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Dialect 'onnx' definition

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Operation definition

onnx.Abs (ONNXAbsOp)

ONNX Abs operation

Description:

"Absolute takes one input data (Tensor) and produces one output data" "(Tensor) where the absolute is, y = abs(x), is applied to" "the tensor elementwise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.Acos (ONNXAcosOp)

ONNX Acos operation

Description:

"Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Acosh (ONNXAcoshOp)

ONNX Acosh operation

Description:

"Calculates the hyperbolic arccosine of the given input tensor element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Add (ONNXAddOp)

ONNX Add operation

Description:

"Performs element-wise binary addition (with Numpy-style broadcasting support)." "" "This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc."

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Results:

C: memref of any type values or tensor of any type values

onnx.And (ONNXAndOp)

ONNX And operation

Description:

"Returns the tensor resulted from performing the and 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 the doc."

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Results:

C: memref of any type values or tensor of any type values

onnx.ArgMax (ONNXArgMaxOp)

ONNX ArgMax operation

Description:

"Computes the indices of the max elements of the input tensor's element along the " "provided axis. The resulted tensor has the same rank as the input if keepdims equal 1." "If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. " "The type of the output tensor is integer."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

reduced: memref of any type values or tensor of any type values

onnx.ArgMin (ONNXArgMinOp)

ONNX ArgMin operation

Description:

"Computes the indices of the min elements of the input tensor's element along the " "provided axis. The resulted tensor has the same rank as the input if keepdims equal 1." "If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. " "The type of the output tensor is integer."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

reduced: memref of any type values or tensor of any type values

onnx.Asin (ONNXAsinOp)

ONNX Asin operation

Description:

"Calculates the arcsine (inverse of sine) of the given input tensor, element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Asinh (ONNXAsinhOp)

ONNX Asinh operation

Description:

"Calculates the hyperbolic arcsine of the given input tensor element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Atan (ONNXAtanOp)

ONNX Atan operation

Description:

"Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Atanh (ONNXAtanhOp)

ONNX Atanh operation

Description:

"Calculates the hyperbolic arctangent of the given input tensor element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.AveragePool (ONNXAveragePoolOp)

ONNX AveragePool operation

Description:

"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 will be following:" " " " output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)" " " " or" " " " output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)" " " " if ceil_mode is enabled" "" " " " * pad_shape[i] is 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:" " " " VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])" " SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])" " " " 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] - 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)." " "

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`auto_pad`	`StringAttr`	string attribute attribute
`ceil_mode`	`IntegerAttr`	64-bit integer attribute attribute
`count_include_pad`	`IntegerAttr`	64-bit integer attribute attribute
`kernel_shape`	`ArrayAttr`	64-bit integer array attribute attribute
`pads`	`ArrayAttr`	64-bit integer array attribute attribute
`strides`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.BatchNormalization (ONNXBatchNormalizationOp)

ONNX BatchNormalization operation

Description:

"Carries out batch normalization as described in the paper" "https://arxiv.org/abs/1502.03167. Depending on the mode it is being run," "there are multiple cases for the number of outputs, which we list below:" "" "Output case #1: Y, mean, var, saved_mean, saved_var (training mode)" "Output case #2: Y (test mode)" "" "For previous (depreciated) non-spatial cases, implementors are suggested" "to flatten the input shape to (N x CD1D2 ..*Dn) before a BatchNormalization Op." "This operator has optional inputs/outputs. See the doc for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted."

Operands:

X: memref of any type values or tensor of any type values
scale: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values
mean: memref of any type values or tensor of any type values
var: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`epsilon`	`FloatAttr`	32-bit float attribute attribute
`momentum`	`FloatAttr`	32-bit float attribute attribute

Results:

Y: memref of any type values or tensor of any type values
out_mean: memref of any type values or tensor of any type values or none type
out_var: memref of any type values or tensor of any type values or none type
saved_mean: memref of any type values or tensor of any type values or none type
saved_var: memref of any type values or tensor of any type values or none type

onnx.BatchNormalizationTestMode (ONNXBatchNormalizationTestModeOp)

ONNX BatchNormalization operation in test mode

Description:

Operands:

X: memref of any type values or tensor of any type values
scale: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values
mean: memref of any type values or tensor of any type values
var: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`epsilon`	`FloatAttr`	32-bit float attribute attribute
`momentum`	`FloatAttr`	32-bit float attribute attribute

Results:

o_Y: memref of any type values or tensor of any type values

onnx.BitShift (ONNXBitShiftOp)

ONNX BitShift operation

Description:

"Bitwise shift operator performs element-wise operation. For each input element, if the" " attribute "direction" is "RIGHT", this operator moves its binary representation toward" " the right side so that the input value is effectively decreased. If the attribute "direction"" " is "LEFT", bits of binary representation moves toward the left side, which results the" " increase of its actual value. The input X is the tensor to be shifted and another input" " Y specifies the amounts of shifting. For example, if "direction" is "Right", X is [1, 4]," " and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with" " X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8]." " " " Because this operator supports Numpy-style broadcasting, X's and Y's shapes are" " not necessarily identical." "This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc."

Operands:

X: memref of any type values or tensor of any type values
Y: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`direction`	`StringAttr`	string attribute attribute

Results:

Z: memref of any type values or tensor of any type values

onnx.Cast (ONNXCastOp)

ONNX Cast operation

Description:

"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" "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."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`to`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Ceil (ONNXCeilOp)

ONNX Ceil operation

Description:

"Ceil takes one input data (Tensor) and produces one output data" "(Tensor) where the ceil is, y = ceil(x), is applied to" "the tensor elementwise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.Clip (ONNXClipOp)

ONNX Clip operation

Description:

"Clip operator limits the given input within an interval. The interval is" "specified by the inputs 'min' and 'max'. They default to" "numeric_limits::lowest() and numeric_limits::max(), respectively."

Operands:

input: memref of any type values or tensor of any type values
min: memref of any type values or tensor of any type values or none type
max: memref of any type values or tensor of any type values or none type

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Compress (ONNXCompressOp)

ONNX Compress operation

Description:

"Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index." " In case axis is not provided, input is flattened before elements are selected." " Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html" " "

Operands:

input: memref of any type values or tensor of any type values
condition: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.ConcatFromSequence (ONNXConcatFromSequenceOp)

ONNX ConcatFromSequence operation

Description:

"Concatenate a sequence of tensors into a single tensor." "All input tensors must have the same shape, except for the dimension size of the axis to concatenate on." "By default 'new_axis' is 0, the behavior is similar to numpy.concatenate." "When 'new_axis' is 1, the behavior is similar to numpy.stack."

Operands:

input_sequence: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute
`new_axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

concat_result: memref of any type values or tensor of any type values

onnx.Concat (ONNXConcatOp)

ONNX Concat operation

Description:

"Concatenate a list of tensors into a single tensor. All input tensors must have the same shape, except for the dimension size of the axis to concatenate on."

Operands:

inputs: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

concat_result: memref of any type values or tensor of any type values

onnx.ConstantOfShape (ONNXConstantOfShapeOp)

ONNX ConstantOfShape operation

Description:

"Generate a tensor with given value and shape."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`value`	`Attribute`	any attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Constant (ONNXConstantOp)

ONNX Constant operation

Description:

"A constant tensor. Exactly one of the two attributes, either value or sparse_value," "must be specified."

Operands:

Attributes:

Attribute	MLIR Type	Description
`sparse_value`	`Attribute`	any attribute attribute
`value`	`Attribute`	any attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.ConvInteger (ONNXConvIntegerOp)

ONNX ConvInteger operation

Description:

"The integer convolution operator consumes an input tensor, its zero-point, a filter, and its zero-point," "and computes the output. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits."

Operands:

x: memref of any type values or tensor of any type values
w: memref of any type values or tensor of any type values
x_zero_point: memref of any type values or tensor of any type values or none type
w_zero_point: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`auto_pad`	`StringAttr`	string attribute attribute
`dilations`	`ArrayAttr`	64-bit integer array attribute attribute
`group`	`IntegerAttr`	64-bit integer attribute attribute
`kernel_shape`	`ArrayAttr`	64-bit integer array attribute attribute
`pads`	`ArrayAttr`	64-bit integer array attribute attribute
`strides`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

y: memref of any type values or tensor of any type values

onnx.ConvNoBias (ONNXConvNoBiasOp)

ONNX Conv operation with no Bias operand.

Description:

"The convolution operator consumes an input tensor and a filter, and" "computes the output."

Operands:

X: memref of any type values or tensor of any type values
W: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`auto_pad`	`StringAttr`	string attribute attribute
`dilations`	`ArrayAttr`	64-bit integer array attribute attribute
`group`	`IntegerAttr`	64-bit integer attribute attribute
`kernel_shape`	`ArrayAttr`	64-bit integer array attribute attribute
`pads`	`ArrayAttr`	64-bit integer array attribute attribute
`strides`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

o_Y: memref of any type values or tensor of any type values

onnx.Conv (ONNXConvOp)

ONNX Conv operation

Description:

"The convolution operator consumes an input tensor and a filter, and" "computes the output."

Operands:

X: memref of any type values or tensor of any type values
W: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`auto_pad`	`StringAttr`	string attribute attribute
`dilations`	`ArrayAttr`	64-bit integer array attribute attribute
`group`	`IntegerAttr`	64-bit integer attribute attribute
`kernel_shape`	`ArrayAttr`	64-bit integer array attribute attribute
`pads`	`ArrayAttr`	64-bit integer array attribute attribute
`strides`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.ConvTranspose (ONNXConvTransposeOp)

ONNX ConvTranspose operation

Description:

"The convolution transpose operator consumes an input tensor and a filter," "and computes the output." "" "If the pads parameter is provided the shape of the output is calculated via the following equation:" "" " output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i]" "" "output_shape can also be explicitly specified in which case pads values are auto generated using these equations:" "" " total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i]" " If (auto_pads != SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2)" " Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2)." "" " "

Operands:

X: memref of any type values or tensor of any type values
W: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`auto_pad`	`StringAttr`	string attribute attribute
`dilations`	`ArrayAttr`	64-bit integer array attribute attribute
`group`	`IntegerAttr`	64-bit integer attribute attribute
`kernel_shape`	`ArrayAttr`	64-bit integer array attribute attribute
`output_padding`	`ArrayAttr`	64-bit integer array attribute attribute
`output_shape`	`ArrayAttr`	64-bit integer array attribute attribute
`pads`	`ArrayAttr`	64-bit integer array attribute attribute
`strides`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.Cos (ONNXCosOp)

ONNX Cos operation

Description:

"Calculates the cosine of the given input tensor, element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Cosh (ONNXCoshOp)

ONNX Cosh operation

Description:

"Calculates the hyperbolic cosine of the given input tensor element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.CumSum (ONNXCumSumOp)

ONNX CumSum operation

Description:

"Performs cumulative sum of the input elements along the given axis." "By default, it will do the sum inclusively meaning the first element is copied as is." "Through an exclusive attribute, this behavior can change to exclude the first element." "It can also perform summation in the opposite direction of the axis. For that, set reverse attribute to 1." "" "Example:" "" "input_x = [1, 2, 3]" "axis=0" "output = [1, 3, 6]" "exclusive=1" "output = [0, 1, 3]" "exclusive=0" "reverse=1" "output = [6, 5, 3]" "exclusive=1" "reverse=1" "output = [5, 3, 0]" "" " "

Operands:

x: memref of any type values or tensor of any type values
axis: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`exclusive`	`IntegerAttr`	64-bit integer attribute attribute
`reverse`	`IntegerAttr`	64-bit integer attribute attribute

Results:

y: memref of any type values or tensor of any type values

onnx.DepthToSpace (ONNXDepthToSpaceOp)

ONNX DepthToSpace operation

Description:

"DepthToSpace rearranges (permutes) data from depth into blocks of spatial data." "This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of" "the input tensor where values from the depth dimension are moved in spatial blocks to the height" "and width dimensions. By default, mode = DCR." "In the DCR mode, elements along the depth dimension from the input tensor are rearranged in the" "following order: depth, column, and then row. The output y is computed from the input x as below:" "" "b, c, h, w = x.shape" "" "tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize2), h, w])" "" "tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])" "" "y = np.reshape(tmp, [b, c // (blocksize2), h * blocksize, w * blocksize])" "" "" "In the CRD mode, elements along the depth dimension from the input tensor are rearranged in the" "following order: column, row, and the depth. The output y is computed from the input x as below:" "" "b, c, h, w = x.shape" "" "tmp = np.reshape(x, [b, c // (blocksize ** 2), blocksize, blocksize, h, w])" "" "tmp = np.transpose(tmp, [0, 1, 4, 2, 5, 3])" "" "y = np.reshape(tmp, [b, c // (blocksize ** 2), h * blocksize, w * blocksize])" ""

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`blocksize`	`IntegerAttr`	64-bit integer attribute attribute
`mode`	`StringAttr`	string attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.DequantizeLinear (ONNXDequantizeLinearOp)

ONNX DequantizeLinear operation

Description:

"The linear dequantization operator. It consumes a quantized tensor, a scale, 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." "'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)."

