Implemented functionality to run binaries and started table generation

llvm/projects/hpvm-tensor-rt/build_pldi/table_generator.py

+import collections
+import glob
+import os 
+import subprocess
+import shutil 
+
+class TableGenerator: 
+    def __init__(self, dir_name, iters, profiler_binary_name):
+        self.__dir_name = dir_name
+        self.__network_name = dir_name
+
+        self.__iters = iters 
+        self.__profiler_binary_name = profiler_binary_name
+
+
+    def __is_binary(self, file_path):
+        # Binary name must start with the network name as per our naming standards
+        return os.path.isfile(file_path) and os.access(file_path, os.X_OK) and \
+                file_path.find(self.__network_name) != -1
+
+
+    def run_binaries_in_input_dir(self):
+        if not os.path.isdir(self.__dir_name):
+            print("ERROR: Directory %s not found" % self.__dir_name)
+            exit(1)
+        self.__results_dir_name = "%s_results" % self.__dir_name 
+
+        try:
+            os.mkdir(self.__results_dir_name)
+        except OSError:
+            if os.path.isdir(self.__results_dir_name):
+                print("Directory already exists. Clearing directory.")
+                for old_file in glob.glob(os.path.join(self.__results_dir_name, "*")):
+                    os.remove(old_file)
+            else:
+                print("ERROR: Directory doesn't exist but failed to create dir")
+
+        for binary_name in os.listdir(self.__dir_name):
+            binary_path = os.path.join(self.__dir_name, binary_name)
+
+            if not self.__is_binary(binary_path):
+                continue
+
+            if not os.path.isfile(binary_path):
+                print("ERROR: Binary %s not found" % binary_path)
+                exit(1)
+
+            output_file = os.path.join(self.__results_dir_name, binary_name + ".txt")
+            # No stdout/stderr piping needed for now
+            subprocess.Popen([profiler_binary_name, binary_path, str(self.__iters), \
+                        output_file]).communicate()
+
+
+    def __get_approximation_type(self, results_filename):
+        approx_type_start_ind = results_file.find(self.__network_name) \
+                + len(self.__network_name) + 1 # + 1 to account for _ delimiter
+        approx_type_end_ind = results_file.find(".txt")
+        return results_filename[approx_type_start_ind : approx_type_end_ind] 
+   
+
+    def generate_table(self):
+        # Copy ops file to results directory to use as empty table 
+        table_filename = os.path.join(self.__results_dir_name, \
+                    "%s_tensors.txt" % self.__network_name) 
+        # TODO un hard code this 
+        soc_operations_file_name = os.path.join("/home/nvidia/soc_simulator", \
+                        "%s_cifar10" % self.__network_name)
+        shutil.copyfile(soc_operations_file_name, table_filename) 
+      
+        # Local table --> avoid setting any global variables in case this class is 
+        # reused multiple times
+        table = defaultdict(defaultdict(str))
+        for results_file_name in os.listdir(self.__results_dir_name):
+            # Ignore if not a results file
+            if not results_file_name.startswith(self.__network_name):
+                continue
+
+            approx_type = self.__get_approximation_type(results_file_name)
+            print(approx_type)
+            results_file = open(os.path.join(self.__results_dir_name, results_file_name), "r")
+            for line in results_file:
+                print(line)
+            results_file.close()
+        # For each output file --> parse and get the storage files 
+        # For each line --> store the time and energy 
+
+        # <operation name> <type of approx> <time> <energy>
+        # Need to map to soc_operations file 
+        # final file should be in the format of the soc_operations file
+
+binary_dir_name = "/home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/build_pldi/mobilenet"
+num_iters = 1 
+profiler_binary_name = "/home/nvidia/awesome_profiler/pp"
+table_gen = TableGenerator(binary_dir_name, num_iters, profiler_binary_name)
+table_gen.run_binaries_in_input_dir()
+table_gen.generate_tables()