Automatically apply chat template in non-chat scenarios

.github/workflows/causal_lm_cpp.yml

@@ -209,7 +209,7 @@ jobs:
           "
           echo "你好！ 你好嗎？" passed
 
-          timeout 1m ${{ matrix.executable }} ./TinyLlama-1.1B-Chat-v1.0/ "Alan Turing was a" "return 0" "你好！ 你好嗎？" > ./pred.txt
+          timeout 1m ${{ matrix.executable }} ./TinyLlama-1.1B-Chat-v1.0/ "Why is the Sun yellow?" "return 0" "你好！ 你好嗎？" > ./pred.txt
           python -c "
           import transformers
           with open('pred.txt', 'r', errors='ignore') as file:
@@ -219,15 +219,14 @@ jobs:
           print('\n\n')
           tokenizer = transformers.AutoTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
           prompts = [
-            'Alan Turing was a',
+            'Why is the Sun yellow?',
             'return 0',
             '你好！ 你好嗎？'
           ]
           for prompt in prompts:
             if tokenizer.chat_template:
                 prompt = tokenizer.apply_chat_template([{'role': 'user', 'content': prompt}], tokenize=False, add_generation_prompt=True)
             tokenized = tokenizer(prompt, return_tensors='pt', add_special_tokens=False)
-            print(tokenized)
             for beam in transformers.LlamaForCausalLM.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0').generate(**tokenized, num_beam_groups=3, num_beams=15, num_return_sequences=15, diversity_penalty=1.0, max_new_tokens=20, early_stopping=False, length_penalty=1.0, no_repeat_ngram_size=9**9, do_sample=False):
                 ref = ': ' + tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True)
                 print(ref)
@@ -277,7 +276,10 @@ jobs:
           echo import transformers > ref.py
           echo predictions = open('cpp.txt', 'r').read() >> ref.py
           echo tokenizer = transformers.AutoTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0', trust_remote_code=True) >> ref.py
-          echo tokenized = tokenizer('69', return_tensors='pt') >> ref.py
+          echo prompt = '69' >> ref.py
+          echo if tokenizer.chat_template: >> ref.py
+          echo     prompt = tokenizer.apply_chat_template([{'role': 'user', 'content': prompt}], tokenize=False, add_generation_prompt=True) >> ref.py
+          echo tokenized = tokenizer(prompt, return_tensors='pt', add_special_tokens=False) >> ref.py
           echo for beam in transformers.AutoModelForCausalLM.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0', trust_remote_code=True).generate(**tokenized, max_new_tokens=100, do_sample=False): >> ref.py
           echo     ref = tokenizer.decode(beam[tokenized['input_ids'].numel():], skip_special_tokens=True) >> ref.py
           echo     idx = predictions.find(ref) >> ref.py
@@ -584,7 +586,10 @@ jobs:
           with open('pred_greedy.txt', 'r') as file:
               predictions = file.read()
           tokenizer = transformers.AutoTokenizer.from_pretrained('microsoft/phi-1_5')
-          tokenized = tokenizer('Alan Turing was a', return_tensors='pt')
+          prompt = 'Alan Turing was a'
+          if tokenizer.chat_template:
+              prompt = tokenizer.apply_chat_template([{'role': 'user', 'content': prompt}], tokenize=False, add_generation_prompt=True)
+          tokenized = tokenizer(prompt, return_tensors='pt', add_special_tokens=False)
           for output in transformers.AutoModelForCausalLM.from_pretrained('microsoft/phi-1_5').generate(**tokenized, max_length=100, do_sample=False):
               ref = tokenizer.decode(output[tokenized['input_ids'].numel():], skip_special_tokens=True)
               idx = predictions.find(ref)
@@ -639,7 +644,10 @@ jobs:
           with open('pred_greedy.txt', 'r') as file:
               predictions = file.read()
           tokenizer = transformers.AutoTokenizer.from_pretrained('ikala/redpajama-3b-chat')
-          tokenized = tokenizer('Alan Turing was a', return_tensors='pt')
+          prompt = 'Alan Turing was a'
+          if tokenizer.chat_template:
+              prompt = tokenizer.apply_chat_template([{'role': 'user', 'content': prompt}], tokenize=False, add_generation_prompt=True)
+          tokenized = tokenizer(prompt, return_tensors='pt', add_special_tokens=False)
           for output in transformers.AutoModelForCausalLM.from_pretrained('ikala/redpajama-3b-chat').generate(**tokenized, max_length=100, do_sample=False):
               ref = tokenizer.decode(output[tokenized['input_ids'].numel():], skip_special_tokens=True)
               idx = predictions.find(ref)

