Repository: IITH-Compilers/IR2Vec (language: python)

Issue:
Seedemb opt



Discussion / hints:

Fix patch:
diff --git a/Manylinux2014_Compliant_Source/pkg/build.sh b/Manylinux2014_Compliant_Source/pkg/build.sh
index a43ec9965..c252eb7b8 100644
--- a/Manylinux2014_Compliant_Source/pkg/build.sh
+++ b/Manylinux2014_Compliant_Source/pkg/build.sh
@@ -18,7 +18,7 @@ cd ..
 cmake -DCMAKE_BUILD_TYPE=Release .. && make -j"$(nproc)" && make install
 
 cd ..
-cp build/vocabulary.h Manylinux2014_Compliant_Source/pkg/ir2vec/
+cp build/include/Vocabulary*.h Manylinux2014_Compliant_Source/pkg/ir2vec/
 cp src/include/utils.h Manylinux2014_Compliant_Source/pkg/ir2vec/
 cp src/include/IR2Vec.h Manylinux2014_Compliant_Source/pkg/ir2vec/
 cp build/src/version.h Manylinux2014_Compliant_Source/pkg/ir2vec/
diff --git a/Manylinux2014_Compliant_Source/pkg/ir2vec/core.cpp b/Manylinux2014_Compliant_Source/pkg/ir2vec/core.cpp
index 360d9a375..565bd775f 100644
--- a/Manylinux2014_Compliant_Source/pkg/ir2vec/core.cpp
+++ b/Manylinux2014_Compliant_Source/pkg/ir2vec/core.cpp
@@ -20,7 +20,6 @@
 #include "llvm/ADT/DenseMap.h"
 #include "llvm/ADT/MapVector.h"
 #include "llvm/ADT/SmallSet.h"
-#include "llvm/ADT/SmallVector.h"
 #include "llvm/ADT/StringExtras.h"
 #include "llvm/ADT/StringRef.h"
 #include "llvm/IR/CFG.h"
@@ -75,11 +74,13 @@ class IR2VecHandler {
   std::string outputFile;
   std::string mode;
   std::string level;
+  unsigned dim;
 
 public:
   IR2VecHandler(std::string fileName, std::string outputFile, std::string mode,
-                std::string level)
-      : fileName(fileName), outputFile(outputFile), mode(mode), level(level) {}
+                std::string level, unsigned dim)
+      : fileName(fileName), outputFile(outputFile), mode(mode), level(level),
+        dim(dim) {}
 
   std::string getFile() { return fileName; }
   std::string getOutputFile() { return outputFile; }
@@ -87,7 +88,7 @@ class IR2VecHandler {
   std::string getLevel() { return level; }
 
   // Function to get Program Vector List
-  PyObject *createProgramVectorList(llvm::SmallVector<double, DIM> llvmPgmVec) {
+  PyObject *createProgramVectorList(IR2Vec::Vector llvmPgmVec) {
     // for PgmVector
     PyObject *PgmList = PyList_New(0);
     for (auto &Pgm_it : llvmPgmVec)
@@ -138,7 +139,6 @@ class IR2VecHandler {
     PyObject *instructionVectorList = PyList_New(0);
     for (auto &Inst_it : llvmInstVecMap) {
       PyObject *instructionVector = PyList_New(0);
-      // copy this SmallVector into c++ Vector
       for (auto &Vec_it : Inst_it.second) {
         PyList_Append(instructionVector, PyFloat_FromDouble(Vec_it));
       }
@@ -166,10 +166,10 @@ class IR2VecHandler {
       ofstream output;
       output.open(outFile, ios_base::app);
       emb = std::move(new IR2Vec::Embeddings(
-          *Module, ir2vecMode, (this->level)[0], &output, funcName));
+          *Module, ir2vecMode, (this->level)[0], &output, this->dim, funcName));
     } else {
       emb = std::move(new IR2Vec::Embeddings(
-          *Module, ir2vecMode, (this->level)[0], nullptr, funcName));
+          *Module, ir2vecMode, (this->level)[0], nullptr, this->dim, funcName));
     }
 
     if (!emb) {
@@ -178,7 +178,7 @@ class IR2VecHandler {
     }
 
     if (type == OpType::Program) {
-      llvm::SmallVector<double, DIM> progVector = emb->getProgramVector();
+      IR2Vec::Vector progVector = emb->getProgramVector();
       return this->createProgramVectorList(progVector);
     } else if (type == OpType::Function) {
       llvm::SmallMapVector<const llvm::Function *, IR2Vec::Vector, 16>
@@ -293,9 +293,10 @@ PyObject *getFunctionVectors(PyObject *self, PyObject *args) {
 
 IR2VecHandlerObject *createIR2VECObject(const char *filename,
                                         const char *output_file,
-                                        const char *mode, const char *level) {
+                                        const char *mode, const char *level,
+                                        unsigned dim) {
   IR2VecHandler *ir2vecObj =
-      new IR2VecHandler(filename, output_file, mode, level);
+      new IR2VecHandler(filename, output_file, mode, level, dim);
   if (!ir2vecObj) {
     return nullptr;
   }
@@ -314,8 +315,9 @@ PyObject *initEmbedding(PyObject *self, PyObject *args) {
   const char *mode = "\0";
   const char *level = "\0";
   const char *output_file = "\0";
+  unsigned dim = 300;
 
-  if (!PyArg_ParseTuple(args, "sss|s", &filename, &mode, &level,
+  if (!PyArg_ParseTuple(args, "sss|Is", &filename, &mode, &level, &dim,
                         &output_file)) {
     // raise error here
     PyErr_SetString(PyExc_TypeError, "Invalid Arguments");
@@ -348,7 +350,7 @@ PyObject *initEmbedding(PyObject *self, PyObject *args) {
   }
 
   IR2VecHandlerObject *ir2vecObj =
-      createIR2VECObject(filename, output_file, mode, level);
+      createIR2VECObject(filename, output_file, mode, level, dim);
 
   if (!ir2vecObj) {
     PyErr_SetString(PyExc_TypeError, "Embedding Object not created");
diff --git a/README.md b/README.md
index 8aa42518d..865ac3d29 100644
--- a/README.md
+++ b/README.md
@@ -113,13 +113,16 @@ To ensure the correctness, run `make check`
 instructions.
 
 ### Using Binary
-> ir2vec -\<mode\> -o \<output-file\> -level \<p|f\> -class \<class-number\> -funcName=\<function-name\> \<input-ll-file\>
+> ir2vec -\<mode\> -dim \<dimensions\> -o \<output-file\> -level \<p|f\> -class \<class-number\> -funcName=\<function-name\> \<input-ll-file\>
 
 #### Command-Line options
 
 - `mode` - can be one of `sym`/`fa`
     - `sym` denotes Symbolic representation
     - `fa` denotes Flow-Aware representation
+- `dim` - Dimensions of embeddings
+    - This is an optional argument. Defaults to `300`.
+    - Other supported dimensions are `75` and `100`
 -  `o` - file in which the embeddings are to be appended;     (Note : If  file doesn’t exist, new file would be created, else embeddings would be appended)
 - `level` - can be one of chars `p`/`f`.
     - `p` denotes `program level` encoding
@@ -141,16 +144,16 @@ Please use `--help` for further details.
 
 #### Flow-Aware Embeddings
 For all functions
-* `` ir2vec -fa -o <output_file> -level <p|f>  -class <class-number> <input_ll_file>``
+* `` ir2vec -fa -dim <dimension> -o <output_file> -level <p|f>  -class <class-number> <input_ll_file>``
 
 For a specific function
-* `` ir2vec -fa -o <output_file> -level f  -class <class-number> -funcName=\<function-name\><input_ll_file>``
+* `` ir2vec -fa -dim <dimension> -o <output_file> -level f  -class <class-number> -funcName=\<function-name\><input_ll_file>``
 
 #### Symbolic Embeddings
 For all functions
- * `` ir2vec -sym -o <output_file> -level <p|f> -class <class-number> <input_ll_file>``
+ * `` ir2vec -sym -dim <dimension> -o <output_file> -level <p|f> -class <class-number> <input_ll_file>``
 For a specific function
- * `` ir2vec -sym -o <output_file> -level f -class <class-number> -funcName=\<function-name\> <input_ll_file>``
+ * `` ir2vec -sym -dim <dimension> -o <output_file> -level f -class <class-number> -funcName=\<function-name\> <input_ll_file>``
 
 ## Using Libraries
 The libraries can be installed by passing the installation location to the `CMAKE_INSTALL_PREFIX` flag during `cmake` followed by `make install`.
@@ -178,7 +181,7 @@ The following example snippet shows how to query the exposed vector representati
 
 // Creating object to generate FlowAware representation
 auto ir2vec =
-      IR2Vec::Embeddings(<LLVM Module>, IR2Vec::IR2VecMode::FlowAware);
+      IR2Vec::Embeddings(<LLVM Module>, IR2Vec::IR2VecMode::FlowAware, <DIM>);
 
 // Getting Instruction vectors corresponding to the instructions in <LLVM Module>
 auto instVecMap = ir2vec.getInstVecMap();
@@ -218,6 +221,8 @@ for (auto val : pgmVec)
 * `file_path`: str - Path to the `.ll` or `.bc` file.
 * `encoding_type`: str - Choose `fa` (Flow-Aware) or `sym` (Symbolic).
 * `level`: str - Choose `p` for program-level or `f` for function-level.
+* `dim`: uint - Choose from `[300, 100, 75]`. Default value is `300`
+* `output_file`: str - If provided, embeddings are saved to this file. Default is an empty string.
 
