t",        "top-n-sigma parmeter (default: %.1f, 0.0 = disabled)", (double)sparams.top_n_sigma});
+    options.push_back({ "*",           "       --adaptive-target",      "adaptive-p sampling: (default: %.2f, <0.0 = disabled)", (double)sparams.adaptive_target});
+    options.push_back({ "*",           "       --adaptive-decay",       "adaptive-p sampling: (default: %.2f)", (double)sparams.adaptive_decay});
     options.push_back({ "*",           "       -l TOKEN_ID(+/-)BIAS",   "modifies the likelihood of token appearing in the completion,\n"
                                                                         "i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"
                                                                         "or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'" });
@@ -4174,6 +4186,8 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
     fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
     fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
+    fprintf(stream, "adaptive_target: %f # default: -1.0\n", sparams.adaptive_target);
+    fprintf(stream, "adaptive_decay: %f # default: 0.9\n", sparams.adaptive_decay);
     fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
     fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
 }

common/sampling.cpp

@@ -99,7 +99,7 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_vocab* vo
         result->n_valid = 0;
     }
     result->grammar = grmr;
-    // init DRY
+    llama_sampling_set_rng_seed(result, params.seed);
     for (const auto& cnstr : params.samplers_sequence)
     {
         switch (cnstr)
@@ -116,11 +116,16 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_vocab* vo
 
                 break;
             }
+            case llama_sampler_type::ADAPTIVE_P:
+            {
+                result->adapt_p_ctx=llama_sampler_init_adaptive_p(params.adaptive_target, params.adaptive_decay, result->rng());
+                break;
+            }
             default:
                 break;
         }
     }
-    llama_sampling_set_rng_seed(result, params.seed);
+
     return result;
 }
 
@@ -247,11 +252,13 @@ std::string llama_sampling_print(const llama_sampling_params & params) {
             "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
             "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n"
             "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f\n"
-            "\txtc_probability = %.3f, xtc_threshold = %.3f, top_n_sigma = %.3f",
+            "\txtc_probability = %.3f, xtc_threshold = %.3f, top_n_sigma = %.3f\n"
+            "\tadaptive_target = %.2f, adaptive_decay = %.2f",
             params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present,
             params.top_k, params.tfs_z, params.top_p, params.min_p, params.typical_p, params.temp,
             params.mirostat, params.mirostat_eta, params.mirostat_tau,
-            params.xtc_probability, params.xtc_threshold, params.top_n_sigma);
+            params.xtc_probability, params.xtc_threshold, params.top_n_sigma,
+            params.adaptive_target, params.adaptive_decay);
 
     return std::string(result);
 }
@@ -283,6 +290,7 @@ std::string llama_sampling_type_to_str(llama_sampler_type sampler_type) {
         case llama_sampler_type::TEMPERATURE: return "temperature";
         case llama_sampler_type::XTC        : return "xtc";
         case llama_sampler_type::TOP_N_SIGMA: return "top_n_sigma";
+        case llama_sampler_type::ADAPTIVE_P : return "adaptive_p";
         default : return "";
     }
 }
@@ -297,7 +305,8 @@ std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vecto
         {"tfs_z",       llama_sampler_type::TFS_Z},
         {"xtc",         llama_sampler_type::XTC},
         {"top_n_sigma", llama_sampler_type::TOP_N_SIGMA},
-        {"temperature", llama_sampler_type::TEMPERATURE}
+        {"temperature", llama_sampler_type::TEMPERATURE},
+        {"adaptive_p",  llama_sampler_type::ADAPTIVE_P},
     };
 
