update test and docs

.github/workflows/causal_lm_cpp.yml

@@ -119,7 +119,10 @@ jobs:
           with open('pred.txt', 'r') as file:
               predictions = file.read()
           tokenizer = transformers.AutoTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
-          tokenized = tokenizer('Why is the Sun yellow?', return_tensors='pt')
+          prompt = 'Why is the Sun yellow?'
+          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')
           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)
               idx = predictions.find(ref)
@@ -135,7 +138,10 @@ jobs:
           with open('pred.txt', 'r') as file:
               predictions = file.read()
           tokenizer = transformers.AutoTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
-          tokenized = tokenizer('69', return_tensors='pt')
+          prompt = '69'
+          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')
           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)
               idx = predictions.find(ref)
@@ -151,7 +157,10 @@ jobs:
           with open('pred.txt', 'r') as file:
               predictions = file.read()
           tokenizer = transformers.AutoTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
-          tokenized = tokenizer('Hi', return_tensors='pt')
+          prompt = 'Hi'
+          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')
           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)
               idx = predictions.find(ref)
@@ -167,7 +176,10 @@ jobs:
           with open('pred.txt', 'r') as file:
               predictions = file.read()
           tokenizer = transformers.AutoTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
-          tokenized = tokenizer('return 0', return_tensors='pt')
+          prompt = 'return 0'
+          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')
           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)
               idx = predictions.find(ref)
@@ -183,7 +195,10 @@ jobs:
           with open('pred.txt', 'r', errors='ignore') as file:
               predictions = file.read()
           tokenizer = transformers.AutoTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
-          tokenized = tokenizer('你好！ 你好嗎？', return_tensors='pt')
+          prompt = '你好！ 你好嗎？'
+          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')
           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)
               idx = predictions.find(ref.replace('�', ''))
@@ -205,6 +220,8 @@ jobs:
             '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')
             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)
@@ -254,7 +271,10 @@ 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'
+          echo  if tokenizer.chat_template:
+          echo    prompt = tokenizer.apply_chat_template([{'role': 'user', 'content': prompt}], tokenize=False, add_generation_prompt=True)
+          echo tokenized = tokenizer(prompt, return_tensors='pt') >> 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
@@ -559,7 +579,10 @@ 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')
           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)
@@ -614,7 +637,10 @@ 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')
           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)

src/README.md

@@ -73,7 +73,7 @@ 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 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("").
+>**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

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

@@ -185,6 +185,8 @@ 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.
+    * Use custom_chat_template = "" to disable it for non-chat mode.
     */
     DecodedResults generate(
         StringInputs inputs,
@@ -199,6 +201,8 @@ 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.
+    * Use custom_chat_template = "" to disable it for non-chat mode.
     */
     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.
+    /// Use custom_chat_template="" to disable it for non-chat mode.
     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.
+    /// Use custom_chat_template="" to disable it for non-chat mode.
     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.
+    /// Use custom_chat_template="" to disable it for non-chat mode.
     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.
+    /// Use custom_chat_template="" to disable it for non-chat mode.
     template <typename... Properties>
     util::EnableIfAllStringAny<VLMDecodedResults, Properties...> generate(
         const std::string& prompt,

src/cpp/src/icontinuous_batching.cpp

@@ -51,12 +51,12 @@ ContinuousBatchingPipeline::IContinuousBatchingPipeline::generate(
         timer.start();
         for (const std::string& prompt : prompts) {
             ov::Tensor encoded_inputs;
-            try {
+            if (!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;
-            } catch (const std::exception& error) {
+            } else {
                 // in case when chat_template was not found in tokenizer_config.json or set
                 encoded_inputs = m_tokenizer.encode(prompt).input_ids;
             }

src/cpp/src/llm_pipeline_stateful.cpp

@@ -90,12 +90,12 @@ DecodedResults StatefulLLMPipeline::generate(
         OPENVINO_ASSERT(!is_chat_conversation, "Can't chat with multiple prompts");
         std::vector<std::string> templated_input_vector;
         for (auto& input : *input_vector) {
-            try {
+            if (!m_tokenizer.get_chat_template().empty()) {
                 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);
-            } catch (const std::exception& error) {
+            } else {
                 // in case when chat_template was not found in tokenizer_config.json or set
                 templated_input_vector.push_back(input);
             }
@@ -116,7 +116,7 @@ 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));
@@ -170,12 +170,12 @@ 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 {
             std::string& prompt = *input_prompt;
-            try {
+            if (!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));
-            } catch (const std::exception& error) {
+            } 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

@@ -770,12 +770,12 @@ 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 {
-        try {
+        if (!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));
-        } catch (const std::exception& error) {
+        } else {
             // in case when chat_template was not found in tokenizer_config.json or set
             tokenized_input = m_tokenizer.encode(prompt);
         }
@@ -1210,12 +1210,12 @@ 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 {
-        try {
+        if (!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));
-        } catch (const std::exception& error) {
+        } else {
             // in case when chat_template was not found in tokenizer_config.json or set
             tokenized_input = m_tokenizer.encode(prompt);
         }

src/cpp/src/tokenizer.cpp

@@ -587,6 +587,10 @@ class Tokenizer::TokenizerImpl {
     void set_chat_template(const std::string& chat_template) {
         m_chat_template = patch_chat_template(chat_template);
     }
+
+    std::string get_chat_template() {
+        return m_chat_template;
+    }
 };
 
 Tokenizer::Tokenizer(const std::filesystem::path& tokenizer_path, const ov::AnyMap& properties) {
@@ -690,6 +694,10 @@ std::string Tokenizer::apply_chat_template(ChatHistory history,
     return m_pimpl->apply_chat_template(history, add_generation_prompt, chat_template);
 }
 
+std::string Tokenizer::get_chat_template() const {
+    return m_pimpl->get_chat_template();
+}
+
 void Tokenizer::set_chat_template(const std::string& chat_template) {
     m_pimpl->set_chat_template(chat_template);
 }