README.md

@@ -133,7 +133,6 @@ from PIL import Image
 
 # Choose GPU instead of CPU in the line below to run the model on Intel integrated or discrete GPU
 pipe = openvino_genai.VLMPipeline("./InternVL2-1B", "CPU")
-pipe.start_chat()
 
 image = Image.open("dog.jpg")
 image_data = np.array(image.getdata()).reshape(1, image.size[1], image.size[0], 3).astype(np.uint8)

samples/cpp/text_generation/README.md

@@ -48,7 +48,7 @@ Recommended models: meta-llama/Llama-2-7b-chat-hf, TinyLlama/TinyLlama-1.1B-Chat
   ./chat_sample <MODEL_DIR>
   ```
 #### Missing chat template
-If you encounter an exception indicating a missing "chat template" when launching the `ov::genai::LLMPipeline` in chat mode, it likely means the model was not tuned for chat functionality. To work this around, manually add the chat template to tokenizer_config.json of your model.
+If you encounter an exception indicating a missing "chat template" when launching the `ov::genai::LLMPipeline` in chat mode, it likely means the model was not tuned for chat functionality. To work this around, manually add the chat template to tokenizer_config.json of your model or update it using call `pipe.get_tokenizer().set_chat_template(new_chat_template)`.
 The following template can be used as a default, but it may not work properly with every model:
 ```
 "chat_template": "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|im_start|>user\n' + message['content'] + '<|im_end|>\n<|im_start|>assistant\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|im_end|>\n'}}{% endif %}{% endfor %}",

samples/python/text_generation/README.md

@@ -48,7 +48,7 @@ Recommended models: meta-llama/Llama-2-7b-chat-hf, TinyLlama/TinyLlama-1.1B-Chat
   python chat_sample.py model_dir
   ```
 #### Missing chat template
-If you encounter an exception indicating a missing "chat template" when launching the `ov::genai::LLMPipeline` in chat mode, it likely means the model was not tuned for chat functionality. To work this around, manually add the chat template to tokenizer_config.json of your model.
+If you encounter an exception indicating a missing "chat template" when launching the `ov::genai::LLMPipeline` in chat mode, it likely means the model was not tuned for chat functionality. To work this around, manually add the chat template to tokenizer_config.json of your model or update it using call `pipe.get_tokenizer().set_chat_template(new_chat_template)`.
 The following template can be used as a default, but it may not work properly with every model:
 ```
 "chat_template": "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|im_start|>user\n' + message['content'] + '<|im_end|>\n<|im_start|>assistant\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|im_end|>\n'}}{% endif %}{% endfor %}",

src/README.md

@@ -73,6 +73,8 @@ output:
 'it is made up of carbon atoms. The carbon atoms are arranged in a linear pattern, which gives the yellow color. The arrangement of carbon atoms in'
 ```
 
+>**Note**: The chat_template from tokenizer_config.json or from tokenizer/detokenizer model will be automatically applied to the prompt at the generation stage. If you want to disable it, you can do it by calling pipe.get_tokenizer().set_chat_template("").
+
 A simple chat in Python:
 ```python
 import openvino_genai as ov_genai

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

@@ -128,6 +128,8 @@ class OPENVINO_GENAI_EXPORTS GenerationConfig {
 
     std::optional<AdapterConfig> adapters;
 
+    bool apply_chat_template = true;
+
     /** @brief sets eos_token_id to tokenizer_eos_token_id if eos_token_id is less than 0.
      * Otherwise verifies eos_token_id == tokenizer_eos_token_id.
      */
@@ -189,6 +191,8 @@ extern OPENVINO_GENAI_EXPORTS ov::Property<size_t> rng_seed;
 static constexpr ov::Property<float> assistant_confidence_threshold{"assistant_confidence_threshold"};
 static constexpr ov::Property<size_t> num_assistant_tokens{"num_assistant_tokens"};
 