 **Returns:**
 
@@ -228,7 +233,14 @@ for (auto val : pgmVec)
 ```python
 import ir2vec
 
+# Approach 1
 initObj = ir2vec.initEmbedding("/path/to/file.ll", "fa", "p")
+
+# Approach 2
+initObj = ir2vec.initEmbedding("/path/to/file.ll", "fa", "p", 100)
+
+# Approach 3
+initObj = ir2vec.initEmbedding("/path/to/file.ll", "fa", "p", 100, "output.txt")
 ```
 
 ### getProgramVector
diff --git a/seed_embeddings/OpenKE/analogy.py b/seed_embeddings/OpenKE/analogy.py
index 3ce055335..effa3cce3 100644
--- a/seed_embeddings/OpenKE/analogy.py
+++ b/seed_embeddings/OpenKE/analogy.py
@@ -6,13 +6,14 @@
 import numpy as np
 from sklearn.metrics.pairwise import euclidean_distances
 
+
 class AnalogyScorer:
     def __init__(self, analogy_file="analogies.txt"):
         self.entity_dict = {}
         self.analogies = self._load_analogies(analogy_file)
 
     def _load_analogies(self, file_path):
-        with open(file_path, 'r') as f:
+        with open(file_path, "r") as f:
             return [tuple(line.strip().split()) for line in f if line.strip()]
 
     def find_vec(self, str1):
@@ -22,12 +23,12 @@ def gen_similarity_table(self, vec):
         keys = list(self.entity_dict.keys())
         entity_matrix = np.array(list(self.entity_dict.values()))
         vec = vec.reshape(1, -1)
-        
+
         # Calculate distances using euclidean_distances
         distances = euclidean_distances(vec, entity_matrix)[0]
-        
+
         return dict(zip(keys, distances))
-    
+
     def findTopk(self, dict1, k, values):
         sortedByVal = dict(sorted(dict1.items(), key=lambda x: x[1]))
         del sortedByVal[values[0].upper()]
@@ -35,11 +36,11 @@ def findTopk(self, dict1, k, values):
         del sortedByVal[values[2].upper()]
         return {k: sortedByVal[k] for k in list(sortedByVal)[:k]}
 
-    def get_analogy_score(self, entity_dict):        
+    def get_analogy_score(self, entity_dict):
         self.entity_dict = entity_dict
         total_count = len(self.analogies)
         correct_count = 0
-        
+
         for values in self.analogies:
             vecA = self.find_vec(values[0])
             vecB = self.find_vec(values[1])
@@ -56,4 +57,4 @@ def get_analogy_score(self, entity_dict):
 
             if values[3].upper() in top_k_dict:
                 correct_count += 1
-        return correct_count
\ No newline at end of file
+        return correct_count
diff --git a/seed_embeddings/OpenKE/config/Trainer.py b/seed_embeddings/OpenKE/config/Trainer.py
index 6d05f1fc3..c1552688a 100755
--- a/seed_embeddings/OpenKE/config/Trainer.py
+++ b/seed_embeddings/OpenKE/config/Trainer.py
@@ -34,7 +34,7 @@ def __init__(
         save_steps=None,
         checkpoint_dir=None,
         index_dir=None,
-        out_path=None,
+        analogy_file="analogies.txt",
     ):
 
         self.work_threads = 8
@@ -52,10 +52,10 @@ def __init__(
         self.save_steps = save_steps
         self.checkpoint_dir = checkpoint_dir
         # self.out_path = out_path
-        
+
         self.entity_names = self.load_entity_names(index_dir)
-        self.analogies = analogy.AnalogyScorer(analogy_file="analogies.txt")
-        
+        self.analogies = analogy.AnalogyScorer(analogy_file=analogy_file)
+
     def load_entity_names(self, index_dir):
         with open(os.path.join(index_dir, "entity2id.txt")) as fEntity:
             content = fEntity.read()
@@ -93,8 +93,8 @@ def getEntityDict(self, ent_embeddings):
         mapping entity names to their corresponding embeddings.
         """
         entity_dict = {}
-        
-        for i, entity_name in enumerate(self.entity_dict):
+
+        for i, entity_name in enumerate(self.entity_names):
             entity_dict[entity_name] = ent_embeddings[i].tolist()
 
         return entity_dict
@@ -139,7 +139,7 @@ def run(
                 weight_decay=self.weight_decay,
             )
         print("Finish initializing...")
-
+        best_metric_val = 0.0
         training_range = tqdm(range(self.train_times))
         for epoch in training_range:
             res = 0.0
@@ -148,6 +148,7 @@ def run(
                 res += loss
             training_range.set_description("Epoch %d | loss: %f" % (epoch, res))
             checkpoint = None
+            save_ckpt = False
             if ray and epoch % freq == 0:
                 metrics = {"loss": res}
                 # Link Prediction
@@ -170,27 +171,45 @@ def run(
                             "hit1": hit1,
                         }
                     )
+                    if best_metric_val <= hit1:
+                        best_metric_val = hit1
+                        save_ckpt = True
                     print("Link Prediction Scores Completed")
 
-                if is_analogy:
+                elif is_analogy:
                     # self.model => Negative Sampling object
                     # self.mode.model => Transe model
 
-                    ent_embeddings = self.model.model.ent_embeddings.weight.data.numpy()
+                    ent_embeddings = (
+                        self.model.model.ent_embeddings.weight.data.cpu().numpy()
+                    )
                     entity_dict = self.getEntityDict(ent_embeddings)
                     analogy_score = self.analogies.get_analogy_score(entity_dict)
                     metrics.update({"AnalogiesScore": analogy_score})
-                    print("Analogy Score Completed")
+                    print("Analogy Score completed")
+
+                    del entity_dict
+
+                    if best_metric_val <= analogy_score:
+                        best_metric_val = analogy_score
+                        save_ckpt = True
+
+                else:  # loss
+                    if best_metric_val >= res:
+                        best_metric_val = res
+                        save_ckpt = True
 
                 with tempfile.TemporaryDirectory() as temp_checkpoint_dir:
                     # Save the checkpoint...
-                    self.model.save_checkpoint(
-                        os.path.join(
-                            temp_checkpoint_dir,
-                            "checkpoint" + "-" + str(epoch) + ".ckpt",
+                    checkpoint = None
+                    if save_ckpt:
+                        self.model.save_checkpoint(
+                            os.path.join(
+                                temp_checkpoint_dir,
+                                "checkpoint" + "-" + str(epoch) + ".ckpt",
+                            )
                         )
-                    )
-                    checkpoint = Checkpoint.from_directory(temp_checkpoint_dir)
+                        checkpoint = Checkpoint.from_directory(temp_checkpoint_dir)
 
                     train.report(metrics, checkpoint=checkpoint)
 
diff --git a/seed_embeddings/OpenKE/generate_embedding_ray.py b/seed_embeddings/OpenKE/generate_embedding_ray.py
index f0f848076..8eb70a73c 100644
--- a/seed_embeddings/OpenKE/generate_embedding_ray.py
+++ b/seed_embeddings/OpenKE/generate_embedding_ray.py
@@ -9,6 +9,7 @@
 import json
 import argparse
 import shutil
+import re
 
 from config import Trainer, Tester
 from module.model import TransE
@@ -25,7 +26,7 @@
 from ray.tune.schedulers import ASHAScheduler
 from ray.tune.search.optuna import OptunaSearch
 
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
 
 
 def test_files(index_dir):
@@ -42,66 +43,56 @@ def test_files(index_dir):
         raise Exception("train2id.txt not found")
 
 
-# TODO :: alpha, lmda, bern, opt_method
-def train(arg_conf):
-
-    try:
-        test_files(arg_conf["index_dir"])
-        print("Files are OK")
-    except:
-        print(arg_conf)
-        print("Error in files")
-        raise Exception("Error in files")
-
+def train(config, args=None):
     # dataloader for training
-
     train_dataloader = TrainDataLoader(
-        in_path=arg_conf["index_dir"],
-        nbatches=arg_conf["nbatches"],
+        in_path=args.index_dir,
+        batch_size=config["batch_size"],
         threads=4,
         sampling_mode="normal",
-        bern_flag=arg_conf["bern"],
+        # bern_flag=config["bern"],
         filter_flag=1,
-        neg_ent=arg_conf["neg_ent"],
-        neg_rel=arg_conf["neg_rel"],
+        # neg_ent=config["neg_ent"],
+        # neg_rel=config["neg_rel"],
     )
     # dataloader for test (link prediction)
-    if arg_conf["link_pred"]:
-        test_dataloader = TestDataLoader(arg_conf["index_dir"], "link")
+    if args.link_pred:
+        test_dataloader = TestDataLoader(args.index_dir, "link")
     else:
         test_dataloader = None
 
     transe = TransE(
         ent_tot=train_dataloader.get_ent_tot(),
         rel_tot=train_dataloader.get_rel_tot(),
-        dim=arg_conf["dim"],
+        dim=args.dim,
         p_norm=1,
         norm_flag=True,
     )
     # define the loss function
     model = NegativeSampling(
         model=transe,
-        loss=MarginLoss(margin=arg_conf["margin"]),
+        loss=MarginLoss(margin=config["margin"]),
         batch_size=train_dataloader.get_batch_size(),
     )
     # train the model
     trainer = Trainer(
         model=model,
         data_loader=train_dataloader,
-        train_times=arg_conf["epoch"],
-        alpha=arg_conf["alpha"],
-        index_dir=arg_conf["index_dir"],
-        use_gpu=arg_conf["use_gpu"],
+        train_times=args.epoch,
+        alpha=config["alpha"],
+        index_dir=args.index_dir,
+        use_gpu=args.use_gpu,
+        analogy_file=args.analogy_file,
     )
     trainer.run(
-        link_prediction=arg_conf["link_pred"],
+        link_prediction=args.link_pred,
         test_dataloader=test_dataloader,
         model=transe,
-        is_analogy=arg_conf["is_analogy"],
+        is_analogy=args.is_analogy,
     )
 
 
-def findRep(src, dest, index_dir, src_type="json"):
+def findRep(src, index_dir, src_type="json"):
     rep = None
     if src_type == "json":
         with open(src) as fSource:
@@ -116,22 +107,44 @@ def findRep(src, dest, index_dir, src_type="json"):
     with open(os.path.join(index_dir, "entity2id.txt")) as fEntity:
         content = fEntity.read()
 
-    with open(dest, "w") as fDest:
-        entities = content.split("\n")
-        toTxt = ""
+    entities = content.split("\n")
+    toTxt = ""
+    for i in range(1, int(entities[0])):
+        toTxt += entities[i].split("\t")[0] + ":" + str(rep[i - 1]) + ",\n"
+    toTxt += (
+        entities[int(entities[0])].split("\t")[0] + ":" + str(rep[int(entities[0]) - 1])
+    )
+    return toTxt
 