     // since samplers names are written multiple ways
@@ -314,7 +323,8 @@ std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vecto
         {"tfs",         llama_sampler_type::TFS_Z},
         {"xtc",         llama_sampler_type::XTC},
         {"top-n-sigma", llama_sampler_type::TOP_N_SIGMA},
-        {"temp",        llama_sampler_type::TEMPERATURE}
+        {"temp",        llama_sampler_type::TEMPERATURE},
+        {"adaptive-p",  llama_sampler_type::ADAPTIVE_P},
     };
 
     std::vector<llama_sampler_type> sampler_types;
@@ -351,7 +361,8 @@ std::vector<llama_sampler_type> llama_sampling_types_from_chars(const std::strin
         {'f', llama_sampler_type::TFS_Z},
         {'x', llama_sampler_type::XTC},
         {'n', llama_sampler_type::TOP_N_SIGMA},
-        {'t', llama_sampler_type::TEMPERATURE}
+        {'t', llama_sampler_type::TEMPERATURE},
+        {'w', llama_sampler_type::ADAPTIVE_P},
     };
 
     std::vector<llama_sampler_type> sampler_types;
@@ -405,6 +416,7 @@ static void sampler_queue(
                     llama_sample_temp(ctx_main, &cur_p, temp);
                 }
                 break;
+            case llama_sampler_type::ADAPTIVE_P: llama_sample_adaptive_p(ctx_main, ctx_sampling->adapt_p_ctx, &cur_p); break;
             default : break;
         }
     }
@@ -422,6 +434,7 @@ static llama_token llama_sampling_sample_impl(
     const int     mirostat        = params.mirostat;
     const float   mirostat_tau    = params.mirostat_tau;
     const float   mirostat_eta    = params.mirostat_eta;
+    const float   adaptive_target = params.adaptive_target;
 
     std::vector<float> original_logits;
     auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, /* apply_grammar= */ is_resampling, &original_logits);
@@ -445,6 +458,17 @@ static llama_token llama_sampling_sample_impl(
         } else if (mirostat == 2) {
             llama_sample_temp(ctx_main, &cur_p, temp);
             id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);
+        } else if (adaptive_target >= 0.0f) {
+            // adaptive p sampling
+            static thread_local std::vector<float> orig_probs;
+            orig_probs.resize(cur_p.size);
+
+            // store original probabilities
+            for (size_t ii = 0; ii < cur_p.size; ++ii) {
+                orig_probs[ii] = cur_p.data[ii].p;
+            }
+            sampler_queue(ctx_main, params, ctx_sampling, cur_p, std::max(1, params.min_keep));
+            id = llama_sample_token_adaptive_p(ctx_main, &cur_p, ctx_sampling->adapt_p_ctx, orig_probs.data());
         } else {
             // temperature sampling
             size_t min_keep = std::max(1, params.min_keep);

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

examples/server/server-context.cpp

@@ -807,6 +807,8 @@ bool server_context::launch_slot_with_task(server_slot& slot, server_task& task)
     slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat);
     slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau);
     slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
+    slot.sparams.adaptive_target = json_value(data, "adaptive_target", default_sparams.adaptive_target);
+    slot.sparams.adaptive_decay = json_value(data, "adaptive_decay", default_sparams.adaptive_decay);
     slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
     slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep);
     slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard);
@@ -1405,6 +1407,8 @@ json server_context::get_formated_generation(const server_slot& slot) const {
         {"mirostat",                  slot.sparams.mirostat},
         {"mirostat_tau",              slot.sparams.mirostat_tau},
         {"mirostat_eta",              slot.sparams.mirostat_eta},
+        {"adaptive_target",           slot.sparams.adaptive_target},
+        {"adaptive_decay",            slot.sparams.adaptive_decay},
         {"penalize_nl",               slot.sparams.penalize_nl},
         {"stop",                      slot.params.antiprompt},
         {"max_tokens",                slot.params.n_predict}, // User configured n_predict

include/llama.h

@@ -1380,6 +1380,21 @@ LLAMA_API struct llama_grammar* llama_sampler_init_grammar_lazy_patterns(
     ///  @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
 
