diff --git a/llvm/projects/hpvm-tensor-rt/build_pldi/table_generator.py b/llvm/projects/hpvm-tensor-rt/build_pldi/table_generator.py
index c610aa658c9e5fa8f8890d09a6638b14d1036c0f..85895d7978dbc7ede8f805ca3053a483b9f7906a 100644
--- a/llvm/projects/hpvm-tensor-rt/build_pldi/table_generator.py
+++ b/llvm/projects/hpvm-tensor-rt/build_pldi/table_generator.py
@@ -39,10 +39,18 @@ class TableGenerator:
def generate_table(self):
'''
- Generates a table file called <network_name>_tensors.txt in the
+ Generates a table file called <network_name>_tensors.txt in the following
+ steps:
+ 1. Runs the offline profiler against the inputted binaries to generate
+ results files
+ 2. Builds an internal table storing all data from the parsed results files
+ the offline profiler generated
+ 3. Writes the internal table to <network_name>_tensors.txt file and uses the
+ <network_name>_ops.txt file as a guideline in terms of row order
'''
+ self.__run_inputted_binaries()
self.__build_internal_table()
- self.__output_table()
+ self.__output_table_to_file()
def __should_execute_file(self, file_path):
@@ -58,7 +66,7 @@ class TableGenerator:
file_path.find(self.__network_name) != -1
- def run_inputted_binaries(self):
+ def __run_inputted_binaries(self):
'''
Invokes the profiler to run all appropriate binaries (must start with the network
name) in the inputted directory. Result files generated by the profiler are
@@ -149,6 +157,11 @@ class TableGenerator:
def __build_internal_table(self):
+ '''
+ Iterates through each results file generated by the runs of the offline
+ profiler and stores the data in a dictionary in the following format:
+ [operation name][approximation type OR conversion type][time/energy]
+ '''
for results_file_name in os.listdir(self.__results_dir_path):
# Ignore if it's not a results file
if results_file_name == self.__table_filename or \
@@ -162,20 +175,18 @@ class TableGenerator:
line = line.strip()
op_name, total_time, total_energy = self.__parse_tensor_operation_line(line)
- # Conv1 --> all approximations --> store f2h and h2f as independent approximations
- # Need to store their full names
- # Handle _f2h and _h2f output for tensor operation
- # Store as columns of original operation rather than independent rows
+ # If the current operation is f2h or h2f
if any(op_name.endswith(prec_conv) for prec_conv in TableGenerator.precision_conversions):
+ # Get the original operation name (without the f2h/h2f) and the conversion type
orig_op_name, conversion_type = self.__get_original_operation_name(op_name)
if orig_op_name not in self.__table:
print("ERROR: Conversion found but original %s is not in the table" % orig_op_name)
exit(1)
- # Need to store f2h and h2f for each type of approximation
- conversion_key = "%s_%s" % (approx_type, conversion_type)
- self.__table[orig_op_name][conversion_key]["time"] = total_time
- self.__table[orig_op_name][conversion_key]["energy"] = total_energy
+
+ # Store f2h and h2f as columns in the row belonging to the original operation
+ self.__table[orig_op_name][approx_type]["time"] = total_time
+ self.__table[orig_op_name][approx_type]["energy"] = total_energy
# Create a new row in the dictionary
else:
@@ -184,72 +195,91 @@ class TableGenerator:
results_file.close()
- def __output_table(self):
+
+ def __output_table_to_file(self):
+ '''
+ Outputs the internally stored table to a file using the <network_name>_ops.txt file as
+ a guideline in the following steps:
+ 1. Opens the ops file and the file to output the table to
+ 2. Reads a line from the ops file (guaranteed to be the layers/NML header)
+ 3. For each operation in the layer (or 1 operation if the "layer" is a NML), we store the
+ time and the energy
+ '''
table_file_path = os.path.join(self.__results_dir_path, self.__table_filename)
- # TODO un hard code this
- soc_operations_file_name = os.path.join("/home/nvidia/soc_simulator", "%s_cifar10" % self.__network_name, "%s_ops.txt" % self.__network_name)
+ soc_operations_file_name = os.path.join("home", "nvidia", "soc_simulator", \
+ "%s_cifar10" % self.__network_name, "%s_ops.txt" % self.__network_name)
- # Don't need to copy the file over --> can use the original file as a reference
soc_operations_file = open(soc_operations_file_name, "r")
table_file = open(table_file_path, "w")
- # TODO possible for operations in the same layer to not have the same # of cols?
-
curr_line = soc_operations_file.readline().strip()
while curr_line:
# First line is always the layers line (#layer_name,num_ops)
layer_name, num_ops = self.__parse_layer_info_line(curr_line)
- print("FIRST LINE", layer_name, num_ops)
-
- # Get each operation in the layer
+
+ # List of strings, where each string is a row corresponding to an operation
+ # in the layer
ops_in_layer = []
+
+ # Stores a list of elements in the header, which will be joined into a string
+ # The header is only generated for the first operation in the layer
+ # CRITICAL ASSUMPTION: All operations within a layer have the same # columns
+ # or everything breaks bc the header is per layer, not per operation
header = ["**", layer_name, str(num_ops)]
-
+
+ # Iterate through all operations within the layer
for op_in_layer_count in range(num_ops):
- # Each line consists of operation name
+ # Contains the operation name
curr_line = soc_operations_file.readline().strip()
- curr_op = [curr_line] # Join into a string later
+
+ # Stores a list of elements that will be joined to make up a row
+ curr_op = [curr_line]
operation_data = self.__table[curr_line]
- # Iterate through time/energy data for each approx type
+ # Iterate through time/energy data for each approximation type corresponding
+ # to the current operation
for approx_type in operation_data:
op_time = operation_data[approx_type]["time"]
op_energy = operation_data[approx_type]["energy"]
- # can either just be the approx time or can be the approx type_h2f/f2h field
curr_op.append(op_time)
curr_op.append(op_energy)
- # CRITICAL ASSUMPTION: All ops within a layer have the same # cols
- # Only fill out the header once for the layer
if op_in_layer_count == 0:
header.append("%s_time" % approx_type)
header.append("%s_energy" % approx_type)
+
ops_in_layer.append(' '.join(curr_op))
# Getting all operation rows and then writing everything because
# calls to write() are slow (memory vs time tradeoff)
- print("%s" % ' '.join(header))
- print("%s" % '\n'.join(ops_in_layer))
table_file.write("%s\n%s\n" % (' '.join(header), '\n'.join(ops_in_layer)))
curr_line = soc_operations_file.readline().strip()
def __parse_layer_info_line(self, layer_info_line): #layer_name,num_ops
+ '''
+ Parses a layer header (from the original ops.txt file) into the layer name
+ and the number of operations
+ Assumed format: #layer_name,num_ops
+
+ Args:
+ layer_info_line: Line at the beginning of each layer in the ops file
+
+ Returns:
+ layer name
+ number of ops in the layer
+ '''
comma_ind = layer_info_line.find(",")
return layer_info_line[layer_info_line.find("#") + 1 : comma_ind], \
int(layer_info_line[comma_ind + 1 : ])
- def __generate_header(self, table):
- # <approx type time/energy> <conversion type at very end>
- # should the header be per tensor op or per layer?
- # Try doing this per layer first
- pass
-
-binary_dir_path = "/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_path, num_iters, profiler_binary_name)
-#table_gen.run_inputted_binaries()
-#table_gen.generate_table()
+
+if __name__ == "__main__":
+ binary_dir_path = "/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_path, num_iters, profiler_binary_name)
+ #table_gen.run_inputted_binaries()
+ table_gen.generate_table()