# -------------------------------------------------------------------------- # ⚠️ WARNING - AUTO-GENERATED CODE - DO NOT EDIT ⚠️ # ⚙️ Generated by 'python -m opgen' # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. # -------------------------------------------------------------------------- # pylint: disable=W0221,W0222,R0901,W0237 # mypy: disable-error-code=override # ruff: noqa: D214, D402, D405, D411, D412, D416 # -------------------------------------------------------------------------- from __future__ import annotations from typing import Optional, Sequence, TypeVar, Union from onnx import GraphProto, SparseTensorProto, TensorProto from onnx.defs import get_schema from typing_extensions import TypeAlias from onnxscript.onnx_opset._impl.opset18 import Opset18 from onnxscript.onnx_types import ( BFLOAT16, BOOL, COMPLEX64, COMPLEX128, DOUBLE, FLOAT, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, FLOAT16, INT8, INT16, INT32, INT64, STRING, UINT8, UINT16, UINT32, UINT64, ) from onnxscript.values import Op, Opset class Opset19(Opset18): def __new__(cls): return Opset.__new__(cls, "", 19) T_AveragePool = TypeVar("T_AveragePool", DOUBLE, FLOAT, FLOAT16) def AveragePool( self, X: T_AveragePool, *, auto_pad: str = "NOTSET", ceil_mode: int = 0, count_include_pad: int = 0, dilations: Optional[Sequence[int]] = None, kernel_shape: Sequence[int], pads: Optional[Sequence[int]] = None, strides: Optional[Sequence[int]] = None, ) -> T_AveragePool: r"""[🌐 AveragePool(19)](https://onnx.ai/onnx/operators/onnx__AveragePool.html#averagepool-19 "Online Documentation") AveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape is calculated differently depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized. With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d): ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D): ``` VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1 ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i] ``` The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). Args: X: (differentiable) Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...]. auto_pad: auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER. ceil_mode: Whether to use ceil or floor (default) to compute the output shape. count_include_pad: Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad. dilations: Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis. kernel_shape: The size of the kernel along each axis. pads: Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis. strides: Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis. """ schema = get_schema("AveragePool", 19, "") op = Op(self, "AveragePool", schema) return op( *self._prepare_inputs(schema, X), auto_pad=auto_pad, ceil_mode=ceil_mode, count_include_pad=count_include_pad, dilations=dilations, kernel_shape=kernel_shape, pads=pads, strides=strides, ) T1_Cast = TypeVar( "T1_Cast", BFLOAT16, BOOL, DOUBLE, FLOAT, FLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ) T2_Cast: TypeAlias = Union[ BFLOAT16, BOOL, DOUBLE, FLOAT, FLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ] def Cast(self, input: T1_Cast, *, saturate: int = 1, to: int) -> T2_Cast: r"""[🌐 Cast(19)](https://onnx.ai/onnx/operators/onnx__Cast.html#cast-19 "Online Documentation") The operator casts the elements of a given input tensor to a data type specified by the 'to' argument and returns an output tensor of the same size in the converted type. The 'to' argument must be one of the data types specified in the 'DataType' enum field in the TensorProto message. Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior. Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type. In more detail, the conversion among numerical types should follow these rules if the destination type is not a float 8 type. * Casting from floating point to: * floating point: +/- infinity if OOR (out of range). * fixed point: undefined if OOR. * bool: +/- 0.0 to False; all else to True. * Casting from fixed point to: * floating point: +/- infinity if OOR. (+ infinity in the case of uint) * fixed point: when OOR, discard higher bits and reinterpret (with respect to two's complement representation for signed types). For example, 200 (int16) -> -56 (int8). * bool: zero to False; nonzero to True. * Casting from bool to: * floating point: `{1.0, 0.0}`. * fixed point: `{1, 0}`. * bool: no change. Float 8 type were introduced to speed up the training of deep models. By default the conversion of a float *x* obeys to the following rules. `[x]` means the value rounded to the target mantissa width. | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | -------- | -------- | -------- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | Inf | FLT_MAX | NaN | FLT_MAX | NaN | | -Inf | -FLT_MAX | NaN | -FLT_MAX | NaN | | \[x\] > FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | | \[x\] \< -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | | else | RNE | RNE | RNE | RNE | The behavior changes if the parameter 'saturate' is