+static constexpr ov::Property<bool> apply_chat_template{"apply_chat_template"};
+
 // Predefined Configs
 
 OPENVINO_DEPRECATED("Please, use individual parameters instead of predefined configs. This method will be removed in 2026.0.0 release")

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

@@ -177,6 +177,8 @@ class OPENVINO_GENAI_EXPORTS LLMPipeline {
     * @param generation_config optional GenerationConfig
     * @param streamer optional streamer
     * @return DecodedResults decoded resulting text
+    * chat_template will be applied to the prompt, run pipe.get_tokenizer().set_chat_template(custom_chat_template) to update it.
+    * To disable it for non-chat mode, please, use custom_chat_template eq "" or set generation_config.apply_chat_template to false.
     */
     DecodedResults generate(
         StringInputs inputs,
@@ -191,6 +193,8 @@ class OPENVINO_GENAI_EXPORTS LLMPipeline {
     * @param inputs input prompt or a vector of prompts
     * @param properties properties
     * @return DecodedResults decoded resulting text
+    * chat_template will be applied to the prompt, run pipe.get_tokenizer().set_chat_template(custom_chat_template) to update it.
+    * To disable it for non-chat mode, please, use custom_chat_template eq "" or set generation_config.apply_chat_template to false.
     */
     template <typename... Properties>
     util::EnableIfAllStringAny<DecodedResults, Properties...> generate(

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

@@ -221,6 +221,9 @@ class OPENVINO_GENAI_EXPORTS Tokenizer {
     /// @param chat_template The new template to override with.
     void set_chat_template(const std::string& chat_template);
 
+    // get information about a chat template to check its status, for example whether it is empty
+    std::string get_chat_template() const;
+
     // information about <bos>, <eos> tokens should be public,
     // they are used at least in StreamerBase descendants
     int64_t get_bos_token_id() const;

src/cpp/include/openvino/genai/visual_language/pipeline.hpp

@@ -98,6 +98,8 @@ class OPENVINO_GENAI_EXPORTS VLMPipeline {
     /// @param generation_config A config to follow for text generation.
     /// @param streamer A streamer to acquire intermediate result.
     /// @return A string generated by a model.
+    /// chat_template will be applied to the prompt, run pipe.set_chat_template(custom_chat_template) to update it.
+    /// To disable it for non-chat mode, please, use custom_chat_template eq "" or set generation_config.apply_chat_template to false.
     VLMDecodedResults generate(
         const std::string& prompt,
         const std::vector<ov::Tensor>& rgbs,
@@ -111,6 +113,8 @@ class OPENVINO_GENAI_EXPORTS VLMPipeline {
     /// @param generation_config A config to follow for text generation.
     /// @param streamer A streamer to acquire intermediate result.
     /// @return A string generated by a model.
+    /// chat_template will be applied to the prompt, run pipe.set_chat_template(custom_chat_template) to update it.
+    /// To disable it for non-chat mode, please, use custom_chat_template eq "" or set generation_config.apply_chat_template to false.
     VLMDecodedResults generate(
         const std::string& prompt,
         const ov::Tensor& rgb,
@@ -124,6 +128,8 @@ class OPENVINO_GENAI_EXPORTS VLMPipeline {
     /// for its members, StreamerVariant a single image or multiple
     /// images.
     /// @return A string generated by a model.
+    /// chat_template will be applied to the prompt, run pipe.set_chat_template(custom_chat_template) to update it.
+    /// To disable it for non-chat mode, please, use custom_chat_template eq "" or set generation_config.apply_chat_template to false.
     VLMDecodedResults generate(
         const std::string& prompt,
         const ov::AnyMap& config_map
@@ -137,6 +143,8 @@ class OPENVINO_GENAI_EXPORTS VLMPipeline {
     /// @param ...properties ov::Property instances to be combined into
     /// ov::AnyMap.
     /// @return A string generated by a model.
+    /// chat_template will be applied to the prompt, run pipe.set_chat_template(custom_chat_template) to update it.
+    /// To disable it for non-chat mode, please, use custom_chat_template eq "" or set generation_config.apply_chat_template to false.
     template <typename... Properties>
     util::EnableIfAllStringAny<VLMDecodedResults, Properties...> generate(
         const std::string& prompt,

