Add CB naive chat

src/cpp/include/openvino/genai/continuous_batching_pipeline.hpp

@@ -10,6 +10,8 @@
 #include "openvino/genai/tokenizer.hpp"
 #include "openvino/genai/generation_config.hpp"
 #include "openvino/genai/generation_handle.hpp"
+#include "openvino/genai/llm_pipeline.hpp"
+#include "openvino/genai/streamer_base.hpp"
 #include "openvino/genai/visibility.hpp"
 
 namespace ov::genai {
@@ -55,13 +57,27 @@ class OPENVINO_GENAI_EXPORTS ContinuousBatchingPipeline {
 
     PipelineMetrics get_metrics() const;
 
-    GenerationHandle add_request(uint64_t request_id, std::string prompt, ov::genai::GenerationConfig sampling_params);
+    GenerationHandle add_request(uint64_t request_id, const ov::Tensor& input_ids, const ov::genai::GenerationConfig& sampling_params);
+    GenerationHandle add_request(uint64_t request_id, const std::string& prompt, const ov::genai::GenerationConfig& sampling_params);
 
     void step();
 
     bool has_non_finished_requests();
 
     // more high level interface, which can process multiple prompts in continuous batching manner
-    std::vector<GenerationResult> generate(const std::vector<std::string>& prompts, std::vector<ov::genai::GenerationConfig> sampling_params);
+    std::vector<EncodedGenerationResult> generate(const std::vector<ov::Tensor>& input_ids, const std::vector<ov::genai::GenerationConfig>& sampling_params, const ov::genai::StreamerVariant& streamer=std::monostate{});
+    std::vector<GenerationResult> generate(const std::vector<std::string>& prompts, const std::vector<ov::genai::GenerationConfig>& sampling_params, const ov::genai::StreamerVariant& streamer=std::monostate{});
+
+    /**
+    * @brief start chat with keeping history in kv cache.
+    *
+    * @param system_message optional system message.
+    */
+    void start_chat(const std::string& system_message = "");
+
+    /**
+    * @brief finish chat and clear kv cache.
+    */
+    void finish_chat();
 };
 }

src/cpp/include/openvino/genai/generation_handle.hpp

@@ -18,6 +18,20 @@ enum class GenerationStatus {
     DROPPED_BY_HANDLE = 4 // Status set when generation handle is dropped
 };
 
+struct EncodedGenerationResult {
+    // request ID - obsolete when handle API is approved as handle will connect results with prompts.
+    uint64_t m_request_id;
+
+    // in a generic case we have multiple generation results per initial prompt
+    // depending on sampling parameters (e.g. beam search or parallel sampling)
+    std::vector<std::vector<int64_t>> m_generation_ids;
+    // scores
+    std::vector<float> m_scores;
+
+    // Status of generation
+    GenerationStatus m_status = GenerationStatus::RUNNING;
+};
+
 struct GenerationResult {
     // request ID - obsolete when handle API is approved as handle will connect results with prompts.
     uint64_t m_request_id;
@@ -60,6 +74,7 @@ class OPENVINO_GENAI_EXPORTS GenerationHandleImpl {
 
     bool can_read();
 
+    GenerationOutputs back();
     // Reads result of a generation for single iteration
     GenerationOutputs read();
     // Reads all generated tokens for all sequences

src/cpp/include/openvino/genai/llm_pipeline.hpp

@@ -14,7 +14,7 @@
 namespace ov {
 namespace genai {
 
-// Return flag corresponds whether generation should be stopped: false means continue generation, true means stop.
+// Return flag correspods whether generation should be stopped: false means continue generation, true means stop.
 using StreamerVariant = std::variant<std::function<bool(std::string)>, std::shared_ptr<StreamerBase>, std::monostate>;
 using OptionalGenerationConfig = std::optional<GenerationConfig>;
 using EncodedInputs = std::variant<ov::Tensor, TokenizedInputs>;

src/cpp/include/openvino/genai/scheduler_config.hpp

@@ -16,7 +16,7 @@ struct SchedulerConfig {
     std::size_t num_kv_blocks = 0;
 
     // total size of KV cache in GB
-    std::size_t cache_size = 0;
+    std::size_t cache_size = 1;
 
