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https://github.com/saymrwulf/onnxruntime.git
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61fa5476d5
update PyTorch Bert SquAD notebooks to use onnxruntim-tools and update usage of intra_op_num_threads. rename python files according to coding style Fix change_input_to_int32. update keras notebook to copy script from rel-1.3.0 branch (Will update them later)
195 lines
9 KiB
Python
195 lines
9 KiB
Python
#-------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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#--------------------------------------------------------------------------
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from typing import Dict
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from logging import getLogger
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from onnx import helper
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from onnx_model import OnnxModel
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from fusion_base import Fusion
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logger = getLogger(__name__)
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class FusionLayerNormalization(Fusion):
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def __init__(self, model: OnnxModel):
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super().__init__(model, "LayerNormalization", "ReduceMean")
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def fuse(self, node, input_name_to_nodes: Dict, output_name_to_node: Dict):
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"""
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Fuse Layer Normalization subgraph into one node LayerNormalization:
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+----------------------+
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| v
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[Root] --> ReduceMean --> Sub --> Pow --> ReduceMean --> Add --> Sqrt --> Div --> Mul --> Add
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(axis=2 or -1) | (Y=2) (axis=2 or -1) (E-6 or E-12 or 0) ^
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+-----------------------------------------------+
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It also handles cases of duplicated sub nodes exported from older version of PyTorch:
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+----------------------+
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| v
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| +-------> Sub-----------------------------------------------+
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[Root] --> ReduceMean --> Sub --> Pow --> ReduceMean --> Add --> Sqrt --> Div --> Mul --> Add
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| ^
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+----------------------+
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"""
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children = self.model.get_children(node, input_name_to_nodes)
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if len(children) == 0 or len(children) > 2:
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return
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parent = self.model.get_parent(node, 0, output_name_to_node)
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if parent is None:
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return
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if children[0].op_type != 'Sub' or self.model.get_parent(children[0], 0, output_name_to_node) != parent:
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return
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if len(children) == 2:
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if children[1].op_type != 'Sub' or self.model.get_parent(children[1], 0, output_name_to_node) != parent:
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return
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div_node = None
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for child in children:
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div_node = self.model.find_first_child_by_type(child, 'Div', input_name_to_nodes, recursive=False)
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if div_node is not None:
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break
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if div_node is None:
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return
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path_id, parent_nodes, _ = self.model.match_parent_paths(
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div_node, [(['Sqrt', 'Add', 'ReduceMean', 'Pow', 'Sub'], [1, 0, 0, 0, 0]),
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(['Sqrt', 'Add', 'ReduceMean', 'Pow', 'Cast', 'Sub'], [1, 0, 0, 0, 0, 0])], output_name_to_node)
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if path_id < 0:
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return
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sub_node = parent_nodes[-1]
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if sub_node not in children:
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return
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second_add_node = parent_nodes[1]
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i, add_weight = self.model.get_constant_input(second_add_node)
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if add_weight is None or add_weight <= 0 or add_weight > 1.0E-4:
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logger.warning(f"epsilon value is not expeced: {add_weight}")
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return
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pow_node = parent_nodes[3]
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if not self.model.find_constant_input(pow_node, 2.0) == 1:
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return
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mul_node = input_name_to_nodes[div_node.output[0]][0]
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if mul_node.op_type != 'Mul':
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return
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last_add_node = input_name_to_nodes[mul_node.output[0]][0]
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if last_add_node.op_type != 'Add':
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return
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subgraph_nodes = [node]
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subgraph_nodes.extend(children)
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subgraph_nodes.extend(parent_nodes[:-1])
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subgraph_nodes.extend([last_add_node, mul_node, div_node])
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if not self.model.is_safe_to_fuse_nodes(subgraph_nodes, last_add_node.output, input_name_to_nodes,
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output_name_to_node):
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logger.debug(f"It is not safe to fuse LayerNormalization node. Skip")
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return
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weight_input = mul_node.input[1 - self.model.input_index(div_node.output[0], mul_node)]
