whisper : avoid some memory allocations

diff --git a/whisper.cpp b/whisper.cpp
index 077607691b3539f382f1e9b514cde119fb630c0d..d23e97feb8a6629fea2c86d738d362b42918191e 100644 (file)
--- a/whisper.cpp
+++ b/whisper.cpp
@@ -204,6 +204,10 @@ struct whisper_vocab {
     std::map<token, id> token_to_id;
     std::map<id, token> id_to_token;
 
+    // used to avoid memory allocations during sampling
+    // TODO: move to whisper_context in the future
+    std::vector<std::pair<double, whisper_vocab::id>> probs_id;
+
     id token_eot  = 50256;
     id token_sot  = 50257;
     id token_prev = 50360;
@@ -551,6 +555,9 @@ static bool whisper_model_load(const std::string & fname, whisper_context & wctx
 
         std::string word;
         std::vector<char> tmp;
+
+        tmp.reserve(128);
+
         for (int i = 0; i < n_vocab; i++) {
             uint32_t len;
             read_safe(fin, len);
@@ -603,6 +610,11 @@ static bool whisper_model_load(const std::string & fname, whisper_context & wctx
                 vocab.id_to_token[i] = word;
             }
         }
+
+        wctx.logits.reserve(vocab.n_vocab*model.hparams.n_text_ctx);
+        wctx.probs.reserve(vocab.n_vocab*model.hparams.n_text_ctx);
+
+        vocab.probs_id.reserve(n_vocab);
     }
 
     {
@@ -1021,7 +1033,7 @@ static bool whisper_model_load(const std::string & fname, whisper_context & wctx
 
             std::string name;
             std::vector<char> tmp(length); // create a buffer
-            fin.read( &tmp[0], tmp.size() ); // read to buffer
+            fin.read(&tmp[0], tmp.size()); // read to buffer
             name.assign(&tmp[0], tmp.size());
 
             if (model.tensors.find(name) == model.tensors.end()) {
@@ -1849,7 +1861,7 @@ static bool whisper_decode(
 
 // the most basic sampling scheme - select the top token
 static whisper_token_data whisper_sample_best(
-        const whisper_vocab & vocab,
+              whisper_vocab & vocab,
         const float * probs,
               bool force_timestamp,
               bool is_initial) {
@@ -1857,11 +1869,11 @@ static whisper_token_data whisper_sample_best(
         0, 0, 0.0f, 0.0f, 0.0f, -1, -1, 0.0f,
     };
 
-    int n_logits = vocab.id_to_token.size();
+    const int n_logits = vocab.n_vocab;
 
-    std::vector<std::pair<double, whisper_vocab::id>> probs_id;
-    probs_id.reserve(n_logits);
+    auto & probs_id = vocab.probs_id;
 
+    probs_id.clear();
     for (int i = 0; i < n_logits; i++) {
         probs_id.emplace_back(probs[i], i);
     }
@@ -2001,6 +2013,9 @@ static void fft(const std::vector<float> & in, std::vector<float> & out) {
     std::vector<float> even;
     std::vector<float> odd;
 
+    even.reserve(N/2);
+    odd.reserve(N/2);
+
     for (int i = 0; i < N; i++) {
         if (i % 2 == 0) {
             even.push_back(in[i]);
@@ -2434,7 +2449,7 @@ int whisper_lang_auto_detect(
     std::vector<std::pair<float, int>> probs_id;
     for (const auto & kv : g_lang) {
         const auto token_lang = whisper_token_lang(ctx, kv.second.first);
-        probs_id.emplace_back( ctx->probs[token_lang], kv.second.first );
+        probs_id.emplace_back(ctx->probs[token_lang], kv.second.first);
     }
 
     // sort descending