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

@@ -97,6 +97,8 @@ class OPENVINO_GENAI_EXPORTS WhisperGenerationConfig : public GenerationConfig {
     // A list containing the non-speech tokens that will be suppressed during generation.
     std::vector<int64_t> suppress_tokens;
 
+    bool apply_chat_template = false;
+
     void update_generation_config(const ov::AnyMap& config_map = {});
 
     template <typename... Properties>

src/cpp/src/generation_config.cpp

@@ -128,6 +128,7 @@ void GenerationConfig::update_generation_config(const ov::AnyMap& properties) {
     read_anymap_param(properties, "logprobs", logprobs);
     read_anymap_param(properties, "num_return_sequences", num_return_sequences);
     read_anymap_param(properties, "adapters", adapters);
+    read_anymap_param(properties, "apply_chat_template", apply_chat_template);
 
     // penalties
     read_anymap_param(properties, "frequency_penalty", frequency_penalty);

src/cpp/src/icontinuous_batching.cpp

@@ -53,9 +53,20 @@ ContinuousBatchingPipeline::IContinuousBatchingPipeline::generate(
     } else {
         input_ids.reserve(prompts.size());
         timer.start();
-        for (const std::string& prompt : prompts) {
+        for (size_t i = 0; i < prompts.size(); i++) {
+            const std::string& prompt = prompts.at(i);
             const auto encode_start = std::chrono::steady_clock::now();
-            input_ids.push_back(m_tokenizer.encode(prompt).input_ids);
+            ov::Tensor encoded_inputs;
+            if (sampling_params.at(i).apply_chat_template && !m_tokenizer.get_chat_template().empty()) {
+                ChatHistory history({{{"role", "user"}, {"content", prompt}}});
+                constexpr bool add_generation_prompt = true;
+                auto templated_prompt = m_tokenizer.apply_chat_template(history, add_generation_prompt);
+                encoded_inputs = m_tokenizer.encode(templated_prompt, ov::genai::add_special_tokens(false)).input_ids;
+            } else {
+                // in case when chat_template was not found in tokenizer_config.json or set
+                encoded_inputs = m_tokenizer.encode(prompt).input_ids;
+            }
+            input_ids.push_back(encoded_inputs);
             tokenization_durations.emplace_back(PerfMetrics::get_microsec(std::chrono::steady_clock::now() - encode_start));
         }
         timer.end();

src/cpp/src/llm_pipeline_stateful.cpp

@@ -9,6 +9,8 @@
 #include "text_callback_streamer.hpp"
 #include "utils.hpp"
 
+#include "debug_utils.hpp"
+
 namespace ov::genai {
 
 StatefulLLMPipeline::StatefulLLMPipeline(
@@ -87,14 +89,19 @@ DecodedResults StatefulLLMPipeline::generate(
     TokenizedInputs encoded_input;
 
     if (auto input_vector = std::get_if<std::vector<std::string>>(&inputs)) {
-        std::vector<std::string> templated_input_vector;
-        for (auto& input : *input_vector) {
-            ChatHistory history({{{"role", "user"}, {"content", input}}});
-            constexpr bool add_generation_prompt = true;
-            auto templated_prompt = m_tokenizer.apply_chat_template(history, add_generation_prompt);
-            templated_input_vector.push_back(templated_prompt);
+        OPENVINO_ASSERT(!is_chat_conversation, "Can't chat with multiple prompts");
+        if (config.apply_chat_template && !m_tokenizer.get_chat_template().empty()) {
+            std::vector<std::string> templated_input_vector;
+            for (auto& input : *input_vector) {
+                ChatHistory history({{{"role", "user"}, {"content", input}}});
+                constexpr bool add_generation_prompt = true;
+                auto templated_prompt = m_tokenizer.apply_chat_template(history, add_generation_prompt);
+                templated_input_vector.push_back(templated_prompt);
+            }
+            encoded_input = m_tokenizer.encode(templated_input_vector, ov::genai::add_special_tokens(false));
+        } else {
+            encoded_input = m_tokenizer.encode(*input_vector);
         }
-        encoded_input = m_tokenizer.encode(templated_input_vector, ov::genai::add_special_tokens(false));
     } else if (auto input_prompt = std::get_if<std::string>(&inputs)) {
         std::string& prompt = *input_prompt;
 
