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..b8182b5b3 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; + uint 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, uint 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 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, funcName, this->dim)); } else { emb = std::move(new IR2Vec::Embeddings( - *Module, ir2vecMode, (this->level)[0], nullptr, funcName)); + *Module, ir2vecMode, (this->level)[0], nullptr, funcName, this->dim)); } if (!emb) { @@ -178,7 +178,7 @@ class IR2VecHandler { } if (type == OpType::Program) { - llvm::SmallVector progVector = emb->getProgramVector(); + IR2Vec::Vector progVector = emb->getProgramVector(); return this->createProgramVectorList(progVector); } else if (type == OpType::Function) { llvm::SmallMapVector @@ -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, + uint 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"; + uint 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/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/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 -\ -o \ -level \ -class \ -funcName=\ \ +> ir2vec -\ -dim \ -o \ -level \ -class \ -funcName=\ \ #### 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 -level -class `` +* `` ir2vec -fa -dim -o -level -class `` For a specific function -* `` ir2vec -fa -o -level f -class -funcName=\`` +* `` ir2vec -fa -dim -o -level f -class -funcName=\`` #### Symbolic Embeddings For all functions - * `` ir2vec -sym -o -level -class `` + * `` ir2vec -sym -dim -o -level -class `` For a specific function - * `` ir2vec -sym -o -level f -class -funcName=\ `` + * `` ir2vec -sym -dim -o -level f -class -funcName=\ `` ## 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(, IR2Vec::IR2VecMode::FlowAware); + IR2Vec::Embeddings(, IR2Vec::IR2VecMode::FlowAware, ); // Getting Instruction vectors corresponding to the instructions in 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. `seedEmbeddingVocabD.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 $) add_library(${IR2VEC_LIB_STATIC} STATIC $) + set_target_properties(${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 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 #include @@ -36,7 +37,9 @@ cl::opt cl_collectIR( cl::init(false), cl::cat(category)); cl::opt cl_iname(cl::Positional, cl::desc("Input file path"), cl::Required, cl::cat(category)); - +cl::opt cl_dim("dim", cl::Optional, cl::init(300), + cl::desc("Dimension of the embeddings"), + cl::cat(category)); cl::opt 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 -#include -#include -#include "IR2Vec.h" - -namespace IR2Vec { - -class Vocabulary { -public: - static const std::map& getVocabulary() { - return vocabulary; - } -private: - static const std::map vocabulary; -}; - -""" - -opening = "\nconst std::map 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 \n" + "#include \n" + "#include \n" + "#include \n" + "#include // For std::invalid_argument\n" + "namespace IR2Vec {\n\n" + "using Vector = std::vector;\n\n" + "class VocabularyBase {\n" + "public:\n" + " virtual ~VocabularyBase() {}\n" + " virtual const std::map& getVocabulary() const = 0;\n" + "};\n\n" + "class VocabularyFactory {\n" + "public:\n" + " static std::unique_ptr 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 \n" + f"#include \n" + f"#include \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& getVocabulary() const override{{\n" + f" return vocabulary;\n" + f" }}\n" + f"private:\n" + f" static const std::map vocabulary;\n" + f"}};\n" + ) + + opening = ( + f"\nconst std::map {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 \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 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 +#include "Vocabulary.h" namespace IR2Vec { -#define