From 926a9cfdbb24d41db96bbdb944d8768772286298 Mon Sep 17 00:00:00 2001
From: sbalandi <sofya.balandina@intel.com>
Date: Tue, 14 Jan 2025 14:39:04 +0000
Subject: [PATCH] ci fix

---
 .github/workflows/causal_lm_cpp.yml           | 36 +++++++--------
 .../openvino/genai/generation_config.hpp      |  2 +
 src/cpp/src/generation_config.cpp             |  1 +
 src/cpp/src/icontinuous_batching.cpp          |  5 ++-
 src/cpp/src/llm_pipeline_stateful.cpp         |  4 +-
 src/cpp/src/llm_pipeline_static.cpp           |  4 +-
 .../src/visual_language/inputs_embedder.cpp   | 44 ++++++++++---------
 .../src/visual_language/inputs_embedder.hpp   |  2 +-
 src/cpp/src/visual_language/pipeline.cpp      |  2 +-
 src/python/py_generation_config.cpp           |  1 +
 tests/python_tests/common.py                  |  2 +-
 11 files changed, 56 insertions(+), 47 deletions(-)

diff --git a/.github/workflows/causal_lm_cpp.yml b/.github/workflows/causal_lm_cpp.yml
index 99104056df..22bf003f9b 100644
--- a/.github/workflows/causal_lm_cpp.yml
+++ b/.github/workflows/causal_lm_cpp.yml
@@ -121,8 +121,8 @@ jobs:
           tokenizer = transformers.AutoTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
           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')
+              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 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)
@@ -140,8 +140,8 @@ jobs:
           tokenizer = transformers.AutoTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
           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')
+              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 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)
@@ -160,7 +160,7 @@ jobs:
           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')
+          tokenized = tokenizer(prompt, return_tensors='pt', add_special_tokens=False)
           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)
@@ -178,8 +178,8 @@ jobs:
           tokenizer = transformers.AutoTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
           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')
+              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 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)
@@ -197,8 +197,8 @@ jobs:
           tokenizer = transformers.AutoTokenizer.from_pretrained('TinyLlama/TinyLlama-1.1B-Chat-v1.0')
           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')
+              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 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('�', ''))
@@ -221,8 +221,8 @@ jobs:
           ]
           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')
+                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 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('�', ''))
@@ -272,9 +272,9 @@ jobs:
           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 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 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', 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
@@ -581,8 +581,8 @@ jobs:
           tokenizer = transformers.AutoTokenizer.from_pretrained('microsoft/phi-1_5')
           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')
+              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)
@@ -639,8 +639,8 @@ jobs:
           tokenizer = transformers.AutoTokenizer.from_pretrained('ikala/redpajama-3b-chat')
           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')
+              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)
diff --git a/src/cpp/include/openvino/genai/generation_config.hpp b/src/cpp/include/openvino/genai/generation_config.hpp
index 3a75fc02ea..cd372e635d 100644
--- a/src/cpp/include/openvino/genai/generation_config.hpp
+++ b/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.
      */
diff --git a/src/cpp/src/generation_config.cpp b/src/cpp/src/generation_config.cpp
index 67682be787..dba5aaa5bd 100644
--- a/src/cpp/src/generation_config.cpp
+++ b/src/cpp/src/generation_config.cpp
@@ -125,6 +125,7 @@ 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);
diff --git a/src/cpp/src/icontinuous_batching.cpp b/src/cpp/src/icontinuous_batching.cpp
index d4fa7c3e5d..6b748d6665 100644
--- a/src/cpp/src/icontinuous_batching.cpp
+++ b/src/cpp/src/icontinuous_batching.cpp
@@ -53,10 +53,11 @@ 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();
             ov::Tensor encoded_inputs;
-            if (!m_tokenizer.get_chat_template().empty()) {
+            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);
diff --git a/src/cpp/src/llm_pipeline_stateful.cpp b/src/cpp/src/llm_pipeline_stateful.cpp
index 18e9d30ebc..14a3f1b651 100644
--- a/src/cpp/src/llm_pipeline_stateful.cpp
+++ b/src/cpp/src/llm_pipeline_stateful.cpp
@@ -90,7 +90,7 @@ 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) {
-            if (!m_tokenizer.get_chat_template().empty()) {
+            if (config.apply_chat_template && !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);
@@ -170,7 +170,7 @@ 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;
-            if (!m_tokenizer.get_chat_template().empty()) {
+            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);
diff --git a/src/cpp/src/llm_pipeline_static.cpp b/src/cpp/src/llm_pipeline_static.cpp
index be3d7552c8..1f4e5ffcec 100644
--- a/src/cpp/src/llm_pipeline_static.cpp
+++ b/src/cpp/src/llm_pipeline_static.cpp
@@ -805,7 +805,7 @@ 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 {
-        if (!m_tokenizer.get_chat_template().empty()) {
+        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);
@@ -1281,7 +1281,7 @@ 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 {
-        if (!m_tokenizer.get_chat_template().empty()) {
+        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);
diff --git a/src/cpp/src/visual_language/inputs_embedder.cpp b/src/cpp/src/visual_language/inputs_embedder.cpp
index 616a12bb6f..e3ce5ab067 100644
--- a/src/cpp/src/visual_language/inputs_embedder.cpp
+++ b/src/cpp/src/visual_language/inputs_embedder.cpp
@@ -50,7 +50,7 @@ class InputsEmbedder::IInputsEmbedder {
     ov::genai::utils::HistoryRemoveManager m_kv_history_manager = {0, 0};
 