Operands:

x: memref of any type values or tensor of any type values
x_scale: memref of any type values or tensor of any type values
x_zero_point: memref of any type values or tensor of any type values or none type

Attributes:

Results:

y: memref of any type values or tensor of any type values

onnx.Det (ONNXDetOp)

ONNX Det operation

Description:

"Det calculates determinant of a square matrix or batches of square matrices." "Det takes one input tensor of shape [*, M, M], where * is zero or more batch dimensions," "and the inner-most 2 dimensions form square matrices." "The output is a tensor of shape [*], containing the determinants of all input submatrices." "e.g., When the input is 2-D, the output is a scalar(shape is empty: [])."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.Div (ONNXDivOp)

ONNX Div operation

Description:

"Performs element-wise binary division (with Numpy-style broadcasting support)." "" "This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc."

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Results:

C: memref of any type values or tensor of any type values

onnx.Dropout (ONNXDropoutOp)

ONNX Dropout operation

Description:

"Dropout takes one input floating tensor and produces two tensor outputs," "output (floating tensor) and mask (Tensor<bool>). Depending on whether it is" "in test mode or not, the output Y will either be a random dropout, or a simple" "copy of the input. Note that our implementation of Dropout does scaling in" "the training phase, so during testing nothing needs to be done." "This operator has optional inputs/outputs. See the doc for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`ratio`	`FloatAttr`	32-bit float attribute attribute

Results:

output: memref of any type values or tensor of any type values
mask: memref of any type values or tensor of any type values or none type

onnx.DynamicQuantizeLinear (ONNXDynamicQuantizeLinearOp)

ONNX DynamicQuantizeLinear operation

Description:

"A Function to fuse calculation for Scale, Zero Point and FP32->8Bit convertion of FP32 Input data." "Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input." "Scale is calculated as:" "" " y_scale = (max(x) - min(x))/(qmax - qmin)" " * where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8" " * data range is adjusted to include 0." "" "Zero point is calculated as:" "" "intermediate_zero_point = (qmin - min(x))/(qmax - qmin)" "y_zero_point = cast(round(saturate(itermediate_zero_point)))" "* where qmax and qmin are max and min values for quantization range .i.e [0, 255] in case of uint8" "* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported." "* rounding to nearest ties to even." "" "Data quantization formula is:" "" "y = saturate (round (x / y_scale) + y_zero_point)" "* for saturation, it saturates to [0, 255] if it's uint8, or [-127, 127] if it's int8. Right now only uint8 is supported." "* rounding to nearest ties to even." ""

Operands:

x: memref of any type values or tensor of any type values

Attributes:

Results:

y: memref of any type values or tensor of any type values
y_scale: memref of any type values or tensor of any type values
y_zero_point: memref of any type values or tensor of any type values

onnx.Elu (ONNXEluOp)

ONNX Elu operation

Description:

"Elu takes one input data (Tensor) and produces one output data" "(Tensor) where the function f(x) = alpha * (exp(x) - 1.) for x <" "0, f(x) = x for x >= 0., is applied to the tensor elementwise." ""

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`alpha`	`FloatAttr`	32-bit float attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.EntryPoint (ONNXEntryPointOp)

Indicate ONNX entry point

Description:

The "onnx.EntryPoint" function indicates the main entry point of ONNX model.

Operands:

Attributes:

Results:

onnx.Equal (ONNXEqualOp)

ONNX Equal operation

Description:

"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 the doc."

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Results:

C: memref of any type values or tensor of any type values

onnx.Erf (ONNXErfOp)

ONNX Erf operation

Description:

"Computes the error function of the given input tensor element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Exp (ONNXExpOp)

ONNX Exp operation

Description:

"Calculates the exponential of the given input tensor, element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Expand (ONNXExpandOp)

ONNX Expand operation

Description:

"Broadcast the input tensor following the given shape and the broadcast rule." "The broadcast rule is similar to numpy.array(input) * numpy.ones(shape):" "Dimensions are right alignment;" "Two corresponding dimension must have the same value, or one of them is equal to 1." "Also, this operator is similar to numpy.broadcast_to(input, shape)," "but the major difference is numpy.broadcast_to() does not allow shape to be smaller than input.size()." "It is possible that the output.shape is not equal to shape, when some dimensions in shape is equal to 1," "or the shape.ndim < input.shape.ndim."

Operands:

input: memref of any type values or tensor of any type values
shape: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.EyeLike (ONNXEyeLikeOp)

ONNX EyeLike operation

Description:

"Generate a 2D tensor (matrix) with ones on the diagonal and zeros everywhere else. Only 2D" "tensors are supported, i.e. input T1 must be of rank 2. The shape of the output tensor is the" "same as the input tensor. The data type can be specified by the 'dtype' argument. If" "'dtype' is not specified, then the type of input tensor is used. By default, the main diagonal" "is populated with ones, but attribute 'k' can be used to populate upper or lower diagonals." "The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the" "TensorProto message and be valid as an output type."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`dtype`	`IntegerAttr`	64-bit integer attribute attribute
`k`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Flatten (ONNXFlattenOp)

ONNX Flatten operation

Description:

"Flattens the input tensor into a 2D matrix. If input tensor has shape" "(d_0, d_1, ... d_n) then the output will have shape" "(d_0 X d_1 ... d_(axis-1), d_axis X d_(axis+1) ... X dn)."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Floor (ONNXFloorOp)

ONNX Floor operation

Description:

"Floor takes one input data (Tensor) and produces one output data" "(Tensor) where the floor is, y = floor(x), is applied to" "the tensor elementwise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.GRU (ONNXGRUOp)

ONNX GRU operation

Description:

"Computes an one-layer GRU. This operator is usually supported via some custom" "implementation such as CuDNN." "" "Notations:" "" "X - input tensor" "" "z - update gate" "" "r - reset gate" "" "h - hidden gate" "" "t - time step (t-1 means previous time step)" "" "W[zrh] - W parameter weight matrix for update, reset, and hidden gates" "" "R[zrh] - R recurrence weight matrix for update, reset, and hidden gates" "" "Wb[zrh] - W bias vectors for update, reset, and hidden gates" "" "Rb[zrh] - R bias vectors for update, reset, and hidden gates" "" "WB[zrh] - W parameter weight matrix for backward update, reset, and hidden gates" "" "RB[zrh] - R recurrence weight matrix for backward update, reset, and hidden gates" "" "WBb[zrh] - W bias vectors for backward update, reset, and hidden gates" "" "RBb[zrh] - R bias vectors for backward update, reset, and hidden gates" "" "H - Hidden state" "" "num_directions - 2 if direction == bidirectional else 1" "" "Activation functions:" "" " Relu(x) - max(0, x)" "" " Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})" "" " Sigmoid(x) - 1/(1 + e^{-x})" "" " (NOTE: Below are optional)" "" " Affine(x) - alphax + beta" "" " LeakyRelu(x) - x if x >= 0 else alpha * x" "" " ThresholdedRelu(x) - x if x >= alpha else 0" "" " ScaledTanh(x) - alphaTanh(betax)" "" " HardSigmoid(x) - min(max(alphax + beta, 0), 1)" "" " Elu(x) - x if x >= 0 else alpha*(e^x - 1)" "" " Softsign(x) - x/(1 + |x|)" "" " Softplus(x) - log(1 + e^x)" "" "Equations (Default: f=Sigmoid, g=Tanh):" "" " - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)" "" " - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)" "" " - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0" "" " - ht = g(Xt(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0" "" " - Ht = (1 - zt) (.) ht + zt (.) Ht-1" "This operator has optional inputs/outputs. See the doc for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted."

Operands:

X: memref of any type values or tensor of any type values
W: memref of any type values or tensor of any type values
R: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values or none type
sequence_lens: memref of any type values or tensor of any type values or none type
initial_h: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`activation_alpha`	`ArrayAttr`	32-bit float array attribute attribute
`activation_beta`	`ArrayAttr`	32-bit float array attribute attribute
`activations`	`ArrayAttr`	string array attribute attribute
`clip`	`FloatAttr`	32-bit float attribute attribute
`direction`	`StringAttr`	string attribute attribute
`hidden_size`	`IntegerAttr`	64-bit integer attribute attribute
`linear_before_reset`	`IntegerAttr`	64-bit integer attribute attribute

Results:

Y: memref of any type values or tensor of any type values or none type
Y_h: memref of any type values or tensor of any type values or none type

onnx.GatherElements (ONNXGatherElementsOp)

ONNX GatherElements operation

Description:

"GatherElements takes two inputs data and indices of the same rank r >= 1" "and an optional attribute axis that identifies an axis of data" "(by default, the outer-most axis, that is axis 0). It is an indexing operation" "that produces its output by indexing into the input data tensor at index" "positions determined by elements of the indices tensor." "Its output shape is the same as the shape of indices and consists of one value" "(gathered from the data) for each element in indices." "" "For instance, in the 3-D case (r = 3), the output produced is determined" "by the following equations: " "" " out[i][j][k] = input[index[i][j][k]][j][k] if axis = 0," " out[i][j][k] = input[i][index[i][j][k]][k] if axis = 1," " out[i][j][k] = input[i][j][index[i][j][k]] if axis = 2," "" "" "This operator is also the inverse of ScatterElements. It is similar to Torch's gather operation." "" "Example 1:" "" " data = [" " [1, 2]," " [3, 4]," " ]" " indices = [" " [0, 0]," " [1, 0]," " ]" " axis = 1" " output = [" " [" " [1, 1]," " [4, 3]," " ]," " ]" "" "Example 2:" "" " data = [" " [1, 2, 3]," " [4, 5, 6]," " [7, 8, 9]," " ]" " indices = [" " [1, 2, 0]," " [2, 0, 0]," " ]" " axis = 0" " output = [" " [" " [4, 8, 3]," " [7, 2, 3]," " ]," " ]" ""

Operands:

data: memref of any type values or tensor of any type values
indices: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.GatherND (ONNXGatherNDOp)

ONNX GatherND operation

Description:

"Given data tensor of rank r >= 1, and indices tensor of rank q >= 1, this operator gathers " "slices of data into an output tensor of rank q + r - indices_shape[-1] - 1." "" "indices is an q-dimensional integer tensor, best thought of as a (q-1)-dimensional tensor of index-tuples into data, " "where each element defines a slice of data" "" "Some salient points about the inputs' rank and shape:" " " "1) r >= 1 and q >= 1 are to be honored. There is no dependency condition to be met between ranks r and q" "" "2) The indices_shape[-1] should have a value between 1 (inclusive) and rank r (inclusive) " "" "3) All values in indices are expected to be within bounds [-s, s-1] along axis of size s (i.e.) -data_shape[i] <= indices[...,i] <= data_shape[i] - 1." " It is an error if any of the index values are out of bounds." "" "The output is computed as follows:" "" "The output tensor is obtained by mapping each index-tuple in the indices tensor to the corresponding slice of the input data." " " "1) If indices_shape[-1] > r => error condition" "" "2) If indices_shape[-1] == r, since the rank of indices is q, indices can be thought of as a (q-1)-dimensional tensor" " containing 1-D tensors of dimension r. Let us think of each such r ranked tensor as indices_slice. " " Each scalar value corresponding to data[indices_slice] is filled into the corresponding location of the (q-1)-dimensional tensor " " to form the output tensor (Example 1 below)" "" "3) If indices_shape[-1] < r, since the rank of indices is q, indices can be thought of as a (q-1)-dimensional tensor" " containing 1-D tensors of dimension < r. Let us think of each such tensors as indices_slice. " " Each tensor slice corresponding to data[indices_slice , :] is filled into the corresponding location of the (q-1)-dimensional tensor " " to form the output tensor (Examples 2, 3, and 4 below)" "" "This operator is the inverse of ScatterND." "" "Example 1" "" " data = 0,1],[2,3 # data_shape = [2, 2]" "" " indices = 0,0],[1,1 # indices_shape = [2, 2]" "" " output = [0,3] # output_shape = [2]" "" "Example 2" "" " data = 0,1],[2,3 # data_shape = [2, 2]" "" " indices = 1],[0 # indices_shape = [2, 1]" "" " output = 2,3],[0,1 # output_shape = [2, 2]" "" "Example 3" "" " data = [0,1],[2,3,4,5],[6,7] # data_shape = [2, 2, 2]" "" " indices = 0,1],[1,0 # indices_shape = [2, 2]" "" " output = 2,3],[4,5 # output_shape = [2, 2] " "" "Example 4" "" " data = [0,1],[2,3,4,5],[6,7] # data_shape = [2, 2, 2]" "" " indices = [0,1,1,0] # indices_shape = [2, 1, 2]" "" " output = [2,3,4,5] # output_shape = [2, 1, 2] " ""

Operands:

data: memref of any type values or tensor of any type values
indices: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Gather (ONNXGatherOp)

ONNX Gather operation

Description:

"Given data tensor of rank r >= 1, and indices tensor of rank q, gather" "entries of the axis dimension of data (by default outer-most one as axis=0) indexed by indices, and concatenates" "them in an output tensor of rank q + (r - 1)." "" "axis = 0 :" "" "Let" "k = indices[i_{0}, ..., i_{q-1}]" "Then" "output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]" "" "" " data = [" " [1.0, 1.2]," " [2.3, 3.4]," " [4.5, 5.7]," " ]" " indices = [" " [0, 1]," " [1, 2]," " ]" " output = [" " [" " [1.0, 1.2]," " [2.3, 3.4]," " ]," " [" " [2.3, 3.4]," " [4.5, 5.7]," " ]," " ]" "" "axis = 1 :" "" "Let" "k = indices[i_{0}, ..., i_{q-1}]" "Then" "output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]" "" "" " data = [" " [1.0, 1.2, 1.9]," " [2.3, 3.4, 3.9]," " [4.5, 5.7, 5.9]," " ]" " indices = [" " [0, 2]," " ]" " axis = 1," " output = [" " [" " [1.0, 1.9]," " [2.3, 3.9]," " [4.5, 5.9]," " ]," " ]" ""

Operands:

data: memref of any type values or tensor of any type values
indices: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.GemmNoBias (ONNXGemmNoBiasOp)

ONNX general matrix multiply operation without bias.

Description:

The "onnx.Gemm" generic matrix multiplication without bias.

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`alpha`	`FloatAttr`	32-bit float attribute attribute
`beta`	`FloatAttr`	32-bit float attribute attribute
`transA`	`IntegerAttr`	64-bit integer attribute attribute
`transB`	`IntegerAttr`	64-bit integer attribute attribute

Results:

o_Y: memref of any type values or tensor of any type values

onnx.Gemm (ONNXGemmOp)

ONNX Gemm operation

Description:

"General Matrix multiplication:" "https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3" "" "A' = transpose(A) if transA else A" "" "B' = transpose(B) if transB else B" "" "Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M)," "input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N)," "and output tensor Y has shape (M, N). A will be transposed before doing the" "computation if attribute transA is non-zero, same for B and transB." "This operator supports unidirectional broadcasting (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check the doc." "This operator has optional inputs/outputs. See the doc for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted."

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values
C: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`alpha`	`FloatAttr`	32-bit float attribute attribute
`beta`	`FloatAttr`	32-bit float attribute attribute
`transA`	`IntegerAttr`	64-bit integer attribute attribute
`transB`	`IntegerAttr`	64-bit integer attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.GlobalAveragePool (ONNXGlobalAveragePoolOp)

ONNX GlobalAveragePool operation

Description:

"GlobalAveragePool consumes an input tensor X and applies average pooling across" " the values in the same channel. This is equivalent to AveragePool with kernel size" " equal to the spatial dimension of input tensor."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.GlobalLpPool (ONNXGlobalLpPoolOp)

ONNX GlobalLpPool operation

Description:

"GlobalLpPool consumes an input tensor X and applies lp pool pooling across" " the values in the same channel. This is equivalent to LpPool with kernel size" " equal to the spatial dimension of input tensor."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`p`	`IntegerAttr`	64-bit integer attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.GlobalMaxPool (ONNXGlobalMaxPoolOp)

ONNX GlobalMaxPool operation

Description:

"GlobalMaxPool consumes an input tensor X and applies max pooling across" " the values in the same channel. This is equivalent to MaxPool with kernel size" " equal to the spatial dimension of input tensor."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.Greater (ONNXGreaterOp)

ONNX Greater operation

Description:

"Returns the tensor resulted from performing the greater 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 the doc."

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Results:

C: memref of any type values or tensor of any type values

onnx.HardSigmoid (ONNXHardSigmoidOp)

ONNX HardSigmoid operation

Description:

"HardSigmoid takes one input data (Tensor) and produces one output data" "(Tensor) where the HardSigmoid function, y = max(0, min(1, alpha * x + beta))," "is applied to the tensor elementwise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`alpha`	`FloatAttr`	32-bit float attribute attribute
`beta`	`FloatAttr`	32-bit float attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.Hardmax (ONNXHardmaxOp)

ONNX Hardmax operation

Description:

"The operator computes the hardmax (1 for the first maximum value, and 0 for all others) values for each layer in the batch" " of the given input." "" "The input does not need to explicitly be a 2D vector; rather, it will be" "coerced into one. For an arbitrary n-dimensional tensor" "input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is" "the axis provided, then input will be coerced into a 2-dimensional tensor with" "dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default" "case where axis=1, this means the input tensor will be coerced into a 2D tensor" "of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size." "In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D." "Each of these dimensions must be matched correctly, or else the operator" "will throw errors. The output tensor has the same shape" "and contains the hardmax values of the corresponding input."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Identity (ONNXIdentityOp)

ONNX Identity operation

Description:

"Identity operator"

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.If (ONNXIfOp)

ONNX If operation

Description:

"If conditional"

Operands:

cond: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`else_branch`	`Attribute`	any attribute attribute
`then_branch`	`Attribute`	any attribute attribute

Results:

outputs: memref of any type values or tensor of any type values

onnx.InstanceNormalization (ONNXInstanceNormalizationOp)

ONNX InstanceNormalization operation

Description:

"Carries out instance normalization as described in the paper" "https://arxiv.org/abs/1607.08022." "" "y = scale * (x - mean) / sqrt(variance + epsilon) + B," "where mean and variance are computed per instance per channel." ""

Operands:

input: memref of any type values or tensor of any type values
scale: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`epsilon`	`FloatAttr`	32-bit float attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.IsInf (ONNXIsInfOp)

ONNX IsInf operation

Description:

"Map infinity to true and other values to false."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`detect_negative`	`IntegerAttr`	64-bit integer attribute attribute
`detect_positive`	`IntegerAttr`	64-bit integer attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.IsNaN (ONNXIsNaNOp)

ONNX IsNaN operation

Description:

"Returns which elements of the input are NaN."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.LRN (ONNXLRNOp)

ONNX LRN operation

Description:

"Local Response Normalization proposed in the AlexNet paper." "It normalizes over local input regions." "The local region is defined across the channels. For an element X[n, c, d1, ..., dk] in a tensor" "of shape (N x C x D1 x D2, ..., Dk), its region is" "{X[n, i, d1, ..., dk] | max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))}." "" "square_sum[n, c, d1, ..., dk] = sum(X[n, i, d1, ..., dk] ^ 2)," "where max(0, c - floor((size - 1) / 2)) <= i <= min(C - 1, c + ceil((size - 1) / 2))." "" "Y[n, c, d1, ..., dk] = X[n, c, d1, ..., dk] / (bias + alpha / size * square_sum[n, c, d1, ..., dk] ) ^ beta"

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`alpha`	`FloatAttr`	32-bit float attribute attribute
`beta`	`FloatAttr`	32-bit float attribute attribute
`bias`	`FloatAttr`	32-bit float attribute attribute
`size`	`IntegerAttr`	64-bit integer attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.LSTM (ONNXLSTMOp)

ONNX LSTM operation

Description:

"Computes an one-layer LSTM. This operator is usually supported via some" "custom implementation such as CuDNN." "" "Notations:" "" "X - input tensor" "" "i - input gate" "" "o - output gate" "" "f - forget gate" "" "c - cell gate" "" "t - time step (t-1 means previous time step)" "" "W[iofc] - W parameter weight matrix for input, output, forget, and cell gates" "" "R[iofc] - R recurrence weight matrix for input, output, forget, and cell gates" "" "Wb[iofc] - W bias vectors for input, output, forget, and cell gates" "" "Rb[iofc] - R bias vectors for input, output, forget, and cell gates" "" "P[iof] - P peephole weight vector for input, output, and forget gates" "" "WB[iofc] - W parameter weight matrix for backward input, output, forget, and cell gates" "" "RB[iofc] - R recurrence weight matrix for backward input, output, forget, and cell gates" "" "WBb[iofc] - W bias vectors for backward input, output, forget, and cell gates" "" "RBb[iofc] - R bias vectors for backward input, output, forget, and cell gates" "" "PB[iof] - P peephole weight vector for backward input, output, and forget gates" "" "H - Hidden state" "" "num_directions - 2 if direction == bidirectional else 1" "" "Activation functions:" "" " Relu(x) - max(0, x)" "" " Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})" "" " Sigmoid(x) - 1/(1 + e^{-x})" "" " (NOTE: Below are optional)" "" " Affine(x) - alphax + beta" "" " LeakyRelu(x) - x if x >= 0 else alpha * x" "" " ThresholdedRelu(x) - x if x >= alpha else 0" "" " ScaledTanh(x) - alphaTanh(betax)" "" " HardSigmoid(x) - min(max(alphax + beta, 0), 1)" "" " Elu(x) - x if x >= 0 else alpha*(e^x - 1)" "" " Softsign(x) - x/(1 + |x|)" "" " Softplus(x) - log(1 + e^x)" "" "Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):" "" " - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)" "" " - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)" "" " - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)" "" " - Ct = ft (.) Ct-1 + it (.) ct" "" " - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)" "" " - Ht = ot (.) h(Ct)" "This operator has optional inputs/outputs. See the doc for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted."

