wehub-resource-sync
228 lines
7.7 KiB
Python
228 lines
7.7 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from onnx_graphsurgeon.logger import G_LOGGER
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from onnx_graphsurgeon.ir.tensor import Tensor
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from onnx_graphsurgeon.util import misc
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from collections import OrderedDict
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from dataclasses import dataclass
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from typing import List, Dict
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class Node(object):
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@dataclass
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class AttributeRef:
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"""
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An AttributeRef is an attribute value which references an attribute in the parent function.
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A node's attribute can only be an AttributeRef if the node lives inside a Function.
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Args:
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name (str): The name of the referenced attribute in the parent Function.
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type (type): The attribute's type.
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"""
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name: str
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type: type
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def __init__(
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self,
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op: str,
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name: str = None,
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attrs: Dict[str, object] = None,
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inputs: List["Tensor"] = None,
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outputs: List["Tensor"] = None,
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domain: str = None,
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):
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"""
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A node represents an operation in a graph, and consumes zero or more Tensors, and produces zero or more Tensors.
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Args:
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op (str): The operation this node performs.
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name (str): The name of this node.
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attrs (Dict[str, object]): A dictionary that maps attribute names to their values.
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inputs (List[Tensor]): A list of zero or more input Tensors.
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outputs (List[Tensor]): A list of zero or more output Tensors.
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domain (str): The domain of this node,
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"""
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self.op = op
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self.name = misc.default_value(name, "")
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self.attrs = misc.default_value(attrs, OrderedDict())
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self.inputs = misc.SynchronizedList(
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self, field_name="outputs", initial=misc.default_value(inputs, [])
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)
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self.outputs = misc.SynchronizedList(
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self, field_name="inputs", initial=misc.default_value(outputs, [])
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)
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self.domain = domain
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def i(self, tensor_idx=0, producer_idx=0):
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"""
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Convenience function to get a producer node of one of this node's input tensors.
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Note that the parameters are swapped compared to the o() function; this is because tensors are likely to have only a single producer
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For example:
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::
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assert node.i() == node.inputs[0].inputs[0]
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assert node.i(1, 2) == node.inputs[1].inputs[2]
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Args:
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tensor_idx (int): The index of the input tensor of this node. Defaults to 0.
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producer_idx (int): The index of the producer of the input tensor, if the tensor has multiple producers. Defaults to 0
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Returns:
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Node: The specified producer (input) node.
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"""
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return self.inputs[tensor_idx].inputs[producer_idx]
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def o(self, consumer_idx=0, tensor_idx=0):
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"""
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Convenience function to get a consumer node of one of this node's output tensors.
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For example:
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::
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assert node.o() == node.outputs[0].outputs[0]
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assert node.o(2, 1) == node.outputs[1].outputs[2]
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Args:
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consumer_idx (int): The index of the consumer of the input tensor. Defaults to 0.
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tensor_idx (int): The index of the output tensor of this node, if the node has multiple outputs. Defaults to 0.
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Returns:
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Node: The specified consumer (output) node
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"""
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return self.outputs[tensor_idx].outputs[consumer_idx]
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def subgraphs(self, recursive=False):
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"""
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Convenience function to iterate over all subgraphs which are contained in this node.
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Node subgraphs are found in attributes of ONNX control flow nodes such as 'If' and 'Loop'.
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Args:
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recursive (bool): Whether to recurse into the subgraph nodes when looking for subgraphs. Defaults to False.
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Returns:
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A generator which iterates over this node's subgraphs.
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"""
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from onnx_graphsurgeon.ir.graph import Graph
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visit_queue = [self]
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# This prevents infinite recursion in the (illegal) case of cyclical graphs.
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visited = set()
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while visit_queue:
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node = visit_queue.pop()
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for attr in node.attrs.values():
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if isinstance(attr, Graph) and id(attr) not in visited:
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visited.add(id(attr))
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if recursive:
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visit_queue.extend(attr.nodes)
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yield attr
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def __setattr__(self, name, value):
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if name in ["inputs", "outputs"]:
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try:
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attr = getattr(self, name)
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if value is attr:
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# This can happen when using things like +=
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# The __iadd__ is executed followed by an assignment
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return
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attr.clear()
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attr.extend(value)
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except AttributeError:
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super().__setattr__(name, value)
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else:
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super().__setattr__(name, value)
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def copy(
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self,
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inputs: List["Tensor"] = None,
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outputs: List["Tensor"] = None,
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tensor_map=None,
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):
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"""
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Makes a shallow copy of this node, overriding input and output information.
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Note: Generally, you should only ever make a copy of a Graph.
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"""
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from onnx_graphsurgeon.ir.graph import Graph
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new_attrs = OrderedDict()
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for name, attr in self.attrs.items():
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if isinstance(attr, Graph):
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new_attrs[name] = attr.copy(tensor_map)
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else:
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new_attrs[name] = attr
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return Node(
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self.op,
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self.name,
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new_attrs,
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inputs=inputs,
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outputs=outputs,
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domain=self.domain,
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)
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def __str__(self):
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ret = "{:} ({:})".format(self.name, self.op)
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def add_io(name, io):
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nonlocal ret
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ret += "\n\t{:}: [".format(name)
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for elem in io:
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ret += "\n\t\t{:}".format(elem)
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ret += "\n\t]"
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add_io("Inputs", self.inputs)
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add_io("Outputs", self.outputs)
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if self.attrs:
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ret += "\nAttributes: {:}".format(self.attrs)
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if self.domain:
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ret += "\nDomain: {:}".format(self.domain)
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return ret
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def __repr__(self):
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return self.__str__()
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def __eq__(self, other):
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"""
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Check whether two nodes are equal by comparing name, attributes, op, inputs, and outputs.
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"""
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G_LOGGER.verbose("Comparing node: {:} with {:}".format(self.name, other.name))
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attrs_match = (
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self.name == other.name
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and self.op == other.op
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and self.attrs == other.attrs
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)
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inputs_match = len(self.inputs) == len(other.inputs) and all(
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[inp == other_inp for inp, other_inp in zip(self.inputs, other.inputs)]
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)
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outputs_match = len(self.outputs) == len(other.outputs) and all(
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[out == other_out for out, other_out in zip(self.outputs, other.outputs)]
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)
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domain_match = self.domain == other.domain
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return attrs_match and inputs_match and outputs_match and domain_match
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