# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. # -------------------------------------------------------------------------- import logging import os import sys from collections import deque from pathlib import Path from typing import Dict, List, Optional, Tuple from float16 import convert_float_to_float16 from onnx import AttributeProto, GraphProto, ModelProto, NodeProto, TensorProto, helper, numpy_helper, save_model from shape_infer_helper import SymbolicShapeInferenceHelper logger = logging.getLogger(__name__) class OnnxModel: def __init__(self, model): self.initialize(model) def initialize(self, model): self.model: ModelProto = model self._node_name_suffix: Dict[str, int] = {} # key is node name prefix, value is the last suffix generated self.shape_infer_helper: SymbolicShapeInferenceHelper = None self.enable_shape_infer: bool = True self.all_graphs: Optional[List[GraphProto]] = None def disable_shape_inference(self): self.enable_shape_infer = False def infer_runtime_shape(self, dynamic_axis_mapping={}, update=False): if self.enable_shape_infer: if self.shape_infer_helper is None or update: self.shape_infer_helper = SymbolicShapeInferenceHelper(self.model) try: if self.shape_infer_helper.infer(dynamic_axis_mapping): return self.shape_infer_helper except: self.enable_shape_infer = False # disable shape inference to suppress same error message. print("failed in shape inference", sys.exc_info()[0]) return None def input_name_to_nodes(self): input_name_to_nodes = {} for node in self.nodes(): for input_name in node.input: if input_name not in input_name_to_nodes: input_name_to_nodes[input_name] = [node] else: input_name_to_nodes[input_name].append(node) return input_name_to_nodes def output_name_to_node(self): output_name_to_node = {} for node in self.nodes(): for output_name in node.output: output_name_to_node[output_name] = node return output_name_to_node def nodes(self): all_nodes = [] for graph in self.graphs(): for node in graph.node: all_nodes.append(node) return all_nodes def graph(self): return self.model.graph def graphs(self): if self.all_graphs is not None: return self.all_graphs self.all_graphs = [] graph_queue = [self.model.graph] while graph_queue: graph = graph_queue.pop(0) self.all_graphs.append(graph) for node in graph.node: for attr in node.attribute: if attr.type == AttributeProto.AttributeType.GRAPH: assert isinstance(attr.g, GraphProto) graph_queue.append(attr.g) if attr.type == AttributeProto.AttributeType.GRAPHS: for g in attr.graphs: assert isinstance(g, GraphProto) graph_queue.append(g) return self.all_graphs def get_graphs_input_names(self): input_names = [] for graph in self.graphs(): for input in graph.input: input_names.append(input.name) return input_names def get_graphs_output_names(self): output_names = [] for graph in self.graphs(): for output in graph.output: output_names.append(output.name) return output_names def get_graph_by_node(self, node): for graph in self.graphs(): if node in graph.node: return graph return None def get_graph_by_name(self, graph_name): for graph in self.graphs(): if graph_name == graph.name: return graph return None def get_topological_insert_id(self, graph, outputs): for idx, node in enumerate(graph.node): for input in node.input: if input in outputs: return idx return len(graph.node) def remove_node(self, node): for graph in self.graphs(): if node in graph.node: graph.node.remove(node) def remove_nodes(self, nodes_to_remove): for node in nodes_to_remove: self.remove_node(node) def add_node(self, node, graph_name=None): if graph_name is None or graph_name == self.model.graph.name: self.model.graph.node.extend([node]) else: graph = self.get_graph_by_name(graph_name) insert_idx = self.get_topological_insert_id(graph, node.output) graph.node.insert(insert_idx, node) def add_nodes(self, nodes_to_add, node_name_to_graph_name=None): if node_name_to_graph_name is None: self.model.graph.node.extend(nodes_to_add) else: for node in nodes_to_add: graph_name = node_name_to_graph_name[node.name] self.add_node(node, graph_name) def add_initializer(self, tensor, graph_name=None): if graph_name is None or graph_name == self.model.graph.name: self.model.graph.initializer.extend([tensor]) else: graph = self.get_graph_by_name(graph_name) graph.initializer.extend([tensor]) def add_input(self, input, graph_name=None): if graph_name is None or graph_name == self.model.graph.name: self.model.graph.input.extend([input]) else: graph = self.get_graph_by_name(graph_name) graph.input.extend([input]) @staticmethod def replace_node_input(node, old_input_name, new_input_name): assert isinstance(old_input_name, str) and