gtest adjustment

src/cpp/src/logit_processor.hpp

@@ -19,7 +19,7 @@ struct Token {
 struct Logits {
     float * m_data = nullptr;
     size_t m_size;
-    // Late initialized
+    // Late initialized for top_p or top_k transforms
     std::vector<Token> m_vector;
 
     Logits(float* data, size_t size): m_data(data), m_size(size) {}
@@ -29,8 +29,11 @@ struct Logits {
         OPENVINO_ASSERT(m_vector.size() == 0, "Logits vector already initialized");
         m_vector.reserve(m_size);
         for (size_t i = 0; i < m_size; i++)
-            m_vector.emplace_back(m_data[i], i);
-
+            m_vector.emplace_back(m_data[i], i);   
+    }
+
+    bool vector_initialized() const {
+        return m_vector.size() > 0;
     }
 
     void resize(size_t new_size) {
@@ -56,7 +59,7 @@ class TopPFilter : public ILogitTransformer {
     TopPFilter(double top_p) : m_top_p(top_p) {}
 
     void apply(Logits& logits) override {
-        if (logits.m_vector.size() == 0) {
+        if (!logits.vector_initialized()) {
             // Initialize and sort vector
             logits.initialize_vector();
             std::sort(logits.m_vector.begin(), logits.m_vector.end(), [](const Token& lhs, const Token& rhs) {return lhs.m_log_prob > rhs.m_log_prob; });
@@ -84,7 +87,7 @@ class TopKFilter : public ILogitTransformer {
         if (m_top_k >= logits.m_size) 
             return;
 
-        if (logits.m_vector.size() == 0) {
+        if (!logits.vector_initialized()) {
             // Initialize and partially sort vector
             logits.initialize_vector();
             std::partial_sort(logits.m_vector.begin(), logits.m_vector.begin() + m_top_k, logits.m_vector.end(), [](const Token& lhs, const Token& rhs) {return lhs.m_log_prob > rhs.m_log_prob; });

src/cpp/src/sampler.hpp

@@ -233,7 +233,7 @@ class Sampler {
         // If top_p or top_k was applied we use sorted vector, if not we go with original buffer.
         std::vector<float> multinomial_weights;
         multinomial_weights.reserve(logits.m_size);
-        if (logits.m_vector.size() > 0)
+        if (logits.vector_initialized())
             for (auto& logit: logits.m_vector) multinomial_weights.emplace_back(logit.m_log_prob);
         else
             multinomial_weights.assign(logits.m_data, logits.m_data + logits.m_size);
@@ -243,7 +243,7 @@ class Sampler {
         std::vector<Token> out_tokens;
         for (size_t token_idx = 0; token_idx < num_tokens_per_sequence; ++token_idx) {
             size_t element_to_pick = dist(rng_engine);
-            if (logits.m_vector.size() > 0)
+            if (logits.vector_initialized())
                 out_tokens.push_back(logits.m_vector[element_to_pick]);
             else
                 out_tokens.emplace_back(logits.m_data[element_to_pick], element_to_pick);