-        for i in range(1, int(entities[0])):
-            toTxt += entities[i].split("\t")[0] + ":" + str(rep[i - 1]) + ",\n"
-        toTxt += (
-            entities[int(entities[0])].split("\t")[0]
-            + ":"
-            + str(rep[int(entities[0]) - 1])
-        )
-        fDest.write(toTxt)
 
+def reformat_embeddings(input_str):
+    # Split the string by '],' to isolate each object
+    entries = input_str.split("],")
 
-if __name__ == "__main__":
+    formatted_entries = []
+
+    for entry in entries:
+        # Remove any newline characters
+        cleaned_entry = entry.replace("\n", " ")
+
+        # Split the object name from the values part
+        obj_name, values = cleaned_entry.split(":[")
 
+        # Remove extra spaces and replace multiple spaces with a single one using regex
+        values = re.sub(r"\s+", " ", values.split("]")[0].strip())
+
+        # Replace spaces between numbers with commas and add the closing bracket
+        formatted_values = values.replace(" ", ", ") + "]"
+
+        # Recombine the object name with the formatted values
+        formatted_entry = f"{obj_name.strip()}:[{formatted_values}"
+        formatted_entries.append(formatted_entry)
+
+    # Join all entries back into one string, separated by newline
+    return "\n".join(formatted_entries)
+
+
+if __name__ == "__main__":
     ray.init()
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -145,7 +158,6 @@ def findRep(src, dest, index_dir, src_type="json"):
     parser.add_argument(
         "--epoch", dest="epoch", help="Epochs", required=False, type=int, default=1000
     )
-
     parser.add_argument(
         "--is_analogy",
         dest="is_analogy",
@@ -170,10 +182,9 @@ def findRep(src, dest, index_dir, src_type="json"):
         type=int,
         default=300,
     )
-
     parser.add_argument(
-        "--nbatches",
-        dest="nbatches",
+        "--batch_size",
+        dest="batch_size",
         help="Number of batches",
         required=False,
         type=int,
@@ -195,69 +206,79 @@ def findRep(src, dest, index_dir, src_type="json"):
         type=bool,
         default=False,
     )
+    parser.add_argument(
+        "--storage_path",
+        dest="storage_path",
+        help="Path to store the ray results",
+        required=False,
+        type=str,
+        default=os.path.join(os.path.expanduser("~"), "ray_results"),
+    )
+    parser.add_argument(
+        "--analogy_file",
+        dest="analogy_file",
+        help="Path to the analogy file",
+        required=False,
+        type=str,
+        default="./analogies.txt",
+    )
+
     arg_conf = parser.parse_args()
+    arg_conf.index_dir = arg_conf.index_dir + "/"
+
+    try:
+        test_files(arg_conf.index_dir)
+        print("Files are OK")
+    except Exception as e:
+        print("Exception: ", e)
+        print("Error in files")
+        raise Exception("Error in files")
 
     search_space = {
-        "epoch": arg_conf.epoch,
-        "dim": tune.sample_from(lambda spec: 100 * np.random.randint(1, 6)),
-        "index_dir": arg_conf.index_dir,
-        "nbatches": tune.sample_from(lambda spec: 2 ** np.random.randint(8, 12)),
-        "margin": tune.quniform(3, 6, 0.5),
+        "batch_size": tune.sample_from(lambda spec: 2 ** np.random.randint(8, 12)),
+        "margin": tune.quniform(0.2, 6, 0.2),
         "alpha": tune.loguniform(1e-4, 1e-1),
-        "neg_ent": tune.randint(1, 30),
-        "neg_rel": tune.randint(1, 30),
-        "bern": tune.randint(0, 2),
-        "opt_method": tune.choice(["SGD", "Adam"]),
-        # "opt_method": tune.choice(["SGD", "Adagrad", "Adam", "Adadelta"]),
-        "is_analogy": arg_conf.is_analogy,
-        "link_pred": arg_conf.link_pred,
-        "use_gpu": arg_conf.use_gpu,
+        # "neg_ent": tune.randint(1, 30),
+        # "neg_rel": tune.randint(1, 30),
+        # "bern": tune.randint(0, 2),
+        "opt_method": "Adam",  # tune.choice(["SGD", "Adam"]),
     }
 
     try:
-        test_files(search_space["index_dir"])
+        test_files(arg_conf.index_dir)
         print("Files are OK")
     except:
         print("Error in files")
         raise Exception("Error in files")
 
     if arg_conf.is_analogy:
-        scheduler = ASHAScheduler(
-            time_attr="training_iteration",
-            max_t=arg_conf.epoch,
-            grace_period=15,
-            reduction_factor=3,
-            metric="AnalogiesScore",
-            mode="max",
-        )
-        optuna = OptunaSearch(metric="AnalogiesScore", mode="max")
+        metric = "AnalogiesScore"
+        mode = "max"
     elif arg_conf.link_pred:
-        scheduler = ASHAScheduler(
-            time_attr="training_iteration",
-            max_t=arg_conf.epoch,
-            grace_period=15,
-            reduction_factor=3,
-            metric="hit1",
-            mode="max",
-        )
-        optuna = OptunaSearch(metric="hit1", mode="max")
+        metric = "hit1"
+        mode = "max"
     else:
-        scheduler = ASHAScheduler(
-            time_attr="training_iteration",
-            max_t=arg_conf.epoch,
-            grace_period=15,
-            reduction_factor=3,
-            metric="loss",
-            mode="min",
-        )
-        optuna = OptunaSearch(metric="loss", mode="min")
+        metric = "loss"
+        mode = "min"
+
+    scheduler = ASHAScheduler(
+        time_attr="training_iteration",
+        max_t=arg_conf.epoch,
+        grace_period=10,
+        reduction_factor=3,
+        metric=metric,
+        mode=mode,
+    )
+    optuna = OptunaSearch(metric="loss", mode="min")
+
     if arg_conf.use_gpu:
         train_with_resources = tune.with_resources(
-            train, resources={"cpu": 0, "gpu": 0.0625}
+            tune.with_parameters(train, args=arg_conf),
+            resources={"cpu": 8, "gpu": 0.15},
         )
     else:
         train_with_resources = tune.with_resources(
-            train, resources={"cpu": 10, "gpu": 0}
+            tune.with_parameters(train, args=arg_conf), resources={"cpu": 10, "gpu": 0}
         )
 
     tuner = tune.Tuner(
@@ -265,79 +286,65 @@ def findRep(src, dest, index_dir, src_type="json"):
         param_space=search_space,
         tune_config=TuneConfig(
             search_alg=optuna,
-            max_concurrent_trials=16,
+            max_concurrent_trials=12,
             scheduler=scheduler,
-            num_samples=512,
+            num_samples=128,
         ),
         run_config=RunConfig(
+            storage_path=arg_conf.storage_path,
             checkpoint_config=CheckpointConfig(
                 num_to_keep=1,
                 # *Best* checkpoints are determined by these params:
-                checkpoint_score_attribute="loss",
-                checkpoint_score_order="min",
-            )
+                checkpoint_score_attribute=metric,
+                checkpoint_score_order=mode,
+            ),
         ),
     )
     results = tuner.fit()
 
     # Write the best result to a file, best_result.txt
-    best_result = None
-    if arg_conf.is_analogy:
-
-        with open(os.path.join(search_space["index_dir"], "best_result.txt"), "a") as f:
-            f.write(
-                "\n" + str(results.get_best_result(metric="AnalogiesScore", mode="max"))
-            )
+    fin_res = results.get_best_result(metric=metric, mode=mode)
+    with open(os.path.join(arg_conf.index_dir, "best_result.txt"), "a") as f:
+        f.write("\n" + str(fin_res))
 
+    if arg_conf.is_analogy:
         print(
             "Best Config Based on Analogy Score : ",
-            results.get_best_result(metric="AnalogiesScore", mode="max"),
+            fin_res,
         )
-
-        best_result = results.get_best_result(metric="AnalogiesScore", mode="max")
-
     elif arg_conf.link_pred:
-
-        with open(os.path.join(search_space["index_dir"], "best_result.txt"), "a") as f:
-            f.write("\n" + str(results.get_best_result(metric="hit1", mode="max")))
-
         print(
             "Best Config Based on Hit1 : ",
-            results.get_best_result(metric="hit1", mode="max"),
+            fin_res,
         )
-        best_result = results.get_best_result(metric="hit1", mode="max")
     else:
-
-        with open(os.path.join(search_space["index_dir"], "best_result.txt"), "a") as f:
-            f.write("\n" + str(results.get_best_result(metric="loss", mode="min")))
-
         print(
             "Best Config Based on Loss : ",
-            results.get_best_result(metric="loss", mode="min"),
+            fin_res,
         )
-        best_result = results.get_best_result(metric="loss", mode="min")
 
     # Get the best configuration
-    best_config = best_result.config
-
+    best_config = fin_res.config
+    print("best_config: ", best_config)
     # Extract the values for constructing the file name
-    epoch = best_config["epoch"]
-    dim = best_config["dim"]
-    nbatches = best_config["nbatches"]
+    dim = arg_conf.dim
+    batch_size = best_config["batch_size"]
     margin = best_config["margin"]
-    index_dir = best_config["index_dir"]
+    alpha = best_config["alpha"]
+    index_dir = arg_conf.index_dir
 
     # Construct the output file name using the best hyperparameters
     outfile = os.path.join(
         index_dir,
-        "seedEmbedding_{}E_{}D_{}batches_{}margin.ckpt".format(
-            epoch,
+        "seedEmbedding_{}_{}Dim_{}Alpha_{}batchsize_{}margin.ckpt".format(
+            metric,
             dim,
-            nbatches,
+            alpha,
+            batch_size,
             margin,
         ),
     )
-    best_checkpoint_path = best_result.checkpoint.path
+    best_checkpoint_path = fin_res.checkpoint.path
     print("best_checkpoint_path is: ", best_checkpoint_path)
     file_name = os.listdir(best_checkpoint_path)[0]
     print("file_name is: ", file_name)
@@ -350,20 +357,24 @@ def findRep(src, dest, index_dir, src_type="json"):
 
         embeddings_path = os.path.join(
             index_dir,
-            "embeddings/seedEmbedding_{}E_{}D_{}batches{}margin.txt".format(
-                epoch,
+            "seedEmbedding_{}_{}Dim_{}Alpha_{}batchsize_{}margin.ckpt".format(
+                metric,
                 dim,
-                nbatches,
+                alpha,
+                batch_size,
                 margin,
             ),
         )
+
+        data = findRep(outfile, index_dir, src_type="ckpt")
+        formatted_data = reformat_embeddings(data)
+
+        # Write the embeddings to outfile
+        embeddings_path = embeddings_path.replace(".ckpt", ".txt")
         print("embeddings_path: ", embeddings_path)
-        findRep(outfile, embeddings_path, index_dir, src_type="ckpt")
+        with open(embeddings_path, "w") as f:
+            f.write(formatted_data)
     else:
         print("No .ckpt file found in the source directory.")
 
