Removed xtc_threshold_max

Author: MaggotHATE

common/arg.cpp

@@ -987,13 +987,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sparams.xtc_threshold = std::stof(value);
         }
     ).set_sparam());
-    add_opt(common_arg(
-        {"-xtc-t-max", "--xtc-threshold-max"}, "N",
-        format("xtc upper threshold (default: %.1f, 0.0 = disabled)", (double)params.sparams.xtc_threshold_max),
-        [](common_params & params, const std::string & value) {
-            params.sparams.xtc_threshold_max = std::stof(value);
-        }
-    ).set_sparam());
     add_opt(common_arg(
         {"--typical"}, "N",
         format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p),

common/common.cpp

@@ -2090,7 +2090,6 @@ void yaml_dump_non_result_info(FILE * stream, const common_params & params, cons
     fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
     fprintf(stream, "xtc_probability: %f # default: 0.5\n", sparams.xtc_probability);
     fprintf(stream, "xtc_threshold: %f # default: 0.1\n", sparams.xtc_threshold);
-    fprintf(stream, "xtc_threshold_max: %f # default: 1.0\n", sparams.xtc_threshold_max);
     fprintf(stream, "typ_p: %f # default: 1.0\n", sparams.typ_p);
     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/common.h

@@ -112,7 +112,6 @@ struct common_sampler_params {
     float   min_p             = 0.05f; // 0.0 = disabled
     float   xtc_probability   = 0.00f; // 0.0 = disabled
     float   xtc_threshold     = 0.10f; // 0.5 = disabled
-    float   xtc_threshold_max = 1.00f; // 0.0 = disabled
     float   tfs_z             = 1.00f; // 1.0 = disabled
     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

common/sampling.cpp

@@ -130,10 +130,10 @@ std::string common_sampler_params::print() const {
 
     snprintf(result, sizeof(result),
             "\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, xtc_probability = %.3f, xtc_threshold = %.3f, xtc_threshold_max = %.3f, typical_p = %.3f, temp = %.3f\n"
+            "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n"
             "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
             penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
-            top_k, tfs_z, top_p, min_p, xtc_probability, xtc_threshold, xtc_threshold_max, typ_p, temp,
+            top_k, tfs_z, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp,
             mirostat, mirostat_eta, mirostat_tau);
 
     return std::string(result);
@@ -185,7 +185,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                         llama_sampler_chain_add(result->chain, llama_sampler_init_min_p    (params.min_p, params.min_keep));
                         break;
                     case COMMON_SAMPLER_TYPE_XTC:
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_xtc      (params.xtc_probability, params.xtc_threshold, params.xtc_threshold_max, params.min_keep, params.seed));
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_xtc      (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
                         break;
                     case COMMON_SAMPLER_TYPE_TFS_Z:
                         llama_sampler_chain_add(result->chain, llama_sampler_init_tail_free(params.tfs_z, params.min_keep));

examples/main/README.md

@@ -245,17 +245,14 @@ Example usage: `--mirostat 2 --mirostat-lr 0.05 --mirostat-ent 3.0`
 
 -   `--xtc-probability N`: Sets the chance for token removal (checked once on sampler start) (default: 0.0).
 -   `--xtc-threshold N`: Sets a minimum probability threshold for tokens to be removed (default: 0.1).
--   `--xtc-threshold-max N`: Sets a maximum probability threshold for tokens to be removed (highly experimental) (default: 1.0).
 
 Exclude Top Choices (XTC) is a unique sampler that is designed to remove top tokens from consideration and avoid more obvious and repetitive outputs. With a chance of `xtc-p` it searches for tokens with probabilities of `xtc-threshold` and above, then removes all such tokens except the least probable one.
 
 By removing top tokens XTC can improve the variety of answers, break writing clichés and inhibit repition, since clichés and repeated phrases are usually more likely to appear. By keeping the last token above the threshold, XTC ensures that the answer is still coherent. XTC is meant to be used for creative tasks, but feel free to experiment with different settings for different models.
 
-The additional `xtc-threshold-max` parameter may help with finetuned models that already give relatively creative output, meaning that clichés and repetitive phrases may appear at lower probabilities. It allows to remove tokens from a middle range which will always be specific to a model, requiring careful experimenting. Leave `xtc-threshold-max` on default 1.0 for all base/instruct models.
-
 Being experimental and unique, XTC is disabled by default. The recommended combination of samplers is Min-P followed by XTC on its default settings: `--sampling-seq mx --min-p 0.02 -xtc-p 0.5`.
 