     // block size for KV cache
     std::size_t block_size = 32;

src/cpp/src/continuous_batching_pipeline.cpp

@@ -6,16 +6,21 @@
 #include <memory>
 
 #include "openvino/genai/continuous_batching_pipeline.hpp"
+#include "openvino/genai/generation_handle.hpp"
 #include "openvino/genai/tokenizer.hpp"
 #include "cache_manager.hpp"
 #include "sampler.hpp"
 #include "model_runner.hpp"
 #include "scheduler.hpp"
+#include "text_callback_streamer.hpp"
 #include "timer.hpp"
 #include "debug_utils.hpp"
 
 using namespace ov::genai;
 
+template<class... Ts> struct overloaded : Ts... {using Ts::operator()...;};
+template<class... Ts> overloaded(Ts...) -> overloaded<Ts...>;
+
 void apply_paged_attention_transformations(std::shared_ptr<ov::Model> model, DeviceConfig& device_config);
 
 class ContinuousBatchingPipeline::Impl {
@@ -51,6 +56,8 @@ class ContinuousBatchingPipeline::Impl {
     std::vector<SequenceGroup::Ptr> m_awaiting_requests;
     // Mutex protecting access to m_awaiting_requests, so add_request and step methods can be called from different threads
     std::mutex m_awaiting_requests_mutex;
+    bool m_is_chat_conversation = false;
+    ChatHistory m_history;
 
 
     void _free_non_running_requests() {
@@ -120,18 +127,9 @@ class ContinuousBatchingPipeline::Impl {
         return m_tokenizer;
     }
 
-    GenerationHandle add_request(uint64_t request_id, std::string prompt, ov::genai::GenerationConfig sampling_params) {
+    GenerationHandle add_request(uint64_t request_id, const ov::Tensor& input_ids, ov::genai::GenerationConfig sampling_params) {
         sampling_params.set_eos_token_id(m_tokenizer.get_eos_token_id());
         sampling_params.validate();
-
-        ov::Tensor input_ids;
-        {
-            static ManualTimer timer("tokenize");
-            timer.start();
-            input_ids = m_tokenizer.encode(prompt).input_ids;
-            timer.end();
-        }
-
         SequenceGroup::Ptr sequence_group = std::make_shared<SequenceGroup>(request_id, input_ids,
                                                                             sampling_params, m_scheduler->get_config().block_size);
         {
@@ -141,6 +139,14 @@ class ContinuousBatchingPipeline::Impl {
         return std::make_unique<GenerationHandleImpl>(sequence_group->get_generation_stream(), sampling_params);
     }
 
+    GenerationHandle add_request(uint64_t request_id, const std::string& prompt, ov::genai::GenerationConfig sampling_params) {
+        static ManualTimer timer("tokenize");
+        timer.start();
+        ov::Tensor input_ids = m_tokenizer.encode(prompt).input_ids;
+        timer.end();
+        return add_request(request_id, input_ids, sampling_params);
+    }
+
     void step() {
         static ManualTimer step_timer("step()");
         step_timer.start();
@@ -238,25 +244,47 @@ class ContinuousBatchingPipeline::Impl {
         return !m_awaiting_requests.empty() || !m_requests.empty();
     }
 
-    std::vector<GenerationResult> generate(const std::vector<std::string> prompts, std::vector<ov::genai::GenerationConfig> sampling_params) {
+    std::vector<EncodedGenerationResult> generate(const std::vector<ov::Tensor>& input_ids, const std::vector<GenerationConfig>& sampling_params, const StreamerVariant& streamer) {
         OPENVINO_ASSERT(!has_non_finished_requests(), "Generate cannot be called while ContinuousBatchingPipeline is already in running state. Use ContinuousBatchingPipeline::add_request");
-        OPENVINO_ASSERT(prompts.size() == sampling_params.size());
+        OPENVINO_ASSERT(input_ids.size() == sampling_params.size());
+        const std::shared_ptr<StreamerBase>& streamer_ptr = std::visit(overloaded{
+            [](std::monostate) -> std::shared_ptr<StreamerBase> {
+                return nullptr;
+            },
+            [](const std::shared_ptr<StreamerBase>& streamer) {
+                return streamer;
+            },
+            [this](const std::function<bool(std::string)>& streamer) -> std::shared_ptr<StreamerBase> {
+                return std::make_unique<TextCallbackStreamer>(m_tokenizer, streamer);
+            }
+        }, streamer);
 
