Custom Op - General

General Custom Ops

qonnx.custom_op.general.bipolar_quant

class qonnx.custom_op.general.bipolar_quant.BipolarQuant(onnx_node: NodeProto, onnx_opset_version: int = 1)

Bases: CustomOp

Bipolar quantization operation for QONNX. Takes four inputs: - input tensor to quantize - the scale

The output is a tensor of the same shape as the input tensor, with quantized values.

execute_node(context, graph): Execute this CustomOp instance, given the execution context and ONNX graph.

get_integer_datatype(model)

get_nodeattr_types(): Returns a dict of permitted attributes for node, where: ret_dict[attribute_name] = (dtype, require, default_value, <allowed_values>) - dtype indicates which member of the ONNX AttributeProto will be utilized - require indicates whether this attribute is required - default_val indicates the default value that will be used if the attribute is not set - <allowed_values> (if specified) indicates that this attribute can only be set to one of the values in the set <allowed_values>. If not specified, all values permitted by dtype are allowed.

get_output_dtype(model)

infer_node_datatype(model): Set the DataType annotations corresponding to the outputs of this node.

make_shape_compatible_op(model): Returns a standard ONNX op which is compatible with this CustomOp for performing shape inference.

verify_node(): Verifies that all attributes the node needs are there and that particular attributes are set correctly. Also checks if the number of inputs is equal to the expected number.

qonnx.custom_op.general.bipolar_quant.binary_quant(inp_tensor, scale)

qonnx.custom_op.general.debugmarker

class qonnx.custom_op.general.debugmarker.DebugMarker(onnx_node: NodeProto, onnx_opset_version: int = 1)

Bases: CustomOp

execute_node(context, graph): Execute this CustomOp instance, given the execution context and ONNX graph.

get_nodeattr_types(): Returns a dict of permitted attributes for node, where: ret_dict[attribute_name] = (dtype, require, default_value, <allowed_values>) - dtype indicates which member of the ONNX AttributeProto will be utilized - require indicates whether this attribute is required - default_val indicates the default value that will be used if the attribute is not set - <allowed_values> (if specified) indicates that this attribute can only be set to one of the values in the set <allowed_values>. If not specified, all values permitted by dtype are allowed.

infer_node_datatype(model): Set the DataType annotations corresponding to the outputs of this node.

make_shape_compatible_op(model): Returns a standard ONNX op which is compatible with this CustomOp for performing shape inference.

verify_node(): Verifies that all attributes the node needs are there and that particular attributes are set correctly. Also checks if the number of inputs is equal to the expected number.

qonnx.custom_op.general.genericpartition

class qonnx.custom_op.general.genericpartition.GenericPartition(onnx_node: NodeProto, onnx_opset_version: int = 1)

Bases: CustomOp

Class that corresponds to the meta/container node GenericPartition which is a placeholder for a group of nodes that have been separated out into an ONNX model of its own.

execute_node(context, graph): Execute this CustomOp instance, given the execution context and ONNX graph.

get_nodeattr_types(): Returns a dict of permitted attributes for node, where: ret_dict[attribute_name] = (dtype, require, default_value, <allowed_values>) - dtype indicates which member of the ONNX AttributeProto will be utilized - require indicates whether this attribute is required - default_val indicates the default value that will be used if the attribute is not set - <allowed_values> (if specified) indicates that this attribute can only be set to one of the values in the set <allowed_values>. If not specified, all values permitted by dtype are allowed.

infer_node_datatype(model): Set the DataType annotations corresponding to the outputs of this node.

make_shape_compatible_op(model): Returns a standard ONNX op which is compatible with this CustomOp for performing shape inference.

verify_node(): Verifies that all attributes the node needs are there and that particular attributes are set correctly. Also checks if the number of inputs is equal to the expected number.

qonnx.custom_op.general.im2col

class qonnx.custom_op.general.im2col.Im2Col(onnx_node: NodeProto, onnx_opset_version: int = 1)

Bases: CustomOp

execute_node(context, graph): Execute this CustomOp instance, given the execution context and ONNX graph.

get_nodeattr_types(): Returns a dict of permitted attributes for node, where: ret_dict[attribute_name] = (dtype, require, default_value, <allowed_values>) - dtype indicates which member of the ONNX AttributeProto will be utilized - require indicates whether this attribute is required - default_val indicates the default value that will be used if the attribute is not set - <allowed_values> (if specified) indicates that this attribute can only be set to one of the values in the set <allowed_values>. If not specified, all values permitted by dtype are allowed.

infer_node_datatype(model): Set the DataType annotations corresponding to the outputs of this node.

make_shape_compatible_op(model): Returns a standard ONNX op which is compatible with this CustomOp for performing shape inference.

