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 85895d7978dbc7ede8f805ca3053a483b9f7906a..e7177d1ca2d632a629178926f23a7827a1c71564 100644
--- a/llvm/projects/hpvm-tensor-rt/build_pldi/table_generator.py
+++ b/llvm/projects/hpvm-tensor-rt/build_pldi/table_generator.py
@@ -48,24 +48,11 @@ class TableGenerator:
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.__run_inputted_binaries()
self.__build_internal_table()
self.__output_table_to_file()
- def __should_execute_file(self, file_path):
- '''
- Checks if the file at the given file path is a binary that should be run
- by the profiler. Must exist, be a binary, and must start with the network
- name as per our naming standards.
-
- Args:
- file_path: Path of the file to check
- '''
- return os.path.isfile(file_path) and os.access(file_path, os.X_OK) and \
- file_path.find(self.__network_name) != -1
-
-
def __run_inputted_binaries(self):
'''
Invokes the profiler to run all appropriate binaries (must start with the network
@@ -99,63 +86,6 @@ class TableGenerator:
output_file]).communicate()
- def __get_approximation_type(self, results_filename):
- '''
- Parses a given results filename for the approximation type.
- Format assumption: <network_name>_<approx_type>.txt
-
- Args:
- results_filename: Name of results file
-
- Returns:
- the approximation technique (ex: fp16)
- '''
- approx_type_start_ind = results_filename.find(self.__network_name) \
- + len(self.__network_name) + 1 # + 1 to account for _ delimiter
- approx_type_end_ind = results_filename.find(".txt")
- return results_filename[approx_type_start_ind : approx_type_end_ind]
-
-
- def __parse_tensor_operation_line(self, tensor_op_line):
- '''
- Parses a tensor operation line (within a output file from the offline
- profiler for the operation name, the total time used, and the total
- energy used
-
- Args:
- tensor_op_line: Tensor operation line from output file
-
- Returns:
- operation name
- total time used
- total energy used
- '''
- line_as_list = tensor_op_line.split(",")
- return line_as_list[0], line_as_list[1], line_as_list[2]
-
-
- def __build_nested_default_dict(self):
- '''
- Builds a nested default dictionary with an arbitrary number of levels
- '''
- return defaultdict(self.__build_nested_default_dict)
-
- def __get_original_operation_name(self, op_name):
- '''
- Parses an operation name containing _<conversion type> for the original
- operation name.
- Format assumption: <original_op_name>_<conversion type>
-
- Args:
- op_name: Name of the operation
-
- Returns:
- the original operation name
- '''
- underscore_ind = op_name.find("_")
- return op_name[ : underscore_ind], op_name[underscore_ind + 1 : ]
-
-
def __build_internal_table(self):
'''
Iterates through each results file generated by the runs of the offline
@@ -180,13 +110,13 @@ class TableGenerator:
# 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:
+ 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)
# 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
+ self.__table[orig_op_name][approx_type]["energy"] = total_energy
# Create a new row in the dictionary
else:
@@ -206,7 +136,7 @@ class TableGenerator:
time and the energy
'''
table_file_path = os.path.join(self.__results_dir_path, self.__table_filename)
- soc_operations_file_name = os.path.join("home", "nvidia", "soc_simulator", \
+ 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 = open(soc_operations_file_name, "r")
@@ -217,7 +147,7 @@ class TableGenerator:
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)
-
+
# List of strings, where each string is a row corresponding to an operation
# in the layer
ops_in_layer = []
@@ -227,14 +157,14 @@ class TableGenerator:
# 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):
+ for op_in_layer_count in range(num_ops):
# Contains the operation name
curr_line = soc_operations_file.readline().strip()
# Stores a list of elements that will be joined to make up a row
- curr_op = [curr_line]
+ curr_op = [curr_line]
operation_data = self.__table[curr_line]
# Iterate through time/energy data for each approximation type corresponding
@@ -247,7 +177,7 @@ class TableGenerator:
curr_op.append(op_energy)
if op_in_layer_count == 0:
- header.append("%s_time" % approx_type)
+ header.append("%s_time" % approx_type)
header.append("%s_energy" % approx_type)
ops_in_layer.append(' '.join(curr_op))
@@ -258,6 +188,77 @@ class TableGenerator:
curr_line = soc_operations_file.readline().strip()
+
+ def __should_execute_file(self, file_path):
+ '''
+ Checks if the file at the given file path is a binary that should be run
+ by the profiler. Must exist, be a binary, and must start with the network
+ name as per our naming standards.
+
+ Args:
+ file_path: Path of the file to check
+ '''
+ return os.path.isfile(file_path) and os.access(file_path, os.X_OK) and \
+ file_path.find(self.__network_name) != -1
+
+
+ def __get_approximation_type(self, results_filename):
+ '''
+ Parses a given results filename for the approximation type.
+ Format assumption: <network_name>_<approx_type>.txt
+
+ Args:
+ results_filename: Name of results file
+
+ Returns:
+ the approximation technique (ex: fp16)
+ '''
+ approx_type_start_ind = results_filename.find(self.__network_name) \
+ + len(self.__network_name) + 1 # + 1 to account for _ delimiter
+ approx_type_end_ind = results_filename.find(".txt")
+ return results_filename[approx_type_start_ind : approx_type_end_ind]
+
+
+ def __parse_tensor_operation_line(self, tensor_op_line):
+ '''
+ Parses a tensor operation line (within a output file from the offline
+ profiler for the operation name, the total time used, and the total
+ energy used
+
+ Args:
+ tensor_op_line: Tensor operation line from output file
+
+ Returns:
+ operation name
+ total time used
+ total energy used
+ '''
+ line_as_list = tensor_op_line.split(",")
+ return line_as_list[0], line_as_list[1], line_as_list[2]
+
+
+ def __build_nested_default_dict(self):
+ '''
+ Builds a nested default dictionary with an arbitrary number of levels
+ '''
+ return defaultdict(self.__build_nested_default_dict)
+
+ def __get_original_operation_name(self, op_name):
+ '''
+ Parses an operation name containing _<conversion type> for the original
+ operation name.
+ Format assumption: <original_op_name>_<conversion type>
+
+ Args:
+ op_name: Name of the operation
+
+ Returns:
+ the original operation name
+ '''
+ underscore_ind = op_name.find("_")
+ return op_name[ : underscore_ind], op_name[underscore_ind + 1 : ]
+
+
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