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- # -------------------------------------------------------------------------
- # Copyright (c) Microsoft Corporation. All rights reserved.
- # Licensed under the MIT License.
- # --------------------------------------------------------------------------
- from logging import getLogger
- from fusion_base import Fusion
- from onnx import helper
- from onnx_model import OnnxModel
- logger = getLogger(__name__)
- class FusionGelu(Fusion):
- def __init__(self, model: OnnxModel):
- super().__init__(model, "Gelu", "Erf")
- def fuse(self, erf_node, input_name_to_nodes: dict, output_name_to_node: dict):
- if self.fuse_1(erf_node, input_name_to_nodes, output_name_to_node):
- return
- if self.fuse_2(erf_node, input_name_to_nodes, output_name_to_node):
- return
- self.fuse_3(erf_node, input_name_to_nodes, output_name_to_node)
- def fuse_1(self, erf_node, input_name_to_nodes: dict, output_name_to_node: dict) -> bool | None:
- """
- This pattern is from PyTorch model
- Fuse Gelu with Erf into one node:
- Pattern 1:
- +-------Mul(0.5)---------------------+
- | |
- | v
- [root] --> Div -----> Erf --> Add --> Mul -->
- (B=1.4142...) (1)
- Pattern 2:
- +------------------------------------+
- | |
- | v
- [root] --> Div -----> Erf --> Add --> Mul -->Mul -->
- (B=1.4142...) (1) (0.5)
- Note that constant input for Add and Mul could be first or second input: like either A=0.5 or B=0.5 is fine.
- """
- if erf_node.output[0] not in input_name_to_nodes:
- return
- children = input_name_to_nodes[erf_node.output[0]]
- if len(children) != 1 or children[0].op_type != "Add":
- return
- add_after_erf = children[0]
- if not self.model.has_constant_input(add_after_erf, 1):
- return
- if add_after_erf.output[0] not in input_name_to_nodes:
- return
- children = input_name_to_nodes[add_after_erf.output[0]]
- if len(children) != 1 or children[0].op_type != "Mul":
- return
- mul_after_erf = children[0]
- div = self.model.match_parent(erf_node, "Div", 0, output_name_to_node)
- if div is None:
- return
- if self.model.find_constant_input(div, 1.4142, delta=0.001) != 1:
- return
- subgraph_input = div.input[0]
- another = 1 if mul_after_erf.input[0] == add_after_erf.output[0] else 0
- if subgraph_input == mul_after_erf.input[another]: # pattern 2
- children = input_name_to_nodes[mul_after_erf.output[0]]
- if len(children) != 1 or children[0].op_type != "Mul":
- return
- mul_half = children[0]
- if not self.model.has_constant_input(mul_half, 0.5):
- return
- subgraph_output = mul_half.output[0]
- else: # pattern 1
- mul_half = self.model.match_parent(mul_after_erf, "Mul", another, output_name_to_node)
- if mul_half is None:
- return
- if not self.model.has_constant_input(mul_half, 0.5):
- return
- if subgraph_input not in mul_half.input:
- return
- subgraph_output = mul_after_erf.output[0]
- subgraph_nodes = [div, erf_node, add_after_erf, mul_after_erf, mul_half]
- if not self.model.is_safe_to_fuse_nodes(
- subgraph_nodes, [subgraph_output], input_name_to_nodes, output_name_to_node
- ):
- return
- self.nodes_to_remove.extend(subgraph_nodes)
- fused_node = helper.make_node(
- "Gelu", inputs=[subgraph_input], outputs=[subgraph_output], name=self.model.create_node_name("Gelu")
- )
- fused_node.domain = "com.microsoft"
- self.nodes_to_add.append(fused_node)
- self.node_name_to_graph_name[fused_node.name] = self.this_graph_name
- self.increase_counter("Gelu")
- return True
- def fuse_2(self, erf_node, input_name_to_nodes: dict, output_name_to_node: dict) -> bool | None:
- """
- This pattern is from Keras model
- Fuse Gelu with Erf into one node:
- +------------------------------------------+
- | |
- | v
- [root] --> Div -----> Erf --> Add --> Mul -->Mul
- (B=1.4142...) (A=1) (A=0.5)
- Note that constant input for Add and Mul could be first or second input: like either A=0.5 or B=0.5 is fine.
