Use unique_ptr with custom deleter for llama

pipeline/includes/data_generation/llama_generator.h

@@ -7,6 +7,7 @@
  */
 
 #include <cstdint>
+#include <memory>
 #include <random>
 #include <string>
 #include <string_view>
@@ -65,6 +66,17 @@ class LlamaGenerator final : public DataGenerator {
   static constexpr uint32_t kDefaultSamplingTopK = 64;
   static constexpr uint32_t kDefaultContextSize = 8192;
 
+  struct ModelDeleter {
+    void operator()(llama_model* model) const noexcept;
+  };
+
+  struct ContextDeleter {
+    void operator()(llama_context* context) const noexcept;
+  };
+
+  using ModelHandle = std::unique_ptr<llama_model, ModelDeleter>;
+  using ContextHandle = std::unique_ptr<llama_context, ContextDeleter>;
+
   struct SamplerState {
     SamplerState() = default;
     ~SamplerState();
@@ -116,8 +128,8 @@ class LlamaGenerator final : public DataGenerator {
    */
   std::string LoadBrewerySystemPrompt(const std::string& prompt_file_path);
 
-  llama_model* model_ = nullptr;
+  ModelHandle model_;
-  llama_context* context_ = nullptr;
+  ContextHandle context_;
   /// @brief Persistent sampler chain reused across inference calls.
   std::unique_ptr<SamplerState> sampler_;
   float sampling_temperature_ = 1.0F;

pipeline/src/data_generation/llama/infer.cc

@@ -22,7 +22,7 @@ static constexpr std::size_t kPromptTokenSlack = 8;
 std::string LlamaGenerator::Infer(const std::string& system_prompt,
                                   const std::string& prompt,
                                   const int max_tokens) {
-  return InferFormatted(ToChatPromptPublic(model_, system_prompt, prompt),
+  return InferFormatted(ToChatPromptPublic(model_.get(), system_prompt, prompt),
                         max_tokens);
 }
 
@@ -31,14 +31,14 @@ std::string LlamaGenerator::InferFormatted(const std::string& formatted_prompt,
   /**
    * Validate that model and context are loaded
    */
-  if (model_ == nullptr || context_ == nullptr) {
+  if (!model_ || !context_) {
     throw std::runtime_error("LlamaGenerator: model not loaded");
   }
 
   /**
    * Get vocabulary for tokenization and token-to-text conversion
    */
-  const llama_vocab* vocab = llama_model_get_vocab(model_);
+  const llama_vocab* vocab = llama_model_get_vocab(model_.get());
   if (vocab == nullptr) {
     throw std::runtime_error("LlamaGenerator: vocab unavailable");
   }
@@ -46,7 +46,7 @@ std::string LlamaGenerator::InferFormatted(const std::string& formatted_prompt,
   /**
    * Clear KV cache to ensure clean inference state (no residual context)
    */
-  llama_memory_clear(llama_get_memory(context_), true);
+  llama_memory_clear(llama_get_memory(context_.get()), true);
 
   /**
    * TOKENIZATION PHASE
@@ -79,8 +79,8 @@ std::string LlamaGenerator::InferFormatted(const std::string& formatted_prompt,
    * Validate and compute effective token budgets based on context window
    * constraints
    */
-  const auto n_ctx = static_cast<int32_t>(llama_n_ctx(context_));
+  const auto n_ctx = static_cast<int32_t>(llama_n_ctx(context_.get()));
-  const auto n_batch = static_cast<int32_t>(llama_n_batch(context_));
+  const auto n_batch = static_cast<int32_t>(llama_n_batch(context_.get()));
   if (n_ctx <= 1 || n_batch <= 0) {
     throw std::runtime_error("LlamaGenerator: invalid context or batch size");
   }
@@ -117,7 +117,7 @@ std::string LlamaGenerator::InferFormatted(const std::string& formatted_prompt,
    */
   const llama_batch prompt_batch = llama_batch_get_one(
       prompt_tokens.data(), static_cast<int32_t>(prompt_tokens.size()));
-  if (llama_decode(context_, prompt_batch) != 0) {
+  if (llama_decode(context_.get(), prompt_batch) != 0) {
     throw std::runtime_error("LlamaGenerator: prompt decode failed");
   }
 