-Example usage: `-xtc-p 0.5 -xtc-t 0.1 -xtc-t-max 1.0`
+Example usage: `-xtc-p 0.5 -xtc-t 0.1
 
 ### Logit Bias
 

examples/server/public/index-new.html

@@ -45,7 +45,6 @@
       min_p: 0.05, // 0 = disabled; recommended for non-english: ~ 0.4
       xtc_probability: 0.0, // 0 = disabled;
       xtc_threshold: 0.1, // 0.5 = disabled;
-      xtc_threshold_max: 1.0, // 0 = disabled;
       tfs_z: 1.0, // 1.0 = disabled
       typical_p: 1.0, // 1.0 = disabled
       presence_penalty: 0.0, // 0.0 = disabled
@@ -841,7 +840,6 @@
           ${FloatField({ label: "Typical-P", title: "Activates local typical sampling, a method used to limit the prediction of tokens that are atypical in the current context. The parameter p controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
           ${FloatField({ label: "XTC probability", title: "Sets the chance for token removal (checked once on sampler start)", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })}
           ${FloatField({ label: "XTC threshold", title: "Sets a minimum probability threshold for tokens to be removed", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })}
-          ${FloatField({ label: "XTC max threshold", title: "Sets a maximum probability threshold for tokens to be removed (highly experimental)", max: 1.0, min: 0.0, name: "xtc_threshold_max", step: 0.01, value: params.value.xtc_threshold_max })}
           ${IntField({ label: "Min Keep", title: "If greater than 0, samplers are forced to return N possible tokens at minimum. Default is 0", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })}
         </fieldset>
 
@@ -1140,7 +1138,6 @@ <h2>llama.cpp</h2>
     min_p: { snapValue: 0.05, snapRangeMultiplier: 2 },
     xtc_probability: { snapValue: 0.0, snapRangeMultiplier: 4 },
     xtc_threshold: { snapValue: 0.5, snapRangeMultiplier: 4 },
-    xtc_threshold_max: { snapValue: 1.0, snapRangeMultiplier: 4 },
     top_p: { snapValue: 1.0, snapRangeMultiplier: 4 },
     tfs_z: { snapValue: 1.0, snapRangeMultiplier: 4 },
     typical_p: { snapValue: 1.0, snapRangeMultiplier: 4 },

examples/server/public/index.html

@@ -309,7 +309,6 @@
       min_p: 0.05, // 0 = disabled
       xtc_probability: 0.0, // 0 = disabled;
       xtc_threshold: 0.1, // 0.5 = disabled;
-      xtc_threshold_max: 1.0, // 0 = disabled;
       tfs_z: 1.0, // 1.0 = disabled
       typical_p: 1.0, // 1.0 = disabled
       presence_penalty: 0.0, // 0.0 = disabled
@@ -1018,7 +1017,6 @@
               ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
               ${FloatField({ label: "XTC probability", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })}
               ${FloatField({ label: "XTC threshold", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })}
-              ${FloatField({ label: "XTC upper threshold", max: 1.0, min: 0.0, name: "xtc_threshold_max", step: 0.01, value: params.value.xtc_threshold_max })}
             </fieldset>
             <hr />
             <fieldset class="three">

examples/server/server.cpp

@@ -893,7 +893,6 @@ struct server_context {
         slot.sparams.min_p             = json_value(data, "min_p",             default_sparams.min_p);
         slot.sparams.xtc_probability   = json_value(data, "xtc_probability",   default_sparams.xtc_probability);
         slot.sparams.xtc_threshold     = json_value(data, "xtc_threshold",     default_sparams.xtc_threshold);
-        slot.sparams.xtc_threshold_max = json_value(data, "xtc_threshold_max", default_sparams.xtc_threshold_max);
         slot.sparams.tfs_z             = json_value(data, "tfs_z",             default_sparams.tfs_z);
         slot.sparams.typ_p             = json_value(data, "typical_p",         default_sparams.typ_p);
         slot.sparams.temp              = json_value(data, "temperature",       default_sparams.temp);
@@ -1244,7 +1243,6 @@ struct server_context {
             {"min_p",                     slot.sparams.min_p},
             {"xtc_probability",           slot.sparams.xtc_probability},
             {"xtc_threshold",             slot.sparams.xtc_threshold},
-            {"xtc_threshold_max",         slot.sparams.xtc_threshold_max},
             {"tfs_z",                     slot.sparams.tfs_z},
             {"typical_p",                 slot.sparams.typ_p},
             {"repeat_last_n",             slot.sparams.penalty_last_n},

include/llama.h

@@ -1095,7 +1095,7 @@ extern "C" {
     LLAMA_API struct llama_sampler * llama_sampler_init_temp_ext   (float   t, float   delta, float exponent);
 