set to False. The rules then become: | x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ | | ----------------- | ------ | -------- | ---- | -------- | | 0 | 0 | 0 | 0 | 0 | | -0 | -0 | 0 | -0 | 0 | | NaN | NaN | NaN | NaN | NaN | | -NaN | -NaN | NaN | -NaN | NaN | | Inf | NaN | NaN | Inf | NaN | | -Inf | -NaN | NaN | -Inf | NaN | | \[x\] > FLT_MAX | NaN | NaN | Inf | NaN | | \[x\] \< -FLT_MAX | NaN | NaN | -Inf | NaN | | else | RNE | RNE | RNE | RNE | Args: input: (differentiable) Input tensor to be cast. saturate: The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description. to: The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto """ schema = get_schema("Cast", 19, "") op = Op(self, "Cast", schema) return op(*self._prepare_inputs(schema, input), saturate=saturate, to=to) T1_CastLike = TypeVar( "T1_CastLike", BFLOAT16, BOOL, DOUBLE, FLOAT, FLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ) T2_CastLike = TypeVar( "T2_CastLike", BFLOAT16, BOOL, DOUBLE, FLOAT, FLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ) def CastLike( self, input: T1_CastLike, target_type: T2_CastLike, *, saturate: int = 1 ) -> T2_CastLike: r"""[🌐 CastLike(19)](https://onnx.ai/onnx/operators/onnx__CastLike.html#castlike-19 "Online Documentation") The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details. Args: input: (differentiable) Input tensor to be cast. target_type: (non-differentiable) The (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor. saturate: The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. Please refer to operator Cast description for further details. """ schema = get_schema("CastLike", 19, "") op = Op(self, "CastLike", schema) return op(*self._prepare_inputs(schema, input, target_type), saturate=saturate) T_Constant: TypeAlias = Union[ BFLOAT16, BOOL, COMPLEX128, COMPLEX64, DOUBLE, FLOAT, FLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ] def Constant( self, *, sparse_value: Optional[SparseTensorProto] = None, value: Optional[TensorProto] = None, value_float: Optional[float] = None, value_floats: Optional[Sequence[float]] = None, value_int: Optional[int] = None, value_ints: Optional[Sequence[int]] = None, value_string: Optional[str] = None, value_strings: Optional[Sequence[str]] = None, ) -> T_Constant: r"""[🌐 Constant(19)](https://onnx.ai/onnx/operators/onnx__Constant.html#constant-19 "Online Documentation") This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must be specified. Args: sparse_value: The value for the elements of the output tensor in sparse format. value: The value for the elements of the output tensor. value_float: The value for the sole element for the scalar, float32, output tensor. value_floats: The values for the elements for the 1D, float32, output tensor. value_int: The value for the sole element for the scalar, int64, output tensor. value_ints: The values for the elements for the 1D, int64, output tensor. value_string: The value for the sole element for the scalar, UTF-8 string, output tensor. value_strings: The values for the elements for the 1D, UTF-8 string, output tensor. """ schema = get_schema("Constant", 19, "") op = Op(self, "Constant", schema) return op( sparse_value=sparse_value, value=value, value_float=value_float, value_floats=value_floats, value_int=value_int, value_ints=value_ints, value_string=value_string, value_strings=value_strings, ) T_DeformConv = TypeVar("T_DeformConv", DOUBLE, FLOAT, FLOAT16) def DeformConv( self, X: T_DeformConv, W: T_DeformConv, offset: T_DeformConv, B: Optional[T_DeformConv] = None, mask: Optional[T_DeformConv] = None, *, dilations: Optional[Sequence[int]] = None, group: int = 1, kernel_shape: Optional[Sequence[int]] = None, offset_group: int = 1, pads: Optional[Sequence[int]] = None, strides: Optional[Sequence[int]] = None, ) -> T_DeformConv: r"""[🌐 DeformConv(19)](https://onnx.ai/onnx/operators/onnx__DeformConv.html#deformconv-19 "Online Documentation") Performs deformable convolution as described in https://arxiv.org/abs/1703.06211 and https://arxiv.org/abs/1811.11168. This operator specification supports the general N-D case. Note that most common use cases have 2D or 3D data. Args: X: Input data tensor. For 2D image data, it has shape (N, C, H, W) where N is the batch size, C is the number of input channels, and H and W are the height and width. In general, the shape is (N, C, D1, D2, ... , Dn) for n-dimensional data, where D1 to Dn are the spatial dimension sizes. Most common use cases have n = 2 or 3. W: Weight tensor that will be used in the convolutions. It has shape (oC, C/group, kH, kW), where oC is the number of output channels and kH and kW are the kernel height and width. For more than 2 dimensions, it has shape (oC, C/group, k1, k2, ... , kn). offset: Offset tensor denoting the offset for the sampling locations in the convolution kernel. It has shape (N, offset_group * kH * kW * 2, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Use linear interpolationfor fractional offset values. Sampling