mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-14 18:12:05 +00:00
221 lines
11 KiB
Python
221 lines
11 KiB
Python
#-------------------------------------------------------------------------
|
|
# Copyright (c) Microsoft Corporation. All rights reserved.
|
|
# Licensed under the MIT License.
|
|
#--------------------------------------------------------------------------
|
|
from typing import Dict
|
|
from logging import getLogger
|
|
from onnx import helper
|
|
from onnx_model import OnnxModel
|
|
from fusion_base import Fusion
|
|
|
|
logger = getLogger(__name__)
|
|
|
|
|
|
class FusionLayerNormalization(Fusion):
|
|
def __init__(self, model: OnnxModel):
|
|
super().__init__(model, "LayerNormalization", "ReduceMean")
|
|
|
|
def fuse(self, node, input_name_to_nodes: Dict, output_name_to_node: Dict):
|
|
"""
|
|
Fuse Layer Normalization subgraph into one node LayerNormalization:
|
|
+----------------------+
|
|
| |
|
|
| v
|
|
[Root] --> ReduceMean --> Sub --> Pow --> ReduceMean --> Add --> Sqrt --> Div --> Mul --> Add
|
|
(axis=2 or -1) | (Y=2) (axis=2 or -1) (E-6 or E-12 or 0) ^
|
|
| |
|
|
+-----------------------------------------------+
|
|
|
|
It also handles cases of duplicated sub nodes exported from older version of PyTorch:
|
|
+----------------------+
|
|
| v
|
|
| +-------> Sub-----------------------------------------------+
|
|
| | |
|
|
| | v
|
|
[Root] --> ReduceMean --> Sub --> Pow --> ReduceMean --> Add --> Sqrt --> Div --> Mul --> Add
|
|
| ^
|
|
| |
|
|
+----------------------+
|
|
"""
|
|
children = self.model.get_children(node, input_name_to_nodes)
|
|
if len(children) == 0 or len(children) > 2:
|
|
return
|
|
|
|
parent = self.model.get_parent(node, 0, output_name_to_node)
|
|
if parent is None:
|
|
return
|
|
|
|
if children[0].op_type != 'Sub' or self.model.get_parent(children[0], 0, output_name_to_node) != parent:
|
|
return
|
|
|
|
if len(children) == 2:
|
|
if children[1].op_type != 'Sub' or self.model.get_parent(children[1], 0, output_name_to_node) != parent:
|
|
return
|
|
|
|
div_node = None
|
|
for child in children:
|
|
div_node = self.model.find_first_child_by_type(child, 'Div', input_name_to_nodes, recursive=False)
|
|
if div_node is not None:
|
|
break
|
|
if div_node is None:
|
|
return
|
|
|
|
path_id, parent_nodes, _ = self.model.match_parent_paths(
|
|
div_node, [(['Sqrt', 'Add', 'ReduceMean', 'Pow', 'Sub'], [1, 0, 0, 0, 0]),
|
|
(['Sqrt', 'Add', 'ReduceMean', 'Pow', 'Cast', 'Sub'], [1, 0, 0, 0, 0, 0])], output_name_to_node)
|
|
if path_id < 0:
|
|
return
|
|
|
|
sub_node = parent_nodes[-1]
|
|
if sub_node not in children:
|
|
return
|
|
|
|
second_add_node = parent_nodes[1]
|
|
i, add_weight = self.model.get_constant_input(second_add_node)
|
|
if add_weight is None or add_weight <= 0 or add_weight > 1.0E-4:
|
|
logger.warning(f"epsilon value is not expeced: {add_weight}")
|
|
return
|
|
|
|
pow_node = parent_nodes[3]
|
|
if not self.model.find_constant_input(pow_node, 2.0) == 1:
|
|
return
|
|
|
|
mul_node = input_name_to_nodes[div_node.output[0]][0]
|
|
if mul_node.op_type != 'Mul':
|
|
return
|
|
|
|
last_add_node = input_name_to_nodes[mul_node.output[0]][0]
|
|
if last_add_node.op_type != 'Add':
|
|
return
|
|
|
|
subgraph_nodes = [node]
|
|
subgraph_nodes.extend(children)
|
|
subgraph_nodes.extend(parent_nodes[:-1])
|
|
|
|
subgraph_nodes.extend([last_add_node, mul_node, div_node])
|
|
if not self.model.is_safe_to_fuse_nodes(subgraph_nodes, last_add_node.output, input_name_to_nodes,
|
|
output_name_to_node):
|
|
logger.debug(f"It is not safe to fuse LayerNormalization node. Skip")
|
|
return
|
|
|
|
weight_input = mul_node.input[1 - self.model.input_index(div_node.output[0], mul_node)]
|
|
if not self.model.is_constant_with_specified_dimension(weight_input, 1, "layernorm weight"):
|
|
return
|
|
|
|
bias_input = last_add_node.input[1 - self.model.input_index(mul_node.output[0], last_add_node)]
|
|
if not self.model.is_constant_with_specified_dimension(bias_input, 1, "layernorm bias"):
|
|
return
|
|
|
|
self.nodes_to_remove.extend(subgraph_nodes)
|
|
|
|
normalize_node = helper.make_node('LayerNormalization',
|
|
inputs=[node.input[0], weight_input, bias_input],
|
|
outputs=[last_add_node.output[0]])
|
|