-    for result in results:
-        print(result)
     del results
-
-    print("Training finished...")
diff --git a/seed_embeddings/README.md b/seed_embeddings/README.md
index e68dae3ff..419906dcd 100644
--- a/seed_embeddings/README.md
+++ b/seed_embeddings/README.md
@@ -1,6 +1,10 @@
 # Pretrained Embeddings
 If you do not want to train the model to generate seed embeddings, and want to use the pretrained vocabulary, please use the vocabulary given [here](../vocabulary) and skip the following.
 
+Currently we support three different embedding dimensions (`75`, `100`, `300`). If you want to use embeddings of different dimensions, you can follow the below steps and copy the resultant
+vocabulary to the [vocabulary](../vocabulary) directory with the following naming convention. `seedEmbeddingVocab<DIM>D.txt`. Such vocabularies would be automatically used during the build
+process of IR2Vec.
+
 # Generation of Seed Embedding Vocabulary
 This directory helps in generating seed embedding vocabulary in 3 steps.
 1. [Building ir2vec](#step-1-building-ir2vec)
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index e4c7adaaf..c8f2b2374 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -1,16 +1,69 @@
-
 configure_file (./include/version.h.cmake version.h @ONLY)
 include_directories(./include ${CMAKE_CURRENT_BINARY_DIR})
 include_directories(${CMAKE_BINARY_DIR})
-set(commonsrc FlowAware.cpp Symbolic.cpp utils.cpp)
-set(libsrc libIR2Vec.cpp ${commonsrc})
-set(binsrc CollectIR.cpp IR2Vec.cpp)
+
 option(LLVM_IR2VEC "where to enable IR2Vec as subproject for LLVM" OFF)
+
+set(GENERATED_HEADERS_DIR "${CMAKE_BINARY_DIR}/include")
+file(MAKE_DIRECTORY ${GENERATED_HEADERS_DIR})
+
+file(GLOB VOCAB_FILES "../vocabulary/seedEmbeddingVocab*D.txt")
+
+# List to hold supported dimensions
+set(DIMENSIONS "")
+
+foreach(VOCAB_FILE IN LISTS VOCAB_FILES)
+    get_filename_component(VOCAB_NAME ${VOCAB_FILE} NAME_WE)  # Extract just the file name
+    string(REGEX MATCH "[0-9]+" DIM ${VOCAB_NAME})
+    execute_process(
+        COMMAND python3 generate_vocabulary.py vocab ${VOCAB_FILE} ${GENERATED_HEADERS_DIR}
+        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
+        RESULT_VARIABLE GENERATE_VOCABULARY_RESULT
+        OUTPUT_VARIABLE GENERATE_VOCABULARY_OUTPUT
+        ERROR_VARIABLE GENERATE_VOCABULARY_ERROR
+    )
+
+    if(NOT GENERATE_VOCABULARY_RESULT EQUAL 0)
+      message(FATAL_ERROR "Failed to generate vocabulary files: ${GENERATE_VOCABULARY_ERROR}")
+    else()
+      message(STATUS "Generated vocabulary file: Vocabulary${DIM}D.h")
+    endif()
+
+    list(APPEND DIMENSIONS ${DIM})
+endforeach()
+
+# Check if DIMENSIONS is not empty
+if(NOT DIMENSIONS)
+  message(FATAL_ERROR "No vocabulary files generated")
+endif()
+
+foreach(DIM ${DIMENSIONS})
+  list(APPEND GENERATED_VOCAB_HEADERS "${GENERATED_HEADERS_DIR}/Vocabulary${DIM}D.h")
+endforeach()
+
+string(REPLACE ";" "," DIMENSIONS "${DIMENSIONS}")
 execute_process(
-    COMMAND python3 generate_vocabulary.py -o ${CMAKE_BINARY_DIR}/vocabulary.h
+    COMMAND python3 generate_vocabulary.py base ${DIMENSIONS} ${GENERATED_HEADERS_DIR}
     WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
-    COMMAND echo "Vocabulary file generated."
+    RESULT_VARIABLE GENERATE_BASE_RESULT
+    OUTPUT_VARIABLE GENERATE_BASE_OUTPUT
+    ERROR_VARIABLE GENERATE_BASE_ERROR
 )
+
+if(NOT GENERATE_BASE_RESULT EQUAL 0)
+  message(FATAL_ERROR "Failed to generate base vocabulary file: ${GENERATE_BASE_ERROR}")
+else()
+  message(STATUS "Generated base vocabulary file")
+endif()
+
+list(APPEND GENERATED_VOCAB_HEADERS "${GENERATED_HEADERS_DIR}/Vocabulary.h")
+
+include_directories(${GENERATED_HEADERS_DIR})
+
+set(commonsrc FlowAware.cpp Symbolic.cpp utils.cpp ${GENERATED_HEADERS_DIR}/VocabularyFactory.cpp)
+set(libsrc libIR2Vec.cpp ${commonsrc})
+set(binsrc CollectIR.cpp IR2Vec.cpp)
+
 if(NOT LLVM_IR2VEC)
 
   set(LT_LLVM_INSTALL_DIR "" CACHE PATH "LLVM installation directory")
@@ -36,10 +89,12 @@ if(NOT LLVM_IR2VEC)
 
   add_library(${IR2VEC_LIB} SHARED $<TARGET_OBJECTS:objlib>)
   add_library(${IR2VEC_LIB_STATIC} STATIC $<TARGET_OBJECTS:objlib>)
+
   set_target_properties(${IR2VEC_LIB} ${IR2VEC_LIB_STATIC} PROPERTIES
       VERSION ${PROJECT_VERSION}
       SOVERSION 1
       PUBLIC_HEADER "./include/IR2Vec.h"
+      PUBLIC_HEADER "${GENERATED_VOCAB_HEADERS}"
       OUTPUT_NAME ${IR2VEC_LIB}
       LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib
       ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib
diff --git a/src/FlowAware.cpp b/src/FlowAware.cpp
index 8e1e539cd..c627dc377 100644
--- a/src/FlowAware.cpp
+++ b/src/FlowAware.cpp
@@ -97,11 +97,11 @@ void IR2Vec_FA::collectWriteDefsMap(Module &M) {
 
 Vector IR2Vec_FA::getValue(std::string key) {
   Vector vec(DIM, 0);
-  if (opcMap.find(key) == opcMap.end()) {
+  if (vocabulary.find(key) == vocabulary.end()) {
     IR2VEC_DEBUG(errs() << "cannot find key in map : " << key << "\n");
     dataMissCounter++;
   } else
-    vec = opcMap[key];
+    vec = vocabulary[key];
   return vec;
 }
 
@@ -493,7 +493,7 @@ Vector IR2Vec_FA::func2Vec(Function &F,
     outs() << sets << " ";
   } outs() << "\n";);
 
-  SmallVector<double, DIM> prevVec;
+  Vector prevVec;
   Instruction *argToKill = nullptr;
 
   while (stack.size() != 0) {
diff --git a/src/IR2Vec.cpp b/src/IR2Vec.cpp
index 3946b1b6d..6fe0b3a98 100644
--- a/src/IR2Vec.cpp
+++ b/src/IR2Vec.cpp
@@ -6,11 +6,12 @@
 //
 //===----------------------------------------------------------------------===//
 
-#include "IR2Vec.h"
 #include "CollectIR.h"
 #include "FlowAware.h"
 #include "Symbolic.h"
+#include "Vocabulary.h"
 #include "version.h"
+
 #include "llvm/Support/CommandLine.h"
 #include <stdio.h>
 #include <time.h>
@@ -36,7 +37,9 @@ cl::opt<bool> cl_collectIR(
     cl::init(false), cl::cat(category));
 cl::opt<std::string> cl_iname(cl::Positional, cl::desc("Input file path"),
                               cl::Required, cl::cat(category));
-
+cl::opt<unsigned> cl_dim("dim", cl::Optional, cl::init(300),
+                         cl::desc("Dimension of the embeddings"),
+                         cl::cat(category));
 cl::opt<std::string> cl_oname("o", cl::Required, cl::desc("Output file path"),
                               cl::cat(category));
 // for on demand generation of embeddings taking function name
@@ -80,7 +83,7 @@ int main(int argc, char **argv) {
   collectIR = cl_collectIR;
   iname = cl_iname;
   oname = cl_oname;
-  // newly added
+  DIM = cl_dim;
   funcName = cl_funcName;
   level = cl_level;
   cls = cl_cls;
@@ -113,9 +116,11 @@ int main(int argc, char **argv) {
     exit(1);
 
   auto M = getLLVMIR();
+  auto vocabulary = VocabularyFactory::createVocabulary(DIM)->getVocabulary();
+
   // newly added
   if (sym && !(funcName.empty())) {
-    IR2Vec_Symbolic SYM(*M);
+    IR2Vec_Symbolic SYM(*M, vocabulary);
     std::ofstream o;
     o.open(oname, std::ios_base::app);
     if (printTime) {
@@ -132,7 +137,7 @@ int main(int argc, char **argv) {
     }
     o.close();
   } else if (fa && !(funcName.empty())) {
-    IR2Vec_FA FA(*M);
+    IR2Vec_FA FA(*M, vocabulary);
     std::ofstream o, missCount, cyclicCount;
     o.open(oname, std::ios_base::app);
     missCount.open("missCount_" + oname, std::ios_base::app);
@@ -153,7 +158,7 @@ int main(int argc, char **argv) {
     }
     o.close();
   } else if (fa) {
-    IR2Vec_FA FA(*M);
+    IR2Vec_FA FA(*M, vocabulary);
     std::ofstream o, missCount, cyclicCount;
     o.open(oname, std::ios_base::app);
     missCount.open("missCount_" + oname, std::ios_base::app);
@@ -172,7 +177,7 @@ int main(int argc, char **argv) {
     }
     o.close();
   } else if (sym) {
-    IR2Vec_Symbolic SYM(*M);
+    IR2Vec_Symbolic SYM(*M, vocabulary);
     std::ofstream o;
     o.open(oname, std::ios_base::app);
     if (printTime) {
diff --git a/src/Symbolic.cpp b/src/Symbolic.cpp
index 68d9014b0..a8f75c9a2 100644
--- a/src/Symbolic.cpp
+++ b/src/Symbolic.cpp
@@ -28,11 +28,11 @@ using namespace IR2Vec;
 using abi::__cxa_demangle;
 