locations outside of the padded input tensor gives zero. B: (optional) Optional 1D bias of length oC to be added to the convolution. Default is a tensor of zeros. mask: (optional) The mask tensor to be applied to each position in the convolution kernel. It has shape (N, offset_group * kH * kW, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Default is a tensor of ones. dilations: Dilation value along each spatial axis of the kernel. Default is 1 along each axis. group: Number of groups the input and output channels, C and oC, are divided into. C and oC must both be divisible by group. Default is 1. kernel_shape: Shape of the convolution kernel. If not present, it is inferred from the shape of input W. offset_group: Number of groups of offset. C must be divisible by offset_group. Default is 1. pads: Padding for the beginning and end along each spatial axis. The values represent the number of pixels added to the beginning and end of the corresponding axis and can take any nonnegative value. The format should be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. Default is 0 along each axis. strides: Stride along each spatial axis. Default is 1 along each axis. """ schema = get_schema("DeformConv", 19, "") op = Op(self, "DeformConv", schema) return op( *self._prepare_inputs(schema, X, W, offset, B, mask), dilations=dilations, group=group, kernel_shape=kernel_shape, offset_group=offset_group, pads=pads, strides=strides, ) T1_DequantizeLinear = TypeVar( "T1_DequantizeLinear", FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT32, INT8, UINT8, ) T2_DequantizeLinear = TypeVar("T2_DequantizeLinear", BFLOAT16, FLOAT, FLOAT16) def DequantizeLinear( self, x: T1_DequantizeLinear, x_scale: T2_DequantizeLinear, x_zero_point: Optional[T1_DequantizeLinear] = None, *, axis: int = 1, ) -> T2_DequantizeLinear: r"""[🌐 DequantizeLinear(19)](https://onnx.ai/onnx/operators/onnx__DequantizeLinear.html#dequantizelinear-19 "Online Documentation") The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor. The dequantization formula is `y = (x - x_zero_point) * x_scale`. `x_scale` and `x_zero_point` must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. `x_zero_point` and `x` must have same type. `x` and `y` must have same shape. In the case of dequantizing int32, there's no zero point (zero point is supposed to be 0). `zero-point` is usually not used in the case of float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz quantization, but the dequantization formula remains the same for consistency and 'x_scale' still determines the output type. Args: x: N-D quantized input tensor to be de-quantized. x_scale: Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. x_zero_point: (optional) Zero point for input 'x'. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified. axis: (Optional) The axis of the dequantizing dimension of the input tensor. Used only for per-axis quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`. When the rank of the input is 1, per-tensor quantization is applied, rendering the axis unnecessary in this scenario. """ schema = get_schema("DequantizeLinear", 19, "") op = Op(self, "DequantizeLinear", schema) return op(*self._prepare_inputs(schema, x, x_scale, x_zero_point), axis=axis) T_Equal = TypeVar( "T_Equal", BFLOAT16, BOOL, DOUBLE, FLOAT, FLOAT16, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ) T1_Equal: TypeAlias = BOOL def Equal(self, A: T_Equal, B: T_Equal) -> T1_Equal: r"""[🌐 Equal(19)](https://onnx.ai/onnx/operators/onnx__Equal.html#equal-19 "Online Documentation") Returns the tensor resulted from performing the `equal` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check `Broadcasting in ONNX `_. Args: A: (non-differentiable) First input operand for the logical operator. B: (non-differentiable) Second input operand for the logical operator. """ schema = get_schema("Equal", 19, "") op = Op(self, "Equal", schema) return op(*self._prepare_inputs(schema, A, B)) V_Identity = TypeVar( "V_Identity", Optional[Sequence[BOOL]], Optional[Sequence[COMPLEX128]], Optional[Sequence[COMPLEX64]], Optional[Sequence[DOUBLE]], Optional[Sequence[FLOAT]], Optional[Sequence[FLOAT16]], Optional[Sequence[INT16]], Optional[Sequence[INT32]], Optional[Sequence[INT64]], Optional[Sequence[INT8]], Optional[Sequence[STRING]], Optional[Sequence[UINT16]], Optional[Sequence[UINT32]], Optional[Sequence[UINT64]], Optional[Sequence[UINT8]], Optional[BOOL], Optional[COMPLEX128], Optional[COMPLEX64], Optional[DOUBLE], Optional[FLOAT], Optional[FLOAT16], Optional[INT16], Optional[INT32], Optional[INT64], Optional[INT8], Optional[STRING], Optional[UINT16], Optional[UINT32], Optional[UINT64], Optional[UINT8], Sequence[BOOL], Sequence[COMPLEX128], Sequence[COMPLEX64], Sequence[DOUBLE], Sequence[FLOAT], Sequence[FLOAT16], Sequence[INT16], Sequence[INT32], Sequence[INT64], Sequence[INT8], Sequence[STRING], Sequence[UINT16], Sequence[UINT32], Sequence[UINT64], Sequence[UINT8], BFLOAT16, BOOL, COMPLEX128, COMPLEX64, DOUBLE, FLOAT, FLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ) def