- """
- if erf_node.output[0] not in input_name_to_nodes:
- return
- children = input_name_to_nodes[erf_node.output[0]]
- if len(children) != 1 or children[0].op_type != "Add":
- return
- add_after_erf = children[0]
- if not self.model.has_constant_input(add_after_erf, 1):
- return
- if add_after_erf.output[0] not in input_name_to_nodes:
- return
- children = input_name_to_nodes[add_after_erf.output[0]]
- if len(children) != 1 or children[0].op_type != "Mul":
- return
- mul_after_erf = children[0]
- if not self.model.has_constant_input(mul_after_erf, 0.5):
- return
- if mul_after_erf.output[0] not in input_name_to_nodes:
- return
- children = input_name_to_nodes[mul_after_erf.output[0]]
- if len(children) != 1 or children[0].op_type != "Mul":
- return
- mul = children[0]
- div = self.model.match_parent(erf_node, "Div", 0, output_name_to_node)
- if div is None:
- return
- sqrt_node = None
- if self.model.find_constant_input(div, 1.4142, delta=0.001) != 1:
- sqrt_node = self.model.match_parent(div, "Sqrt", 1, output_name_to_node)
- if sqrt_node is None:
- return
- if not self.model.has_constant_input(sqrt_node, 2.0):
- return
- root_node = self.model.get_parent(div, 0, output_name_to_node)
- if root_node is None:
- return
- if root_node.output[0] not in mul.input:
- return
- subgraph_nodes = [div, erf_node, add_after_erf, mul_after_erf, mul]
- if sqrt_node:
- subgraph_nodes.append(sqrt_node)
- if not self.model.is_safe_to_fuse_nodes(
- subgraph_nodes, [mul.output[0]], input_name_to_nodes, output_name_to_node
- ):
- return
- self.nodes_to_remove.extend(subgraph_nodes)
- fused_node = helper.make_node(
- "Gelu", inputs=[root_node.output[0]], outputs=[mul.output[0]], name=self.model.create_node_name("Gelu")
- )
- fused_node.domain = "com.microsoft"
- self.nodes_to_add.append(fused_node)
- self.node_name_to_graph_name[fused_node.name] = self.this_graph_name
- self.increase_counter("Gelu")
- return True
- def fuse_3(self, erf_node, input_name_to_nodes: dict, output_name_to_node: dict) -> bool | None:
- """
- This pattern is from TensorFlow model
- Fuse Gelu with Erf into one node:
- +----------------------------------------------+
- | |
- | v
- [root] --> Mul -----> Erf --> Add --> Mul -->Mul
- (A=0.7071067690849304) (B=1) (B=0.5)
- Note that constant input for Add and Mul could be first or second input: like either A=0.5 or B=0.5 is fine.
- """
- if erf_node.output[0] not in input_name_to_nodes:
- return
- children = input_name_to_nodes[erf_node.output[0]]
- if len(children) != 1 or children[0].op_type != "Add":
- return
- add_after_erf = children[0]
- if not self.model.has_constant_input(add_after_erf, 1):
- return
- if add_after_erf.output[0] not in input_name_to_nodes:
- return
- children = input_name_to_nodes[add_after_erf.output[0]]
- if len(children) != 1 or children[0].op_type != "Mul":
- return
- mul_half = children[0]
- if not self.model.has_constant_input(mul_half, 0.5):
- return
- first_mul = self.model.match_parent(erf_node, "Mul", 0, output_name_to_node)
- if first_mul is None:
- return
- i = self.model.find_constant_input(first_mul, 0.7071067690849304, delta=0.001)
- if i < 0:
- return
- root_node = self.model.get_parent(first_mul, 0 if i == 1 else 1, output_name_to_node)
- if root_node is None:
- return
- if mul_half.output[0] not in input_name_to_nodes:
- return
- children = input_name_to_nodes[mul_half.output[0]]
- if len(children) != 1 or children[0].op_type != "Mul":
- return
- last_mul = children[0]
- if not (last_mul.input[0] == root_node.output[0] or last_mul.input[1] == root_node.output[0]):
- return
- subgraph_nodes = [first_mul, erf_node, add_after_erf, mul_half, last_mul]
- if not self.model.is_safe_to_fuse_nodes(
- subgraph_nodes,
- [last_mul.output[0]],
- input_name_to_nodes,
- output_name_to_node,
- ):
- return
- self.nodes_to_remove.extend(subgraph_nodes)
- fused_node = helper.make_node(
- "Gelu", inputs=[root_node.output[0]], outputs=[last_mul.output[0]], name=self.model.create_node_name("Gelu")
- )
- fused_node.domain = "com.microsoft"
- self.nodes_to_add.append(fused_node)
- self.node_name_to_graph_name[fused_node.name] = self.this_graph_name
- self.increase_counter("Gelu")
- return True
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