@@ -110,7 +117,7 @@ DecodedResults StatefulLLMPipeline::generate(
 
             m_history.push_back({{"role", "user"}, {"content", prompt}});
             constexpr bool add_generation_prompt = true;
-            auto new_templated_chat_history  = m_tokenizer.apply_chat_template(m_history, add_generation_prompt);
+            auto new_templated_chat_history = m_tokenizer.apply_chat_template(m_history, add_generation_prompt);
             // Do not add special tokens in chat scenario to be aligned with HF.
             auto new_chat_tokens = m_tokenizer.encode(new_templated_chat_history, ov::genai::add_special_tokens(false));
             auto prev_chat_tokens = m_tokenizer.encode(m_templated_chat_history, ov::genai::add_special_tokens(false));
@@ -163,7 +170,16 @@ DecodedResults StatefulLLMPipeline::generate(
 
             // TODO: Forbid LoRA config change if we are in the chat mode, because it requires regenerating the history with LoRA applied
         } else {
-            encoded_input = m_tokenizer.encode(prompt);
+            std::string& prompt = *input_prompt;
+            if (config.apply_chat_template && !m_tokenizer.get_chat_template().empty()) {
+                ChatHistory history({{{"role", "user"}, {"content", prompt}}});
+                constexpr bool add_generation_prompt = true;
+                auto templated_prompt = m_tokenizer.apply_chat_template(history, add_generation_prompt);
+                encoded_input = m_tokenizer.encode(templated_prompt, ov::genai::add_special_tokens(false));
+            } else {
+                // in case when chat_template was not found in tokenizer_config.json or set
+                encoded_input = m_tokenizer.encode(prompt);
+            }
         }
     }
 

src/cpp/src/llm_pipeline_static.cpp

@@ -827,7 +827,15 @@ DecodedResults StatefulLLMPipeline::generate(
         // for chat ov::genai::add_special_tokens(false) is aligned with stateful pipeline and HF
         tokenized_input = m_tokenizer.encode(prompt, ov::genai::add_special_tokens(false));
     } else {
-        tokenized_input = m_tokenizer.encode(prompt);
+        if (config.apply_chat_template && !m_tokenizer.get_chat_template().empty()) {
+            ChatHistory history({{{"role", "user"}, {"content", prompt}}});
+            constexpr bool add_generation_prompt = true;
+            auto templated_prompt = m_tokenizer.apply_chat_template(history, add_generation_prompt);
+            tokenized_input = m_tokenizer.encode(templated_prompt, ov::genai::add_special_tokens(false));
+        } else {
+            // in case when chat_template was not found in tokenizer_config.json or set
+            tokenized_input = m_tokenizer.encode(prompt);
+        }
     }
 
     auto encode_stop_time =  std::chrono::steady_clock::now();
@@ -1294,7 +1302,15 @@ DecodedResults StatelessLLMPipeline::generate(
         // for chat ov::genai::add_special_tokens(false) is aligned with stateful pipeline and HF
         tokenized_input = m_tokenizer.encode(prompt, ov::genai::add_special_tokens(false));
     } else {
-        tokenized_input = m_tokenizer.encode(prompt);
+        if (config.apply_chat_template && !m_tokenizer.get_chat_template().empty()) {
+            ChatHistory history({{{"role", "user"}, {"content", prompt}}});
+            constexpr bool add_generation_prompt = true;
+            auto templated_prompt = m_tokenizer.apply_chat_template(history, add_generation_prompt);
+            tokenized_input = m_tokenizer.encode(templated_prompt, ov::genai::add_special_tokens(false));
+        } else {
+            // in case when chat_template was not found in tokenizer_config.json or set
+            tokenized_input = m_tokenizer.encode(prompt);
+        }
     }
 
     auto encode_stop_time =  std::chrono::steady_clock::now();