Operands:

X: memref of any type values or tensor of any type values
W: memref of any type values or tensor of any type values
R: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values or none type
sequence_lens: memref of any type values or tensor of any type values or none type
initial_h: memref of any type values or tensor of any type values or none type
initial_c: memref of any type values or tensor of any type values or none type
P: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`activation_alpha`	`ArrayAttr`	32-bit float array attribute attribute
`activation_beta`	`ArrayAttr`	32-bit float array attribute attribute
`activations`	`ArrayAttr`	string array attribute attribute
`clip`	`FloatAttr`	32-bit float attribute attribute
`direction`	`StringAttr`	string attribute attribute
`hidden_size`	`IntegerAttr`	64-bit integer attribute attribute
`input_forget`	`IntegerAttr`	64-bit integer attribute attribute

Results:

Y: memref of any type values or tensor of any type values or none type
Y_h: memref of any type values or tensor of any type values or none type
Y_c: memref of any type values or tensor of any type values or none type

onnx.LeakyRelu (ONNXLeakyReluOp)

ONNX LeakyRelu operation

Description:

"LeakyRelu takes input data (Tensor) and an argument alpha, and produces one" "output data (Tensor) where the function f(x) = alpha * x for x < 0," "f(x) = x for x >= 0, is applied to the data tensor elementwise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`alpha`	`FloatAttr`	32-bit float attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.Less (ONNXLessOp)

ONNX Less operation

Description:

"Returns the tensor resulted from performing the less 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 the doc."

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Results:

C: memref of any type values or tensor of any type values

onnx.Log (ONNXLogOp)

ONNX Log operation

Description:

"Calculates the natural log of the given input tensor, element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.LogSoftmax (ONNXLogSoftmaxOp)

ONNX LogSoftmax operation

Description:

"The operator computes the logsoftmax (log of softmax) values for each layer in the batch" " of the given input." "" "The input does not need to explicitly be a 2D vector; rather, it will be" "coerced into one. For an arbitrary n-dimensional tensor" "input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is" "the axis provided, then input will be coerced into a 2-dimensional tensor with" "dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default" "case where axis=1, this means the input tensor will be coerced into a 2D tensor" "of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size." "In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D." "Each of these dimensions must be matched correctly, or else the operator" "will throw errors. The output tensor has the same shape" "and contains the logsoftmax values of the corresponding input."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Loop (ONNXLoopOp)

ONNX Loop operation

Description:

"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 = Constantvalue = <Scalar Tensor [3]>" " %b = Constantvalue = <Scalar Tensor [6]>" " %keepgoing = Constantvalue = <Scalar Tensor [1]>" " %max_trip_count = Constantvalue = <Scalar Tensor [10]>" " %keepgoing_out, %b_out, %user_defined_vals = Loop[body = ](%max_trip_count, %keepgoing, %b)" " return" " }" "" " graph body-net (" " %i[INT32, scalar]" " %keepgoing[BOOL, scalar]" " %b[INT32, scalar]" " ) {" " %my_local = Add(%a, %b)" " %b_out = Sub(%a, %b)" " %keepgoing_out = Greater(%my_local, %b_out)" " %user_defined_vals = Add(%b, %b)" " return %keepgoing_out, %b_out, %user_defined_vals" " }" "" "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 /" " for (int i=0; i < max_trip_count && keepgoing; ++i) {" " / User-defined code (loop body) /" " int my_local = a + b; // Reading values in the enclosing scope is fine" " b = a - b; // writes fine if we specify b as a loop-carried dependency" " keepgoing = my_local > b; // keepgoing is a loop-carried dependency" " user_defined_vals[i] = b + b;" " / End user-defined code */" " }" " // my_local = 123; // Can't do this. my_local was defined in the the body" "" " // These below values are live-out from 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 variables which you wish to make available in the enclosing scope (i.e." " the variables b and keepgoing) must be declared as either loop-carried" " dependencies (both at the op inputs and output and at the body net input and" " output) or scan_outputs." "3) Values created in the body cannot be accessed in the enclosing scope." "" "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)."

Operands:

M: memref of any type values or tensor of any type values or none type
cond: memref of any type values or tensor of any type values or none type
v_initial: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`body`	`Attribute`	any attribute attribute

Results:

v_final_and_scan_outputs: memref of any type values or tensor of any type values

onnx.LpNormalization (ONNXLpNormalizationOp)

ONNX LpNormalization operation

Description:

"Given a matrix, apply Lp-normalization along the provided axis."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute
`p`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.LpPool (ONNXLpPoolOp)

ONNX LpPool operation

Description:

"LpPool consumes an input tensor X and applies Lp pooling across" " the tensor according to kernel sizes, stride sizes, and pad lengths." " Lp pooling consisting of computing the Lp norm 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."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`auto_pad`	`StringAttr`	string attribute attribute
`kernel_shape`	`ArrayAttr`	64-bit integer array attribute attribute
`p`	`IntegerAttr`	64-bit integer attribute attribute
`pads`	`ArrayAttr`	64-bit integer array attribute attribute
`strides`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.MatMulInteger (ONNXMatMulIntegerOp)

ONNX MatMulInteger operation

Description:

"Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html." "The production MUST never overflow. The accumulation may overflow if and only if in 32 bits."

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values
a_zero_point: memref of any type values or tensor of any type values or none type
b_zero_point: memref of any type values or tensor of any type values or none type

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.MatMul (ONNXMatMulOp)

ONNX MatMul operation

Description:

"Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html"

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.Max (ONNXMaxOp)

ONNX Max operation

Description:

"Element-wise max of each of the input tensors (with Numpy-style broadcasting support)." "All inputs and outputs must have the same data type." "This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc."

Operands:

data_0: memref of any type values or tensor of any type values

Attributes:

Results:

max: memref of any type values or tensor of any type values

onnx.MaxPool (ONNXMaxPoolOp)

ONNX MaxPool operation

Description:

"MaxPool consumes an input tensor X and applies max pooling across" " the tensor according to kernel sizes, stride sizes, and pad lengths." " max pooling consisting of computing the max 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 will be following:" " " " output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)" " " " or" " " " output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)" " " " if ceil_mode is enabled" "" " " " * pad_shape[i] is 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:" " " " 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])" " " " 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 maximum number of elements exclude pad." " "

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`auto_pad`	`StringAttr`	string attribute attribute
`ceil_mode`	`IntegerAttr`	64-bit integer attribute attribute
`dilations`	`ArrayAttr`	64-bit integer array attribute attribute
`kernel_shape`	`ArrayAttr`	64-bit integer array attribute attribute
`pads`	`ArrayAttr`	64-bit integer array attribute attribute
`storage_order`	`IntegerAttr`	64-bit integer attribute attribute
`strides`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

Y: memref of any type values or tensor of any type values
Indices: memref of any type values or tensor of any type values or none type

onnx.MaxPoolSingleOut (ONNXMaxPoolSingleOutOp)

ONNX MaxPool operation with a single output.

Description:

"ONNX MaxPool operation with a single output." "See ONNXMaxPoolOp for a full description of the MaxPool semantics."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`auto_pad`	`StringAttr`	string attribute attribute
`ceil_mode`	`IntegerAttr`	64-bit integer attribute attribute
`dilations`	`ArrayAttr`	64-bit integer array attribute attribute
`kernel_shape`	`ArrayAttr`	64-bit integer array attribute attribute
`pads`	`ArrayAttr`	64-bit integer array attribute attribute
`storage_order`	`IntegerAttr`	64-bit integer attribute attribute
`strides`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

o_Y: memref of any type values or tensor of any type values

onnx.MaxRoiPool (ONNXMaxRoiPoolOp)

ONNX MaxRoiPool operation

Description:

"ROI max pool consumes an input tensor X and region of interests (RoIs) to" " apply max pooling across each RoI, to produce output 4-D tensor of shape" " (num_rois, channels, pooled_shape[0], pooled_shape[1])."

Operands:

X: memref of any type values or tensor of any type values
rois: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`pooled_shape`	`ArrayAttr`	64-bit integer array attribute attribute
`spatial_scale`	`FloatAttr`	32-bit float attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.MaxUnpool (ONNXMaxUnpoolOp)

ONNX MaxUnpool operation

Description:

"MaxUnpool essentially computes the partial inverse of the MaxPool op." " The input information to this op is typically the the output information from a MaxPool op. The first" " input tensor X is the tensor that needs to be unpooled, which is typically the pooled tensor (first output)" " from MaxPool. The second input tensor, I, contains the indices to the (locally maximal) elements corrsponding" " to the elements in the first input tensor X. Input tensor I is typically the second output of the MaxPool op." " The third (optional) input is a tensor that specifies the output size of the unpooling operation." "" "MaxUnpool is intended to do 'partial' inverse of the MaxPool op. 'Partial' because all the non-maximal" " values from the original input to MaxPool are set to zero in the output of the MaxUnpool op. Pooling" " the result of an unpooling operation should give back the original input to the unpooling op." "" "MaxUnpool can produce the same output size for several input sizes, which makes unpooling op ambiguous." " The third input argument, output_size, is meant to disambiguate the op and produce output tensor of" " known/predictable size." "" "In addition to the inputs, MaxUnpool takes three attributes, namely kernel_shape, strides, and pads," " which define the exact unpooling op. The attributes typically have the same values as the corrsponding" " pooling op that the unpooling op is trying to invert."

Operands:

X: memref of any type values or tensor of any type values
I: memref of any type values or tensor of any type values
output_shape: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`kernel_shape`	`ArrayAttr`	64-bit integer array attribute attribute
`pads`	`ArrayAttr`	64-bit integer array attribute attribute
`strides`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Mean (ONNXMeanOp)

ONNX Mean operation

Description:

"Element-wise mean of each of the input tensors (with Numpy-style broadcasting support)." "All inputs and outputs must have the same data type." "This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc."

Operands:

data_0: memref of any type values or tensor of any type values

Attributes:

Results:

mean: memref of any type values or tensor of any type values

onnx.MeanVarianceNormalization (ONNXMeanVarianceNormalizationOp)

ONNX MeanVarianceNormalization operation

Description:

"A MeanVarianceNormalization Function: Perform mean variance normalization" " on the input tensor X using formula:
(X-EX)/sqrt(E(X-EX)^2)"

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.Min (ONNXMinOp)

ONNX Min operation

Description:

"Element-wise min of each of the input tensors (with Numpy-style broadcasting support)." "All inputs and outputs must have the same data type." "This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc."

Operands:

data_0: memref of any type values or tensor of any type values

Attributes:

Results:

min: memref of any type values or tensor of any type values

onnx.Mod (ONNXModOp)

ONNX Mod operation

Description:

"Performs element-wise binary modulus (with Numpy-style broadcasting support). " " The sign of the remainder is the same as that of the Divisor." " " " Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend " " (in contrast to integer mod). To force a behavior like numpy.fmod() an 'fmod' Attribute is provided." " This attribute is set to 0 by default causing the behavior to be like integer mod. " " Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod()." "" " If the input type is floating point, then fmod attribute must be set to 1." " " " In case of dividend being zero, the results will be platform dependent." "" " This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc."

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`fmod`	`IntegerAttr`	64-bit integer attribute attribute

Results:

C: memref of any type values or tensor of any type values

onnx.Mul (ONNXMulOp)

ONNX Mul operation

Description:

"Performs element-wise binary multiplication (with Numpy-style broadcasting support)." "" "This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc."

