Read EOS token from model runtime information for speculative_decodin…

…g_lm (#353)

Extension to issue #277, Added the functionality to read EOS token from
model runtime information in the speculative_decoding_lm.

text_generation/causal_lm/cpp/speculative_decoding_lm.cpp

@@ -11,8 +11,7 @@ constexpr size_t BATCH_SIZE = 1;
 // threfore usually SEQ_LEN_AXIS = 2
 constexpr size_t SEQ_LEN_AXIS = 2;
 
-// There's no way to extract special token values from the detokenizer for now
-constexpr int64_t SPECIAL_EOS_TOKEN = 2;
+int64_t SPECIAL_EOS_TOKEN;
 
 namespace {
 std::pair<ov::Tensor, ov::Tensor> tokenize(ov::InferRequest& tokenizer, std::string&& prompt) {
@@ -117,9 +116,10 @@ int main(int argc, char* argv[]) try {
     // tokenizer model
     ov::Core core;
     core.add_extension(OPENVINO_TOKENIZERS_PATH);  // OPENVINO_TOKENIZERS_PATH is defined in CMakeLists.txt
+    auto tokenizer_model = core.read_model(std::string{argv[1]} + "/openvino_tokenizer.xml");
     // tokenizer and detokenizer work on CPU only
     ov::InferRequest tokenizer = core.compile_model(
-        std::string{argv[1]} + "/openvino_tokenizer.xml", "CPU").create_infer_request();
+        tokenizer_model, "CPU").create_infer_request();
     auto [draft_input_ids, draft_attention_mask] = tokenize(tokenizer, argv[3]);
     ov::InferRequest detokenizer = core.compile_model(
         std::string{argv[1]} + "/openvino_detokenizer.xml", "CPU").create_infer_request();
@@ -183,6 +183,14 @@ int main(int argc, char* argv[]) try {
     draft_input_ids.set_shape({BATCH_SIZE, 1});
     draft_position_ids.set_shape({BATCH_SIZE, 1});
 
+    auto rt_info = tokenizer_model->get_rt_info(); //Get the runtime info for the model
+
+    if (rt_info.count("eos_token_id") > 0) { //check if the runtime information has a valid EOS token ID
+        SPECIAL_EOS_TOKEN = rt_info["eos_token_id"].as<int64_t>();
+    } else {
+        throw std::runtime_error("EOS token ID not found in model's runtime information.");
+    }
+
 /* Speculative decoding works the following way. The draft model predicts the next K
    tokens one by one in an autoregressive manner, while the main model validates these
    predictions and corrects them if necessary. We go through each predicted token, and