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Commit f08a6b06 authored by Elizabeth's avatar Elizabeth
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Began porting to class format for encapsulation + state storage

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llvm/projects/soc_simulator/src/driver.py
+ 85
− 68
View file @ f08a6b06
......@@ -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()
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