diff --git a/llvm/projects/soc_simulator/src/driver.py b/llvm/projects/soc_simulator/src/driver.py
index b685170da40c17dc45b7258cbbe2166ac52736fc..895a72d9e90f1a0559716944d598cb410e8d0d9b 100644
--- a/llvm/projects/soc_simulator/src/driver.py
+++ b/llvm/projects/soc_simulator/src/driver.py
@@ -3,62 +3,82 @@ import os
import subprocess
import sys
-def build_nested_default_dict():
- return defaultdict(build_nested_default_dict)
-
-def is_conv(operation_name):
- return operation_name.startswith("Conv")
-
-def is_nml(operation_name):
- return operation_name.startswith("NML")
-
-def is_fc(operation_name):
- return operation_name.startswith("FC")
-
-# NOTE: Use an OrderedDict if we want to search by operation name
-# Using a list bc we care about the order the data is read in
-# since it corresponds to the data in the configuration file
-tensor_layers = []
-def parse_tensor_layer_file(layer_filename):
- if not os.path.isfile(layer_filename):
- print("ERROR: %s was not found." % layer_filename)
- exit(1)
-
- layer_file = open(layer_filename, "r")
- for line in layer_file:
- layer_data = line.strip().split(',')
- layer_name = layer_data[0]
-
- tensor_layer = defaultdict(str)
- tensor_layer["Name"] = layer_name
-
- if is_conv(layer_name):
- tensor_layer["N"] = float(layer_data[1])
- tensor_layer["Cin"] = float(layer_data[2])
- tensor_layer["H"] = float(layer_data[3])
- tensor_layer["W"] = float(layer_data[4])
- tensor_layer["Cout"] = float(layer_data[5])
- tensor_layer["Kh"] = float(layer_data[7])
- tensor_layer["Kw"] = float(layer_data[8])
- tensor_layer["Sh"] = float(layer_data[9])
- tensor_layer["Sw"] = float(layer_data[10])
-
- elif is_fc(layer_name):
- tensor_layer["RA"] = float(layer_data[1])
- tensor_layer["CA"] = float(layer_data[2])
- tensor_layer["RB"] = float(layer_data[3])
- tensor_layer["CB"] = float(layer_data[4])
-
- elif not is_nml(layer_name): # TODO should we store data for NMLs?
- print("ERROR: Invalid layer name %s" % layer_name)
- exit(1)
-
- tensor_layers.append(tensor_layer)
- layer_file.close()
-
+class Driver:
+ def driver(self):
+ self.parse_tensor_layer_file()
+ #self.parse_tensor_table()
+ #self.run_simulations()
+ #self.display_results()
+
+ def __init__(self, layer_filename, table_filename, config_filename, results_filename):
+ self.__layer_filename = layer_filename
+ self.__table_filename = table_filename
+ self.__config_filename = config_filename
+ self.__results_filename = results_filename
+
+ # NOTE: Use an OrderedDict if we want to search by operation name
+ # Using a list bc we care about the order the data is read in
+ # since it corresponds to the data in the configuration file
+ self.__tensor_layers = []
+
+ @staticmethod
+ def is_conv(operation_name):
+ return operation_name.startswith("Conv")
+
+ @staticmethod
+ def is_nml(operation_name):
+ return operation_name.startswith("NML")
+
+ @staticmethod
+ def is_fc(operation_name):
+ return operation_name.startswith("FC")
+
+ def driver(self):
+ self.parse_tensor_layer_file()
+ #self.parse_tensor_table()
+ #self.run_simulations()
+ #self.display_results()
+
+ def parse_tensor_layer_file(self):
+ if not os.path.isfile(self.__layer_filename):
+ print("ERROR: %s was not found." % self.__layer_filename)
+ exit(1)
+
+ layer_file = open(self.__layer_filename, "r")
+ for line in layer_file:
+ layer_data = line.strip().split(',')
+ layer_name = layer_data[0]
+
+ tensor_layer = defaultdict(str)
+ tensor_layer["Name"] = layer_name
+
+ if Driver.is_conv(layer_name):
+ tensor_layer["N"] = float(layer_data[1])
+ tensor_layer["Cin"] = float(layer_data[2])
+ tensor_layer["H"] = float(layer_data[3])
+ tensor_layer["W"] = float(layer_data[4])
+ tensor_layer["Cout"] = float(layer_data[5])
+ tensor_layer["Kh"] = float(layer_data[7])
+ tensor_layer["Kw"] = float(layer_data[8])
+ tensor_layer["Sh"] = float(layer_data[9])
+ tensor_layer["Sw"] = float(layer_data[10])
+
+ elif Driver.is_fc(layer_name):
+ tensor_layer["RA"] = float(layer_data[1])
+ tensor_layer["CA"] = float(layer_data[2])
+ tensor_layer["RB"] = float(layer_data[3])
+ tensor_layer["CB"] = float(layer_data[4])
+
+ elif not Driver.is_nml(layer_name): # TODO should we store data for NMLs?
