sample.yml

# SPDX-FileCopyrightText: Copyright (c) <year> NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
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# custom IPluginV2IOExt
# IPluginV2IOExt will insert a implicit batch dimension the custom onnx operator by default.
# IPluginV2IOExt will use implicit batch, which means that the first dimension of each input and output
# must be -1, and other dimension must be no dynamic. as shown below.
sample_io_ext:
    plugin_type: IPluginV2IOExt
    inputs:
        input0:
            shape: -1x16x16
        input1:
            shape: -1x16x32x32
    outputs:
        output:
            shape: -1x64
    support_format_combination: ["float32+float32+float32"]

# custom IPluginV2DynamicExt
# IPluginV2DynamicExt use explicit batch dimension.
sample_dynamic_ext_static_shape:
    plugin_type: IPluginV2DynamicExt
    inputs:
        input0:
            shape: 4x8x16
        input1:
            shape: 3x9x2
    outputs:
        output0:
            shape: 1x72
        output1:
            shape: 4x7x8x1
    support_format_combination: ["float32+float32+float32+float32"]

# custom IPluginV2DynamicExt
# IPluginV2DynamicExt use explicit batch dimension.
sample_dynamic_ext_dynamic_shape:
    plugin_type: IPluginV2DynamicExt
    inputs:
        input0:
            shape: -1x-1x16
        input1:
            shape: -1x-1x-1
    outputs:
        output0:
            shape: -1x72
        output1:
            shape: -1x-1x-1x1
    support_format_combination: ["float32+float32+float32+float32"]

# custom IPluginV2DynamicExt with attribute
sample_dynamic_ext_with_attributes:
    plugin_type: IPluginV2DynamicExt
    inputs:
        input0:
            shape: -1x-1x16
        input1:
            shape: -1x-1x-1
    outputs:
        output0:
            shape: -1x72
        output1:
            shape: -1x-1x-1x1
    support_format_combination: ["float32+float32+float32+float32"]
    attributes:
        attr_a:
            datatype: int32[]
            default_value: [1,1,2,2]
        attr_b:
            datatype: float32
            default_value: 1.5
        attr_c:
            datatype: char[]
            default_value: ['a', 'b', 'c']
        attr_d:
            datatype: float16

# custom operator name
custom_add:
    plugin_type: IPluginV2DynamicExt
    # list all inputs here
    inputs:
        # each input must contain type, datatype and shape
        input0:
            # -1 mean dynamic shape
            shape: 1x16x-1x-1
        input1:
            # -1 mean dynamic shape
            shape: 1x16x-1x-1
    outputs:
        output:
            # the output shape can be completely unknown, and calculated by getOutputDimensions()
            shape: 1x16x-1x-1
    # the support format comes form the inputs tensor and outputs tensor(initializer inputs are datatype determined),
    # the combination must be specify in the order of inputs and outputs, e.g. in this case input0+input1+output
    support_format_combination: ["float32+float32+float32", "float16+float16+float16"]

    # attributes can be empty if doesn't exist.

# custom operator name
custom_conv:
    # available option: IPluginV2DynamicExt, IPluginV2IOExt
    plugin_type: IPluginV2DynamicExt
    # list all inputs here
    inputs:
        # each input must contain type, datatype and shape
        input.3:
            # -1 mean dynamic shape
            shape: 1x16x32x32
        conv.weight:
            shape: 16x32x3x3
        conv.bias:
            shape: 16
    outputs:
        output:
            shape: 1x32x32x32
    # the support format comes form the inputs tensor and outputs tensor(initializer inputs are datatype determined),
    # please note that some operators has initializer inputs, then the initializer's datatype is determined. e.g. weights and bias
    # are always float32 in this case.
    # the support_format_combination below means we can support FP32 and FP16 calculation. 
    support_format_combination: ["float32+float32+float32+float32", "float16+float32+float32+float16", "int8+float32+float32+int8"]
    # each attribute must at least contain the datatype, the value can be deserialized from onnx operator during
    # deserialization.
    attributes:
        dilations:
            # the datatype may be a scalar or a vector
            # availabe option: float64, float32, float16, int32, int16, int8, char
            # [2] means vector size == 2.
            # can be seen from the onnx operator, e.g. netron.
            datatype: int32[2]
        group:
            datatype: int32
        kernel_shape:
            datatype: int32[2]
        pads:
            datatype: int32[2]
        strides:
            datatype: int32[2]