DIM 300 -using Vector = llvm::SmallVector; - 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 instVecMap; llvm::SmallMapVector funcVecMap; Vector pgmVector; + std::map 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; +using Vector = std::vector; +using VocabTy = std::map; 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 opcMap; +extern unsigned DIM; std::unique_ptr 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/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 @@ -16914.214172 1840.963612 -8056.768579 21579.305453 -906.262205 -14727.750711 4137.897728 12411.245494 -6586.577729 -37492.964734 -23503.579107 24019.665282 3178.140591 -4723.500753 5407.842244 7912.921167 -3458.007608 3013.286848 -43144.929418 15115.546246 13531.053632 4908.258806 -5820.302126 -7871.660645 3889.551273 -10608.906240 1448.299046 10127.834420 1014.800933 22875.036123 -11217.396606 7132.652507 18359.679961 -541.432431 9754.327113 19447.189792 -5458.297947 1001.803515 1260.266054 429.515246 30198.629348 18262.393044 -3520.028532 10885.129589 9038.867032 23509.461660 -2190.924487 20668.772705 -4464.237518 -15003.251139 10138.776080 7383.103845 -17749.750384 -2971.023222 -14480.273423 9624.178060 19172.138324 8872.303230 -11147.777716 11520.362299 -11789.698651 -2464.035909 7416.324752 381.487262 27537.998172 14634.787225 15946.290784 14708.178722 30142.986762 -11951.156505 -2847.544498 13036.279684 11946.873288 -2281.002892 14178.835480 -9445.563955 -21959.072204 -11427.626110 14.950048 -38164.969681 -15326.407916 -604.005724 40969.720254 -22921.026771 1021.245459 6482.292367 8335.949495 22709.037213 -15671.772052 -634.916992 -16388.880672 -88.372465 -10411.435811 38141.528995 1444.237535 7092.407774 -3015.810814 9673.987822 10480.803898 17367.615576 -5099.230566 -15713.135321 -16277.581293 -37081.163429 -8470.125739 -8772.651484 3261.668365 7589.841179 -31083.117699 -6787.744450 7673.834177 27727.294920 -34264.453388 -1024.019126 18412.358452 9384.182441 16855.866440 7579.575966 -20625.224866 4570.472021 1587.196202 1714.091642 13996.430750 -8729.059297 -1445.531903 -5345.869665 6737.841282 747.071812 5712.435997 1316.571824 -37393.893115 -11137.285625 3457.923217 -7597.644020 934.439596 4310.015852 8189.372290 -13761.423625 12699.496546 -14782.421306 18183.527744 -33691.703352 -517.262789 -11239.399834 -19974.966452 -3504.060581 16885.975472 25761.155805 14053.165475 -6681.317758 -6153.418469 -7325.079859 -30356.658229 -34656.871696 6283.765911 -1758.163001 8988.464773 -1522.551626 -1673.995728 5009.828211 -9656.368676 1426.665633 9335.348034 -11649.556808 -1566.318860 -6295.734684 31544.197891 661.649733 -4830.674434 -14478.650575 -8427.805505 -545.973874 2502.690463 15002.196104 -6300.676337 -33845.085246 -34194.679896 -18256.095681 19120.451916 -4796.204741 -1357.198324 -13606.642437 -3021.046722 -18445.044067 3332.247063 -17445.313795 6821.379552 -1561.129509 -13353.268278 -23115.474322 472.936692 -1898.996769 -9484.504976 -5017.053747 -15333.591479 -5749.949470 18458.335412 4047.862621 8084.542286 3028.814311 1129.188741 -21428.210033 -19353.038396 5786.410664 -7532.951615 21430.806942 19577.147353 459.479687 -18445.971763 -4433.705096 -27620.540913 19828.835893 -17033.978568 4474.063320 -6138.081727 -9289.859715 29276.565507 -13631.410108 -7187.016573 -18083.697502 8781.137185 3854.077762 -34757.682776 32445.021522 20843.803376 -11541.272980 -4382.951260 -1828.406638 2398.068724 18359.047156 21539.259133 -15484.985073 -13341.607581 3250.685126 -6791.923469 -1860.182208 -395.840937 16642.290996 -16803.371117 11532.507834 -15286.484050 -9243.681272 12537.733834 16148.825960 493.864148 8958.987453 -833.191497 -2730.725876 8574.123858 17163.311834 13874.951473 -6250.890749 -19190.295017 -44012.399335 13558.447731 -8566.129714 -6785.466744 -403.105960 -9429.032390 -19264.410796 3993.546581 -5802.231716 24354.598056 802.353023 -11928.318532 16617.764262 -27415.844372 -24580.951602 -13247.186538 2753.917039 -291.838695 -5530.368520 7072.636033 -7053.490037 6451.836519 9084.519734 856.179694 1720.038980 -17071.204665 -10241.571031 -236.670560 18695.744940 8736.999602 -4602.100388 24403.244235 5754.482284 -10343.410231 -6480.548159 858.463332 31.853096 -2285.052285 -20497.891846 -9698.522648 -9221.781745 8000.340915 -9367.694417 14972.296732 1337.978384 1709.723559 9941.290745 +16914.239737 1840.981802 -8056.817911 21579.296520 -906.316497 -14727.797115 4137.876401 12411.280953 -6586.562078 -37492.964788 -23503.569943 24019.669262 3178.205893 -4723.530913 5407.877824 7912.934175 -3458.056365 3013.332508 -43144.954157 15115.587790 13531.105645 4908.255795 -5820.292873 -7871.662108 3889.509265 -10608.904523 1448.248686 10127.800801 1014.830101 22875.078128 -11217.396718 7132.679162 18359.717864 -541.438802 9754.257737 19447.217860 -5458.309037 1001.817028 1260.314788 429.579484 30198.570108 18262.427973 -3520.038288 10885.075944 9038.812628 23509.483794 -2190.944232 20668.774651 -4464.250265 -15003.294026 10138.764168 7383.131005 -17749.769020 -2971.040689 -14480.221331 9624.128167 19172.139926 8872.318439 -11147.725663 11520.408401 -11789.705205 -2464.030694 7416.382130 381.455867 27538.019036 14634.785688 15946.268038 14708.151110 30142.996306 -11951.106543 -2847.509524 13036.231473 11946.824998 -2281.054380 14178.859285 -9445.563182 -21959.103018 -11427.606841 14.912112 -38164.977695 -15326.420244 -603.970317 40969.728375 -22921.064083 1021.286277 6482.304357 8335.991977 22709.055046 -15671.783603 -634.899949 -16388.840622 -88.332475 -10411.444594 38141.554630 1444.259569 7092.443766 -3015.865027 9673.946988 10480.841084 17367.560567 -5099.253692 -15713.142826 -16277.614827 -37081.121866 -8470.087895 -8772.695145 3261.617294 7589.888150 -31083.167349 -6787.786026 7673.836229 27727.297982 -34264.481233 -1024.028217 18412.394089 9384.169209 16855.899739 7579.575724 -20625.235935 4570.505635 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4047.839714 8084.505585 3028.808299 1129.165422 -21428.166980 -19353.033854 5786.461144 -7532.987514 21430.789465 19577.137261 459.527968 -18446.000678 -4433.673779 -27620.511413 19828.856589 -17033.992846 4474.089827 -6138.113014 -9289.834124 29276.613087 -13631.383000 -7187.036561 -18083.712847 8781.180054 3854.047879 -34757.666827 32445.019073 20843.795104 -11541.262047 -4382.903431 -1828.347616 2398.051750 18359.092437 21539.233307 -15484.982620 -13341.666013 3250.718549 -6791.955915 -1860.230467 -395.803710 16642.242498 -16803.428262 11532.492267 -15286.435404 -9243.626667 12537.684768 16148.850751 493.906823 8958.963143 -833.235873 -2730.757479 8574.134740 17163.255209 13874.967743 -6250.944147 -19190.288741 -44012.376401 13558.481189 -8566.152492 -6785.430969 -403.129459 -9429.027801 -19264.395602 3993.598922 -5802.239763 24354.552599 802.349434 -11928.311191 16617.765536 -27415.878160 -24581.004090 -13247.229632 2753.975665 -291.792378 -5530.310934 7072.628982 -7053.494335 6451.878346 9084.561345 856.200951 1720.079651 -17071.271865 -10241.539163 -236.615965 18695.779908 8736.988008 -4602.147639 24403.284676 5754.462882 -10343.425092 -6480.598526 858.419776 31.810921 -2285.101590 -20497.857665 -9698.512085 -9221.734122 8000.390882 -9367.662544 14972.344512 1337.949051 1709.686658 9941.308840 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 #include using namespace llvm; @@ -27,8 +27,8 @@ float IR2Vec::WO; float IR2Vec::WA; float IR2Vec::WT; bool IR2Vec::debug; -std::map IR2Vec::opcMap = - IR2Vec::Vocabulary::getVocabulary(); +unsigned IR2Vec::DIM; + std::unique_ptr 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, 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