+ print("ERROR: Invalid layer name %s" % layer_name)
+ exit(1)
+
+ self.__tensor_layers.append(tensor_layer)
+ layer_file.close()
+ '''
# [layer_name][operation_name][cols]
# Operation names need to be stored in order of insertion
-tensor_table = defaultdict(lambda: list(defaultdict(str)))
+ tensor_table = defaultdict(lambda: list(defaultdict(str)))
def parse_tensor_table(table_filename):
if not os.path.isfile(table_filename):
@@ -115,7 +135,7 @@ def get_approx_type(approx_type):
return "fp32"
return "promise"
-def is_promise(config_layer):
+def Driver.is_promise(config_layer):
# TODO overhead in call to split?
return float(config_layer.split(' ')[0]) < fp16_swing
@@ -128,8 +148,8 @@ def quantize(curr_layer, prev_layer, h2f_f2h_operation_ind, layer_data):
layer_name = layer_data["Name"]
- # NOTE: Ignoring logic where curr == promise or prev == promise bc
- # smartDMA is always true so we'd return near the beginning of the method
+ # NOTE: Ignoring logic where curr == promise or prev == promise bc
+ # smartDMA is always true so we'd return near the beginning of the method
# Get h2f/f2h data using the first tensor operation in the layer
# (which is why order matters in the tensor table)
@@ -148,14 +168,14 @@ def run_promise_simulation(swing, layer_data):
layer_name = layer_data["Name"]
patch_factor = 1
- if is_conv(layer_name):
+ if Driver.is_conv(layer_name):
rows_a = layer_data["N"] * layer_data["H"] * layer_data["W"] \
- / (layer_data["Sh"] * layer_data["Sw"])
+ / (layer_data["Sh"] * layer_data["Sw"])
cols_a = layer_data["Cin"] * layer_data["Kh"] * layer_data["Kw"]
rows_b = cols_a
cols_b = layer_data["Cout"]
patch_factor = layer_data["Kh"] * layer_data["Kw"]
- elif is_fc(layer_name):
+ elif Driver.is_fc(layer_name):
rows_a = layer_data["RA"]
cols_a = layer_data["CA"]
rows_b = cols_a
@@ -164,8 +184,8 @@ def run_promise_simulation(swing, layer_data):
print("PROMISE can't run whatever this layer is.")
exit(1)
#print("[%f x %f] x [%f x %f] : %f" % (rows_a, cols_a, rows_b, cols_b, swing))
- # Run promise simulator
- # TODO need to print time and energy in the ptm runner so we can pipe it
+ # Run promise simulator
+ # TODO need to print time and energy in the ptm runner so we can pipe it
output = subprocess.Popen(["./ptm", str(rows_a), str(cols_a), str(rows_b), \
str(cols_b), str(patch_factor), str(swing)], \
stdout = subprocess.PIPE, stderr = subprocess.PIPE).communicate()[0]
@@ -215,7 +235,7 @@ def run_simulations(config_filename):
layer_data = tensor_layers[layer_ind] # layer
layer_name = layer_data["Name"]
- if is_promise(config_layer):
+ if Driver.is_promise(config_layer):
print("Running layer %s on PROMISE" % layer_name)
curr_layer = ApproxTypes.PROMISE
quant_time, quant_energy = quantize(curr_layer, prev_layer, 0, layer_data)
@@ -282,12 +302,9 @@ def driver(tensor_layers_file, tensor_table_file, conf_file, output_file):
parse_tensor_table(tensor_table_file)
run_simulations(conf_file)
display_results(output_file)
-
+'''
if __name__ == "__main__":
if len(sys.argv) != 5:
print("Usage: python driver.py <layer info> <tensor info> <configurations> <results file>")
exit(1)
- test_layers_file = "/home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/build_mobilenet/mobilenet_layers.txt"
- test_table_file = "/home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/build_pldi/mobilenet_results/mobilenet_tensors.txt"
- test_conf_file = "/home/nvidia/Gitlab/hpvm/llvm/projects/hpvm-tensor-rt/build_mobilenet/mobilenet_conf2.txt"
- driver(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])
+ Driver(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4]).driver()