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fusion_utils.py
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fusion_utils.py
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#-------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#--------------------------------------------------------------------------
from logging import getLogger
from typing import Tuple
from onnx import helper, numpy_helper, TensorProto
from numpy import ndarray, array_equal
from onnx_model import OnnxModel
logger = getLogger(__name__)
class FusionUtils:
def __init__(self, model: OnnxModel):
self.model: OnnxModel = model
def cast_graph_input_to_int32(self, input_name: str) -> Tuple[bool, str]:
graph_input = self.model.find_graph_input(input_name)
if graph_input is not None and graph_input.type.tensor_type.elem_type != TensorProto.INT32:
cast_output, cast_node = self.cast_input_to_int32(input_name)
logger.debug(f"Casted graph input {input_name} to int32")
return True, cast_output
logger.debug(f"Did not cast graph input {input_name} to int32: found {graph_input is not None}")
return False, input_name
def cast_input_to_int32(self, input_name: str):
cast_output = input_name + '_int32'
# Avoid consequent Cast nodes.
inputs = [input_name]
output_name_to_node = self.model.output_name_to_node()
if input_name in output_name_to_node:
parent_node = output_name_to_node[input_name]
if parent_node and parent_node.op_type == 'Cast':
inputs = [parent_node.input[0]]
cast_node = helper.make_node('Cast', inputs=inputs, outputs=[cast_output])
cast_node.attribute.extend([helper.make_attribute("to", int(TensorProto.INT32))])
self.model.add_node(cast_node)
return cast_output, cast_node
def remove_cast_int32(self, input_name: str):
input_name_to_nodes = self.model.input_name_to_nodes()
nodes = input_name_to_nodes[input_name]
for node in nodes:
if node.op_type == "Cast":
is_int32 = False
for att in node.attribute:
if att.name == 'to' and att.i == int(TensorProto.INT32):
is_int32 = True
break
if is_int32:
output_name = node.output[0]
self.model.remove_node(node)
self.model.replace_input_of_all_nodes(output_name, input_name)
@staticmethod
def check_node_attribute(node, attribute_name: str, expected_value, default_value=None):
"""Verify that a node has expected value for an attribute.
Args:
node (NodeProto): a node to check
attribute_name (str): name of attribute
expected_value (Any): expected value of the attribute
default_value (Any, optional): default value if the attribute does not exist. Defaults to None.
Returns:
bool: whether the check is passed or not
"""
value = default_value
for attr in node.attribute:
if attr.name == attribute_name:
value = helper.get_attribute_value(attr)
if isinstance(expected_value, list):
return (isinstance(value, ndarray) or isinstance(value, list)) and array_equal(
expected_value, value, equal_nan=False)
else:
return value == expected_value
def check_node_input_value(self, node, input_index: int, expected_value):
"""Verify that a node has expected input value
Args:
node (NodeProto): a node to check
input_index (int): index of its input to be verified
expected_value (Any): expected value of the input
Returns:
bool: whether the check is passed or not
"""
assert len(node.input) > input_index
value = self.model.get_constant_value(node.input[input_index])
if isinstance(expected_value, list):
return (isinstance(value, ndarray) or isinstance(value, list)) and array_equal(
expected_value, value, equal_nan=False)
else:
return value == expected_value
def get_dtype(self, shape_infer_helper, input_or_output_name: str) -> int:
"""Get data type of an input or output.
Args:
shape_infer_helper (SymbolicShapeInferenceHelper): object of symbolic shape inference
input_or_output_name (str): name of input or output
Returns:
int: tensor data type
"""
dtype = self.model.get_dtype(input_or_output_name)
if dtype is not None:
return dtype
if shape_infer_helper:
tensor_proto = shape_infer_helper.known_vi_[input_or_output_name]
if tensor_proto.type.tensor_type.HasField('elem_type'):
return tensor_proto.type.tensor_type.elem_type
return None
def remove_useless_cast_nodes(self):
"""Remove cast nodes that are not needed: input and output has same data type.
"""
shape_infer = self.model.infer_runtime_shape(update=True)
if shape_infer is None:
return
nodes_to_remove = []
for node in self.model.nodes():
if node.op_type == 'Cast':
input_dtype = self.get_dtype(shape_infer, node.input[0])
output_dtype = self.get_dtype(shape_infer, node.output[0])
if input_dtype and input_dtype == output_dtype:
nodes_to_remove.append(node)
if nodes_to_remove:
graph_input_names = set(self.model.get_graphs_input_names())
graph_output_names = set(self.model.get_graphs_output_names())
for node in nodes_to_remove:
if bool(set(node.output) & graph_output_names):
if not bool(set(node.input) & graph_input_names):
self.model.replace_output_of_all_nodes(node.input[0], node.output[0])
else:
continue
else:
self.model.replace_input_of_all_nodes(node.output[0], node.input[0])
self.model.remove_node(node)
logger.info(f"Removed {len(nodes_to_remove)} Cast nodes with output type same as input")
def remove_useless_reshape_nodes(self):
"""Remove reshape node that is not needed based on symbolic shape inference: input and output has same shape
"""
shape_infer = self.model.infer_runtime_shape(update=True)
if shape_infer is None:
return
nodes_to_remove = []
for node in self.model.nodes():
if node.op_type == 'Reshape':
input_shape = shape_infer.get_edge_shape(node.input[0])
output_shape = shape_infer.get_edge_shape(node.output[0])
if input_shape and output_shape and input_shape == output_shape:
logger.info(
f"Remove reshape node {node.name} since its input shape is same as output: {input_shape}")
nodes_to_remove.append(node)
if nodes_to_remove:
graph_input_names = set(self.model.get_graphs_input_names())
graph_output_names = set(self.model.get_graphs_output_names())
for node in nodes_to_remove:
if bool(set(node.output) & graph_output_names):
if not bool(set(node.input) & graph_input_names):
self.model.replace_output_of_all_nodes(node.input[0], node.output[0])
else:
continue
else:
self.model.replace_input_of_all_nodes(node.output[0], node.input[0])
self.model.remove_node(node)
class NumpyHelper:
@staticmethod
def to_array(tensor: TensorProto, fill_zeros: bool = False) -> ndarray:
# When weights are in external data format but not presented, we can still test the optimizer with two changes:
# (1) set fill_zeros = True (2) change load_external_data=False in optimizer.py
if fill_zeros:
from onnx import mapping
return ndarray(shape=tensor.dims, dtype=mapping.TENSOR_TYPE_TO_NP_TYPE[tensor.data_type])
return numpy_helper.to_array(tensor)