 Vector IR2Vec_Symbolic::getValue(std::string key) {
-  Vector vec;
-  if (opcMap.find(key) == opcMap.end())
+  Vector vec(DIM, 0);
+  if (vocabulary.find(key) == vocabulary.end())
     IR2VEC_DEBUG(errs() << "cannot find key in map : " << key << "\n");
   else
-    vec = opcMap[key];
+    vec = vocabulary[key];
   return vec;
 }
 
diff --git a/src/generate_vocabulary.py b/src/generate_vocabulary.py
index bca3aadb5..80068717e 100644
--- a/src/generate_vocabulary.py
+++ b/src/generate_vocabulary.py
@@ -7,73 +7,198 @@
 # ------------------------------------------------------------------------------
 
 import argparse
-
-parser = argparse.ArgumentParser()
-parser.add_argument("-o", "--output", type=str, help="Output file name")
-args = parser.parse_args()
-
-if args.output is None:
-    print("Error: Output file path not provided.")
-    exit(1)
-
-output_file = args.output
-vocab_file = "../vocabulary/seedEmbeddingVocab.txt"
-
-# Define headers and opening/closing of map
-header = """\
-// Generated by IR2Vec. DO NOT EDIT!
-// This file contains the learned vocabulary used by IR2Vec.
-//
-// clang-format off
-
-#ifndef __VOCABULARY__
-#define __VOCABULARY__
-
-#include <map>
-#include <string>
-#include <vector>
-#include "IR2Vec.h"
-
-namespace IR2Vec {
-
-class Vocabulary {
-public:
-    static const std::map<std::string, IR2Vec::Vector>& getVocabulary() {
-        return vocabulary;
-    }
-private:
-    static const std::map<std::string, IR2Vec::Vector> vocabulary;
-};
-
-"""
-
-opening = "\nconst std::map<std::string, IR2Vec::Vector> Vocabulary::vocabulary = {\n"
-closing = """\
+import sys
+import os
+
+
+class VocabularyError(Exception):
+    """Custom exception for vocabulary errors."""
+
+    pass
+
+
+# Common header strings
+HEADER_GENERATED = "// Generated by IR2Vec. DO NOT EDIT!\n"
+HEADER_VOCABULARY_BASE = "// This file defines the base class for vocabulary and the factory \n// for creating vocabulary instances used by IR2Vec.\n"
+HEADER_VOCABULARY_CLASS = (
+    "// This file defines the {class_name} vocabulary class used by IR2Vec.\n"
+)
+HEADER_CLANG_FORMAT_OFF = "// clang-format off\n"
+
+
+def write_file(file_path, content):
+    try:
+        with open(file_path, "w") as fw:
+            fw.write(content)
+    except IOError as e:
+        raise VocabularyError(f"Failed to write to {file_path}: {e}")
+
+
+def generate_base_vocabulary_header():
+    return (
+        f"{HEADER_GENERATED}"
+        f"{HEADER_VOCABULARY_BASE}"
+        f"{HEADER_CLANG_FORMAT_OFF}\n"
+        "#ifndef __VOCABULARY_H__\n"
+        "#define __VOCABULARY_H__\n\n"
+        "#include <map>\n"
+        "#include <string>\n"
+        "#include <vector>\n"
+        "#include <memory>\n"
+        "#include <stdexcept> // For std::invalid_argument\n"
+        "namespace IR2Vec {\n\n"
+        "using Vector = std::vector<double>;\n\n"
+        "class VocabularyBase {\n"
+        "public:\n"
+        "    virtual ~VocabularyBase() {}\n"
+        "    virtual const std::map<std::string, IR2Vec::Vector>& getVocabulary() const = 0;\n"
+        "};\n\n"
+        "class VocabularyFactory {\n"
+        "public:\n"
+        "    static std::unique_ptr<VocabularyBase> createVocabulary(unsigned dim);\n"
+        "};\n\n"
+        "} // namespace IR2Vec\n\n"
+        "#endif // __VOCABULARY_H__\n"
+    )
+
+
+def generate_vocabulary_class(vocab_file, class_name):
+    class_header = (
+        f"{HEADER_GENERATED}"
+        f"{HEADER_VOCABULARY_CLASS.format(class_name=class_name)}"
+        f"{HEADER_CLANG_FORMAT_OFF}\n"
+        f"#ifndef __{class_name.upper()}__\n"
+        f"#define __{class_name.upper()}__\n\n"
+        f"#include <map>\n"
+        f"#include <string>\n"
+        f"#include <vector>\n"
+        f'#include "Vocabulary.h" // Include the base class\n\n'
+        f"namespace IR2Vec {{\n\n"
+        f"class {class_name} : public VocabularyBase {{\n"
+        f"public:\n"
+        f"    const std::map<std::string, IR2Vec::Vector>& getVocabulary() const override{{\n"
+        f"        return vocabulary;\n"
+        f"    }}\n"
+        f"private:\n"
+        f"    static const std::map<std::string, IR2Vec::Vector> vocabulary;\n"
+        f"}};\n"
+    )
+
+    opening = (
+        f"\nconst std::map<std::string, IR2Vec::Vector> {class_name}::vocabulary = {{\n"
+    )
+    closing = """\
 };
 } // namespace IR2Vec
 
-#endif // __VOCABULARY__
+#endif // __{class_name.upper()}__
 """
-
-try:
-    with open(output_file, "w") as fw:
-        fw.write(header)
-        with open(vocab_file, "r") as fr:
-            # Write vector declarations to the output file
-            for line in fr.readlines():
-                key, val = line.strip().split(":")
-                e = val.find("]")
-                fw.write(f"const IR2Vec::Vector {key}_vector = {{  {val[1:e]} }};\n")
-
-        fw.write(opening)
-        with open(vocab_file, "r") as fr:
-            # Write map entries to the output file
-            for line in fr.readlines():
-                key, _ = line.strip().split(":")
-                fw.write(f'    {{ "{key}", {key}_vector }},\n')
-        fw.write(closing)
-
-    print(f"Generated {output_file}")
-
-except FileNotFoundError:
-    print(f"Error: Vocabulary file '{vocab_file}' not found.")
+    dim = class_name.replace("Vocabulary", "")
+    vector_definitions = ""
+    with open(vocab_file, "r") as fr:
+        for line in fr.readlines():
+            key, val = line.strip().split(":")
+            e = val.find("]")
+            vector_definitions += (
+                f"const IR2Vec::Vector {key}_vector_{dim} = {{  {val[1:e]} }};\n"
+            )
+
+    vocabulary_entries = ""
+    with open(vocab_file, "r") as fr:
+        for line in fr.readlines():
+            key, _ = line.strip().split(":")
+            vocabulary_entries += f'    {{ "{key}", {key}_vector_{dim} }},\n'
+
+    return class_header + vector_definitions + opening + vocabulary_entries + closing
+
+
+def generate_vocabulary_factory(class_names):
+    factory_header = (
+        f"{HEADER_GENERATED}"
+        f"{HEADER_VOCABULARY_BASE}"
+        f"{HEADER_CLANG_FORMAT_OFF}\n"
+        "#include <memory>\n"  # For std::unique_ptr
+        '#include "Vocabulary.h"\n\n'
+    )
+
+    # Include headers for each vocabulary class
+    for class_name in class_names:
+        factory_header += f'#include "{class_name}.h"\n'
+
+    factory_header += "\nnamespace IR2Vec {\n\n"
+
+    factory_function = (
+        "std::unique_ptr<VocabularyBase> VocabularyFactory::createVocabulary(unsigned dim) {\n"
+        "    switch (dim) {\n"
+    )
+
+    factory_cases = ""
+    for class_name in class_names:
+        dim = class_name.replace("Vocabulary", "").replace("D", "")
+        factory_cases += f"        case {dim}:\n            return std::make_unique<{class_name}>();\n"
+
+    factory_footer = """\
+        default:
+            throw std::invalid_argument("Invalid dimension for vocabulary creation.");
+    }
+}
+} // namespace IR2Vec\n
+"""
+    return factory_header + factory_function + factory_cases + factory_footer
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Generate vocabulary and factory files."
+    )
+    parser.add_argument(
+        "type", choices=["vocab", "base"], help="Type of files to generate."
+    )
+    parser.add_argument(
+        "input",
+        help="Input vocabulary text file or dimensions as a comma-separated list.",
+    )
+    parser.add_argument("output", help="Output file path.")
+
+    args = parser.parse_args()
+    if args.type is None or args.input is None or args.output is None:
+        raise VocabularyError("Type, input, or output not provided.")
+
+    if args.type == "vocab":
+        # Generate vocabulary class based on input file
+        vocab_file = args.input
+
+        # Extract dimension from the file name
+        dim = (
+            os.path.splitext(os.path.basename(vocab_file))[0]
+            .split("D")[0]
+            .split("Vocab")[-1]
+        )
+        class_name = f"Vocabulary{dim}D"
+        vocab_class_code = generate_vocabulary_class(vocab_file, class_name)
+        output_file = os.path.join(args.output, f"{class_name}.h")
+        write_file(output_file, vocab_class_code)
+
+    elif args.type == "base":
+        # Generate base vocabulary header and factory implementation
+        base_header_code = generate_base_vocabulary_header()
+        output_file = os.path.join(args.output, "Vocabulary.h")
+        write_file(output_file, base_header_code)
+
+        # Parse the dimensions from the input and generate class names
+        dimensions = args.input.split(",")
+        class_names = [
+            f"Vocabulary{dim}D" for dim in dimensions
+        ]  # Create class names based on dimensions
+
+        factory_code = generate_vocabulary_factory(class_names)
+        output_file = os.path.join(args.output, "VocabularyFactory.cpp")
+        write_file(output_file, factory_code)
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except VocabularyError as e:
+        sys.stderr.write(f"Error: {e}\n")  # Write errors to stderr
+        sys.exit(1)
diff --git a/src/include/FlowAware.h b/src/include/FlowAware.h
index 1f1b4eb83..b27d27510 100644
--- a/src/include/FlowAware.h
+++ b/src/include/FlowAware.h
@@ -28,6 +28,7 @@ class IR2Vec_FA {
 private:
   llvm::Module &M;
   std::string res;
+  IR2Vec::VocabTy &vocabulary;
   IR2Vec::Vector pgmVector;
   unsigned dataMissCounter;
   unsigned cyclicCounter;
@@ -130,9 +131,9 @@ class IR2Vec_FA {
   void updateFuncVecMapWithCallee(const llvm::Function *function);
 