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Results:

C: memref of any type values or tensor of any type values

onnx.Multinomial (ONNXMultinomialOp)

ONNX Multinomial operation

Description:

"Generate a tensor of samples from a multinomial distribution according to the probabilities" "of each of the possible outcomes."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`dtype`	`IntegerAttr`	64-bit integer attribute attribute
`sample_size`	`IntegerAttr`	64-bit integer attribute attribute
`seed`	`FloatAttr`	32-bit float attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Neg (ONNXNegOp)

ONNX Neg operation

Description:

"Neg takes one input data (Tensor) and produces one output data" "(Tensor) where each element flipped sign, y = -x, is applied to" "the tensor elementwise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.NonMaxSuppression (ONNXNonMaxSuppressionOp)

ONNX NonMaxSuppression operation

Description:

"Filter out boxes that have high intersection-over-union (IOU) overlap with previously selected boxes." "Bounding boxes with score less than score_threshold are removed. Bounding box format is indicated by attribute center_point_box." "Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to" "orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system" "result in the same boxes being selected by the algorithm." "The selected_indices output is a set of integers indexing into the input collection of bounding boxes representing the selected boxes." "The bounding box coordinates corresponding to the selected indices can then be obtained using the Gather or GatherND operation."

Operands:

boxes: memref of any type values or tensor of any type values
scores: memref of any type values or tensor of any type values
max_output_boxes_per_class: memref of any type values or tensor of any type values or none type
iou_threshold: memref of any type values or tensor of any type values or none type
score_threshold: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`center_point_box`	`IntegerAttr`	64-bit integer attribute attribute

Results:

selected_indices: memref of any type values or tensor of any type values

onnx.NonZero (ONNXNonZeroOp)

ONNX NonZero operation

Description:

"Returns the indices of the elements that are non-zero" " (in row-major order - by dimension)." " NonZero behaves similar to numpy.nonzero:" " https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html"

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.Not (ONNXNotOp)

ONNX Not operation

Description:

"Returns the negation of the input tensor element-wise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.OneHot (ONNXOneHotOp)

ONNX OneHot operation

Description:

"Produces a one-hot tensor based on inputs." " The locations represented by the index values in the 'indices' input tensor will have 'on_value'" " and the other locations will have 'off_value' in the output tensor, where 'on_value' and 'off_value'" " are specified as part of required input argument 'values', which is a two-element tensor of format" " [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the" " input tensor. The additional dimension is for one-hot representation. The additional dimension will" " be inserted at the position specified by 'axis'. If 'axis' is not specified then then additional" " dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional" " dimension is specified by required scalar input 'depth'. The type of the output tensor is the same" " as the type of the 'values' input. Any entries in the 'indices' input tensor with values outside" " the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the" " output tensor." "" " when axis = 0:" " output[input[i, j, k], i, j, k] = 1 for all i, j, k and 0 otherwise." "" " when axis = -1:" " output[i, j, k, input[i, j, k]] = 1 for all i, j, k and 0 otherwise." ""

Operands:

indices: memref of any type values or tensor of any type values
depth: memref of any type values or tensor of any type values
values: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Or (ONNXOrOp)

ONNX Or operation

Description:

"Returns the tensor resulted from performing the or 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 the doc."

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Results:

C: memref of any type values or tensor of any type values

onnx.PRelu (ONNXPReluOp)

ONNX PRelu operation

Description:

"PRelu takes input data (Tensor) and slope tensor as input, and produces one" "output data (Tensor) where the function f(x) = slope * x for x < 0," "f(x) = x for x >= 0., is applied to the data tensor elementwise." "This operator supports unidirectional broadcasting (tensor slope should be unidirectional broadcastable to input tensor X); for more details please check the doc."

Operands:

X: memref of any type values or tensor of any type values
slope: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.PadConstantValue (ONNXPadConstantValueOp)

ONNX Pad operation with constant padding value

Description:

"this operation is introduced to handle situation" " in which the padding value is a constant. " The value will become an attribute." "This operation is also used to handle the optional value input is missing and the default value 0." "is used."

Operands:

data: memref of any type values or tensor of any type values
pads: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`constant_value`	`FloatAttr`	32-bit float attribute attribute
`mode`	`StringAttr`	string attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.PadConstatValuePad (ONNXPadConstantValuePadOp)

ONNX Pad operation with constant padding value

Description:

"this operation is introduced to handle situation" " in which the padding value and padding are constants" "They will become attributes."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`pads`	`ArrayAttr`	64-bit integer array attribute attribute
`constant_value`	`FloatAttr`	32-bit float attribute attribute
`mode`	`StringAttr`	string attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Pad (ONNXPadOp)

ONNX Pad operation

Description:

"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)" "" "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" "" "" "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]," " ]," " ]" ""

Operands:

data: memref of any type values or tensor of any type values
pads: memref of any type values or tensor of any type values
constant_value: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`mode`	`StringAttr`	string attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Pow (ONNXPowOp)

ONNX Pow operation

Description:

"Pow takes input data (Tensor) and exponent Tensor, and" "produces one output data (Tensor) where the function f(x) = x^exponent," "is applied to the data tensor elementwise." "This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc."

Operands:

X: memref of any type values or tensor of any type values
Y: memref of any type values or tensor of any type values

Attributes:

Results:

Z: memref of any type values or tensor of any type values

onnx.QLinearConv (ONNXQLinearConvOp)

ONNX QLinearConv operation

Description:

"The convolution operator consumes a quantized input tensor, its scale and zero point," "a quantized filter, its scale and zero point, and output's scale and zero point," "and computes the quantized output. Each scale and zero-point pair must have same shape." "It means they must be either scalars (per tensor) or 1-D tensors (per output channel)." "Each input or output and its related zero point must have same type."

Operands:

x: memref of any type values or tensor of any type values
x_scale: memref of any type values or tensor of any type values
x_zero_point: memref of any type values or tensor of any type values
w: memref of any type values or tensor of any type values
w_scale: memref of any type values or tensor of any type values
w_zero_point: memref of any type values or tensor of any type values
y_scale: memref of any type values or tensor of any type values
y_zero_point: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`auto_pad`	`StringAttr`	string attribute attribute
`dilations`	`ArrayAttr`	64-bit integer array attribute attribute
`group`	`IntegerAttr`	64-bit integer attribute attribute
`kernel_shape`	`ArrayAttr`	64-bit integer array attribute attribute
`pads`	`ArrayAttr`	64-bit integer array attribute attribute
`strides`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

y: memref of any type values or tensor of any type values

onnx.QLinearMatMul (ONNXQLinearMatMulOp)

ONNX QLinearMatMul operation

Description:

"Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html." "It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output." "The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even." "Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape." "They must be either scalar (per tensor) or 1-D tensor (per row for 'a' and per column for 'b'). If scale and zero point are 1-D tensor," "the number of elements of scale and zero point tensor of input 'a' and output 'y' should be equal to the number of rows of input 'a'," "and the number of elements of scale and zero point tensor of input 'b' should be equal to the number of columns of input 'b'." "Production must never overflow, and accumulation may overflow if and only if in 32 bits."

Operands:

a: memref of any type values or tensor of any type values
a_scale: memref of any type values or tensor of any type values
a_zero_point: memref of any type values or tensor of any type values
b: memref of any type values or tensor of any type values
b_scale: memref of any type values or tensor of any type values
b_zero_point: memref of any type values or tensor of any type values
y_scale: memref of any type values or tensor of any type values
y_zero_point: memref of any type values or tensor of any type values

Attributes:

Results:

y: memref of any type values or tensor of any type values

onnx.QuantizeLinear (ONNXQuantizeLinearOp)

ONNX QuantizeLinear operation

Description:

"The linear per-tensor/layer quantization operator. It consumes a high precision tensor, a scale, a zero point to compute the low precision / quantized tensor." "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 nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type."

Operands:

x: memref of any type values or tensor of any type values
y_scale: memref of any type values or tensor of any type values
y_zero_point: memref of any type values or tensor of any type values or none type

Attributes:

Results:

y: memref of any type values or tensor of any type values

onnx.RNN (ONNXRNNOp)

ONNX RNN operation

Description:

"Computes an one-layer simple RNN. This operator is usually supported" "via some custom implementation such as CuDNN." "" "Notations:" "" "X - input tensor" "" "i - input gate" "" "t - time step (t-1 means previous time step)" "" "Wi - W parameter weight matrix for input gate" "" "Ri - R recurrence weight matrix for input gate" "" "Wbi - W parameter bias vector for input gate" "" "Rbi - R parameter bias vector for input gate" "" "WBi - W parameter weight matrix for backward input gate" "" "RBi - R recurrence weight matrix for backward input gate" "" "WBbi - WR bias vectors for backward input gate" "" "RBbi - RR bias vectors for backward input gate" "" "H - Hidden state" "" "num_directions - 2 if direction == bidirectional else 1" "" "Activation functions:" "" " Relu(x) - max(0, x)" "" " Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})" "" " Sigmoid(x) - 1/(1 + e^{-x})" "" " (NOTE: Below are optional)" "" " Affine(x) - alphax + beta" "" " LeakyRelu(x) - x if x >= 0 else alpha * x" "" " ThresholdedRelu(x) - x if x >= alpha else 0" "" " ScaledTanh(x) - alphaTanh(betax)" "" " HardSigmoid(x) - min(max(alphax + beta, 0), 1)" "" " Elu(x) - x if x >= 0 else alpha*(e^x - 1)" "" " Softsign(x) - x/(1 + |x|)" "" " Softplus(x) - log(1 + e^x)" "" "Equations (Default: f=Tanh):" "" " - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)" "This operator has optional inputs/outputs. See the doc for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted."

Operands:

X: memref of any type values or tensor of any type values
W: memref of any type values or tensor of any type values
R: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values or none type
sequence_lens: memref of any type values or tensor of any type values or none type
initial_h: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`activation_alpha`	`ArrayAttr`	32-bit float array attribute attribute
`activation_beta`	`ArrayAttr`	32-bit float array attribute attribute
`activations`	`ArrayAttr`	string array attribute attribute
`clip`	`FloatAttr`	32-bit float attribute attribute
`direction`	`StringAttr`	string attribute attribute
`hidden_size`	`IntegerAttr`	64-bit integer attribute attribute

Results:

Y: memref of any type values or tensor of any type values or none type
Y_h: memref of any type values or tensor of any type values or none type

onnx.RandomNormalLike (ONNXRandomNormalLikeOp)

ONNX RandomNormalLike operation

Description:

"Generate a tensor with random values drawn from a normal distribution." "The shape of the output tensor is copied from the shape of the input tensor," "and the parameters of the normal distribution are specified by mean and scale." "" "The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided." "The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the" "TensorProto message, and be valid as an output type."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`dtype`	`IntegerAttr`	64-bit integer attribute attribute
`mean`	`FloatAttr`	32-bit float attribute attribute
`scale`	`FloatAttr`	32-bit float attribute attribute
`seed`	`FloatAttr`	32-bit float attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.RandomNormal (ONNXRandomNormalOp)

ONNX RandomNormal operation

Description:

"Generate a tensor with random values drawn from a normal distribution. The shape" "of the tensor is specified by the shape argument and the parameter of the normal distribution" "specified by mean and scale." "" "The data type is specified by the 'dtype' argument. The 'dtype' argument must" "be one of the data types specified in the 'DataType' enum field in the" "TensorProto message."

Operands:

Attributes:

Attribute	MLIR Type	Description
`dtype`	`IntegerAttr`	64-bit integer attribute attribute
`mean`	`FloatAttr`	32-bit float attribute attribute
`scale`	`FloatAttr`	32-bit float attribute attribute
`seed`	`FloatAttr`	32-bit float attribute attribute
`shape`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.RandomUniformLike (ONNXRandomUniformLikeOp)

ONNX RandomUniformLike operation

Description:

"Generate a tensor with random values drawn from a uniform distribution." "The shape of the output tensor is copied from the shape of the input tensor," "and the parameters of the uniform distribution are specified by low and high." "" "The data type is specified by the 'dtype' argument, or copied from the input tensor if not provided." "The 'dtype' argument must be one of the data types specified in the 'DataType' enum field in the" "TensorProto message and be valid as an output type."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`dtype`	`IntegerAttr`	64-bit integer attribute attribute
`high`	`FloatAttr`	32-bit float attribute attribute
`low`	`FloatAttr`	32-bit float attribute attribute
`seed`	`FloatAttr`	32-bit float attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.RandomUniform (ONNXRandomUniformOp)

ONNX RandomUniform operation

Description:

"Generate a tensor with random values drawn from a uniform distribution. The shape" "of the tensor is specified by the shape argument and the range by low and high." "" "The data type is specified by the 'dtype' argument. The 'dtype' argument must" "be one of the data types specified in the 'DataType' enum field in the" "TensorProto message."