Identity(self, input: V_Identity) -> V_Identity: r"""[🌐 Identity(19)](https://onnx.ai/onnx/operators/onnx__Identity.html#identity-19 "Online Documentation") Identity operator Args: input: (differentiable) Input tensor """ schema = get_schema("Identity", 19, "") op = Op(self, "Identity", schema) return op(*self._prepare_inputs(schema, input)) B_If: TypeAlias = BOOL V_If: TypeAlias = Union[ None, Sequence[BFLOAT16], Sequence[BOOL], Sequence[COMPLEX128], Sequence[COMPLEX64], Sequence[DOUBLE], Sequence[FLOAT], Sequence[FLOAT16], Sequence[INT16], Sequence[INT32], Sequence[INT64], Sequence[INT8], Sequence[STRING], Sequence[UINT16], Sequence[UINT32], Sequence[UINT64], Sequence[UINT8], BFLOAT16, BOOL, COMPLEX128, COMPLEX64, DOUBLE, FLOAT, FLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, Sequence[FLOAT8E4M3FN], Sequence[FLOAT8E4M3FNUZ], Sequence[FLOAT8E5M2], Sequence[FLOAT8E5M2FNUZ], ] def If(self, cond: B_If, *, else_branch: GraphProto, then_branch: GraphProto) -> V_If: r"""[🌐 If(19)](https://onnx.ai/onnx/operators/onnx__If.html#if-19 "Online Documentation") If conditional Args: cond: Condition for the if. The tensor must contain a single element. else_branch: Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch. then_branch: Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch. """ schema = get_schema("If", 19, "") op = Op(self, "If", schema) return op( *self._prepare_inputs(schema, cond), else_branch=else_branch, then_branch=then_branch, ) I_Loop: TypeAlias = INT64 B_Loop: TypeAlias = BOOL V_Loop = TypeVar( "V_Loop", Optional[Sequence[BFLOAT16]], Optional[Sequence[BOOL]], Optional[Sequence[COMPLEX128]], Optional[Sequence[COMPLEX64]], Optional[Sequence[DOUBLE]], Optional[Sequence[FLOAT]], Optional[Sequence[FLOAT16]], Optional[Sequence[INT16]], Optional[Sequence[INT32]], Optional[Sequence[INT64]], Optional[Sequence[INT8]], Optional[Sequence[STRING]], Optional[Sequence[UINT16]], Optional[Sequence[UINT32]], Optional[Sequence[UINT64]], Optional[Sequence[UINT8]], Optional[BFLOAT16], Optional[BOOL], Optional[COMPLEX128], Optional[COMPLEX64], Optional[DOUBLE], Optional[FLOAT], Optional[FLOAT16], Optional[FLOAT8E4M3FN], Optional[FLOAT8E4M3FNUZ], Optional[FLOAT8E5M2], Optional[FLOAT8E5M2FNUZ], Optional[INT16], Optional[INT32], Optional[INT64], Optional[INT8], Optional[STRING], Optional[UINT16], Optional[UINT32], Optional[UINT64], Optional[UINT8], Sequence[BFLOAT16], Sequence[BOOL], Sequence[COMPLEX128], Sequence[COMPLEX64], Sequence[DOUBLE], Sequence[FLOAT], Sequence[FLOAT16], Sequence[FLOAT8E4M3FN], Sequence[FLOAT8E4M3FNUZ], Sequence[FLOAT8E5M2], Sequence[FLOAT8E5M2FNUZ], Sequence[INT16], Sequence[INT32], Sequence[INT64], Sequence[INT8], Sequence[STRING], Sequence[UINT16], Sequence[UINT32], Sequence[UINT64], Sequence[UINT8], BFLOAT16, BOOL, COMPLEX128, COMPLEX64, DOUBLE, FLOAT, FLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ) def Loop( self, M: Optional[I_Loop], cond: Optional[B_Loop], *v_initial: V_Loop, body: GraphProto, ) -> V_Loop: r"""[🌐 Loop(19)](https://onnx.ai/onnx/operators/onnx__Loop.html#loop-19 "Online Documentation") Generic Looping construct. This loop has multiple termination conditions: 1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not. This table summarizes the operating modes of this operator with equivalent C-style code: Operator inputs defined as (max_trip_count, condition_var). * input ("", ""): for (int i=0; ; ++i) { cond = ... // Note this value is ignored, but is required in the body } * input ("", cond) // Note this is analogous to a while loop bool cond = ...; for (int i=0; cond; ++i) { cond = ...; } * input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = ...; } * input (trip_count, "") // Note this is analogous to a for loop int trip_count = ... for (int i=0; i < trip_count; ++i) { cond = ...; // ignored } * input (trip_count, cond) int trip_count = ...; bool cond = ...; for (int i=0; i < trip_count && cond; ++i) { cond = ...; } *Sample usage - cond as well as trip count* graph predict-net { %a = Constant[value = ]() %b = Constant[value = ]() %keepgoing = Constant[value = ]() %max_trip_count = Constant[value = ]() %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b) return } graph body-net ( %i[INT32, scalar] // iteration number %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used %b_in[INT32, scalar] // incoming value of loop-carried-dependency b ) { %my_local = Add(%a, %b_in) %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated return %keepgoing_out, %b_out, %user_defined_val } *Sample equivalent C code* { /* User-defined code (enclosing scope) */ int a = 3, b = 6; bool keepgoing = true; // Analogous to input cond /* End user-defined code */ /* Implicitly-defined code */ const int max_trip_count = 10; // Analogous to input M int user_defined_vals[]; // Imagine this is resizable /* End implicitly-defined code */ /* initialize loop-carried variables and scan-output variables */ bool keepgoing_out = keepgoing int b_out = b for (int i=0; i < max_trip_count && keepgoing_out; ++i) { /* Implicitly-defined code: bind actual parameter values to formal parameter variables of loop-body */ bool keepgoing_in = keepgoing_out; bool b_in = b_out; /* User-defined code (loop body) */ int my_local = a + b_in; // Reading value "a" from the enclosing scope is fine b_out = a - b_in; keepgoing_out = my_local > b_out; user_defined_val = b_in + b_in; // b_in and b_out are different variables /* End user-defined code */ /* Implicitly defined-code */ user_defined_vals[i] = user_defined_val // accumulate scan-output values } // int t = my_local; // Can't do this. my_local is not accessible here. // The values below are bound to the output variables of the loop and therefore accessible // b_out; user_defined_vals; keepgoing_out; } There are several things of note in this code snippet: 1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above. Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer). The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order. Args: M: (optional) A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip. cond: (optional) A boolean termination condition. Optional. Pass empty string to skip. v_initial: (variadic, heterogeneous) The initial values of any loop-carried dependencies (values that change across loop iterations) body: The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations. """ schema = get_schema("Loop", 19, "") op = Op(self, "Loop", schema) return op(*self._prepare_inputs(schema, M, cond, *v_initial), body=body) T_Pad = TypeVar( "T_Pad", BFLOAT16, BOOL, COMPLEX128, COMPLEX64, DOUBLE, FLOAT, FLOAT16, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ) Tind_Pad = TypeVar("Tind_Pad", INT32, INT64) def Pad( self, data: T_Pad, pads: INT64, constant_value: Optional[T_Pad] = None, axes: Optional[Tind_Pad] = None, *, mode: str = "constant", ) -> T_Pad: r"""[🌐 Pad(19)](https://onnx.ai/onnx/operators/onnx__Pad.html#pad-19 "Online Documentation") Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array 4) `wrap` - wrap-around padding as if the data tensor forms a torus Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. :: data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] Example 2 (`reflect` mode): :: data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] Example 3 (`edge` mode): :: data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] Example 4 (`wrap` mode): :: data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [2, 1, 1, 1] mode = 'wrap' output = [ [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], ] Args: data: (differentiable) Input tensor. pads: (non-differentiable) Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to the number of elements in the `axes` input or the input rank if `axes` are not provided explicitly. `pads` format should be: [x1_begin, x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `axes[i]` and xi_end, the number of pad values added at the end of axis `axes[i]`. constant_value: (optional, non-differentiable) (Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False). axes: (optional, non-differentiable) 1-D tensor of axes that `pads` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated. If not provided, all axes are assumed (`[0, 1, ..., input_rank-1]`). mode: Supported modes: `constant`(default), `reflect`, `edge`, `wrap` """ schema = get_schema("Pad", 19, "") op = Op(self, "Pad", schema) return op(*self._prepare_inputs(schema, data, pads, constant_value, axes), mode=mode) T1_QuantizeLinear = TypeVar("T1_QuantizeLinear", BFLOAT16, FLOAT, FLOAT16, INT32) T2_QuantizeLinear = TypeVar( "T2_QuantizeLinear", FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT8, UINT8, ) def QuantizeLinear( self, x: T1_QuantizeLinear, y_scale: T1_QuantizeLinear, y_zero_point: Optional[T2_QuantizeLinear] = None, *, axis: int = 1, saturate: int = 1, ) -> T2_QuantizeLinear: r"""[🌐 QuantizeLinear(19)](https://onnx.ai/onnx/operators/onnx__QuantizeLinear.html#quantizelinear-19 "Online Documentation") The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor. The scale factor and zero point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. The quantization formula is `y = saturate ((x / y_scale) + y_zero_point)`. For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8. For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type. 