 public:
-  IR2Vec_FA(llvm::Module &M) : M{M} {
+  IR2Vec_FA(llvm::Module &M, IR2Vec::VocabTy &vocab) : M{M}, vocabulary{vocab} {
 
-    pgmVector = IR2Vec::Vector(DIM, 0);
+    pgmVector = IR2Vec::Vector(IR2Vec::DIM, 0);
     res = "";
 
     memWriteOps.try_emplace("store", 1);
diff --git a/src/include/IR2Vec.h b/src/include/IR2Vec.h
index 32a1aaa5c..b67d7a0c1 100644
--- a/src/include/IR2Vec.h
+++ b/src/include/IR2Vec.h
@@ -13,37 +13,39 @@
 #include "llvm/IR/Module.h"
 #include <string>
 
+#include "Vocabulary.h"
 namespace IR2Vec {
 
-#define DIM 300
-using Vector = llvm::SmallVector<double, DIM>;
-
 enum IR2VecMode { FlowAware, Symbolic };
 
 class Embeddings {
   int generateEncodings(llvm::Module &M, IR2VecMode mode, char level = '\0',
-                        std::string funcName = "", std::ostream *o = nullptr,
-                        int cls = -1, float WO = 1, float WA = 0.2,
-                        float WT = 0.5);
+                        std::string funcName = "", unsigned dim = 300,
+                        std::ostream *o = nullptr, int cls = -1, float WO = 1,
+                        float WA = 0.2, float WT = 0.5);
 
   llvm::SmallMapVector<const llvm::Instruction *, Vector, 128> instVecMap;
   llvm::SmallMapVector<const llvm::Function *, Vector, 16> funcVecMap;
   Vector pgmVector;
+  std::map<std::string, IR2Vec::Vector> vocabulary;
 
 public:
   Embeddings() = default;
-  Embeddings(llvm::Module &M, IR2VecMode mode, std::string funcName = "",
-             float WO = 1, float WA = 0.2, float WT = 0.5) {
-    generateEncodings(M, mode, '\0', funcName, nullptr, -1, WO, WA, WT);
+  Embeddings(llvm::Module &M, IR2VecMode mode, unsigned dim = 300,
+             std::string funcName = "", float WO = 1, float WA = 0.2,
+             float WT = 0.5) {
+    vocabulary = VocabularyFactory::createVocabulary(dim)->getVocabulary();
+    generateEncodings(M, mode, '\0', funcName, dim, nullptr, -1, WO, WA, WT);
   }
 
   // Use this constructor if the representations ought to be written to a
   // file. Analogous to the command line options that are being used in IR2Vec
   // binary.
   Embeddings(llvm::Module &M, IR2VecMode mode, char level, std::ostream *o,
-             std::string funcName = "", float WO = 1, float WA = 0.2,
-             float WT = 0.5) {
-    generateEncodings(M, mode, level, funcName, o, -1, WO, WA, WT);
+             unsigned dim = 300, std::string funcName = "", float WO = 1,
+             float WA = 0.2, float WT = 0.5) {
+    vocabulary = VocabularyFactory::createVocabulary(dim)->getVocabulary();
+    generateEncodings(M, mode, level, funcName, dim, o, -1, WO, WA, WT);
   }
 
   // Returns a map containing instructions and the corresponding vector
diff --git a/src/include/Symbolic.h b/src/include/Symbolic.h
index 618672b9f..efad7e0f7 100644
--- a/src/include/Symbolic.h
+++ b/src/include/Symbolic.h
@@ -24,6 +24,7 @@ class IR2Vec_Symbolic {
 
 private:
   llvm::Module &M;
+  IR2Vec::VocabTy &vocabulary;
   IR2Vec::Vector pgmVector;
 
   IR2Vec::Vector getValue(std::string key);
@@ -37,8 +38,9 @@ class IR2Vec_Symbolic {
       instVecMap;
 
 public:
-  IR2Vec_Symbolic(llvm::Module &M) : M{M} {
-    pgmVector = IR2Vec::Vector(DIM, 0);
+  IR2Vec_Symbolic(llvm::Module &M, IR2Vec::VocabTy &vocab)
+      : M{M}, vocabulary{vocab} {
+    pgmVector = IR2Vec::Vector(IR2Vec::DIM, 0);
     res = "";
   }
 
diff --git a/src/include/utils.h b/src/include/utils.h
index ab7921ddf..1541f1c64 100644
--- a/src/include/utils.h
+++ b/src/include/utils.h
@@ -29,8 +29,8 @@ namespace IR2Vec {
     }                                                                          \
   })
 
-#define DIM 300
-using Vector = llvm::SmallVector<double, DIM>;
+using Vector = std::vector<double>;
+using VocabTy = std::map<std::string, Vector>;
 using abi::__cxa_demangle;
 
 extern bool fa;
@@ -46,7 +46,7 @@ extern float WO;
 extern float WA;
 extern float WT;
 extern bool debug;
-extern std::map<std::string, Vector> opcMap;
+extern unsigned DIM;
 std::unique_ptr<llvm::Module> getLLVMIR();
 void scaleVector(Vector &vec, float factor);
 // newly added
diff --git a/src/libIR2Vec.cpp b/src/libIR2Vec.cpp
index 69fd45214..6cceb48ad 100644
--- a/src/libIR2Vec.cpp
+++ b/src/libIR2Vec.cpp
@@ -17,9 +17,9 @@
 
 int IR2Vec::Embeddings::generateEncodings(llvm::Module &M,
                                           IR2Vec::IR2VecMode mode, char level,
-                                          std::string funcName, std::ostream *o,
-                                          int cls, float WO, float WA,
-                                          float WT) {
+                                          std::string funcName, unsigned dim,
+                                          std::ostream *o, int cls, float WO,
+                                          float WA, float WT) {
 
   IR2Vec::level = level;
   IR2Vec::cls = cls;
@@ -27,25 +27,26 @@ int IR2Vec::Embeddings::generateEncodings(llvm::Module &M,
   IR2Vec::WA = WA;
   IR2Vec::WT = WT;
   IR2Vec::funcName = funcName;
+  IR2Vec::DIM = dim;
 
   if (mode == IR2Vec::IR2VecMode::FlowAware && !funcName.empty()) {
-    IR2Vec_FA FA(M);
+    IR2Vec_FA FA(M, vocabulary);
     FA.generateFlowAwareEncodingsForFunction(o, funcName);
     instVecMap = FA.getInstVecMap();
     funcVecMap = FA.getFuncVecMap();
   } else if (mode == IR2Vec::IR2VecMode::FlowAware) {
-    IR2Vec_FA FA(M);
+    IR2Vec_FA FA(M, vocabulary);
     FA.generateFlowAwareEncodings(o);
     instVecMap = FA.getInstVecMap();
     funcVecMap = FA.getFuncVecMap();
     pgmVector = FA.getProgramVector();
   } else if (mode == IR2Vec::IR2VecMode::Symbolic && !funcName.empty()) {
-    IR2Vec_Symbolic SYM(M);
+    IR2Vec_Symbolic SYM(M, vocabulary);
     SYM.generateSymbolicEncodingsForFunction(0, funcName);
     instVecMap = SYM.getInstVecMap();
     funcVecMap = SYM.getFuncVecMap();
   } else if (mode == IR2Vec::IR2VecMode::Symbolic) {
-    IR2Vec_Symbolic SYM(M);
+    IR2Vec_Symbolic SYM(M, vocabulary);
     SYM.generateSymbolicEncodings(o);
     instVecMap = SYM.getInstVecMap();
     funcVecMap = SYM.getFuncVecMap();
diff --git a/src/utils.cpp b/src/utils.cpp
index 515afeb38..b9fc0e580 100644
--- a/src/utils.cpp
+++ b/src/utils.cpp
@@ -8,7 +8,7 @@
 