verify_node(): Verifies that all attributes the node needs are there and that particular attributes are set correctly. Also checks if the number of inputs is equal to the expected number.

qonnx.custom_op.general.im2col.compute_conv_output_dim(ifm_dim, k, stride, total_pad=0, dilation=1): Returns spatial output dimension size for convolution with given params. total_pad gives the total amount of padding along the entire axis (both sides included).

qonnx.custom_op.general.im2col.get_im2col_indices_nchw(x_shape, field_height, field_width, padding=0, stride_h=1, stride_w=1, dilation_h=1, dilation_w=1): Returns im2col indices.

qonnx.custom_op.general.im2col.im2col_indices_nchw(x, ifm_h, ifm_w, field_height, field_width, padding=[0, 0, 0, 0], stride_h=1, stride_w=1, pad_val=0, dilation_h=1, dilation_w=1): Performs im2col on image (2D tensor, possibly with 1-length dummy dimensions) x with given field height and width, as well as values for padding and stride size. Returns result of im2col.

qonnx.custom_op.general.maxpoolnhwc

class qonnx.custom_op.general.maxpoolnhwc.MaxPoolNHWC(onnx_node: NodeProto, onnx_opset_version: int = 1)

Bases: CustomOp

execute_node(context, graph): Execute this CustomOp instance, given the execution context and ONNX graph.

get_nodeattr_types(): Returns a dict of permitted attributes for node, where: ret_dict[attribute_name] = (dtype, require, default_value, <allowed_values>) - dtype indicates which member of the ONNX AttributeProto will be utilized - require indicates whether this attribute is required - default_val indicates the default value that will be used if the attribute is not set - <allowed_values> (if specified) indicates that this attribute can only be set to one of the values in the set <allowed_values>. If not specified, all values permitted by dtype are allowed.

infer_node_datatype(model): Set the DataType annotations corresponding to the outputs of this node.

make_shape_compatible_op(model): Returns a standard ONNX op which is compatible with this CustomOp for performing shape inference.

verify_node(): Verifies that all attributes the node needs are there and that particular attributes are set correctly. Also checks if the number of inputs is equal to the expected number.

qonnx.custom_op.general.maxpoolnhwc.compute_pool_output_dim(ifm_dim, k, stride, pad=0, ceil_mode=0): Return spatial output dimension size for pooling with given params.

qonnx.custom_op.general.multithreshold

class qonnx.custom_op.general.multithreshold.MultiThreshold(onnx_node: NodeProto, onnx_opset_version: int = 1)

Bases: CustomOp

Class that corresponds to a multithresholding node.

execute_node(context, graph): Execute this CustomOp instance, given the execution context and ONNX graph.

get_nodeattr_types(): Returns a dict of permitted attributes for node, where: ret_dict[attribute_name] = (dtype, require, default_value, <allowed_values>) - dtype indicates which member of the ONNX AttributeProto will be utilized - require indicates whether this attribute is required - default_val indicates the default value that will be used if the attribute is not set - <allowed_values> (if specified) indicates that this attribute can only be set to one of the values in the set <allowed_values>. If not specified, all values permitted by dtype are allowed.

infer_node_datatype(model): Set the DataType annotations corresponding to the outputs of this node.

make_shape_compatible_op(model): Returns a standard ONNX op which is compatible with this CustomOp for performing shape inference.

verify_node(): Verifies that all attributes the node needs are there and that particular attributes are set correctly. Also checks if the number of inputs is equal to the expected number.

qonnx.custom_op.general.multithreshold.multithreshold(v, thresholds, out_scale=None, out_bias=None, channels_last=False)

Given a set of threshold values t={t_0, t_1 … t_n} the successive thresholding maps any real number x to an integer in the interval [0, n], where the returned integer is the number of thresholds x is greater than or equal to.

The output tensor will be scaled by out_scale and biased by out_bias.

qonnx.custom_op.general.quant

qonnx.custom_op.general.quantavgpool2d

class qonnx.custom_op.general.quantavgpool2d.QuantAvgPool2d(onnx_node: NodeProto, onnx_opset_version: int = 1)

Bases: CustomOp

CustomOp that corresponds to the quantized average pooling layer from Brevitas

execute_node(context, graph): Execute this CustomOp instance, given the execution context and ONNX graph.

get_accum_size()

get_nodeattr_types(): Returns a dict of permitted attributes for node, where: ret_dict[attribute_name] = (dtype, require, default_value, <allowed_values>) - dtype indicates which member of the ONNX AttributeProto will be utilized - require indicates whether this attribute is required - default_val indicates the default value that will be used if the attribute is not set - <allowed_values> (if specified) indicates that this attribute can only be set to one of the values in the set <allowed_values>. If not specified, all values permitted by dtype are allowed.

