import inspect
from typing import List, Optional
from onnx import NodeProto
from ..patterns_api import MatchResult, PatternOptimization
[docs]
class ConvBiasNullPattern(PatternOptimization):
"""
Checks that a Conv has a null bias.
Model with nodes to be fused:
.. gdot::
:script: DOT-SECTION
:process:
from experimental_experiment.doc import to_dot
import numpy as np
import ml_dtypes
import onnx
import onnx.helper as oh
import onnx.numpy_helper as onh
opset_imports = [
oh.make_opsetid("", 18),
]
inputs = []
outputs = []
nodes = []
initializers = []
sparse_initializers = []
functions = []
inputs.append(
oh.make_tensor_value_info("X", onnx.TensorProto.FLOAT, shape=(512, 3, 64, 64))
)
inputs.append(
oh.make_tensor_value_info("W", onnx.TensorProto.FLOAT, shape=(64, 3, 4, 4))
)
initializers.append(onh.from_array(np.zeros((64,), dtype=np.float32), name="B2"))
nodes.append(
oh.make_node(
"Conv",
["X", "W", "B2"],
["Y"],
dilations=[1, 1],
group=1,
kernel_shape=[4, 4],
pads=[1, 1, 1, 1],
strides=[2, 2],
)
)
outputs.append(
oh.make_tensor_value_info("Y", onnx.TensorProto.FLOAT, shape=(512, 64, 32, 32))
)
graph = oh.make_graph(
nodes,
"pattern",
inputs,
outputs,
initializers,
sparse_initializer=sparse_initializers,
)
model = oh.make_model(graph, functions=functions, opset_imports=opset_imports)
print("DOT-SECTION", to_dot(model))
Outcome of the fusion:
.. gdot::
:script: DOT-SECTION
:process:
from experimental_experiment.doc import to_dot
import numpy as np
import ml_dtypes
import onnx
import onnx.helper as oh
import onnx.numpy_helper as onh
opset_imports = [
oh.make_opsetid("", 18),
]
inputs = []
outputs = []
nodes = []
initializers = []
sparse_initializers = []
functions = []
inputs.append(
oh.make_tensor_value_info("X", onnx.TensorProto.FLOAT, shape=(512, 3, 64, 64))
)
inputs.append(
oh.make_tensor_value_info("W", onnx.TensorProto.FLOAT, shape=(64, 3, 4, 4))
)
nodes.append(
oh.make_node(
"Conv",
["X", "W"],
["Y"],
dilations=[1, 1],
group=1,
kernel_shape=[4, 4],
pads=[1, 1, 1, 1],
strides=[2, 2],
)
)
outputs.append(
oh.make_tensor_value_info("Y", onnx.TensorProto.FLOAT, shape=(512, 64, 32, 32))
)
graph = oh.make_graph(
nodes,
"pattern",
inputs,
outputs,
initializers,
sparse_initializer=sparse_initializers,
)
model = oh.make_model(graph, functions=functions, opset_imports=opset_imports)
print("DOT-SECTION", to_dot(model))
"""
def __init__(self, verbose: int = 0, priority: int = 0):
super().__init__(verbose, priority)
[docs]
def match(
self,
g: "GraphBuilderPatternOptimization", # noqa: F821
node: NodeProto,
matched: List[MatchResult],
) -> Optional[MatchResult]:
if node.op_type != "Conv" or node.domain != "":
return self.none()
if len(node.input) < 3:
return self.none(node, inspect.currentframe().f_lineno)
if not g.is_constant(node.input[2]):
return self.none(node, inspect.currentframe().f_lineno)
cst = g.get_computed_constant(node.input[2])
if cst is None or cst.min() != 0 or cst.max() != 0:
return self.none(node, inspect.currentframe().f_lineno)
return MatchResult(self, [node], self.apply, insert_at=node)
[docs]
def apply(
self,
g: "GraphBuilder", # noqa: F821
node: NodeProto,
) -> List[NodeProto]:
new_node = g.make_node(
"Conv",
node.input[:2],
node.output,
name=f"{self.__class__.__name__}--{node.name}",
doc_string=node.doc_string,
)
new_node.attribute.extend(node.attribute)
return [new_node]