 public:
-    virtual ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics) = 0;
+    virtual ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics, bool apply_chat_template) = 0;
 
     virtual std::pair<ov::Tensor, std::optional<int64_t>> get_position_ids(const size_t inputs_embeds_size, const size_t history_size) {
         ov::Tensor position_ids = ov::Tensor{ov::element::i64, { 1, inputs_embeds_size }};
@@ -155,7 +155,7 @@ class InputsEmbedder::IInputsEmbedder {
         ),
         m_tokenizer(tokenizer) { }
 
-    ov::Tensor get_encoded_input_ids(const std::string& prompt, ov::genai::VLMPerfMetrics& metrics, const std::string& chat_template_fallback = {}) {
+    ov::Tensor get_encoded_input_ids(const std::string& prompt, ov::genai::VLMPerfMetrics& metrics, const std::string& chat_template_fallback = {}, bool apply_chat_template = true) {
         ov::Tensor encoded_input_ids;
         if (m_is_chat_conversation) {
             // KV cache in model already contains prompts and answers from previous iterations.
@@ -223,15 +223,19 @@ class InputsEmbedder::IInputsEmbedder {
             m_tokenized_history.clear();
             std::copy_n(new_chat_tokens.data<int64_t>(), new_chat_tokens.get_size(), std::back_inserter(m_tokenized_history));
         } else {
-            std::string templated_prompt;
+            std::string templated_prompt = prompt;
             ChatHistory history({{{"role", "user"}, {"content", prompt}}});
             constexpr bool add_generation_prompt = true;
 
-            if (!m_tokenizer.get_chat_template().empty()) {
-                templated_prompt = m_tokenizer.apply_chat_template(history, add_generation_prompt);
-            } else {
-                // Use fallback chat template if it was not found in tokenizer_config.json
-                templated_prompt = m_tokenizer.apply_chat_template(history, add_generation_prompt, chat_template_fallback);
+            if (apply_chat_template) {
+                if (!m_tokenizer.get_chat_template().empty()) {
+                    templated_prompt = m_tokenizer.apply_chat_template(history, add_generation_prompt);
+                    std::cout << " m_tokenizer 1 " << std::endl;
+                } else {
+                    // Use fallback chat template if it was not found in tokenizer_config.json
+                    templated_prompt = m_tokenizer.apply_chat_template(history, add_generation_prompt, chat_template_fallback);
+                    std::cout << " m_tokenizer 2 " << std::endl;
+                }
             }
 
             auto start_tokenizer_time = std::chrono::steady_clock::now();
@@ -331,7 +335,7 @@ class InputsEmbedderMiniCPM : public InputsEmbedder::IInputsEmbedder {
             m_pos_embed_cache = get_2d_sincos_pos_embed(m_vlm_config.hidden_size, {70, 70});
         }
 
-    virtual ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics) override {
+    virtual ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics, bool apply_chat_template) override {
         std::string images_prompt;
         std::vector<EncodedImage> embeds;
 
@@ -366,7 +370,7 @@ class InputsEmbedderMiniCPM : public InputsEmbedder::IInputsEmbedder {
         }
         images_prompt += prompt;
 
-        ov::Tensor encoded_input = get_encoded_input_ids(images_prompt, metrics);
+        ov::Tensor encoded_input = get_encoded_input_ids(images_prompt, metrics, {}, apply_chat_template);
 
         ov::Tensor inputs_embeds = m_embedding.infer(encoded_input);
         OPENVINO_ASSERT(
@@ -629,7 +633,7 @@ class InputsEmbedderLLaVA : public InputsEmbedder::IInputsEmbedder {
         const ov::AnyMap device_config) :
         IInputsEmbedder(vlm_config, models_map, tokenizer, config_dir_path, device, device_config) { }
 