get_shifts()

infer_node_datatype(model): Set the DataType annotations corresponding to the outputs of this node.

make_shape_compatible_op(model): Returns a standard ONNX op which is compatible with this CustomOp for performing shape inference.

verify_node(): Verifies that all attributes the node needs are there and that particular attributes are set correctly. Also checks if the number of inputs is equal to the expected number.

qonnx.custom_op.general.trunc

class qonnx.custom_op.general.trunc.Trunc_v1(onnx_node, onnx_opset_version=1)

Bases: CustomOp

Generic truncation operation for QONNX. Takes four inputs: - input tensor to truncate - the scale - the zero-point - the truncation bit-width

The output is a tensor of the same shape as the input tensor, with truncated values.

execute_node(context, graph): Execute this CustomOp instance, given the execution context and ONNX graph.

get_nodeattr_types(): Returns a dict of permitted attributes for node, where: ret_dict[attribute_name] = (dtype, require, default_value, <allowed_values>) - dtype indicates which member of the ONNX AttributeProto will be utilized - require indicates whether this attribute is required - default_val indicates the default value that will be used if the attribute is not set - <allowed_values> (if specified) indicates that this attribute can only be set to one of the values in the set <allowed_values>. If not specified, all values permitted by dtype are allowed.

infer_node_datatype(model): Set the DataType annotations corresponding to the outputs of this node.

make_shape_compatible_op(model): Returns a standard ONNX op which is compatible with this CustomOp for performing shape inference.

verify_node(): Verifies that all attributes the node needs are there and that particular attributes are set correctly. Also checks if the number of inputs is equal to the expected number.

class qonnx.custom_op.general.trunc.Trunc_v2(onnx_node, onnx_opset_version=1)

Bases: CustomOp

Generic truncation operation for QONNX. Takes four inputs: - input tensor to truncate - the scale - the zero-point - the truncation scale - the truncation bit-width

The output is a tensor of the same shape as the input tensor, with truncated values.

execute_node(context, graph): Execute this CustomOp instance, given the execution context and ONNX graph.

get_nodeattr_types(): Returns a dict of permitted attributes for node, where: ret_dict[attribute_name] = (dtype, require, default_value, <allowed_values>) - dtype indicates which member of the ONNX AttributeProto will be utilized - require indicates whether this attribute is required - default_val indicates the default value that will be used if the attribute is not set - <allowed_values> (if specified) indicates that this attribute can only be set to one of the values in the set <allowed_values>. If not specified, all values permitted by dtype are allowed.

infer_node_datatype(model): Set the DataType annotations corresponding to the outputs of this node.

make_shape_compatible_op(model): Returns a standard ONNX op which is compatible with this CustomOp for performing shape inference.

verify_node(): Verifies that all attributes the node needs are there and that particular attributes are set correctly. Also checks if the number of inputs is equal to the expected number.

qonnx.custom_op.general.trunc.trunc_v1(inp_tensor, scale, zeropt, input_bit_width, output_bit_width, rounding_mode)

qonnx.custom_op.general.trunc.trunc_v2(inp_tensor, scale, zeropt, input_bit_width, narrow, signed, output_scale, output_bit_width, rounding_mode)

qonnx.custom_op.general.xnorpopcount

class qonnx.custom_op.general.xnorpopcount.XnorPopcountMatMul(onnx_node: NodeProto, onnx_opset_version: int = 1)

Bases: CustomOp

Class that corresponds to a XNOR-popcount matrix multiplication node.

execute_node(context, graph): Execute this CustomOp instance, given the execution context and ONNX graph.

get_nodeattr_types(): Returns a dict of permitted attributes for node, where: ret_dict[attribute_name] = (dtype, require, default_value, <allowed_values>) - dtype indicates which member of the ONNX AttributeProto will be utilized - require indicates whether this attribute is required - default_val indicates the default value that will be used if the attribute is not set - <allowed_values> (if specified) indicates that this attribute can only be set to one of the values in the set <allowed_values>. If not specified, all values permitted by dtype are allowed.

infer_node_datatype(model): Set the DataType annotations corresponding to the outputs of this node.

make_shape_compatible_op(model): Returns a standard ONNX op which is compatible with this CustomOp for performing shape inference.

verify_node(): Verifies that all attributes the node needs are there and that particular attributes are set correctly. Also checks if the number of inputs is equal to the expected number.

qonnx.custom_op.general.xnorpopcount.xnorpopcountmatmul(inp0, inp1): Simulates XNOR-popcount matrix multiplication as a regular bipolar matrix multiplication followed by some post processing.