'y_zero_point' is usually not used for quantization to float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz, but the quantization formula remains the same for consistency and the type of the attribute 'y_zero_point' still determines the quantization type. Args: x: N-D full precision Input tensor to be quantized. y_scale: Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization. y_zero_point: (optional) Zero point for doing quantization to get 'y'. Shape must match y_scale. Default is uint8 with zero point of 0 if it's not specified. axis: (Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). saturate: The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description. """ schema = get_schema("QuantizeLinear", 19, "") op = Op(self, "QuantizeLinear", schema) return op( *self._prepare_inputs(schema, x, y_scale, y_zero_point), axis=axis, saturate=saturate, ) T_Reshape = TypeVar( "T_Reshape", BFLOAT16, BOOL, COMPLEX128, COMPLEX64, DOUBLE, FLOAT, FLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ) def Reshape(self, data: T_Reshape, shape: INT64, *, allowzero: int = 0) -> T_Reshape: r"""[🌐 Reshape(19)](https://onnx.ai/onnx/operators/onnx__Reshape.html#reshape-19 "Online Documentation") Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). If 'allowzero' is set, and the new shape includes 0, the dimension will be set explicitly to zero (i.e. not taken from input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor's shape and the output tensor's shape are required to have the same number of elements. If the attribute 'allowzero' is set, it is invalid for the specified shape to contain both a zero value and -1, as the value of the dimension corresponding to -1 cannot be determined uniquely. Args: data: (differentiable) An input tensor. shape: (non-differentiable) Specified shape for output. allowzero: (Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy. """ schema = get_schema("Reshape", 19, "") op = Op(self, "Reshape", schema) return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero) T1_Resize = TypeVar( "T1_Resize", BFLOAT16, BOOL, COMPLEX128, COMPLEX64, DOUBLE, FLOAT, FLOAT16, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ) T2_Resize = TypeVar("T2_Resize", DOUBLE, FLOAT, FLOAT16) def Resize( self, X: T1_Resize, roi: Optional[T2_Resize] = None, scales: Optional[FLOAT] = None, sizes: Optional[INT64] = None, *, antialias: int = 0, axes: Optional[Sequence[int]] = None, coordinate_transformation_mode: str = "half_pixel", cubic_coeff_a: float = -0.75, exclude_outside: int = 0, extrapolation_value: float = 0.0, keep_aspect_ratio_policy: str = "stretch", mode: str = "nearest", nearest_mode: str = "round_prefer_floor", ) -> T1_Resize: r"""[🌐 Resize(19)](https://onnx.ai/onnx/operators/onnx__Resize.html#resize-19 "Online Documentation") Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor. Each dimension value of the output tensor is: :: output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input \"sizes\" is not specified. Args: X: (differentiable) N-D tensor roi: (optional, non-differentiable) 1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X or the length of axes, if provided. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize" scales: (optional, non-differentiable) The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X' or the length of 'axes', if provided. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list. sizes: (optional, non-differentiable) Target size of the output tensor. Its interpretation depends on the 'keep_aspect_ratio_policy' value.The number of elements of 'sizes' should be the same as the rank of input 'X', or the length of 'axes', if provided. Only one of 'scales' and 'sizes' can be specified. antialias: If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel. axes: If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Non-specified dimensions are interpreted as non-resizable. Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated. coordinate_transformation_mode: This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote `x_resized` as the coordinate of axis x in the resized tensor, `x_original` as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, `length_resized` as the length of the resized tensor in axis x, `scale = length_resized / length_original`, `output_width` the target length on the axis x which can be a fractional number when it is calculated out of a scale factor, and `output_width_int` the effective output width as an integer. if coordinate_transformation_mode is `"half_pixel"`, ``` x_original = (x_resized + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"half_pixel_symmetric"`, ``` adjustment = output_width_int / output_width center = input_width / 2 offset = center * (1 - adjustment) x_ori = offset + (x + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"pytorch_half_pixel"`, ``` x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0 ``` if coordinate_transformation_mode is `"align_corners"`, ``` x_original = x_resized * (length_original - 1) / (length_resized - 1) ``` if coordinate_transformation_mode is `"asymmetric"`, ``` x_original = x_resized / scale ``` if coordinate_transformation_mode is `"tf_crop_and_resize"`, ``` x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1) ``` . cubic_coeff_a: The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if mode is "cubic". exclude_outside: If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0. extrapolation_value: When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f. keep_aspect_ratio_policy: This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used. Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`. If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size: `out_size[d] = sizes[i]` If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio: ``` scale = Min(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[d]) ``` If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio: ``` scale = Max(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[d]) ``` For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up. mode: Three interpolation modes: "nearest" (default), "linear" and "cubic". The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor). nearest_mode: Four modes: "round_prefer_floor" (default, as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest". """ schema = get_schema("Resize", 19, "") op = Op(self, "Resize", schema) return op( *self._prepare_inputs(schema, X, roi, scales, sizes), antialias=antialias, axes=axes, coordinate_transformation_mode=coordinate_transformation_mode, cubic_coeff_a=cubic_coeff_a, exclude_outside=exclude_outside, extrapolation_value=extrapolation_value, keep_aspect_ratio_policy=keep_aspect_ratio_policy, mode=mode, nearest_mode=nearest_mode, ) V_Scan = TypeVar( "V_Scan", BFLOAT16, BOOL, COMPLEX128, COMPLEX64, DOUBLE, FLOAT, FLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ) def Scan( self, *initial_state_and_scan_inputs: V_Scan, body: GraphProto, num_scan_inputs: int, scan_input_axes: Optional[Sequence[int]] = None, scan_input_directions: Optional[Sequence[int]] = None, scan_output_axes: Optional[Sequence[int]] = None, scan_output_directions: Optional[Sequence[int]] = None, ) -> V_Scan: r"""[🌐 Scan(19)](https://onnx.ai/onnx/operators/onnx__Scan.html#scan-19 "Online Documentation") Scan can be used to iterate over one or more scan_input tensors, constructing zero or more scan_output tensors. It combines ideas from general recurrences, functional programming constructs such as scan, fold, map, and zip, and is intended to enable generalizations of RNN-like constructs for sequence-to-sequence processing. Other tensors (referred to as state_variables here) can be used to carry a state when iterating from one element to another (similar to hidden-state in RNNs, also referred to as loop-carried dependences in the context of loops). Many common usages involve a single scan_input tensor (where functionality similar to scan, fold and map can be obtained). When more than one scan_input is used, a behavior similar to zip is obtained. The attribute body must be a graph, specifying the computation to be performed in every iteration. It takes as input the current values of the state_variables and the current iterated element of the scan_inputs. It must return the (updated) values of the state_variables and zero or more scan_output_element tensors. The values of the scan_output_element tensors are concatenated over all the iterations to produce the scan_output values of the scan construct (similar to the concatenated intermediate hidden-state values of RNN-like constructs). All the output tensors (state_variables as well as scan_output_element tensors) are required to have the same shape in each iteration of the loop (a restriction imposed to enable efficient memory allocation). Note that the iterated element passed to the body subgraph does not have a sequence axis. It will have a rank one less than the rank of the corresponding scan_input. The scan operation returns the final values of the state_variables as well as the scan_outputs. The optional attribute scan_input_directions specifies the direction (forward or backward) for each scan input. If this attribute is omitted, all sequences are scanned in the forward direction. A bidirectional scan may be performed by specifying the same tensor input twice in the scan_inputs, once with a forward direction, and once with a backward direction. The scan_output of the operation is produced by concatenating the scan_output_element values produced by the body in each iteration. The optional attribute scan_output_directions specifies the direction in which scan_output is constructed (by appending or prepending the scan_output_element to scan_output in each iteration) for each scan_output. If this attribute is omitted, the scan_output_element is appended to the scan_output in each iteration. The optional attribute scan_input_axes specifies the axis to be scanned for each scan_input. If omitted, every scan_input will be scanned in axis 0. For example, if axis 0 is the batch axis and axis 1 is the time axis (to be scanned), specify an axis value of 1. Note that scanning a non-zero axis may be less efficient than scanning axis zero. The optional attribute scan_output_axes specifies the axis along which the scan_outputs are accumulated for each scan_output. For example, if axis 1 is the time axis (to be scanned) for both inputs and outputs, specify a scan_input axis and scan_output axis value of 1. Note that because of the ONNX restriction that only the last parameter of an operator can be variadic, the initial-states