-    virtual ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics) override {
+    virtual ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics, bool apply_chat_template) override {
         std::string image_token = m_vlm_config.im_start;
         // Adapted from llava-1.5-7b-hf chat_template.json
         std::string chat_template_fallback = "{% for message in messages %}{% if message['role'] == 'user' %}{{ 'USER: ' + message['content'] + ' ' }}{% else %}{{ 'ASSISTANT: ' + message['content'] + ' ' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}";
@@ -647,7 +651,7 @@ class InputsEmbedderLLaVA : public InputsEmbedder::IInputsEmbedder {
         }
         formatted_prompt += prompt;
 
-        ov::Tensor input_ids = get_encoded_input_ids(formatted_prompt, metrics, chat_template_fallback);
+        ov::Tensor input_ids = get_encoded_input_ids(formatted_prompt, metrics, chat_template_fallback, apply_chat_template);
         ov::Tensor text_embeds = m_embedding.infer(input_ids);
 
         if (images.empty()) {
@@ -742,7 +746,7 @@ class InputsEmbedderLLaVANext : public InputsEmbedderLLaVA {
         const ov::AnyMap device_config) :
         InputsEmbedderLLaVA(vlm_config, models_map, tokenizer, config_dir_path, device, device_config) { }
 
-    virtual ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics) override {
+    virtual ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics, bool apply_chat_template) override {
         std::string image_token = m_vlm_config.im_start;
         // Adapted from llava-1.5-7b-hf chat_template.json
         std::string chat_template_fallback = "{% for message in messages %}{% if message['role'] == 'user' %}{{ 'USER: ' + message['content'] + ' ' }}{% else %}{{ 'ASSISTANT: ' + message['content'] + ' ' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}";
@@ -774,7 +778,7 @@ class InputsEmbedderLLaVANext : public InputsEmbedderLLaVA {
         }
         formatted_prompt += prompt;
 
-        ov::Tensor input_ids = get_encoded_input_ids(formatted_prompt, metrics, chat_template_fallback);
+        ov::Tensor input_ids = get_encoded_input_ids(formatted_prompt, metrics, chat_template_fallback, apply_chat_template);
         ov::Tensor text_embeds = m_embedding.infer(input_ids);
 
         if (images.empty()) {
@@ -1069,7 +1073,7 @@ class InputsEmbedderInternVLChat : public InputsEmbedder::IInputsEmbedder {
         const ov::AnyMap device_config) :
         IInputsEmbedder(vlm_config, models_map, tokenizer, config_dir_path, device, device_config) { }
 
-    virtual ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics) override {
+    virtual ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics, bool apply_chat_template) override {
         std::string image_start_token = m_vlm_config.image_start_token;
         std::string image_context_token = m_vlm_config.image_context_token;
         std::string image_end_token = m_vlm_config.image_end_token;
@@ -1097,7 +1101,7 @@ class InputsEmbedderInternVLChat : public InputsEmbedder::IInputsEmbedder {
         }
         formatted_prompt += prompt;
 
-        ov::Tensor input_ids = get_encoded_input_ids(formatted_prompt, metrics);
+        ov::Tensor input_ids = get_encoded_input_ids(formatted_prompt, metrics, {}, apply_chat_template);
         ov::Tensor text_embeds = m_embedding.infer(input_ids);
 
         if (images.empty()) {
@@ -1214,7 +1218,7 @@ class InputsEmbedderQwen2VL : public InputsEmbedder::IInputsEmbedder {
             ).create_infer_request();
         }
     
-    virtual ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics) override {
+    virtual ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics, bool apply_chat_template) override {
         std::string formatted_prompt;
 
         std::vector<ov::Tensor> single_images = to_single_image_tensors(images);
@@ -1246,7 +1250,7 @@ class InputsEmbedderQwen2VL : public InputsEmbedder::IInputsEmbedder {
 
         // Adapted from Qwen/Qwen2-7B-Instruct
         std::string chat_template_fallback = "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}";
-        ov::Tensor input_ids = get_encoded_input_ids(formatted_prompt, metrics, chat_template_fallback);
+        ov::Tensor input_ids = get_encoded_input_ids(formatted_prompt, metrics, chat_template_fallback, apply_chat_template);
         ov::Tensor text_embeds = m_embedding.infer(input_ids);
 
         if (images.empty()) {
@@ -1616,8 +1620,8 @@ InputsEmbedder::InputsEmbedder(const VLMConfig& vlm_config,
     }
 }
 