         std::vector<GenerationHandle> generations;
-        for (size_t request_id = 0; request_id < prompts.size(); ++request_id) {
-            generations.push_back(add_request(request_id, prompts[request_id], sampling_params[request_id]));
+        for (size_t request_id = 0; request_id < input_ids.size(); ++request_id) {
+            OPENVINO_ASSERT(1 == input_ids[request_id].get_shape().at(0), "Use multiple tensors to pass a batch.");
+            generations.push_back(add_request(request_id, input_ids[request_id], sampling_params[request_id]));
         }
 
-        std::vector<GenerationResult> results;
+        std::vector<EncodedGenerationResult> results;
         results.reserve(m_awaiting_requests.size());
 
-        while (has_non_finished_requests()) {
+        bool continue_generation = true;
+        while (has_non_finished_requests() && continue_generation) {
             step();
+            if (streamer_ptr) {
+                std::unordered_map<uint64_t, GenerationOutput> token = generations.at(0).get()->back();
+                OPENVINO_ASSERT(1 == token.size());
+                OPENVINO_ASSERT(1 == token.begin()->second.generated_token_ids.size());
+                continue_generation = !streamer_ptr->put(token.begin()->second.generated_token_ids.at(0));
+            }
+        }
+        if (streamer_ptr) {
+            streamer_ptr->end();
         }
 
         for (size_t generation_idx = 0; generation_idx < generations.size(); ++generation_idx) {
             const auto& generation = generations[generation_idx];
-            GenerationResult result;
+            EncodedGenerationResult result;
             result.m_request_id = 1;
             std::vector<GenerationOutput> generation_outputs = generation->read_all();
             std::sort(generation_outputs.begin(), generation_outputs.end(), [=] (GenerationOutput& r1, GenerationOutput& r2) {
@@ -266,17 +294,69 @@ class ContinuousBatchingPipeline::Impl {
             auto num_outputs = std::min(sampling_params[generation_idx].num_return_sequences, generation_outputs.size());
             for (size_t generation_output_idx = 0; generation_output_idx < num_outputs; ++generation_output_idx) {
                 const auto& generation_output = generation_outputs[generation_output_idx];
-                std::string output_text = m_tokenizer.decode(generation_output.generated_token_ids);
-                result.m_generation_ids.push_back(output_text);
+                result.m_generation_ids.push_back(std::move(generation_output.generated_token_ids));
                 result.m_scores.push_back(generation_output.score);
             }
             result.m_status = generation->get_status();
-            results.push_back(result);
+            results.push_back(std::move(result));
         }
 
-        OPENVINO_ASSERT(results.size() == prompts.size());
+        OPENVINO_ASSERT(results.size() == input_ids.size());
         return results;
     }
+
+    std::vector<GenerationResult> generate(const std::vector<std::string>& prompts, std::vector<ov::genai::GenerationConfig> sampling_params, const StreamerVariant& streamer) {
+        std::vector<ov::Tensor> input_ids;
+        static ManualTimer timer("tokenize");
+        if (m_is_chat_conversation) {
+            OPENVINO_ASSERT(1 == prompts.size(), "Can't chat with multiple prompts");
+            m_history.push_back({{"role", "user"}, {"content", prompts.at(0)}});
+            constexpr bool add_generation_prompt = true;
+            std::string history = m_tokenizer.apply_chat_template(m_history, add_generation_prompt);
+            timer.start();
+            input_ids.push_back(m_tokenizer.encode(history).input_ids);
+            timer.end();
+        } else {
+            input_ids.reserve(prompts.size());
+            for (const std::string& prompt : prompts) {
+                timer.start();
+                input_ids.push_back(m_tokenizer.encode(prompt).input_ids);
+                timer.end();
+            }
+        }
+        std::vector<EncodedGenerationResult> encoded = generate(input_ids, sampling_params, streamer);
+        std::vector<GenerationResult> decoded;
+        decoded.reserve(encoded.size());
+        for (EncodedGenerationResult& res : encoded) {
+            std::vector<std::string> generated;
+            generated.reserve(res.m_generation_ids.size());
+            for (size_t idx = 0; idx < res.m_generation_ids.size(); ++idx) {
+                generated.push_back(m_tokenizer.decode(res.m_generation_ids.at(idx)));
+                if (m_is_chat_conversation && 0 == idx) {
+                    m_history.push_back({{"role", "assistant"}, {"content", generated.back()}});
+                }
+            }
+            decoded.push_back(GenerationResult{
+                res.m_request_id,
+                std::move(generated),
+                std::move(res.m_scores),
+                res.m_status
+            });
+        }
+        return decoded;
+    }
+
+    void start_chat(const std::string& system_message) {
+        if (!system_message.empty()) {
+            m_history.push_back({{"role", "system"}, {"content", system_message}});
+        }
+        m_is_chat_conversation = true;
+    };
+
+    void finish_chat() {
+        m_is_chat_conversation = false;
+        m_history.clear();
+    };
 };
 