 #include "utils.h"
 #include "IR2Vec.h"
-#include "vocabulary.h"
+#include "Vocabulary.h"
 #include <fstream>
 #include <string>
 using namespace llvm;
@@ -27,8 +27,8 @@ float IR2Vec::WO;
 float IR2Vec::WA;
 float IR2Vec::WT;
 bool IR2Vec::debug;
-std::map<std::string, Vector> IR2Vec::opcMap =
-    IR2Vec::Vocabulary::getVocabulary();
+unsigned IR2Vec::DIM;
+
 std::unique_ptr<Module> IR2Vec::getLLVMIR() {
   SMDiagnostic err;
   static LLVMContext context;
diff --git a/vocabulary/seedEmbeddingVocab100D.txt b/vocabulary/seedEmbeddingVocab100D.txt
new file mode 100644
index 000000000..f026c5764
--- /dev/null
+++ b/vocabulary/seedEmbeddingVocab100D.txt
@@ -0,0 +1,63 @@
+add:[-8.52362216e-02, -2.45945796e-01, -4.24607731e-02, 2.64923483e-01, -1.41214542e-02, -1.07707255e-01, 4.51593101e-02, 1.02487214e-01, -1.48341414e-02, 1.81787178e-01, 6.67016208e-02, 4.46189977e-02, -8.23750347e-02, 1.83093518e-01, 3.32678035e-02, -1.46622390e-01, -4.92643900e-02, -2.55224146e-02, 6.04326949e-02, 1.88715130e-01, -3.68193164e-02, 6.94231167e-02, 4.10126410e-02, -6.84764013e-02, 1.23058945e-01, -9.83736390e-05, -5.54158911e-03, 2.94519085e-02, 2.26749685e-02, 1.17240392e-01, 6.11324720e-02, -1.81771517e-01, -1.76415052e-02, -4.01592515e-02, 1.78097621e-01, 7.06670852e-03, -4.57172357e-02, -1.50619969e-01, -9.44827124e-02, -1.75480068e-01, -7.60255978e-02, 1.07171066e-01, 2.22498715e-01, 7.22258165e-02, 7.20588341e-02, -1.16421148e-01, -9.41838771e-02, 1.80813923e-01, -8.05889741e-02, 1.46086052e-01, -9.98349935e-02, 3.29788476e-02, -9.92206857e-02, 9.08804759e-02, 3.78689431e-02, 5.88943586e-02, -4.59491797e-02, -8.29120278e-02, 7.95054361e-02, 1.78439990e-02, -2.58509982e-02, 7.56616369e-02, 1.04568325e-01, -1.56202599e-01, -2.57734563e-02, 1.93596855e-02, 1.29040807e-01, -8.24761093e-02, -1.73545837e-01, 1.42969966e-01, -1.28778622e-01, -3.95107232e-02, 1.33133652e-02, -3.37289199e-02, -6.24400079e-02, -1.31070897e-01, -4.30826917e-02, -1.26567945e-01, -2.15651900e-01, -5.67506291e-02, -3.29258330e-02, 1.65608257e-01, -1.88492145e-02, 2.33287752e-01, 3.00378054e-02, -1.76738545e-01, -8.04325193e-02, 1.87702760e-01, -2.39736848e-02, -9.85645577e-02, 3.72615233e-02, -1.12956636e-01, -9.50063169e-02, 3.42431143e-02, 1.22220606e-01, -5.39523130e-03, 1.98720489e-03, 9.11523402e-03, -6.93807155e-02, 6.17282577e-02]
+alloca:[-0.09549255, -0.12557219, 0.03379089, 0.12109557, -0.03531531, -0.2551805, 0.10788739, 0.09700596, -0.01370677, 0.1135722, 0.07690978, 0.05116574, -0.09614895, 0.19678918, 0.14742346, -0.00976098, 0.24440478, -0.03487564, -0.05972337, -0.07113709, -0.04069309, -0.00905408, 0.09381753, -0.05227873, 0.0185564, 0.00785588, 0.10815099, 0.09986073, 0.0029005, 0.12109574, 0.08769628, -0.07261439, -0.04622762, -0.11434357, 0.31857088, 0.05211561, -0.0501864, -0.08464269, -0.05009941, -0.16629553, -0.10458375, 0.03947812, 0.08374927, 0.02248335, 0.07524205, 0.12703352, 0.02529348, 0.05168853, -0.05606319, -0.13958237, -0.0585564, -0.05144294, -0.11440989, 0.12794414, 0.01429109, -0.02604704, 0.04282403, -0.17824344, -0.00103301, -0.00441383, -0.1290221, 0.11616968, 0.17841731, -0.17370644, 0.01878525, -0.13822994, 0.29534182, -0.08290374, -0.26018453, -0.2423333, 0.06269401, 0.15722081, 0.05572377, 0.03796894, 0.0097774, -0.09503853, -0.03461437, -0.18860193, 0.05917948, -0.05421929, 0.1554059, 0.06737203, 0.12789263, 0.18676774, -0.03264643, -0.14613546, 0.10640658, 0.04788931, 0.05284823, -0.07678876, -0.15884072, -0.09681277, -0.20900938, 0.13614763, 0.11053005, -0.01231453, 0.0082564, 0.01286345, 0.05174101, -0.03757114]
+and:[-0.0881869, -0.1832501, -0.02236259, -0.08309272, -0.01707565, -0.09593864, 0.05584465, 0.11377291, 0.0052051, 0.33537892, -0.02086726, -0.04760219, -0.07862076, 0.17689508, 0.06404671, -0.24831565, -0.04806908, -0.01249005, 0.06386451, 0.17768936, -0.03290997, 0.07213664, 0.0320717, -0.06150476, 0.08116434, 0.00066787, -0.09663099, 0.09602754, 0.02356921, 0.12101333, 0.05900703, -0.16276178, -0.03739112, -0.03569444, 0.15475409, -0.07160591, -0.04432394, -0.09916383, -0.00070206, -0.13641287, -0.0825344, 0.09731495, 0.21391882, 0.05829814, 0.16758765, 0.17819895, -0.10077256, 0.01100022, -0.15785712, -0.10925666, -0.19227467, 0.02036687, 0.03744258, 0.0705176, 0.01200844, 0.06924565, 0.02984295, 0.00166557, 0.01469694, 0.01338413, 0.00133804, 0.0685681, 0.06490856, -0.13547117, 0.13122046, -0.04225351, 0.12522636, -0.07368809, -0.23429681, 0.07449005, 0.08751217, -0.01046039, 0.03592191, 0.02029108, -0.10386209, 0.05748203, -0.27302966, -0.12220228, 0.09895118, -0.04531586, -0.02545726, 0.03405684, -0.01663586, -0.09172897, -0.02909598, -0.1601327, 0.08839633, 0.0608284, -0.09534211, -0.07028268, 0.03571701, -0.10825088, -0.11168069, 0.02237209, 0.09868738, -0.0014685, -0.00517186, -0.01344037, -0.09333953, -0.03477098]
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diff --git a/vocabulary/seedEmbeddingVocab.txt b/vocabulary/seedEmbeddingVocab300D.txt
similarity index 100%
rename from vocabulary/seedEmbeddingVocab.txt
rename to vocabulary/seedEmbeddingVocab300D.txt
diff --git a/vocabulary/seedEmbeddingVocab75D.txt b/vocabulary/seedEmbeddingVocab75D.txt
new file mode 100644
index 000000000..1db19858f
--- /dev/null
+++ b/vocabulary/seedEmbeddingVocab75D.txt
@@ -0,0 +1,63 @@
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+trunc:[-1.36811793e-01, 1.67938128e-01, -1.12058200e-01, 6.62409887e-02, 9.16510969e-02, 1.42627805e-01, -1.72531992e-01, -2.63230264e-01, -1.89903647e-01, -7.43066799e-03, 1.61957592e-01, 3.00346557e-02, 3.52388211e-02, 1.99403167e-01, -1.29218757e-01, 1.84870679e-02, 2.83326745e-01, -1.31465584e-01, 1.91873118e-01, 7.08255842e-02, -5.70347626e-03, -1.06859475e-01, -5.71674518e-02, -8.79198536e-02, -4.90500145e-02, -1.76253095e-01, -2.41765901e-01, -1.98071256e-01, -1.65196046e-01, -9.84440148e-02, -8.40751976e-02, -2.14823738e-01, 4.22967784e-02, -1.08100109e-01, -2.89799571e-02, -1.76890388e-01, 1.99554786e-01, -1.94301888e-01, 1.54805914e-01, -1.54455468e-01, -3.22737806e-02, -1.45465598e-01, -2.85377522e-04, 1.12740539e-01, 1.54975608e-01, 8.14621672e-02, -9.96743236e-03, 5.00754155e-02, -1.70285389e-01, 4.13373224e-02, -1.81011677e-01, 3.25282156e-01, 1.26180872e-02, 1.21963099e-01, -1.98182613e-01, -9.64406803e-02, 1.62552461e-01, 1.30482644e-01, -4.22334485e-03, -3.61689553e-02, 1.39273763e-01, 6.64202794e-02, -4.54185046e-02, 8.14628601e-02, -1.43102348e-01, 1.70172676e-01, 1.54201342e-02, -6.93341419e-02, 1.19819410e-01, -1.10864714e-02, 7.98286218e-03, -5.97647242e-02, -5.83026856e-02, 5.12241088e-02, 2.02081446e-03]
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+uitofp:[-0.28474295, 0.13217203, -0.16568871, 0.18379283, 0.11226938, 0.19207269, -0.19832176, -0.08162867, -0.12924019, -0.13496576, -0.00762922, 0.07567707, -0.15030566, 0.12156913, -0.1307342, -0.19167385, 0.11801702, -0.14493088, 0.10906817, 0.01311873, -0.01061422, -0.09030879, -0.251764, -0.03967099, 0.1586867, -0.14576115, -0.12991518, 0.10370931, -0.1244427, -0.14580499, -0.06180171, -0.14415021, 0.04207605, -0.11499359, -0.03921367, -0.12659079, 0.07339595, -0.15185502, 0.1766244, 0.17538792, 0.17453255, -0.1522221, 0.05713944, 0.0651423, 0.15804505, 0.08223064, -0.19757104, 0.04586276, -0.18602233, 0.03112394, -0.12740982, 0.10457055, 0.00761122, 0.1563707, -0.14385206, -0.10981123, 0.17529841, 0.1344809, -0.0042707, -0.01513807, 0.14462957, 0.09708796, 0.1768233, -0.15789527, -0.19911379, 0.08259769, 0.04403876, -0.07122029, 0.12193058, 0.00843644, -0.20593181, -0.04928745, -0.05748281, 0.06752896, -0.33122623]
+unreachable:[0.13830788, -0.1384795, 0.12985048, -0.01590925, -0.17242672, -0.16921625, -0.08732756, 0.06238424, -0.19026113, -0.0158169, 0.16435188, 0.02325607, -0.00133849, -0.12819748, -0.15801033, 0.06894314, -0.11699118, -0.03206338, -0.04867816, 0.08262745, -0.02899568, -0.17304559, 0.00456284, -0.1043093, -0.06316522, -0.19096501, 0.10812564, -0.17962073, -0.20548488, 0.1455722, 0.06589094, 0.03311252, -0.07200453, -0.18936579, 0.12057041, -0.19025533, -0.12212797, 0.1456004, -0.14757317, -0.15557618, -0.05640632, 0.14078732, 0.14334233, -0.16418514, 0.14397267, 0.0625571, 0.15891595, 0.07323151, -0.10111858, -0.14292835, 0.01567381, -0.10903962, 0.17538103, 0.08803182, -0.00309126, -0.14454287, 0.0691599, -0.11123948, 0.05696773, 0.09039135, 0.09839866, -0.05023813, -0.03404732, 0.02872319, 0.17865536, 0.13223949, -0.17297149, -0.16898467, 0.14986424, -0.10918473, 0.1600209, 0.15127546, 0.16981383, -0.1279901, 0.14220221]
+urem:[-0.11265117, 0.20686236, -0.12146873, -0.23542368, 0.09378337, 0.02999638, 0.14878128, -0.28188494, -0.20065261, 0.00433275, 0.16977304, -0.01879602, 0.22663146, 0.0617098, -0.1831158, 0.01779491, 0.03017136, -0.08971473, 0.07763614, 0.08042387, 0.09445225, -0.08793671, 0.0159521, -0.07481197, -0.05264295, -0.18723217, -0.08170687, -0.21142115, -0.1702957, -0.0912115, -0.15084253, -0.06110323, -0.1994154, 0.10485501, 0.02635208, -0.17529666, 0.08368196, -0.20547396, -0.13216047, -0.1626017, -0.03278501, -0.12043266, -0.13597806, 0.11791392, 0.16410512, 0.085006, 0.17119788, 0.20151798, 0.22284089, 0.03112911, -0.19075638, 0.10404129, 0.01210428, -0.01493562, -0.21803944, -0.00028815, 0.17211886, 0.1743311, -0.02144206, -0.18241248, 0.09792571, -0.04942631, -0.12507392, 0.14260796, -0.1524944, 0.19653419, -0.20759608, 0.22784927, 0.18324168, -0.03458956, 0.00935992, -0.05326611, -0.07238707, 0.04781368, -0.2394482]
+variable:[-0.04416586, -0.06529783, 0.06804077, -0.17281267, 0.21564505, -0.10317826, -0.04053801, -0.28083235, 0.11455013, -0.25391814, -0.09901162, -0.32934445, 0.22513658, -0.10261232, 0.10434292, -0.19085433, -0.02302103, 0.27024737, -0.11197262, -0.2376181, 0.28228357, 0.15263258, 0.12693042, 0.16975136, 0.29924616, 0.13014257, 0.02674635, -0.10427801, 0.1129915, 0.02840486, 0.17082089, 0.02872703, 0.3129407, 0.14322756, -0.20241754, 0.06491784, -0.08436896, 0.03400991, 0.07390775, -0.07466827, 0.3478845, 0.01303683, 0.26052088, 0.21157041, -0.25165147, -0.18953912, 0.07487449, -0.32147086, -0.072584, 0.20075838, 0.10203002, -0.06976248, -0.22996348, -0.15062493, -0.08361167, 0.1157522, 0.02569508, 0.0058547, -0.1650832, -0.1911111, 0.01633538, -0.13533501, -0.27804825, -0.05999075, 0.02358659, -0.07764025, -0.07134418, 0.14262564, -0.20307393, 0.17827222, 0.13546483, -0.24436948, 0.16881269, -0.08427223, -0.05620972]
+vectorTy:[0.06019246, -0.08325528, 0.06796158, -0.1322501, -0.05250492, -0.05546387, 0.00084803, -0.13031249, 0.10292219, 0.16610621, -0.10459585, 0.21548297, -0.26862848, 0.08505963, 0.07657417, 0.16639674, 0.06083921, -0.00245328, 0.01956977, 0.21975385, -0.16639455, 0.14512713, -0.15006405, -0.2066719, -0.18236274, 0.12637317, -0.09601986, 0.17929196, 0.15004157, -0.2437613, 0.11610509, -0.12576523, -0.13349846, 0.00924278, 0.05477205, 0.11079396, 0.0149577, 0.08251344, 0.02648669, 0.28141958, -0.16844293, 0.02850299, -0.1849258, -0.00724132, -0.19553263, 0.16303328, -0.09641571, -0.08541372, -0.01167152, -0.0498387, 0.13118263, 0.03702982, 0.16826656, -0.12130306, 0.05498985, 0.07180171, 0.01047648, 0.19805974, 0.1132893, 0.12827364, -0.15292895, -0.0969883, 0.15015276, 0.25131726, 0.03910673, -0.12237693, 0.12764196, 0.14949943, -0.11722989, -0.142063, 0.07540323, 0.00866951, -0.16423495, 0.1803801, 0.08819818]
+voidTy:[0.02707169, -0.1911424, 0.18000233, -0.10855684, -0.15720911, -0.16028677, 0.08442728, 0.14063193, 0.14310814, 0.15267418, 0.03571516, 0.17472863, -0.1096759, -0.04809512, 0.15041178, 0.1852203, -0.08891776, 0.06437854, 0.07771051, 0.17514206, -0.18457355, 0.17228611, 0.14382029, 0.17567936, -0.19985509, 0.14512071, 0.00597054, -0.0005212, 0.16043086, 0.01015274, 0.1932167, 0.10049, -0.18615878, -0.14139535, -0.07989334, 0.13810824, 0.00811669, 0.16482183, -0.0484471, 0.05603446, -0.19822212, 0.21622814, -0.13391475, -0.07386104, -0.01220543, -0.17700416, 0.02550385, -0.04609942, 0.19161303, -0.08407282, 0.17597687, 0.01678882, 0.17494556, -0.15565564, 0.18086468, 0.01991182, -0.07939529, -0.04929006, 0.17783763, 0.18024826, -0.14163949, 0.18041377, 0.11955009, 0.00620418, 0.1386835, -0.18642457, 0.09366929, 0.16274834, -0.12151442, -0.19673795, 0.0550727, 0.10491449, 0.14478931, -0.02286906, 0.05187593]
+xor:[-0.14333177, 0.17120288, -0.11569388, -0.22889645, 0.0944928, 0.13705893, -0.1748709, -0.27198055, -0.20403583, -0.00204914, 0.16587155, 0.03005969, 0.03635887, 0.12084309, -0.17170708, 0.01672503, 0.2855867, -0.13425359, 0.2921407, 0.0620962, 0.00266978, -0.0903588, -0.06116274, -0.08814006, -0.04630399, -0.18787035, -0.1754261, -0.20075771, -0.18573092, -0.11999942, -0.14531347, -0.171928, 0.04110451, -0.10397294, -0.03485578, -0.17938605, 0.08046042, -0.19675075, -0.14346175, -0.15566549, -0.0307311, -0.14872791, -0.03878547, -0.00254308, 0.1565295, 0.08097854, 0.16432391, 0.04949491, -0.17471117, 0.2079512, -0.17290911, 0.10197662, 0.01110592, 0.09702181, -0.21049088, -0.05485036, 0.16571115, 0.13224575, -0.00631514, -0.0666507, 0.10324501, 0.04556064, -0.0946413, 0.1366175, -0.14613271, 0.1439316, 0.01403525, -0.06756077, 0.15449193, 0.01570368, 0.00935933, -0.07182793, -0.05895546, 0.05251422, 0.00088228]
+zext:[0.14294301, 0.16004492, -0.133936, 0.17041178, 0.10354671, 0.14033805, -0.18445012, -0.25389433, -0.18157399, -0.06085023, 0.15627292, 0.05945444, 0.0152561, 0.11567469, -0.12275951, 0.01612445, 0.2721165, -0.12822908, 0.29646817, 0.06210494, -0.00723742, -0.1003171, -0.06575806, -0.05716129, -0.04435528, -0.16020222, -0.23530036, -0.18981609, -0.14945146, -0.11926567, -0.08064298, -0.2043876, 0.04202923, -0.10597136, -0.03096929, -0.17040831, 0.0771085, -0.18618186, 0.16527407, -0.14749476, -0.02966438, -0.14139591, 0.0018809, -0.02142575, 0.14795087, 0.07683742, -0.01349613, 0.04579562, -0.17163049, 0.02995836, -0.17292565, 0.11137506, 0.01039091, 0.13139422, -0.19006667, -0.09951284, 0.15722732, 0.12452687, -0.00450555, -0.02189622, 0.13461596, 0.06696767, -0.04037055, -0.13414587, -0.1399019, 0.1544194, 0.01091198, -0.0665535, 0.11422376, 0.00154535, -0.13661395, -0.04903502, -0.05472786, 0.05032588, 0.00220357]