-ov::Tensor InputsEmbedder::get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics) {
-    return m_impl->get_inputs_embeds(prompt, images, metrics);
+ov::Tensor InputsEmbedder::get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics, bool apply_chat_template) {
+    return m_impl->get_inputs_embeds(prompt, images, metrics, apply_chat_template);
 }
 
 std::pair<ov::Tensor, std::optional<int64_t>> InputsEmbedder::get_position_ids(const size_t inputs_embeds_size, const size_t history_size) {
diff --git a/src/cpp/src/visual_language/inputs_embedder.hpp b/src/cpp/src/visual_language/inputs_embedder.hpp
index 223d090b22..b21d0198cc 100644
--- a/src/cpp/src/visual_language/inputs_embedder.hpp
+++ b/src/cpp/src/visual_language/inputs_embedder.hpp
@@ -32,7 +32,7 @@ class InputsEmbedder {
                    const ov::AnyMap device_config);
 
     // compute input embedding for prompt and multiple images
-    ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics);
+    ov::Tensor get_inputs_embeds(const std::string& prompt, const std::vector<ov::Tensor>& images, ov::genai::VLMPerfMetrics& metrics, bool apply_chat_template);
 
     // compute position ids for language model input
     std::pair<ov::Tensor, std::optional<int64_t>> get_position_ids(const size_t inputs_embeds_size, const size_t history_size);
diff --git a/src/cpp/src/visual_language/pipeline.cpp b/src/cpp/src/visual_language/pipeline.cpp
index 95e3064548..415e7fcae9 100644
--- a/src/cpp/src/visual_language/pipeline.cpp
+++ b/src/cpp/src/visual_language/pipeline.cpp
@@ -166,7 +166,7 @@ class ov::genai::VLMPipeline::VLMPipelineImpl {
         generation_config.validate();
 
         auto start_get_inputs_embeds = std::chrono::steady_clock::now();
-        ov::Tensor inputs_embeds = m_inputs_embedder->get_inputs_embeds(prompt, rgbs, perf_metrics);
+        ov::Tensor inputs_embeds = m_inputs_embedder->get_inputs_embeds(prompt, rgbs, perf_metrics, generation_config.apply_chat_template);
         auto end_get_inputs_embeds = std::chrono::steady_clock::now();
 
         auto to_remove_from_hist = m_inputs_embedder->get_num_tokens_to_remove_from_hist();
diff --git a/src/python/py_generation_config.cpp b/src/python/py_generation_config.cpp
index e2a6d7062c..a7d7789a55 100644
--- a/src/python/py_generation_config.cpp
+++ b/src/python/py_generation_config.cpp
@@ -115,6 +115,7 @@ void init_generation_config(py::module_& m) {
         .def_readwrite("include_stop_str_in_output", &GenerationConfig::include_stop_str_in_output)
         .def_readwrite("stop_token_ids", &GenerationConfig::stop_token_ids)
         .def_readwrite("adapters", &GenerationConfig::adapters)
+        .def_readwrite("apply_chat_template", &GenerationConfig::apply_chat_template)
         .def("set_eos_token_id", &GenerationConfig::set_eos_token_id, py::arg("tokenizer_eos_token_id"))
         .def("is_beam_search", &GenerationConfig::is_beam_search)
         .def("is_greedy_decoding", &GenerationConfig::is_greedy_decoding)
diff --git a/tests/python_tests/common.py b/tests/python_tests/common.py
index ed6263a284..8f2d2b05b9 100644
--- a/tests/python_tests/common.py
+++ b/tests/python_tests/common.py
@@ -274,7 +274,7 @@ def run_hugging_face(
         else:
             processed_prompts = prompts
         # process all prompts as a single batch as we have a single generation config for all prompts
-        inputs = hf_tokenizer(processed_prompts, return_tensors='pt', padding=True, truncation=True, add_special_tokens=True, padding_side='left')
+        inputs = hf_tokenizer(processed_prompts, return_tensors='pt', padding=True, truncation=True, add_special_tokens=False, padding_side='left')
         input_ids, attention_mask = inputs['input_ids'], inputs['attention_mask']
         hf_generation_config = convert_to_hf(opt_model.generation_config, generation_configs)
         hf_encoded_outputs = opt_model.generate(input_ids, attention_mask=attention_mask, generation_config=hf_generation_config, tokenizer=hf_tokenizer)