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Add more kernels that have typed registrations to the operators we track type usage for. #6565

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skottmckay merged 2 commits into from
Feb 5, 2021
Merged

Add more kernels that have typed registrations to the operators we track type usage for. #6565

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Add more kernels that have typed registrations to the operators we tr…
skottmckay Feb 4, 2021
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skottmckay Feb 5, 2021
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32 changes: 23 additions & 9 deletions tools/python/util/ort_format_model/operator_type_usage_processors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -260,16 +260,31 @@ def add(processor):
#
# Ops we are ignoring currently so as not to produce meaningless/unused output:
# - Implementation is type agnostic:
# DynamicQuantizeMatMul, If, Loop, Reshape, Scan, Shape, Squeeze, Unsqueeze
# ai.onnx: If, Loop, Reshape, Scan, Shape, Squeeze, Unsqueeze
# com.microsoft: DynamicQuantizeMatMul, MatMulIntegerToFloat
# - Only one type supported in the ORT implementation:
# FusedConv, FusedGemm, FusedMatMul, TransposeMatMul
# com.microsoft: FusedConv, FusedGemm, FusedMatMul, TransposeMatMul
# - Implementation does not have any significant type specific code:
# Concat, Flatten, Not, QLinearConv, Reshape, Shape, Squeeze, Unsqueeze
default_processor_onnx_ops = ['Add', 'AveragePool', 'BatchNormalization', 'Clip', 'Conv',
'DequantizeLinear', 'Div', 'Equal', 'Exp', 'Expand',
'Gemm', 'Greater', 'Less', 'MatMul', 'Max', 'Min', 'Mul',
'NonMaxSuppression', 'NonZero', 'Pad', 'Range', 'Relu', 'Resize',
'Sigmoid', 'Softmax', 'Split', 'Sub', 'Tile', 'TopK', 'Transpose']
# ai.onnx: Concat, Flatten, Not, QLinearConv, Reshape, Shape, Squeeze, Unsqueeze
#
default_processor_onnx_ops = ['Abs', 'Add', 'ArgMax', 'ArgMin', 'AveragePool',
'BatchNormalization', 'BitShift',
'Ceil', 'Clip', 'Conv', 'CumSum',
'DequantizeLinear', 'Div',
'Equal', 'Exp', 'Expand',
'Floor',
'Gemm', 'Greater',
'IsNaN'
'Less', 'Log', 'LogSoftmax', 'LpNormalization',
'MatMul', 'Max', 'Min', 'Mul',
'Neg', 'NonMaxSuppression', 'NonZero',
'Pad',
'Range', 'Reciprocal', 'ReduceL1', 'ReduceL2', 'ReduceLogSum', 'ReduceLogSumExp',
'ReduceMax', 'ReduceMean', 'ReduceMin', 'ReduceProd', 'ReduceSum', 'ReduceSumSquare',
'Relu', 'Resize', 'RoiAlign', 'Round',
'Sigmoid', 'Sin', 'Softmax', 'Split', 'Sqrt', 'Sub',
'Tanh', 'Tile', 'TopK', 'Transpose',
'Where']

internal_ops = ['QLinearAdd', 'QLinearMul']

Expand Down Expand Up @@ -303,7 +318,6 @@ def add(processor):

# Operators that switch on output type
add(DefaultTypeUsageProcessor('ai.onnx', 'ConstantOfShape', inputs=[], outputs=[0]))
add(DefaultTypeUsageProcessor('com.microsoft', 'DynamicQuantizeMatMul', inputs=[], outputs=[0]))

# Random generator ops produce new data so we track the output type
onnx_random_ops = ['RandomNormal', 'RandomNormalLike', 'RandomUniform', 'RandomUniformLike', 'Multinomial']
Expand Down