def apply(self, model):
streamline_transformations = [
ConvertSubToAdd(),
ConvertDivToMul(),
BatchNormToAffine(),
ConvertSignToThres(),
MoveMulPastMaxPool(),
MoveScalarLinearPastInvariants(),
AbsorbSignBiasIntoMultiThreshold(),
MoveAddPastMul(),
MoveScalarAddPastMatMul(),
MoveAddPastConv(),
MoveScalarMulPastMatMul(),
MoveScalarMulPastConv(),
MoveAddPastMul(),
CollapseRepeatedAdd(),
CollapseRepeatedMul(),
MoveMulPastMaxPool(),
AbsorbAddIntoMultiThreshold(),
FactorOutMulSignMagnitude(),
AbsorbMulIntoMultiThreshold(),
Absorb1BitMulIntoMatMul(),
Absorb1BitMulIntoConv(),
RoundAndClipThresholds(),
]
for trn in streamline_transformations:
model = model.transform(trn)
model = model.transform(RemoveIdentityOps())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
model = model.transform(InferDataTypes())
return (model, False)
def step_resnet50_streamline_linear(model: ModelWrapper,
cfg: DataflowBuildConfig):
streamline_transformations = [
AbsorbScalarMulAddIntoTopK(
), # before MoveAddPastMul to avoid int->float
ConvertSubToAdd(),
ConvertDivToMul(),
RemoveIdentityOps(),
CollapseRepeatedMul(),
BatchNormToAffine(),
ConvertSignToThres(),
MoveAddPastMul(),
MoveScalarAddPastMatMul(),
MoveAddPastConv(),
MoveScalarMulPastMatMul(),
MoveScalarMulPastConv(),
MoveScalarLinearPastInvariants(),
MoveAddPastMul(),
CollapseRepeatedAdd(),
CollapseRepeatedMul(),
AbsorbAddIntoMultiThreshold(),
FactorOutMulSignMagnitude(),
MoveMaxPoolPastMultiThreshold(),
AbsorbMulIntoMultiThreshold(),
Absorb1BitMulIntoMatMul(),
Absorb1BitMulIntoConv(),
RoundAndClipThresholds(),
]
for trn in streamline_transformations:
model = model.transform(trn)
model = model.transform(GiveUniqueNodeNames())
return model