 
+    /// @details Adaptive p sampler initializer
+    /// @param target Select tokens near this probability (valid range 0.0 to 1.0; <0 = disabled)
+    /// @param decay Decay rate for target adaptation over time. lower values -> faster but less stable adaptation. (valid range 0.0 to 1.0; ≤0 = no adaptation)
+    LLAMA_API struct llama_sampler_adaptive_p * llama_sampler_init_adaptive_p(
+           const float target,
+           const float decay,
+        const uint32_t seed);
+
+    /// @details Adaptive p sampler described in https://github.com/MrJackSpade/adaptive-p-docs/blob/main/README.md
+    void llama_sample_adaptive_p(
+            struct llama_context * ctx,
+ struct llama_sampler_adaptive_p * adapt_p_ctx,
+          llama_token_data_array * candidates);
+
+
     /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
     /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
     /// @param 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.
@@ -1417,6 +1432,13 @@ LLAMA_API struct llama_grammar* llama_sampler_init_grammar_lazy_patterns(
             struct llama_context * ctx,
           llama_token_data_array * candidates);
 
+    /// @details Randonly selects a token from the candidates following adaptive p sampler.
+    llama_token llama_sample_token_adaptive_p(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+ struct llama_sampler_adaptive_p * adapt_p_ctx,
+                           float * orig_probs);
+
     //
     // Model split
     //

src/llama-sampling.cpp

@@ -1033,6 +1033,111 @@ struct llama_sampler_dry* llama_sampler_init_dry_impl(const struct llama_vocab&
 }
 
 
+// adaptive p
+
+llama_token llama_sample_token_adaptive_p_impl(
+              struct llama_sampling * smpl,
+             llama_token_data_array * candidates,
+    struct llama_sampler_adaptive_p * adapt_p_ctx,
+                              float * orig_probs)
+{
+    GGML_ASSERT(candidates->size > 0);
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    const size_t count = candidates->size;
+    adapt_p_ctx->probs.resize(count);
+
+    // cumulative distribution
+    const float max_logit = adapt_p_ctx->max_logit;
+    float cum_prob = 0.0f;
+    for (size_t i = 0; i < count; ++i) {
+        cum_prob += expf(candidates->data[i].logit - max_logit);
+        adapt_p_ctx->probs[i] = cum_prob;
+    }
+    adapt_p_ctx->probs.back() += 1.0f;  // safety margin in case rng() ~= rng.max()
+
+    // find token with cum_prob > target_cum_prob
+    const float target_cum_prob = cum_prob * (float)adapt_p_ctx->rng() / (float)adapt_p_ctx->rng.max();
+    auto iter = std::upper_bound(adapt_p_ctx->probs.begin(), adapt_p_ctx->probs.end(), target_cum_prob);
+    GGML_ASSERT(iter != adapt_p_ctx->probs.end());
+    llama_token id = candidates->data[std::distance(adapt_p_ctx->probs.begin(), iter)].id;
+
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    smpl->n_sample++;
+
+    // update history with original probability of selected token
+    adapt_p_ctx->weighted_sum = adapt_p_ctx->decay * adapt_p_ctx->weighted_sum + orig_probs[id];
+    adapt_p_ctx->total_weight = adapt_p_ctx->decay * adapt_p_ctx->total_weight + 1.0f;
+
+    return id;
+}
+
+void llama_sampler_adaptive_p_apply(struct llama_sampler_adaptive_p * adapt_p_ctx, llama_token_data_array * candidates)
+{
+    if (adapt_p_ctx->target < 0.0f) {
+        // sampler is disabled
+        llama_sample_softmax_impl(nullptr, candidates);
+        return;
+    }
+
+    // incomplete softmax because final division can be fused
+    float max_l = candidates->data[0].logit;
+    for (size_t i = 1; i < candidates->size; ++i) {
+        max_l = std::max(max_l, candidates->data[i].logit);
+    }
+    float cum_sum = 0.0f;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        const float p = expf(candidates->data[i].logit - max_l);
+        candidates->data[i].p = p;
+        cum_sum += p;
+    }
+
+    // compute adapted target probability
+    const float target = std::clamp(adapt_p_ctx->target, 0.0f, 1.0f);
+    const float adapted_target = std::clamp(adapt_p_ctx->total_weight == 0.0f
+        ? target
+        : 2.0f * target - (adapt_p_ctx->weighted_sum / adapt_p_ctx->total_weight),
+        0.0f, 1.0f);
+
+    // transformation constants
+    static constexpr float peak_logit_value = 5.0f;
+    static constexpr float inv_width = 1.0f / 0.3f;
+    static constexpr float sharpness = 10.0f;
+
+    const float fused_target = adapted_target * inv_width;
+    const float fused_width = inv_width / cum_sum;
+
+    // quadratic near target for finite differentiation, transitioning to linear decay in tails
+    // unbounded negative logits suppress far-from-target tokens after softmax
+    float max_logit = -INFINITY;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        const float dist = std::abs(candidates->data[i].p * fused_width - fused_target);
+        const float logit = peak_logit_value - sharpness * dist * dist / (1.0f + dist);
+        candidates->data[i].logit = logit;
+        max_logit = std::max(max_logit, logit);
+    }
+    candidates->sorted = false;
+    adapt_p_ctx->max_logit = max_logit;
+}
+
+struct llama_sampler_adaptive_p * llama_sampler_init_adaptive_p_impl(
+       const float target,
+       const float decay,
+    const uint32_t seed)
+{
+    const float clamped_decay = std::clamp(decay, 0.0f, 0.99f);
+    return new llama_sampler_adaptive_p {
+        /* .target          = */ target,
+        /* .decay           = */ clamped_decay,
+        /* .rng             = */ std::mt19937(seed),
+        /* .weighted_sum    = */ target / (1.0f - clamped_decay),
+        /* .total_weight    = */ 1.0f / (1.0f - clamped_decay),
+        /* .max_logit       = */ 0.0f,
+        /* .probs           = */ {},
+    };
+}
+
+
 // grammar
 
