Add min-p sampling

* ggerganov/llama.cpp#3841

Sources/llmfarm_core_cpp/ggml/common.cpp

@@ -50,6 +50,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             params.top_k = std::stoi(get_next_arg(i, argc, argv, arg, params));
         } else if (arg == "--top_p") {
             params.top_p = std::stof(get_next_arg(i, argc, argv, arg, params));
+        } else if (arg == "min_p") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.min_p = std::stof(argv[i]);
         } else if (arg == "--temp") {
             params.temp = std::stof(get_next_arg(i, argc, argv, arg, params));
             params.temp = std::max(params.temp, 0.0f);
@@ -110,6 +116,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stderr, "  -n N, --n_predict N   number of tokens to predict (default: %d)\n", params.n_predict);
     fprintf(stderr, "  --top_k N             top-k sampling (default: %d)\n", params.top_k);
     fprintf(stderr, "  --top_p N             top-p sampling (default: %.1f)\n", params.top_p);
+    fprintf(stderr, "  --min_p N             min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)params.min_p);
     fprintf(stderr, "  --temp N              temperature (default: %.1f)\n", params.temp);
     fprintf(stderr, "  --repeat-last-n N     last n tokens to consider for penalize (default: %d, 0 = disabled)\n", params.repeat_last_n);
     fprintf(stderr, "  --repeat-penalty N    penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)\n", (double)params.repeat_penalty);

Sources/llmfarm_core_cpp/ggml/sampling.cpp

@@ -62,6 +62,7 @@ llama_token llama_sampling_sample(
     const float   temp            = params.temp;
     const int32_t top_k           = params.top_k <= 0 ? n_vocab : params.top_k;
     const float   top_p           = params.top_p;
+    const float   min_p           = params.min_p;
     const float   tfs_z           = params.tfs_z;
     const float   typical_p       = params.typical_p;
     const int32_t repeat_last_n   = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
@@ -143,6 +144,7 @@ llama_token llama_sampling_sample(
             llama_sample_tail_free  (ctx, &cur_p, tfs_z, min_keep);
             llama_sample_typical    (ctx, &cur_p, typical_p, min_keep);
             llama_sample_top_p      (ctx, &cur_p, top_p, min_keep);
+            llama_sample_min_p      (ctx_main, &cur_p, min_p,     min_keep);
             llama_sample_temp(ctx, &cur_p, temp);
 
             {

Sources/llmfarm_core_cpp/ggml/sampling.h

@@ -10,6 +10,7 @@
 typedef struct llama_sampling_params {
     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   tfs_z             = 1.00f; // 1.0 = disabled
     float   typical_p         = 1.00f; // 1.0 = disabled
     float   temp              = 0.80f; // 1.0 = disabled

Sources/llmfarm_core_cpp/llama/llama.cpp

@@ -7617,6 +7617,32 @@ void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * can
     }
 }
 
+void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+    if (p <= 0.0f || !candidates->size) {
+        return;
+    }
+
+    llama_sample_softmax(ctx, candidates);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    float scale = candidates->data[0].p; // scale by max prob
+    size_t i = 1; // first token always matches
+
+    for (; i < candidates->size; ++i) {
+        if (candidates->data[i].p < p * scale && i >= min_keep) {
+            break; // prob too small
+        }
+    }
+
+    // Resize the output vector to keep only the matching tokens
+    candidates->size = i;
+
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
 void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
     if (z >= 1.0f || candidates->size <= 2) {
         return;

Sources/llmfarm_core_cpp/spm-headers/llama.h

@@ -599,6 +599,13 @@ extern "C" {
                            float   p,
                           size_t   min_keep);
 
+    /// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
+    LLAMA_API void llama_sample_min_p(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+                           float   p,
+                          size_t   min_keep);
+
     /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
     LLAMA_API void llama_sample_tail_free(
             struct llama_context * ctx,