 ContinuousBatchingPipeline::ContinuousBatchingPipeline( const std::string& models_path,
@@ -306,10 +386,14 @@ PipelineMetrics ContinuousBatchingPipeline::get_metrics() const{
     return m_impl->get_metrics();
 }
 
-GenerationHandle ContinuousBatchingPipeline::add_request(uint64_t request_id, std::string prompt, ov::genai::GenerationConfig sampling_params) {
+GenerationHandle ContinuousBatchingPipeline::add_request(uint64_t request_id, const std::string& prompt, const ov::genai::GenerationConfig& sampling_params) {
     return m_impl->add_request(request_id, prompt, sampling_params);
 }
 
+GenerationHandle ContinuousBatchingPipeline::add_request(uint64_t request_id, const ov::Tensor& input_ids, const ov::genai::GenerationConfig& sampling_params) {
+    return m_impl->add_request(request_id, input_ids, sampling_params);
+}
+
 void ContinuousBatchingPipeline::step() {
     m_impl->step();
 }
@@ -318,6 +402,18 @@ bool ContinuousBatchingPipeline::has_non_finished_requests() {
     return m_impl->has_non_finished_requests();
 }
 
-std::vector<GenerationResult> ContinuousBatchingPipeline::generate(const std::vector<std::string>& prompts, std::vector<ov::genai::GenerationConfig> sampling_params) {
-    return m_impl->generate(prompts, sampling_params);
-}
+std::vector<EncodedGenerationResult> ContinuousBatchingPipeline::generate(const std::vector<ov::Tensor>& input_ids, const std::vector<ov::genai::GenerationConfig>& sampling_params, const StreamerVariant& streamer) {
+    return m_impl->generate(input_ids, sampling_params, streamer);
+}
+
+std::vector<GenerationResult> ContinuousBatchingPipeline::generate(const std::vector<std::string>& prompts, const std::vector<ov::genai::GenerationConfig>& sampling_params, const StreamerVariant& streamer) {
+    return m_impl->generate(prompts, sampling_params, streamer);
+}
+
+void ContinuousBatchingPipeline::start_chat(const std::string& system_message) {
+    m_impl->start_chat(system_message);
+};
+
+void ContinuousBatchingPipeline::finish_chat() {
+    m_impl->finish_chat();
+};

src/cpp/src/generation_handle.cpp

@@ -20,6 +20,10 @@ bool GenerationHandleImpl::can_read() {
     return m_generation_stream->can_read();
 }
 
+std::unordered_map<uint64_t, GenerationOutput> GenerationHandleImpl::back() {
+    return m_generation_stream->back();
+}
+
 std::unordered_map<uint64_t, GenerationOutput> GenerationHandleImpl::read() {
     return m_generation_stream->read();
 }

src/cpp/src/generation_stream.hpp

@@ -31,6 +31,9 @@ class GenerationStream {
     }
 
     // Retriving vector of pairs <sequence_id, token_id> as we can generate multiple outputs for a single prompt
+    GenerationOutputs back() {
+        return m_output_queue.back();
+    }
     GenerationOutputs read() {
         return m_output_queue.pull();
     }