and scan-inputs are listed together as one input parameter. Similarly, the final-states and scan-outputs are listed together as one output parameter. The attribute num_scan_inputs indicates the number M of scan-inputs. The behavior of Scan < num_scan_inputs = m, body = loop-body, scan_input_axes = [axis_1, ..., axis_m] > (init_1, ..., init_n, scan_1, ..., scan_m) is equivalent to the following pseudo-code: // scan_i.shape[axis_i] denotes the (max) sequence-length of scan_i // scan_i.shape[axis_i] is required to be equal to scan_j.shape[axis_j] for all i,j. sequence_length = scan_1.shape[axis_1]; // initialize state-variables st_1 = init_1; ... st_n = init_n; // initialize scan-output variables: [] denotes an empty tensor scan_out_1 = []; ...; scan_out_k = []; // identify number of iterations: // execute loop for (int t = 0; t < sequence_length; ++t) { // generate the scan-input elements: the notation T[t] indicates the sub-tensor // of rank one less than T obtained by indexing T at position t along axis k. si_1 = scan_1[t]; ... ; si_m = scan_m[t]; // execute loop-body st_1, ..., st_n, so_1, ..., so_k = loop-body(st_1, ..., st_n, si_1, ..., si_m) // accumulate the scan-output elements scan_out_1 = Concat(scan_out_1, so_1); ... ; scan_out_k = Concat(scan_out_k, so_k); } return st_1, ..., st_n, scan_out_1, ..., scan_out_k; *Sample usage: Encoding RNN using a Scan* The following example shows how a simple RNN over an input tensor %X, with weight tensor %Wi, recurrence weight tensor %Ri, bias tensors %Wbi and %Rbi, and initial hidden-state %H_0 can be encoded as a ScanLoop. Note that the loop-body is a nested graph, and it directly computes %Wi, %Ri, %Wbi, and %Rbi (typically constants or initializers in the body graph). If these values are computed in the outer graph, they need to be passed in as extra state_variables. graph rnn-encoding { %H_0 = ... %X = ... %Y_h, %Y = Scan[body = , num_scan_inputs=1](%H_0, %X) return %Y, %Y_h } graph rnn-cell-1 ( %H_tminus1[FLOAT, tensor] %X_t[FLOAT, tensor] ) { %Wi = ... %Ri = ... %Wbi = ... %Rbi = ... %t1 = X_t * (Wi^T) %t2 = H_tminus1*(Ri^T) %t3 = Add(%t1, %t2) %t4 = Add(%t3, %Wbi) %t5 = Add(%t4, %Rbi) %Ht = Tanh(%t5) %Accumulate = Identity(%Ht) return %Ht, %Accumulate } Args: initial_state_and_scan_inputs: (variadic, heterogeneous) Initial values of the loop's N state variables followed by M scan_inputs body: The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations. num_scan_inputs: An attribute specifying the number of scan_inputs M. scan_input_axes: An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). scan_input_directions: An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction. scan_output_axes: An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1]. scan_output_directions: An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration. """ schema = get_schema("Scan", 19, "") op = Op(self, "Scan", schema) return op( *self._prepare_inputs(schema, *initial_state_and_scan_inputs), body=body, num_scan_inputs=num_scan_inputs, scan_input_axes=scan_input_axes, scan_input_directions=scan_input_directions, scan_output_axes=scan_output_axes, scan_output_directions=scan_output_directions, ) T_Shape = TypeVar( "T_Shape", BFLOAT16, BOOL, COMPLEX128, COMPLEX64, DOUBLE, FLOAT, FLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ) T1_Shape: TypeAlias = INT64 def Shape(self, data: T_Shape, *, end: Optional[int] = None, start: int = 0) -> T1_Shape: r"""[🌐 Shape(19)](https://onnx.ai/onnx/operators/onnx__Shape.html#shape-19 "Online Documentation") Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor's shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clamped to the range [0, r], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0. If start > end, the result will be an empty shape. Examples: :: Input tensor with shape: [2, 3, 4] No attributes specified. Output: [2, 3, 4] :: Input tensor with shape: [2, 3, 4] start: -1 Output: [4] :: Input tensor with shape: [2, 3, 4] end: -1 Output: [2, 3] :: Input tensor with shape: [2, 3, 4] start: 1 end: 2 Output: [3] Args: data: (non-differentiable) An input tensor. end: (Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included. start: (Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back. """ schema = get_schema("Shape", 19, "") op = Op(self, "Shape", schema) return op(*self._prepare_inputs(schema, data), end=end, start=start) T_Size = TypeVar( "T_Size", BFLOAT16, BOOL, COMPLEX128, COMPLEX64, DOUBLE, FLOAT, FLOAT16, FLOAT8E4M3FN, FLOAT8E4M3FNUZ, FLOAT8E5M2, FLOAT8E5M2FNUZ, INT16, INT32, INT64, INT8, STRING, UINT16, UINT32, UINT64, UINT8, ) T1_Size: TypeAlias = INT64 def Size(self, data: T_Size) -> T1_Size: r"""[🌐 Size(19)](https://onnx.ai/onnx/operators/onnx__Size.html#size-19 "Online Documentation") Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor. Args: data: (non-differentiable) An input tensor. """ schema = get_schema("Size", 19, "") op = Op(self, "Size", schema) return op(*self._prepare_inputs(schema, data))