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if not self.model.is_constant_with_specified_dimension(weight_input, 1, "layernorm weight"):
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return
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bias_input = last_add_node.input[1 - self.model.input_index(mul_node.output[0], last_add_node)]
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if not self.model.is_constant_with_specified_dimension(bias_input, 1, "layernorm bias"):
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return
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self.nodes_to_remove.extend(subgraph_nodes)
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normalize_node = helper.make_node('LayerNormalization',
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inputs=[node.input[0], weight_input, bias_input],
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outputs=[last_add_node.output[0]])
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normalize_node.attribute.extend([helper.make_attribute("epsilon", float(add_weight))])
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self.nodes_to_add.append(normalize_node)
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class FusionLayerNormalizationTF(Fusion):
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def __init__(self, model: OnnxModel):
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super().__init__(model, "LayerNormalization", "Add", "TF")
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def fuse(self, node, input_name_to_nodes: Dict, output_name_to_node: Dict):
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"""
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Layer Norm from Keras in Tensorflow:
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+----------------------+
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| v (B) (B) (A)
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Add --> ReduceMean --> Sub --> Mul --> ReduceMean --> Add --> Sqrt --> Reciprocol --> Mul --> Mul --> Sub --> Add
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| | | ^ ^
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| | | | |
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| +----------------------------------------------------------------------------|-------+ |
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| v |
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+-------------------------------------------------------------------------------------> Mul--------------------+
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"""
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return_indice = []
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parent_nodes = self.model.match_parent_path(
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node,
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['Sub', 'Mul', 'Mul', 'Reciprocal', 'Sqrt', 'Add', 'ReduceMean', 'Mul', 'Sub', 'ReduceMean'],
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[ 1, 1, None, 0, 0, 0, None, 0, 0, None],
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output_name_to_node,
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return_indice=return_indice) # yapf: disable
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if parent_nodes is None:
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return
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assert len(return_indice) == 3
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if not (return_indice[0] in [0, 1] and return_indice[1] in [0, 1] and return_indice[2] in [0, 1]):
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logger.debug("return indice is exepected in [0, 1], but got {return_indice}")
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return
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sub_node_0, mul_node_0, mul_node_1, reciprocol_node, sqrt_node, add_node_0, reduce_mean_node_0, mul_node_2, sub_node_1, reduce_mean_node_1 = parent_nodes
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mul_node_3 = self.model.match_parent(node, 'Mul', 0, output_name_to_node)
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if mul_node_3 is None:
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logger.debug("mul_node_3 not found")
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return
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root_node = self.model.get_parent(reduce_mean_node_1, 0, output_name_to_node)
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if root_node is None:
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logger.debug("root node is none")
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return
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i, epsilon = self.model.get_constant_input(add_node_0)
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if epsilon is None or epsilon <= 0 or epsilon > 1.0E-5:
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logger.debug("epsilon is not matched")
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return
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if reduce_mean_node_1.input[0] not in mul_node_3.input or reduce_mean_node_1.input[0] not in sub_node_1.input:
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logger.debug("reduce_mean_node_1 and mul_node_3 shall link from root node")
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return
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if mul_node_2.input[0] != mul_node_2.input[1]:
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logger.debug("mul_node_2 shall have two same inputs")
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return
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subgraph_nodes = [
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node, sub_node_0, mul_node_0, mul_node_1, reciprocol_node, sqrt_node, add_node_0, reduce_mean_node_0,
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mul_node_2, sub_node_1, reduce_mean_node_1, mul_node_3
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]
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if not self.model.is_safe_to_fuse_nodes(subgraph_nodes, node.output, self.model.input_name_to_nodes(),
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self.model.output_name_to_node()):
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logger.debug("not safe to fuse layer normalization")
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return
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self.nodes_to_remove.extend(subgraph_nodes)
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weight_input = mul_node_1.input[1]
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bias_input = sub_node_0.input[0]
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#TODO: add epsilon attribute
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fused_node = helper.make_node('LayerNormalization',
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inputs=[reduce_mean_node_1.input[0], weight_input, bias_input],
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outputs=[node.output[0]])
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fused_node.attribute.extend([helper.make_attribute("epsilon", float(epsilon))])
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self.nodes_to_add.append(fused_node)
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