normalize_node.attribute.extend([helper.make_attribute("epsilon", float(add_weight))])
|
|
self.nodes_to_add.append(normalize_node)
|
|
|
|
|
|
class FusionLayerNormalizationTF(Fusion):
|
|
def __init__(self, model: OnnxModel):
|
|
super().__init__(model, "LayerNormalization", "Add", "TF")
|
|
|
|
def fuse(self, node, input_name_to_nodes: Dict, output_name_to_node: Dict):
|
|
"""
|
|
Layer Norm from Tensorflow model(using keras2onnx or tf2onnx):
|
|
+------------------------------------+
|
|
| |
|
|
| |
|
|
(Cast_1) |
|
|
| |
|
|
| v (B) (B) (A)
|
|
Add --> (Cast_1) --> ReduceMean --> Sub --> Mul --> ReduceMean --> (Cast_3) --> Add --> Sqrt --> Reciprocol --> Mul --> Mul --> Sub --> Add
|
|
| | | ^ ^
|
|
| | | | |
|
|
| +--------------------------------------------------(Cast_2)-------------------------------|-------+ |
|
|
| v |
|
|
+---------------------------------------------------------------------------------------------------------------> Mul--------------------+
|
|
"""
|
|
return_indice = []
|
|
_, parent_nodes, return_indice = self.model.match_parent_paths(
|
|
node,
|
|
[(['Sub', 'Mul', 'Mul', 'Reciprocal', 'Sqrt', 'Add', 'ReduceMean', 'Mul', 'Sub', 'ReduceMean'],
|
|
[ 1, 1, None, 0, 0, 0, None, 0, 0, None]),
|
|
(['Sub', 'Mul', 'Mul', 'Reciprocal', 'Sqrt', 'Add', 'Cast', 'ReduceMean', 'Mul', 'Sub', 'ReduceMean'],
|
|
[ 1, 1, None, 0, 0, 0, 0, None, 0, 0, None])],
|
|
output_name_to_node) # yapf: disable
|
|
|
|
if parent_nodes is None:
|
|
return
|
|
|
|
assert len(return_indice) == 3
|
|
if not (return_indice[0] in [0, 1] and return_indice[1] in [0, 1] and return_indice[2] in [0, 1]):
|
|
logger.debug("return indice is exepected in [0, 1], but got {return_indice}")
|
|
return
|
|
|
|
sub_node_0, mul_node_0, mul_node_1, reciprocol_node, sqrt_node, add_node_0 = parent_nodes[:6]
|
|
reduce_mean_node_0, mul_node_2, sub_node_1, reduce_mean_node_1 = parent_nodes[-4:]
|
|
|
|
cast_node_3 = None
|
|
if len(parent_nodes) == 11:
|
|
cast_node_3 = parent_nodes[6]
|
|
assert (cast_node_3.op_type == 'Cast')
|
|
|
|
mul_node_3 = self.model.match_parent(node, 'Mul', 0, output_name_to_node)
|
|
if mul_node_3 is None:
|
|
logger.debug("mul_node_3 not found")
|
|
return
|
|
|
|
node_before_reduce = self.model.get_parent(reduce_mean_node_1, 0, output_name_to_node)
|
|
root_node = node_before_reduce if cast_node_3 is None else self.model.get_parent(
|
|
node_before_reduce, 0, output_name_to_node)
|
|
if root_node is None:
|
|
logger.debug("root node is none")
|
|
return
|
|
|
|
i, epsilon = self.model.get_constant_input(add_node_0)
|
|
if epsilon is None or epsilon <= 0 or (epsilon > 1.0E-5 and cast_node_3 is None):
|
|
logger.debug("epsilon is not matched")
|
|
return
|
|
|
|
if cast_node_3 is None and (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):
|
|
logger.debug("reduce_mean_node_1 and mul_node_3 shall link from root node")
|
|
return
|
|
|
|
if cast_node_3 is not None and (node_before_reduce.input[0] not in mul_node_3.input
|
|
or reduce_mean_node_1.input[0] not in sub_node_1.input):
|
|
logger.debug("reduce_mean_node_1 and mul_node_3 shall link from root node")
|
|
return
|
|
|
|
if mul_node_2.input[0] != mul_node_2.input[1]:
|
|
logger.debug("mul_node_2 shall have two same inputs")
|
|
return
|
|
|
|
subgraph_nodes = [
|
|
node, 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, mul_node_3
|
|
]
|
|
|
|
if cast_node_3 is not None:
|
|
cast_node_2 = self.model.match_parent(mul_node_0, 'Cast', 0, output_name_to_node)
|
|
if cast_node_2 is None:
|
|
logger.debug("cast_node_2 not found")
|
|
return
|
|
subgraph_nodes.extend([node_before_reduce, cast_node_2, cast_node_3])
|
|
|
|
if not self.model.is_safe_to_fuse_nodes(subgraph_nodes, node.output, self.model.input_name_to_nodes(),
|
|
self.model.output_name_to_node()):
|
|
logger.debug("not safe to fuse layer normalization")
|
|
return
|
|
|
|
self.nodes_to_remove.extend(subgraph_nodes)
|
|
|
|
weight_input = mul_node_1.input[1]
|
|
bias_input = sub_node_0.input[0]
|
|
|
|
#TODO: add epsilon attribute
|
|
fused_node = helper.make_node('LayerNormalization',
|
|
inputs=[mul_node_3.input[0], weight_input, bias_input],
|
|
outputs=[node.output[0]])
|
|
fused_node.attribute.extend([helper.make_attribute("epsilon", float(epsilon))])
|
|
self.nodes_to_add.append(fused_node)
|