isinstance(new_input_name, str) for j in range(len(node.input)): if node.input[j] == old_input_name: node.input[j] = new_input_name def replace_input_of_all_nodes(self, old_input_name, new_input_name): for node in self.model.graph.node: OnnxModel.replace_node_input(node, old_input_name, new_input_name) @staticmethod def replace_node_output(node, old_output_name, new_output_name): assert isinstance(old_output_name, str) and isinstance(new_output_name, str) for j in range(len(node.output)): if node.output[j] == old_output_name: node.output[j] = new_output_name def replace_output_of_all_nodes(self, old_output_name, new_output_name): for node in self.model.graph.node: OnnxModel.replace_node_output(node, old_output_name, new_output_name) def get_initializer(self, name): for graph in self.graphs(): for tensor in graph.initializer: if tensor.name == name: return tensor return None def get_nodes_by_op_type(self, op_type): nodes = [] for node in self.nodes(): if node.op_type == op_type: nodes.append(node) return nodes def get_children(self, node, input_name_to_nodes=None): if input_name_to_nodes is None: input_name_to_nodes = self.input_name_to_nodes() children = [] for output in node.output: if output in input_name_to_nodes: for node in input_name_to_nodes[output]: children.append(node) return children def get_parents(self, node, output_name_to_node=None): if output_name_to_node is None: output_name_to_node = self.output_name_to_node() parents = [] for input in node.input: if input in output_name_to_node: parents.append(output_name_to_node[input]) return parents def get_parent(self, node, i, output_name_to_node=None): if output_name_to_node is None: output_name_to_node = self.output_name_to_node() if len(node.input) <= i: return None input = node.input[i] if input not in output_name_to_node: return None return output_name_to_node[input] def match_first_parent(self, node, parent_op_type, output_name_to_node, exclude=[]): """ Find parent node based on constraints on op_type. Args: node (str): current node name. parent_op_type (str): constraint of parent node op_type. output_name_to_node (dict): dictionary with output name as key, and node as value. exclude (list): list of nodes that are excluded (not allowed to match as parent). Returns: parent: The matched parent node. None if not found. index: The input index of matched parent node. None if not found. """ for i, input in enumerate(node.input): if input in output_name_to_node: parent = output_name_to_node[input] if parent.op_type == parent_op_type and parent not in exclude: return parent, i else: logger.debug(f"To find first {parent_op_type}, current {parent.op_type}") return None, None def match_parent( self, node, parent_op_type, input_index=None, output_name_to_node=None, exclude=[], return_indice=None, ): """ Find parent node based on constraints on op_type and index. When input_index is None, we will find the first parent node based on constraints, and return_indice will be appended the corresponding input index. Args: node (str): current node name. parent_op_type (str): constraint of parent node op_type. input_index (int or None): only check the parent given input index of current node. output_name_to_node (dict): dictionary with output name as key, and node as value. exclude (list): list of nodes that are excluded (not allowed to match as parent). return_indice (list): a list to append the input index when input_index is None. Returns: parent: The matched parent node. """ assert node is not None assert input_index is None or input_index >= 0 if output_name_to_node is None: output_name_to_node = self.output_name_to_node() if input_index is None: parent, index = self.match_first_parent(node, parent_op_type, output_name_to_node, exclude) if return_indice is not None: return_indice.append(index) return parent if input_index >= len(node.input): logger.debug(f"input_index {input_index} >= node inputs {len(node.input)}") return None parent = self.get_parent(node, input_index, output_name_to_node) if parent is not None and parent.op_type == parent_op_type and parent not in exclude: return parent if parent is not None: logger.debug(f"Expect {parent_op_type}, Got {parent.op_type}") return None def match_parent_paths(self, node, paths, output_name_to_node): for i, path in enumerate(paths): assert isinstance(path, List) or isinstance(path, Tuple) return_indice = [] matched = self.match_parent_path(node, path[0], path[1], output_name_to_node, return_indice) if matched: return i, matched, return_indice return -1, None, None def match_parent_path( self, node, parent_op_types, parent_input_index, output_name_to_node=None, return_indice=None, ): """ Find a sequence of input edges based on constraints on parent op_type and index. When input_index is None, we will find the first parent node based on constraints, and return_indice will be appended the corresponding input index. Args: node (str): current node name. parent_op_types (str): constraint of parent node op_type of each input edge. parent_input_index (list): constraint of input index of each input edge. None means no constraint. output_name_to_node (dict): dictionary with output name as key, and node as value. return_indice (list): a list to append the input index when there is no constraint on input index of an edge. Returns: parents: a list of matched parent node. """ assert len(parent_input_index) == len(parent_op_types) if output_name_to_node is None: output_name_to_node = self.output_name_to_node() current_node = node matched_parents = [] for i, op_type in enumerate(parent_op_types): matched_parent = self.match_parent( current_node, op_type, parent_input_index[i], output_name_to_node, exclude=[], return_indice=return_indice, ) if matched_parent is None: logger.debug( f"Failed to match index={i} parent_input_index={parent_input_index[i]} op_type={op_type}", stack_info=True, ) return None matched_parents.append(matched_parent) current_node = matched_parent return matched_parents def find_first_child_by_type(self, node, child_type, input_name_to_nodes=None, recursive=True): children = self.get_children(node, input_name_to_nodes) dq = deque(children) while len(dq) > 0: current_node = dq.pop() if current_node.op_type == child_type: return current_node if recursive: children = self.get_children(current_node, input_name_to_nodes) for child in children: dq.appendleft(child) return None def find_first_parent_by_type(self, node, parent_type, output_name_to_node=None, recursive=True): if output_name_to_node is None: output_name_to_node = self.output_name_to_node() parents = self.get_parents(node, output_name_to_node) dq = deque(parents) while len(dq) > 0: current_node = dq.pop() if current_node.op_type == parent_type: return current_node if recursive: parents = self.get_parents(current_node, output_name_to_node) for parent in parents: dq.appendleft(parent) return None def get_constant_value(self, output_name): for node in self.get_nodes_by_op_type("Constant"): if node.output[0] == output_name: for att in node.attribute: if att.name == "value": return numpy_helper.to_array(att.t) # Fall back to intializer since constant folding might have been applied. initializer = self.get_initializer(output_name) if initializer is not None: return numpy_helper.to_array(initializer) return None def get_constant_input(self, node): for i, input in enumerate(node.input): value = self.get_constant_value(input) if value is not None: return i, value return None, None def find_constant_input(self, node, expected_value, delta=0.000001): i, value = self.get_constant_input(node) if value is not None and value.size == 1 and abs(value - expected_value) < delta: return i return -1 def is_constant_with_specified_dimension(self, output_name, dimensions, description): value = self.get_constant_value(output_name) if value is None: logger.debug(f"{description} {output_name} is not initializer.") return False if len(value.shape) != dimensions: logger.debug(f"{description} {output_name} shall have {dimensions} dimensions. Got shape {value.shape}") return False return True def has_constant_input(self, node, expected_value, delta=0.000001): return self.find_constant_input(node, expected_value, delta) >= 0 def get_children_subgraph_nodes(self, root_node, stop_nodes, input_name_to_nodes=None): if input_name_to_nodes is None: input_name_to_nodes = self.input_name_to_nodes() children = input_name_to_nodes[root_node.output[0]] unique_nodes = [] dq = deque(children) while len(dq) > 0: current_node = dq.pop() if current_node in stop_nodes: continue if current_node not in unique_nodes: unique_nodes.append(current_node) for output in current_node.output: if output in input_name_to_nodes: children = input_name_to_nodes[output] for child in children: dq.appendleft(child) return unique_nodes def tensor_shape_to_list(self, tensor_type): """Convert tensor shape to list""" shape_list = [] for d in tensor_type.shape.dim: if d.HasField("dim_value"): shape_list.append(d.dim_value) # known dimension elif d.HasField("dim_param"): shape_list.append(d.dim_param) # unknown dimension with symbolic name else: shape_list.append("?") # shall not happen return shape_list def get_dtype(self, input_or_output: str): """Try get data type given a name (could be initializer, graph input or output).""" tensor_type_map = {obj.name: obj.type for obj in self.model.graph.value_info} if input_or_output in tensor_type_map: return tensor_type_map[input_or_output].tensor_type.elem_type graph_input = self.find_graph_input(input_or_output) if graph_input: return graph_input.type.tensor_type.elem_type graph_output = self.find_graph_output(input_or_output) if graph_output: return graph_output.type.tensor_type.elem_type return None @staticmethod def get_node_attribute(node: NodeProto, attribute_name: str): for attr in node.attribute: if attr.name == attribute_name: value = helper.get_attribute_value(attr) return value return None def remove_cascaded_cast_nodes(self): """Remove Cast node that are followed by another Cast node like --> Cast --> Cast --> Note that this shall be used carefully since it might introduce semantic change. For example, float -> int -> float could get different value than the original float value. So, it is recommended to used only in post-processing of mixed precision conversion. """ output_name_to_node = self.output_name_to_node() removed_count = 0 for node in self.nodes(): if node.op_type == "Cast": parent = self.get_parent(node, 0, output_name_to_node=output_name_to_node) if parent and parent.op_type == "Cast": node.input[0] = parent.input[0] removed_count += 1 if removed_count > 0: logger.info("Removed %d cascaded Cast nodes", removed_count) self.prune_graph() def remove_useless_cast_nodes(self): """Remove cast nodes that are not needed: input and output has same data type.""" shape_infer = self.infer_runtime_shape(update=True) if shape_infer is None: logger.info(f"Skip removing useless cast nodes since shape inference failed.") return def get_data_type(input_or_output_name): dtype = self.get_dtype(input_or_output_name) if dtype: return dtype if shape_infer.known_vi_[input_or_output_name].type.tensor_type.HasField("elem_type"): return shape_infer.known_vi_[input_or_output_name].type.tensor_type.elem_type return None nodes_to_remove = [] for node in self.nodes(): if node.op_type == "Cast": input_dtype = get_data_type(node.input[0]) output_dtype = get_data_type(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.get_graphs_input_names()) graph_output_names = set(self.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.replace_output_of_all_nodes(node.input[0], node.output[0]) else: continue else: self.replace_input_of_all_nodes(node.output[0], node.input[0]) self.remove_node(node) logger.info("Removed %d Cast nodes with output type same as input", len(nodes_to_remove)) def convert_model_float32_to_float16(self, cast_input_output=True): logger.warning( "The function convert_model_float32_to_float16 is deprecated. Use convert_float_to_float16 instead!" ) self.convert_float_to_float16(use_symbolic_shape_infer=True, keep_io_types=cast_input_output) def convert_float_to_float16(self, use_symbolic_shape_infer=True, **kwargs): """Convert a model to half (default) or mixed precision. To use mixed precision, user need specify which graph inputs, outputs, operator type or list of nodes shall keep in float32. By default, we use symbolic shape inference to get shape and type information. If not, ONNX shape inference will be used. Note that symbolic/ONNX shape inference might fail, and the conversion might not proceed without shape and type information. Args: use_symbolic_shape_infer (bool, optional): use symbolic shape inference instead of onnx shape inference. Defaults to True. keep_io_types (Union[bool, List[str]], optional): It could be boolean or a list of float32 input/output names. If True, model inputs/outputs should be left as float32. Defaults to False. op_block_list (List[str], optional): List of operator types to leave as float32. Defaults to None, which will use `float16.DEFAULT_OP_BLOCK_LIST` as default. node_block_list (List[str], optional): List of node names to leave as float32. Defaults to None. force_fp16_initializers(bool): force converting all float initializers to float16. Default to false, which will convert only the one needed to avoid precision loss. min_positive_val (float, optional): minimal positive value. Defaults to 1e-7. max_finite_val (float, optional): maximal finite value. Defaults to 1e4. """ if "keep_io_types" not in kwargs: kwargs["keep_io_types"] = True model = self.model if use_symbolic_shape_infer: # Use symbolic shape inference since custom operators (like Gelu, SkipLayerNormalization etc) are not recognized by onnx shape inference. shape_infer_helper = SymbolicShapeInferenceHelper(model) model = shape_infer_helper.infer_shapes(model, auto_merge=True, guess_output_rank=False) parameters = {"disable_shape_infer": use_symbolic_shape_infer} parameters.update( { key: kwargs[key] for key in [ "keep_io_types", "min_positive_val", "max_finite_val", "op_block_list", "node_block_list", "force_fp16_initializers", ] if key in kwargs } ) fp16_model = convert_float_to_float16(model, **parameters) self.initialize(fp16_model) self.remove_cascaded_cast_nodes() self.remove_useless_cast_nodes() def create_node_name(self, op_type, name_prefix=None): """Create a unique node name that starts with a prefix (default is operator type). The name will not be duplicated with any name that generated or existed in current graphs. Args: op_type (str): operator type name_prefix (str, optional): prefix of node name. Defaults to None. Returns: str: node name """ if name_prefix: prefix = name_prefix if name_prefix.endswith("_") else (name_prefix + "_") else: prefix = op_type + "_" suffix: int = 0 if prefix in self._node_name_suffix: suffix = self._node_name_suffix[prefix] + 1 else: # Check existed node name only once for a prefix as we assume create_node_name is called for every new node in fusion. for node in self.nodes(): if node.name and node.name.startswith(prefix): try: index = int(node.name[len(prefix) :]) suffix = max(index + 1, suffix) except ValueError: continue # Record the generated suffix so that we can avoid generating duplicated name. self._node_name_suffix[prefix] = suffix return prefix + str(suffix) def find_graph_input(self, input_name): for input in self.model.graph.input: if input.name == input_name: return input return None def find_graph_output(self, output_name): for output in self.model.graph.output: if output.name == output_name: return output return None def get_parent_subgraph_nodes(self, node, stop_nodes, output_name_to_node=None): if output_name_to_node is None: output_name_to_node = self.output_name_to_node() unique_nodes = [] parents = self.get_parents(node, output_name_to_node) dq = deque(parents) while len(dq) > 0: current_node = dq.pop() if current_node in stop_nodes: continue if current_node not in unique_nodes: unique_nodes.append(current_node) for input in current_node.input: if input in output_name_to_node: dq.appendleft(output_name_to_node[input]) return unique_nodes def get_graph_inputs(self, current_node, recursive=False): """ Find graph inputs that linked to current node. """ graph_inputs = [] for input in current_node.input: if self.find_graph_input(input) and input not in graph_inputs: graph_inputs.append(input) if recursive: parent_nodes = self.get_parent_subgraph_nodes(current_node, []) for node in parent_nodes: for input in node.input: if self.find_graph_input(input) and input not in graph_inputs: graph_inputs.append(input) return graph_inputs @staticmethod def input_index(node_output, child_node): index = 0 for input in child_node.input: if input == node_output: return index index += 1 return -1 def remove_unused_constant(self): input_name_to_nodes = self.input_name_to_nodes() # remove unused constant unused_nodes = [] nodes = self.nodes() for node in nodes: if node.op_type == "Constant" and node.output[0] not in input_name_to_nodes: unused_nodes.append(node) self.remove_nodes(unused_nodes) if len(unused_nodes) > 0: logger.debug(f"Removed unused constant nodes: {len(unused_nodes)}") def prune_graph(self, outputs=None): """ Prune graph to keep only required outputs. It removes unnecessary inputs and nodes. Nodes are not linked (directly or indirectly) to any required output will be removed. Args: outputs (list): a list of graph outputs to retain. If it is None, all graph outputs will be kept. """ if len(self.graphs()) > 1: logger.debug(f"Skip prune_graph since graph has subgraph") return if outputs is None: outputs = [output.name for output in self.model.graph.output] output_name_to_node = self.output_name_to_node() all_nodes = [] for output in outputs: if output in output_name_to_node: last_node = output_name_to_node[output] if last_node in all_nodes: continue nodes = self.get_parent_subgraph_nodes(last_node, []) all_nodes.append(last_node) all_nodes.extend(nodes) nodes_to_remove = [] for node in self.model.graph.node: if node not in all_nodes: nodes_to_remove.append(node) self.remove_nodes(nodes_to_remove) # remove outputs not in list output_to_remove = [] for output in self.model.graph.output: if output.name not in outputs: output_to_remove.append(output) for output in output_to_remove: self.model.graph.output.remove(output) # remove inputs not used by any node. input_name_to_nodes = self.input_name_to_nodes() input_to_remove = [] for input in self.model.graph.input: if input.name not in input_name_to_nodes: input_to_remove.append(input) for input in input_to_remove: self.model.graph.input.remove(input) if input_to_remove or output_to_remove or nodes_to_remove: logger.info( "Graph pruned: {} inputs, {} outputs and {} nodes are removed".format( len(input_to_remove), len(output_to_remove), len(nodes_to_remove) ) ) self.update_graph() def update_graph(self, verbose=False): graph = self.model.graph remaining_input_names = [] for node in graph.node: if node.op_type in ["Loop", "Scan", "If"]: # TODO: handle inner graph logger.debug(f"Skip update_graph since graph has operator: {node.op_type}") return if node.op_type != "Constant": for input_name in node.input: if input_name not in remaining_input_names: remaining_input_names.append(input_name) if verbose: logger.debug(f"remaining input names: {remaining_input_names}") # remove graph input that is not used inputs_to_remove = [] for input in graph.input: if input.name not in remaining_input_names: inputs_to_remove.append(input) for input in inputs_to_remove: graph.input.remove(input) names_to_remove = [input.name for input in inputs_to_remove] logger.debug(f"remove {len(inputs_to_remove)} unused inputs: {names_to_remove}") # remove weights that are not used weights_to_remove = [] weights_to_keep = [] for initializer in graph.initializer: if initializer.name not in remaining_input_names and not self.find_graph_output(initializer.name): weights_to_remove.append(initializer) else: weights_to_keep.append(initializer.name) for initializer in weights_to_remove: graph.initializer.remove(initializer) names_to_remove = [initializer.name for initializer in weights_to_remove] logger.debug(f"remove {len(weights_to_remove)} unused initializers: {names_to_remove}") if verbose: logger.debug(f"remaining initializers:{weights_to_keep}") self.remove_unused_constant() def is_safe_to_fuse_nodes(self, nodes_to_remove, keep_outputs, input_name_to_nodes, output_name_to_node): for node_to_remove in nodes_to_remove: for output_to_remove in node_to_remove.output: if output_to_remove in keep_outputs: continue if output_to_remove in input_name_to_nodes: for impacted_node in input_name_to_nodes[output_to_remove]: if impacted_node not in nodes_to_remove: logger.debug( f"it is not safe to remove nodes since output {output_to_remove} is used by {impacted_node}" ) return False return True @staticmethod def graph_topological_sort(graph): deps_count = [0] * len(graph.node) # dependency count of each node deps_to_nodes = {} # input to node indice sorted_nodes = [] # initialize sorted_nodes for node_idx, node in enumerate(graph.node): # CANNOT use len(node.input) directly because input can be optional deps_count[node_idx] = sum(1 for _ in node.input if _) if deps_count[node_idx] == 0: # Constant doesn't depend on any inputs sorted_nodes.append(graph.node[node_idx]) continue for input_name in node.input: if input_name not in deps_to_nodes: deps_to_nodes[input_name] = [node_idx] else: deps_to_nodes[input_name].append(node_idx) # Note: this logic only applies to top level graph since a sub graph could use intializer from parent graph initializer_names = [init.name for init in graph.initializer] graph_input_names = [input.name for input in graph.input] input_names = initializer_names + graph_input_names input_names.sort() prev_input_name = None for input_name in input_names: if prev_input_name == input_name: continue prev_input_name = input_name if input_name in deps_to_nodes: for node_idx in deps_to_nodes[input_name]: deps_count[node_idx] = deps_count[node_idx] - 1 if deps_count[node_idx] == 0: sorted_nodes.append(graph.node[node_idx]) start = 0 end = len(sorted_nodes) while start < end: for output in sorted_nodes[start].output: if output in deps_to_nodes: for node_idx in deps_to_nodes[output]: deps_count[node_idx] = deps_count[node_idx] - 1 if deps_count[node_idx] == 0: sorted_nodes.append(graph.node[node_idx]) end = end + 1 start = start + 1 if end != len(graph.node): raise RuntimeError( f"Graph is not a DAG: end={end}, len(graph.node)={len(graph.node)}, graph.node[end]={graph.node[end]}" ) graph.ClearField("node") graph.node.extend(sorted_nodes) def topological_sort(self): # TODO: support graph_topological_sort() in subgraphs # for graph in self.graphs(): # self.graph_topological_sort(graph) OnnxModel.graph_topological_sort(self.model.graph) @staticmethod def save( model, output_path, save_as_external_data=False, all_tensors_to_one_file=True, size_threshold=1024, convert_attribute=False, ): Path(output_path).parent.mkdir(parents=True, exist_ok=True) if save_as_external_data: # Save model to external data, which is needed for model size > 2GB output_dir = Path(output_path).parent output_dir.mkdir(parents=True, exist_ok=True) external_data_path = output_path + ".data" location = Path(external_data_path).name if all_tensors_to_one_file else None if os.path.exists(output_path): logger.info(f"Delete the existed onnx file: {output_path}") os.remove(output_path) if all_tensors_to_one_file: if os.path.exists(external_data_path): # Delete the external data file. Otherwise, data will be appended to existing file. logger.info(f"Delete the existed external data file: {external_data_path}") os.remove(external_data_path) else: if os.listdir(output_dir): raise RuntimeError(f"Output directory ({output_dir}) for external data is not empty.") save_model( model, output_path, save_as_external_data=True, all_tensors_to_one_file=all_tensors_to_one_file, location=location, size_threshold=size_threshold, convert_attribute=convert_attribute, ) else: save_model(model, output_path) def save_model_to_file(self, output_path, use_external_data_format=False, all_tensors_to_one_file=True): logger.info(f"Sort graphs in topological order") self.topological_sort() if output_path.endswith(".json"): # Output text for testing small model. with open(output_path, "w") as out: out.write(str(model)) else: OnnxModel.save(self.model, output_path, use_external_data_format, all_tensors_to_one_file) logger.info(f"Model saved to {output_path}") def get_graph_inputs_excluding_initializers(self): """ Returns real graph inputs (excluding initializers from older onnx model). """ graph_inputs = [] for input in self.model.graph.input: if self.get_initializer(input.name) is None: graph_inputs.append(input) return graph_inputs def get_opset_version(self): """Get opset version of onnx domain Raises: RuntimeError: ONNX model has no opset for default domain. Returns: int: opset version of onnx domain. """ for opset in self.model.opset_import: if opset.domain in ["", "ai.onnx"]: return opset.version raise RuntimeError("ONNX model has no opset for default domain") @staticmethod def has_same_value(tensor1: TensorProto, tensor2: TensorProto) -> bool: """Returns True when two tensors have same value. Note that name can be different. Args: tensor1 (TensorProto): initializer 1 tensor2 (TensorProto): initializer 2 Returns: bool: True when two intializers has same value. """ if tensor1.data_type != tensor2.data_type or tensor1.dims != tensor2.dims: return False if tensor1.HasField("raw_data") and tensor2.HasField("raw_data"): return tensor1.raw_data == tensor2.raw_data return numpy_helper.to_array(tensor1) == numpy_helper.to_array(tensor2) def remove_duplicated_initializer(self): """Remove initializers with duplicated values, and only keep the first one. It could help reduce size of models (like ALBert) with shared weights. Note: this function does not process subgraph. """ if len(self.graphs()) > 1: logger.warning("remove_duplicated_initializer does not process subgraphs.") initializer_count = len(self.model.graph.initializer) same = [-1] * initializer_count for i in range(initializer_count - 1): if same[i] >= 0: continue for j in range(i + 1, initializer_count): if OnnxModel.has_same_value(self.model.graph.initializer[i], self.model.graph.initializer[j]): same[j] = i count = 0 for i in range(initializer_count): if same[i] >= 0: count += 1 self.replace_input_of_all_nodes( self.model.graph.initializer[i].name, self.model.graph.initializer[same[i]].name ) if count > 0: self.update_graph() print(f"Removed {count} initializers with duplicated value") def add_prefix_to_names(self, prefix: str): """Add prefix to initializer or intermediate outputs in graph. Main graph inputs and outputs are excluded. It could help avoid conflicting in name of node_args when merging two graphs. Note: this function does not process subgraph. """ if len(self.graphs()) > 1: logger.warning("add_prefix_to_names does not process subgraphs.") # Exclude the names of inputs and outputs of main graph (but not subgraphs) excluded = [i.name for i in self.model.graph.input] + [o.name for o in self.model.graph.output] for initializer in self.model.graph.initializer: if initializer.name not in excluded: if prefix + initializer.name not in excluded: initializer.name = prefix + initializer.name for node in self.model.graph.node: # update name of node inputs for j in range(len(node.input)): if node.input[j] not in excluded: if prefix + node.input[j] not in excluded: node.input[j] = prefix + node.input[j] # update name of node outputs for j in range(len(node.output)): if node.output[j] not in excluded: if prefix + node.output[j] not in excluded: node.output[j] = prefix + node.output[j] for value_info in self.model.graph.value_info: if value_info.name not in excluded: value_info.name = prefix + value_info.name