@@ -139,7 +139,7 @@ std::string LlamaGenerator::InferFormatted(const std::string& formatted_prompt,
      * Index -1 means use the last output position from previous batch
      */
     const llama_token next =
-        llama_sampler_sample(sampler_->chain, context_, -1);
+        llama_sampler_sample(sampler_->chain, context_.get(), -1);
     /**
      * Stop if model predicts end-of-generation token (EOS/EOT)
      */
@@ -153,7 +153,7 @@ std::string LlamaGenerator::InferFormatted(const std::string& formatted_prompt,
      */
     llama_token decode_token = next;
     const llama_batch one_token_batch = llama_batch_get_one(&decode_token, 1);
-    if (llama_decode(context_, one_token_batch) != 0) {
+    if (llama_decode(context_.get(), one_token_batch) != 0) {
       throw std::runtime_error(
           "LlamaGenerator: decode failed during generation");
     }

pipeline/src/data_generation/llama/llama_generator.cc

@@ -24,6 +24,18 @@ struct SamplerConfig {
 using SamplerPtr =
     std::unique_ptr<llama_sampler, decltype(&llama_sampler_free)>;
 
+void LlamaGenerator::ModelDeleter::operator()(llama_model* model) const noexcept {
+  if (model != nullptr) {
+    llama_model_free(model);
+  }
+}
+
+void LlamaGenerator::ContextDeleter::operator()(llama_context* context) const noexcept {
+  if (context != nullptr) {
+    llama_free(context);
+  }
+}
+
 static SamplerPtr CreateSamplerChain(const SamplerConfig& config,
                                      std::mt19937& rng) {
   const llama_sampler_chain_params sampler_params =
@@ -88,6 +100,7 @@ LlamaGenerator::LlamaGenerator(const ApplicationOptions& options,
   sampling_temperature_ = options.temperature;
   sampling_top_p_ = options.top_p;
   sampling_top_k_ = options.top_k;
+
   if (options.seed == -1) {
     std::random_device random_device;
     rng_.seed(random_device());
@@ -100,26 +113,8 @@ LlamaGenerator::LlamaGenerator(const ApplicationOptions& options,
   const SamplerConfig sampler_config{sampling_temperature_, sampling_top_p_,
                                      sampling_top_k_};
   auto sampler_chain = CreateSamplerChain(sampler_config, rng_);
-  sampler_.reset(new SamplerState());
+  sampler_ = std::make_unique<SamplerState>();
   sampler_->chain = sampler_chain.release();
 }
 
-LlamaGenerator::~LlamaGenerator() {
+LlamaGenerator::~LlamaGenerator() = default;
-  sampler_.reset();
-
-  /**
-   * Free the inference context (contains KV cache and computation state)
-   */
-  if (context_ != nullptr) {
-    llama_free(context_);
-    context_ = nullptr;
-  }
-
-  /**
-   * Free the loaded model (contains weights and vocabulary)
-   */
-  if (model_ != nullptr) {
-    llama_model_free(model_);
-    model_ = nullptr;
-  }
-}

pipeline/src/data_generation/llama/load.cc

@@ -9,23 +9,19 @@
 #include <algorithm>
 #include <stdexcept>
 #include <string>
+#include <utility>
 
 #include "data_generation/llama_generator.h"
 #include "llama.h"
 
 void LlamaGenerator::Load(const std::string& model_path) {
-  if (context_ != nullptr) {
+  context_.reset();
-    llama_free(context_);
+  model_.reset();
-    context_ = nullptr;
-  }
-  if (model_ != nullptr) {
-    llama_model_free(model_);
-    model_ = nullptr;
-  }
 
   const llama_model_params model_params = llama_model_default_params();
-  model_ = llama_model_load_from_file(model_path.c_str(), model_params);
+  LlamaGenerator::ModelHandle loaded_model(
-  if (model_ == nullptr) {
+      llama_model_load_from_file(model_path.c_str(), model_params));
+  if (!loaded_model) {
     throw std::runtime_error(
         "LlamaGenerator: failed to load model from path: " + model_path);
   }
@@ -34,12 +30,14 @@ void LlamaGenerator::Load(const std::string& model_path) {
   context_params.n_ctx = n_ctx_;
   context_params.n_batch = std::min(n_ctx_, static_cast<uint32_t>(5000));
 
-  context_ = llama_init_from_model(model_, context_params);
+  LlamaGenerator::ContextHandle loaded_context(
-  if (context_ == nullptr) {
+      llama_init_from_model(loaded_model.get(), context_params));
-    llama_model_free(model_);
+  if (!loaded_context) {
-    model_ = nullptr;
     throw std::runtime_error("LlamaGenerator: failed to create context");
   }
 
+  model_ = std::move(loaded_model);
+  context_ = std::move(loaded_context);
+
   spdlog::info("[LlamaGenerator] Loaded model: {}", model_path);
 }