 struct llama_sampler_grammar {

src/llama-sampling.h

@@ -61,6 +61,24 @@ struct llama_sampler_dry * llama_sampler_init_dry_impl(
 
 void llama_sampler_dry_apply(struct llama_sampler_dry* smpl, llama_token_data_array* cur_p);
 
+// maintains an exponential moving average of the *ORIGINAL* probabilities of selected tokens
+// used to compute an adapted target at each sampling step.
+// see llama.h for a full description of the sampler
+struct llama_sampler_adaptive_p {
+    const float target;     // target probability (0.0 - 1.0; negative = disabled)
+    const float decay;      // EMA decay; history ≈ 1/(1-decay) tokens (0.0 - 0.99)
+    std::mt19937 rng;       // RNG
+    float weighted_sum;     // sum(p_n * decay^N)
+    float total_weight;     // sum(decay^i), converges to 1/(1-decay)
+    float max_logit;        // maximum logit found during transform
+    std::vector<float> probs;   // cumulative probabilities
+};
+
+void llama_sampler_adaptive_p_apply(
+    struct llama_sampler_adaptive_p * adapt_p_ctx,
+             llama_token_data_array * candidates);
+
+struct llama_sampler_adaptive_p * llama_sampler_init_adaptive_p_impl(const float target, const float decay, const uint32_t seed);
 
 
 void llama_sample_repetition_penalties_impl(
@@ -83,6 +101,6 @@ llama_token llama_sample_token_mirostat_v2_impl(struct llama_sampling * smpl, ll
 llama_token llama_sample_token_greedy_impl     (struct llama_sampling * smpl, llama_token_data_array * candidates);
 llama_token llama_sample_token_with_rng_impl   (struct llama_sampling * smpl, llama_token_data_array * candidates, std::mt19937 & rng);
 llama_token llama_sample_token_impl            (struct llama_sampling * smpl, llama_token_data_array * candidates);
-
+llama_token llama_sample_token_adaptive_p_impl (struct llama_sampling * smpl, llama_token_data_array * candidates, struct llama_sampler_adaptive_p * adapt_p_ctx, float * orig_probs);