Test patch:
diff --git a/Manylinux2014_Compliant_Source/pkg/tests/test_ir2vec.py b/Manylinux2014_Compliant_Source/pkg/tests/test_ir2vec.py
index 5399eab3f..118bc99ac 100644
--- a/Manylinux2014_Compliant_Source/pkg/tests/test_ir2vec.py
+++ b/Manylinux2014_Compliant_Source/pkg/tests/test_ir2vec.py
@@ -160,7 +160,7 @@ def test_fa_f():
         path = (TEST_SUITE_DIR / file).resolve()
         full_path = str(path).strip()
 
-        initObj = ir2vec.initEmbedding(full_path, "fa", "f")
+        initObj = ir2vec.initEmbedding(full_path, "fa", "f", 300)
         assert initObj is not None
 
         functionVectorMap = ir2vec.getFunctionVectors(initObj)
diff --git a/src/test-suite/oracle/SYM_llvm17_p/sqlite3.txt b/src/test-suite/oracle/SYM_llvm17_p/sqlite3.txt
index 7e5ab73b1..80aeff055 100644
--- a/src/test-suite/oracle/SYM_llvm17_p/sqlite3.txt
+++ b/src/test-suite/oracle/SYM_llvm17_p/sqlite3.txt
@@ -1,1 +1,1 @@
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