     /// @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335
-    LLAMA_API struct llama_sampler * llama_sampler_init_xtc        (float   p, float   t, float     t_max, size_t min_keep, uint32_t seed);
+    LLAMA_API struct llama_sampler * llama_sampler_init_xtc        (float   p, float   t,     size_t min_keep, uint32_t seed);
 
     /// @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.

src/llama-sampling.cpp

@@ -1064,7 +1064,6 @@ struct llama_sampler * llama_sampler_init_temp_ext(float temp, float delta, floa
 struct llama_sampler_xtc {
     const float    probability;
     const float    threshold;
-    const float    threshold_max;
     const size_t   min_keep;
 
     const uint32_t seed;
@@ -1082,8 +1081,6 @@ static void llama_sample_xtc_apply(struct llama_sampler * smpl, llama_token_data
 
     if (ctx->probability <= 0.0f
         || ctx->threshold > 0.5f
-        || ctx->threshold_max <= 0.0f
-        || ctx->threshold_max <= ctx->threshold
         || cur_p->size <= 2) {
         return;
     }
@@ -1095,35 +1092,29 @@ static void llama_sample_xtc_apply(struct llama_sampler * smpl, llama_token_data
     // in case it's not sorted/recalculated yet
     llama_sampler_softmax_impl(cur_p);
 
-    int pos_first = -1;
     int pos_last = 0;
 
     for (size_t i = 0; i < cur_p->size; ++i) {
         if (cur_p->data[i].p - ctx->threshold >= -1e-5) {
-            if (cur_p->data[i].p - ctx->threshold_max > 1e-3) pos_first = i;
             pos_last = i;
-        } else {
-            break;
-        }
+        } else break;
     }
 
-    int to_remove = pos_last - (1 + pos_first);
-
-    if (cur_p->size - to_remove >= ctx->min_keep && to_remove > 0) {
+    if (cur_p->size - pos_last >= ctx->min_keep && pos_last > 0) {
 
-        size_t last_idx = cur_p->size - to_remove;
+        size_t last_idx = cur_p->size - pos_last;
 
-        for (size_t i = pos_first + 1; i <= last_idx; ++i) {
-            cur_p->data[i] = cur_p->data[i + to_remove];
+        for (size_t i = 0; i <= last_idx; ++i) {
+            cur_p->data[i] = cur_p->data[i + pos_last];
         }
 
-        cur_p->size = cur_p->size - to_remove;
+        cur_p->size = cur_p->size - pos_last;
     }
 }
 
 static struct llama_sampler * llama_sampler_xtc_clone(const struct llama_sampler * smpl) {
     const auto * ctx = (const llama_sampler_xtc *) smpl->ctx;
-    auto * result = llama_sampler_init_xtc(ctx->probability, ctx->threshold, ctx->threshold_max, ctx->min_keep, ctx->seed);
+    auto * result = llama_sampler_init_xtc(ctx->probability, ctx->threshold, ctx->min_keep, ctx->seed);
 
     // copy the state
     {
@@ -1154,14 +1145,13 @@ static struct llama_sampler_i llama_sampler_xtc_i = {
     /* .free   = */ llama_sampler_xtc_free,
 };
 
-struct llama_sampler * llama_sampler_init_xtc(float p, float t, float t_max, size_t min_keep, uint32_t seed) {
+struct llama_sampler * llama_sampler_init_xtc(float p, float t, size_t min_keep, uint32_t seed) {
     auto seed_cur = get_rng_seed(seed);
     return new llama_sampler {
         /* .iface = */ &llama_sampler_xtc_i,
         /* .ctx   = */ new llama_sampler_xtc {
             /* .probability   = */ p,
             /* .threshold     = */ t,
-            /* .threshold_max = */ t_max,
             /* .min_keep      = */ min_keep,
             /* .seed          = */ seed,
             /* .seed_cur      = */ seed_cur,