Operands:

Attributes:

Attribute	MLIR Type	Description
`dtype`	`IntegerAttr`	64-bit integer attribute attribute
`high`	`FloatAttr`	32-bit float attribute attribute
`low`	`FloatAttr`	32-bit float attribute attribute
`seed`	`FloatAttr`	32-bit float attribute attribute
`shape`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Range (ONNXRangeOp)

ONNX Range operation

Description:

"Generate a tensor containing a sequence of numbers that begin at start and extends by increments of delta " "up to limit (exclusive)." "" "The number of elements in the output of range is computed as below-" "" "number_of_elements = max( ceil( (limit - start) / delta ) , 0 )" "" "The pseudocode determining the contents of the output is shown below-" "" "for(int i=0; i<number_of_elements; ++i)" "" "{" " " " output[i] = start + (i * delta); " "" "} " "" "Example 1" "Inputs: start = 3, limit = 9, delta = 3" "Output: [3, 6]" "" "Example 2" "Inputs: start = 10, limit = 4, delta = -2" "Output: [10, 8, 6]" ""

Operands:

start: memref of any type values or tensor of any type values
limit: memref of any type values or tensor of any type values
delta: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Reciprocal (ONNXReciprocalOp)

ONNX Reciprocal operation

Description:

"Reciprocal takes one input data (Tensor) and produces one output data" "(Tensor) where the reciprocal is, y = 1/x, is applied to" "the tensor elementwise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.ReduceL1 (ONNXReduceL1Op)

ONNX ReduceL1 operation

Description:

"Computes the L1 norm of the input tensor's element along the provided axes. The resulted" "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then" "the resulted tensor have the reduced dimension pruned." "" "The above behavior is similar to numpy, with the exception that numpy default keepdims to" "False instead of True."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

reduced: memref of any type values or tensor of any type values

onnx.ReduceL2 (ONNXReduceL2Op)

ONNX ReduceL2 operation

Description:

"Computes the L2 norm of the input tensor's element along the provided axes. The resulted" "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then" "the resulted tensor have the reduced dimension pruned." "" "The above behavior is similar to numpy, with the exception that numpy default keepdims to" "False instead of True."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

reduced: memref of any type values or tensor of any type values

onnx.ReduceLogSumExp (ONNXReduceLogSumExpOp)

ONNX ReduceLogSumExp operation

Description:

"Computes the log sum exponent of the input tensor's element along the provided axes. The resulted" "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then" "the resulted tensor have the reduced dimension pruned." "" "The above behavior is similar to numpy, with the exception that numpy default keepdims to" "False instead of True."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

reduced: memref of any type values or tensor of any type values

onnx.ReduceLogSum (ONNXReduceLogSumOp)

ONNX ReduceLogSum operation

Description:

"Computes the log sum of the input tensor's element along the provided axes. The resulted" "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then" "the resulted tensor have the reduced dimension pruned." "" "The above behavior is similar to numpy, with the exception that numpy default keepdims to" "False instead of True."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

reduced: memref of any type values or tensor of any type values

onnx.ReduceMax (ONNXReduceMaxOp)

ONNX ReduceMax operation

Description:

"Computes the max of the input tensor's element along the provided axes. The resulted" "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then" "the resulted tensor have the reduced dimension pruned." "" "The above behavior is similar to numpy, with the exception that numpy default keepdims to" "False instead of True."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

reduced: memref of any type values or tensor of any type values

onnx.ReduceMean (ONNXReduceMeanOp)

ONNX ReduceMean operation

Description:

"Computes the mean of the input tensor's element along the provided axes. The resulted" "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then" "the resulted tensor have the reduced dimension pruned." "" "The above behavior is similar to numpy, with the exception that numpy default keepdims to" "False instead of True."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

reduced: memref of any type values or tensor of any type values

onnx.ReduceMin (ONNXReduceMinOp)

ONNX ReduceMin operation

Description:

"Computes the min of the input tensor's element along the provided axes. The resulted" "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then" "the resulted tensor have the reduced dimension pruned." "" "The above behavior is similar to numpy, with the exception that numpy default keepdims to" "False instead of True."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

reduced: memref of any type values or tensor of any type values

onnx.ReduceProd (ONNXReduceProdOp)

ONNX ReduceProd operation

Description:

"Computes the product of the input tensor's element along the provided axes. The resulted" "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then" "the resulted tensor have the reduced dimension pruned." "" "The above behavior is similar to numpy, with the exception that numpy default keepdims to" "False instead of True."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

reduced: memref of any type values or tensor of any type values

onnx.ReduceSum (ONNXReduceSumOp)

ONNX ReduceSum operation

Description:

"Computes the sum of the input tensor's element along the provided axes. The resulted" "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then" "the resulted tensor have the reduced dimension pruned." "" "The above behavior is similar to numpy, with the exception that numpy default keepdims to" "False instead of True."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

reduced: memref of any type values or tensor of any type values

onnx.ReduceSumSquare (ONNXReduceSumSquareOp)

ONNX ReduceSumSquare operation

Description:

"Computes the sum square of the input tensor's element along the provided axes. The resulted" "tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then" "the resulted tensor have the reduced dimension pruned." "" "The above behavior is similar to numpy, with the exception that numpy default keepdims to" "False instead of True."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

reduced: memref of any type values or tensor of any type values

onnx.Relu (ONNXReluOp)

ONNX Relu operation

Description:

"Relu takes one input data (Tensor) and produces one output data" "(Tensor) where the rectified linear function, y = max(0, x), is applied to" "the tensor elementwise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.Reshape (ONNXReshapeOp)

ONNX Reshape operation

Description:

"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)."

Operands:

data: memref of any type values or tensor of any type values
shape: memref of any type values or tensor of any type values

Attributes:

Results:

reshaped: memref of any type values or tensor of any type values

onnx.Resize (ONNXResizeOp)

ONNX Resize operation

Description:

"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."

Operands:

X: memref of any type values or tensor of any type values
roi: memref of any type values or tensor of any type values
scales: memref of any type values or tensor of any type values
sizes: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`coordinate_transformation_mode`	`StringAttr`	string attribute attribute
`cubic_coeff_a`	`FloatAttr`	32-bit float attribute attribute
`exclude_outside`	`IntegerAttr`	64-bit integer attribute attribute
`extrapolation_value`	`FloatAttr`	32-bit float attribute attribute
`mode`	`StringAttr`	string attribute attribute
`nearest_mode`	`StringAttr`	string attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.ReverseSequence (ONNXReverseSequenceOp)

ONNX ReverseSequence operation

Description:

"Reverse batch of sequences having different lengths specified by sequence_lens." "" "For each slice i iterating on batch axis, the operator reverses the first sequence_lens[i] elements on time axis," "and copies elements whose index's beyond sequence_lens[i] to the output. So the output slice i contains reversed" "sequences on the first sequence_lens[i] elements, then have original values copied for the other elements." "" "Example 1:" " input = [[0.0, 4.0, 8.0, 12.0]," " [1.0, 5.0, 9.0, 13.0]," " [2.0, 6.0, 10.0, 14.0]," " [3.0, 7.0, 11.0, 15.0]]" " sequence_lens = [4, 3, 2, 1]" " time_axis = 0" " batch_axis = 1" "" " output = [[3.0, 6.0, 9.0, 12.0]," " [2.0, 5.0, 8.0, 13.0]," " [1.0, 4.0, 10.0, 14.0]," " [0.0, 7.0, 11.0, 15.0]]" "" "Example 2:" " input = [[0.0, 1.0, 2.0, 3.0 ]," " [4.0, 5.0, 6.0, 7.0 ]," " [8.0, 9.0, 10.0, 11.0]," " [12.0, 13.0, 14.0, 15.0]]" " sequence_lens = [1, 2, 3, 4]" " time_axis = 1" " batch_axis = 0" "" " output = [[0.0, 1.0, 2.0, 3.0 ]," " [5.0, 4.0, 6.0, 7.0 ]," " [10.0, 9.0, 8.0, 11.0]," " [15.0, 14.0, 13.0, 12.0]]"

Operands:

input: memref of any type values or tensor of any type values
sequence_lens: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`batch_axis`	`IntegerAttr`	64-bit integer attribute attribute
`time_axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.RoiAlign (ONNXRoiAlignOp)

ONNX RoiAlign operation

Description:

"Region of Interest (RoI) align operation described in the" "Mask R-CNN paper." "RoiAlign consumes an input tensor X and region of interests (rois)" "to apply pooling across each RoI; it produces a 4-D tensor of shape" "(num_rois, C, output_height, output_width)." "" "RoiAlign is proposed to avoid the misalignment by removing" "quantizations while converting from original image into feature" "map and from feature map into RoI feature; in each ROI bin," "the value of the sampled locations are computed directly" "through bilinear interpolation."

Operands:

X: memref of any type values or tensor of any type values
rois: memref of any type values or tensor of any type values
batch_indices: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`mode`	`StringAttr`	string attribute attribute
`output_height`	`IntegerAttr`	64-bit integer attribute attribute
`output_width`	`IntegerAttr`	64-bit integer attribute attribute
`sampling_ratio`	`IntegerAttr`	64-bit integer attribute attribute
`spatial_scale`	`FloatAttr`	32-bit float attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.Round (ONNXRoundOp)

ONNX Round operation

Description:

"Round takes one input Tensor and rounds the values, element-wise, meaning" "it finds the nearest integer for each value." "In case of halfs, the rule is to round them to the nearest even integer." "The output tensor has the same shape and type as the input." "" "Examples:" "" "round([0.9]) = [1.0]" "round([2.5]) = [2.0]" "round([2.3]) = [2.0]" "round([1.5]) = [2.0]" "round([-4.5]) = [-4.0]" ""

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.Scan (ONNXScanOp)

ONNX Scan operation

Description:

"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<axis=k>[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<axis=axis_1>[t];" " ... ;" " si_m = scan_m<axis=axis_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<axis=0>(scan_out_1, so_1); ... ; scan_out_k = Concat<axis=0>(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" " }" ""

Operands:

initial_state_and_scan_inputs: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`body`	`Attribute`	any attribute attribute
`num_scan_inputs`	`IntegerAttr`	64-bit integer attribute attribute
`scan_input_axes`	`ArrayAttr`	64-bit integer array attribute attribute
`scan_input_directions`	`ArrayAttr`	64-bit integer array attribute attribute
`scan_output_axes`	`ArrayAttr`	64-bit integer array attribute attribute
`scan_output_directions`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

final_state_and_scan_outputs: memref of any type values or tensor of any type values

onnx.ScatterElements (ONNXScatterElementsOp)

ONNX ScatterElements operation

Description:

"ScatterElements takes three inputs data, updates, and indices of the same" "rank r >= 1 and an optional attribute axis that identifies an axis of data" "(by default, the outer-most axis, that is axis 0). The output of the operation" "is produced by creating a copy of the input data, and then updating its value" "to values specified by updates at specific index positions specified by" "indices. Its output shape is the same as the shape of data." "" "For each entry in updates, the target index in data is obtained by combining" "the corresponding entry in indices with the index of the entry itself: the" "index-value for dimension = axis is obtained from the value of the corresponding" "entry in indices and the index-value for dimension != axis is obtained from the" "index of the entry itself." "" "For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry" "is performed as below:" "" " output[indices[i][j]][j] = updates[i][j] if axis = 0, " " output[i][indices[i][j]] = updates[i][j] if axis = 1," "" "" "This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation." "" "Example 1:" "" " data = [" " [0.0, 0.0, 0.0]," " [0.0, 0.0, 0.0]," " [0.0, 0.0, 0.0]," " ]" " indices = [" " [1, 0, 2]," " [0, 2, 1]," " ]" " updates = [" " [1.0, 1.1, 1.2]," " [2.0, 2.1, 2.2]," " ]" " output = [" " [2.0, 1.1, 0.0]" " [1.0, 0.0, 2.2]" " [0.0, 2.1, 1.2]" " ]" "" "Example 2:" "" " data = [[1.0, 2.0, 3.0, 4.0, 5.0]]" " indices = [[1, 3]]" " updates = [[1.1, 2.1]]" " axis = 1" " output = [[1.0, 1.1, 3.0, 2.1, 5.0]]" ""

Operands:

data: memref of any type values or tensor of any type values
indices: memref of any type values or tensor of any type values
updates: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.ScatterND (ONNXScatterNDOp)

ONNX ScatterND operation

Description:

"ScatterND takes three inputs data tensor of rank r >= 1, indices tensor of rank q >= 1," "and updates tensor of rank q + r - indices.shape[-1] - 1. The output of the operation" "is produced by creating a copy of the input data, and then updating its value to values" "specified by updates at specific index positions specified by indices. Its output shape" "is the same as the shape of data. Note that indices should not have duplicate entries." "That is, two or more updates for the same index-location is not supported." "" "indices is an integer tensor. Let k denote indices.shape[-1], the last dimension in the shape of indices." " indices is treated as a (q-1)-dimensional tensor of k-tuples, where each k-tuple is a partial-index into data." "Hence, k can be a value at most the rank of data. When k equals rank(data), each update entry specifies an" "update to a single element of the tensor. When k is less than rank(data) each update entry specifies an" "update to a slice of the tensor." "" "updates is treated as a (q-1)-dimensional tensor of replacement-slice-values. Thus, the" "first (q-1) dimensions of updates.shape must match the first (q-1) dimensions of indices.shape." "The remaining dimensions of updates correspond to the dimensions of the" "replacement-slice-values. Each replacement-slice-value is a (r-k) dimensional tensor," "corresponding to the trailing (r-k) dimensions of data. Thus, the shape of updates" "must equal indices.shape[0:q-1] ++ data.shape[k:r-1], where ++ denotes the concatenation" "of shapes." "" "The output is calculated via the following equation:" "" " output = np.copy(data)" " update_indices = indices.shape[:-1]" " for idx in np.ndindex(update_indices):" " output[indices[idx]] = updates[idx]" "" "The order of iteration in the above loop is not specified." "In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]." "This ensures that the output value does not depend on the iteration order." "" "This operator is the inverse of GatherND." "" "Example 1:" "" " data = [1, 2, 3, 4, 5, 6, 7, 8]" " indices = [[4], [3], [1], [7]]" " updates = [9, 10, 11, 12]" " output = [1, 11, 3, 10, 9, 6, 7, 12]" "" "" "Example 2:" "" " data = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]]," " [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]]," " [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]," " [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]" " indices = [[0], [2]]" " updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]," " [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]" " output = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]," " [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]]," " [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]," " [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]" ""

Operands:

data: memref of any type values or tensor of any type values
indices: memref of any type values or tensor of any type values
updates: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Scatter (ONNXScatterOp)

ONNX Scatter operation

Description:

"This operator is deprecated. Please use ScatterElements, which provides the same functionality." "" "Scatter takes three inputs data, updates, and indices of the same" "rank r >= 1 and an optional attribute axis that identifies an axis of data" "(by default, the outer-most axis, that is axis 0). The output of the operation" "is produced by creating a copy of the input data, and then updating its value" "to values specified by updates at specific index positions specified by" "indices. Its output shape is the same as the shape of data." "" "For each entry in updates, the target index in data is obtained by combining" "the corresponding entry in indices with the index of the entry itself: the" "index-value for dimension = axis is obtained from the value of the corresponding" "entry in indices and the index-value for dimension != axis is obtained from the" "index of the entry itself." "" "For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry" "is performed as below:" "" " output[indices[i][j]][j] = updates[i][j] if axis = 0, " " output[i][indices[i][j]] = updates[i][j] if axis = 1," "" "" "This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation." "" "Example 1:" "" " data = [" " [0.0, 0.0, 0.0]," " [0.0, 0.0, 0.0]," " [0.0, 0.0, 0.0]," " ]" " indices = [" " [1, 0, 2]," " [0, 2, 1]," " ]" " updates = [" " [1.0, 1.1, 1.2]," " [2.0, 2.1, 2.2]," " ]" " output = [" " [2.0, 1.1, 0.0]" " [1.0, 0.0, 2.2]" " [0.0, 2.1, 1.2]" " ]" "" "Example 2:" "" " data = [[1.0, 2.0, 3.0, 4.0, 5.0]]" " indices = [[1, 3]]" " updates = [[1.1, 2.1]]" " axis = 1" " output = [[1.0, 1.1, 3.0, 2.1, 5.0]]" ""

Operands:

data: memref of any type values or tensor of any type values
indices: memref of any type values or tensor of any type values
updates: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Selu (ONNXSeluOp)

ONNX Selu operation

Description:

"Selu takes one input data (Tensor) and produces one output data" "(Tensor) where the scaled exponential linear unit function," "y = gamma * (alpha * e^x - alpha) for x <= 0, y = gamma * x for x > 0," "is applied to the tensor elementwise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`alpha`	`FloatAttr`	32-bit float attribute attribute
`gamma`	`FloatAttr`	32-bit float attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.SequenceAt (ONNXSequenceAtOp)

ONNX SequenceAt operation

Description:

"Outputs a tensor copy from the tensor at 'position' in 'input_sequence'." "Accepted range for 'position' is in [-n, n - 1], where n is the number of tensors in 'input_sequence'." "Negative value means counting positions from the back."

Operands:

input_sequence: memref of any type values or tensor of any type values
position: memref of any type values or tensor of any type values

Attributes:

Results:

tensor: memref of any type values or tensor of any type values

onnx.SequenceConstruct (ONNXSequenceConstructOp)

ONNX SequenceConstruct operation

Description:

"Construct a tensor sequence containing 'inputs' tensors." "All tensors in 'inputs' must have the same data type."

Operands:

inputs: memref of any type values or tensor of any type values

Attributes:

Results:

output_sequence: memref of any type values or tensor of any type values

onnx.SequenceEmpty (ONNXSequenceEmptyOp)

ONNX SequenceEmpty operation

Description:

"Construct an empty tensor sequence, with given data type."

Operands:

Attributes:

Attribute	MLIR Type	Description
`dtype`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.SequenceErase (ONNXSequenceEraseOp)

ONNX SequenceErase operation

Description:

"Outputs a tensor sequence that removes the tensor at 'position' from 'input_sequence'." "Accepted range for 'position' is in [-n, n - 1], where n is the number of tensors in 'input_sequence'." "Negative value means counting positions from the back." "'position' is optional, by default it erases the last tensor from 'input_sequence'."

Operands:

input_sequence: memref of any type values or tensor of any type values
position: memref of any type values or tensor of any type values or none type

Attributes:

Results:

output_sequence: memref of any type values or tensor of any type values

onnx.SequenceInsert (ONNXSequenceInsertOp)

ONNX SequenceInsert operation

Description:

"Outputs a tensor sequence that inserts 'tensor' into 'input_sequence' at 'position'." "'tensor' must have the same data type as 'input_sequence'." "Accepted range for 'position' is in [-n, n], where n is the number of tensors in 'input_sequence'." "Negative value means counting positions from the back." "'position' is optional, by default it inserts 'tensor' to the back of 'input_sequence'."

Operands:

input_sequence: memref of any type values or tensor of any type values
tensor: memref of any type values or tensor of any type values
position: memref of any type values or tensor of any type values or none type

Attributes:

Results:

output_sequence: memref of any type values or tensor of any type values

onnx.SequenceLength (ONNXSequenceLengthOp)

ONNX SequenceLength operation

Description:

"Produces a scalar(tensor of empty shape) containing the number of tensors in 'input_sequence'."

Operands:

input_sequence: memref of any type values or tensor of any type values

Attributes:

Results:

length: memref of any type values or tensor of any type values

onnx.Shape (ONNXShapeOp)

ONNX Shape operation

Description:

"Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Results:

shape: memref of any type values or tensor of any type values

onnx.Shrink (ONNXShrinkOp)

ONNX Shrink operation

Description:

"Shrink takes one input data (Tensor) and produces one Tensor output," "having same datatype and shape with input. It has two attributes, lambd and" "bias. The formula of this operator is: If x < -lambd, y = x + bias;" "If x > lambd, y = x - bias; Otherwise, y = 0."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`bias`	`FloatAttr`	32-bit float attribute attribute
`lambd`	`FloatAttr`	32-bit float attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Sigmoid (ONNXSigmoidOp)

ONNX Sigmoid operation

Description:

"Sigmoid takes one input data (Tensor) and produces one output data" "(Tensor) where the sigmoid function, y = 1 / (1 + exp(-x)), is applied to the" "tensor elementwise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.Sign (ONNXSignOp)

ONNX Sign operation

Description:

"Calculate the sign of the given input tensor element-wise." "If input > 0, output 1. if input < 0, output -1. if input == 0, output 0."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Sin (ONNXSinOp)

ONNX Sin operation

Description:

"Calculates the sine of the given input tensor, element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Sinh (ONNXSinhOp)

ONNX Sinh operation

Description:

"Calculates the hyperbolic sine of the given input tensor element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Size (ONNXSizeOp)

ONNX Size operation

Description:

"Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Results:

size: memref of any type values or tensor of any type values

onnx.Slice (ONNXSliceOp)

ONNX Slice operation

Description:

"Produces a slice of the input tensor along multiple axes. Similar to numpy:" "https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html" "Slices uses starts, ends, axes and steps inputs to specify the start and end" "dimension and step for each axis in the list of axes, it uses this information to" "slice the input data tensor. If a negative value is passed for any of the" "start or end indices, it represent number of elements before the end of that" "dimension. If the value passed to start or end is larger than the n (the" "number of elements in this dimension), it represents n. For slicing to the" "end of a dimension with unknown size, it is recommended to pass in INT_MAX." "If a negative value is passed for step, it represents slicing backward." "If axes are omitted, they are set to [0, ..., ndim-1]." "If steps are omitted, they are set to [1, ..., 1] of length len(starts)" "Example 1:" " data = [" " [1, 2, 3, 4]," " [5, 6, 7, 8]," " ]" " axes = [0, 1]" " starts = [1, 0]" " ends = [2, 3]" " steps = [1, 2]" " result = [" " [5, 7]," " ]" "Example 2:" " data = [" " [1, 2, 3, 4]," " [5, 6, 7, 8]," " ]" " starts = [0, 1]" " ends = [-1, 1000]" " result = [" " [2, 3, 4]," " ]"

Operands:

data: memref of any type values or tensor of any type values
starts: memref of any type values or tensor of any type values
ends: memref of any type values or tensor of any type values
axes: memref of any type values or tensor of any type values or none type
steps: memref of any type values or tensor of any type values or none type

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Softmax (ONNXSoftmaxOp)

ONNX Softmax operation

Description:

"The operator computes the softmax (normalized exponential) values for each layer in the batch" " of the given input." "" "The input does not need to explicitly be a 2D vector; rather, it will be" "coerced into one. For an arbitrary n-dimensional tensor" "input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is" "the axis provided, then input will be coerced into a 2-dimensional tensor with" "dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default" "case where axis=1, this means the input tensor will be coerced into a 2D tensor" "of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size." "In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D." "Each of these dimensions must be matched correctly, or else the operator" "will throw errors. The output tensor has the same shape" "and contains the softmax values of the corresponding input."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Softplus (ONNXSoftplusOp)

ONNX Softplus operation

Description:

"Softplus takes one input data (Tensor) and produces one output data" "(Tensor) where the softplus function, y = ln(exp(x) + 1), is applied to" "the tensor elementwise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.Softsign (ONNXSoftsignOp)

ONNX Softsign operation

Description:

"Calculates the softsign (x/(1+|x|)) of the given input tensor element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.SpaceToDepth (ONNXSpaceToDepthOp)

ONNX SpaceToDepth operation

Description:

"SpaceToDepth rearranges blocks of spatial data into depth. More specifically," "this op outputs a copy of the input tensor where values from the height and width dimensions" "are moved to the depth dimension."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`blocksize`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output: memref of any type values or tensor of any type values

onnx.Split (ONNXSplitOp)

ONNX Split operation

Description:

"Split a tensor into a list of tensors, along the specified" "'axis'. Lengths of the parts can be specified using argument 'split'." "Otherwise, the tensor is split to equal sized parts."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute
`split`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

outputs: memref of any type values or tensor of any type values

onnx.SplitToSequence (ONNXSplitToSequenceOp)

ONNX SplitToSequence operation

Description:

"Split a tensor into a sequence of tensors, along the specified" "'axis'. Lengths of the parts can be specified using argument 'split'." "'split' must contain only positive numbers." "'split' is either a scalar (tensor of empty shape), or a 1-D tensor." "If 'split' is a scalar, then 'input' will be split into equally sized chunks(if possible)." "Last chunk will be smaller if the 'input' size along the given axis 'axis' is not divisible" "by 'split'." "Otherwise, the tensor is split into 'size(split)' chunks, with lengths of the parts on 'axis'" "specified in 'split'. In this scenario, the sum of entries in 'split' must be equal to the" "dimension size of input tensor on 'axis'."

Operands:

input: memref of any type values or tensor of any type values
split: memref of any type values or tensor of any type values or none type

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute
`keepdims`	`IntegerAttr`	64-bit integer attribute attribute

Results:

output_sequence: memref of any type values or tensor of any type values

onnx.Sqrt (ONNXSqrtOp)

ONNX Sqrt operation

Description:

"Square root takes one input data (Tensor) and produces one output data" "(Tensor) where the square root is, y = x^0.5, is applied to" "the tensor elementwise. If x is negative, then it will return NaN."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Results:

Y: memref of any type values or tensor of any type values

onnx.Squeeze (ONNXSqueezeOp)

ONNX Squeeze operation

Description:

"Remove single-dimensional entries from the shape of a tensor." "Takes a parameter axes with a list of axes to squeeze." "If axes is not provided, all the single dimensions will be removed from" "the shape. If an axis is selected with shape entry not equal to one, an error is raised."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

squeezed: memref of any type values or tensor of any type values

onnx.StringNormalizer (ONNXStringNormalizerOp)

ONNX StringNormalizer operation

Description:

"StringNormalization performs string operations for basic cleaning." "This operator has only one input (denoted by X) and only one output" "(denoted by Y). This operator first examines the elements in the X," "and removes elements specified in "stopwords" attribute." "After removing stop words, the intermediate result can be further lowercased," "uppercased, or just returned depending the "case_change_action" attribute." "This operator only accepts [C]- and [1, C]-tensor." "If all elements in X are dropped, the output will be the empty value of string tensor with shape [1]" "if input shape is [C] and shape [1, 1] if input shape is [1, C]."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`case_change_action`	`StringAttr`	string attribute attribute
`is_case_sensitive`	`IntegerAttr`	64-bit integer attribute attribute
`locale`	`StringAttr`	string attribute attribute
`stopwords`	`ArrayAttr`	string array attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.Sub (ONNXSubOp)

ONNX Sub operation

Description:

"Performs element-wise binary subtraction (with Numpy-style broadcasting support)." "" "This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc."

Operands:

A: memref of any type values or tensor of any type values
B: memref of any type values or tensor of any type values

Attributes:

Results:

C: memref of any type values or tensor of any type values

onnx.Sum (ONNXSumOp)

ONNX Sum operation

Description:

"Element-wise sum of each of the input tensors (with Numpy-style broadcasting support)." "All inputs and outputs must have the same data type." "This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc."

Operands:

data_0: memref of any type values or tensor of any type values

Attributes:

Results:

sum: memref of any type values or tensor of any type values

onnx.Tan (ONNXTanOp)

ONNX Tan operation

Description:

"Calculates the tangent of the given input tensor, element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Tanh (ONNXTanhOp)

ONNX Tanh operation

Description:

"Calculates the hyperbolic tangent of the given input tensor element-wise."

Operands:

input: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.TfIdfVectorizer (ONNXTfIdfVectorizerOp)

ONNX TfIdfVectorizer operation

Description:

"This transform extracts n-grams from the input sequence and save them as a vector. Input can" "be either a 1-D or 2-D tensor. For 1-D input, output is the n-gram representation of that input." "For 2-D input, the output is also a 2-D tensor whose i-th row is the n-gram representation of the i-th input row." "More specifically, if input shape is [C], the corresponding output shape would be [max(ngram_indexes) + 1]." "If input shape is [N, C], this operator produces a [N, max(ngram_indexes) + 1]-tensor." "" "In contrast to standard n-gram extraction, here, the indexes of extracting an n-gram from the original" "sequence are not necessarily consecutive numbers. The discontinuity between indexes are controlled by the number of skips." "If the number of skips is 2, we should skip two tokens when scanning through the original sequence." "Let's consider an example. Assume that input sequence is [94, 17, 36, 12, 28] and the number of skips is 2." "The associated 2-grams are [94, 12] and [17, 28] respectively indexed by [0, 3] and [1, 4]." "If the number of skips becomes 0, the 2-grams generated are [94, 17], [17, 36], [36, 12], [12, 28]" "indexed by [0, 1], [1, 2], [2, 3], [3, 4], respectively." "" "The output vector (denoted by Y) stores the count of each n-gram;" "Y[ngram_indexes[i]] indicates the times that the i-th n-gram is found. The attribute ngram_indexes is used to determine the mapping" "between index i and the corresponding n-gram's output coordinate. If pool_int64s is [94, 17, 17, 36], ngram_indexes is [1, 0]," "ngram_counts=[0, 0], then the Y[0] (first element in Y) and Y[1] (second element in Y) are the counts of [17, 36] and [94, 17]," "respectively. An n-gram which cannot be found in pool_strings/pool_int64s should be ignored and has no effect on the output." "Note that we may consider all skips up to S when generating the n-grams." "" "The examples used above are true if mode is "TF". If mode is "IDF", all the counts larger than 1 would be truncated to 1 and" "the i-th element in weights would be used to scale (by multiplication) the count of the i-th n-gram in pool. If mode is "TFIDF"," "this operator first computes the counts of all n-grams and then scale them by the associated values in the weights attribute." "" "Only one of pool_strings and pool_int64s can be set. If pool_int64s is set, the input should be an integer tensor." "If pool_strings is set, the input must be a string tensor."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`max_gram_length`	`IntegerAttr`	64-bit integer attribute attribute
`max_skip_count`	`IntegerAttr`	64-bit integer attribute attribute
`min_gram_length`	`IntegerAttr`	64-bit integer attribute attribute
`mode`	`StringAttr`	string attribute attribute
`ngram_counts`	`ArrayAttr`	64-bit integer array attribute attribute
`ngram_indexes`	`ArrayAttr`	64-bit integer array attribute attribute
`pool_int64s`	`ArrayAttr`	64-bit integer array attribute attribute
`pool_strings`	`ArrayAttr`	string array attribute attribute
`weights`	`ArrayAttr`	32-bit float array attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.ThresholdedRelu (ONNXThresholdedReluOp)

ONNX ThresholdedRelu operation

Description:

"ThresholdedRelu takes one input data (Tensor) and produces one output data" "(Tensor) where the rectified linear function, y = x for x > alpha, y = 0 otherwise," "is applied to the tensor elementwise."

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`alpha`	`FloatAttr`	32-bit float attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.Tile (ONNXTileOp)

ONNX Tile operation

Description:

"Constructs a tensor by tiling a given tensor." "This is the same as function tile in Numpy, but no broadcast." "For example A = 1, 2], [3, 4, B = [1, 2], tile(A, B) = 1, 2, 1, 2], [3, 4, 3, 4"

Operands:

input: memref of any type values or tensor of any type values
repeats: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.TopK (ONNXTopKOp)

ONNX TopK operation

Description:

"Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of" "shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:" " -Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]" " which contains the values of the top k elements along the specified axis" " -Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which" " contains the indices of the top k elements (original indices from the input" " tensor)." "" "If "largest" is 1 (the default value) then the k largest elements are returned." "If "sorted" is 1 (the default value) then the resulting k elements will be sorted." "If "sorted" is 0, order of returned 'Values' and 'Indices' are undefined." "" "Given two equivalent values, this operator uses the indices along the axis as" " a tiebreaker. That is, the element with the lower index will appear first."

Operands:

X: memref of any type values or tensor of any type values
K: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute
`largest`	`IntegerAttr`	64-bit integer attribute attribute
`sorted`	`IntegerAttr`	64-bit integer attribute attribute

Results:

Values: memref of any type values or tensor of any type values
Indices: memref of any type values or tensor of any type values

onnx.Transpose (ONNXTransposeOp)

ONNX Transpose operation

Description:

"Transpose the input tensor similar to numpy.transpose. For example, when" "perm=(1, 0, 2), given an input tensor of shape (1, 2, 3), the output shape" "will be (2, 1, 3)."

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`perm`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

transposed: memref of any type values or tensor of any type values

onnx.Unique (ONNXUniqueOp)

ONNX Unique operation

Description:

"Find the unique elements of a tensor. When an optional attribute 'axis' is provided, unique subtensors sliced along the 'axis' are returned. " "Otherwise the input tensor is flattened and unique values of the flattened tensor are returned. " "" "This operator returns the unique values or sliced unique subtensors of the input tensor and three optional outputs. " "The first output tensor 'Y' contains all unique values or subtensors of the input. " "The second optional output tensor 'indices' contains indices of 'Y' elements' first occurance in 'X'.. " "The third optional output tensor 'inverse_indices' contains, for elements of 'X', its corresponding indices in 'Y'. ". " "The fourth optional output tensor 'counts' contains the count of each element of 'Y' in the input. " "" "Outputs are either sorted in ascending order or optionally in the order of the first occurrence of the values in the input. " "" "https://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html" "" "Example 1:" " input_X = [2, 1, 1, 3, 4, 3]" " attribute_sorted = 0" " attribute_axis = None" " output_Y = [2, 1, 3, 4]" " output_indices = [0, 1, 3, 4]" " output_inverse_indices = [0, 1, 1, 2, 3, 2]" " output_counts = [1, 2, 2, 1]" "" "Example 2:" " input_X = 1, 3], [2, 3" " attribute_sorted = 1" " attribute_axis = None" " output_Y = [1, 2, 3]" " output_indices = [0, 2, 1]" " output_inverse_indices = [0, 2, 1, 2]" " output_counts = [1, 1, 2]" "" "Example 3:" " input_X = 1, 0, 0], [1, 0, 0], [2, 3, 4" " attribute_sorted = 1" " attribute_axis = 0" " output_Y = 1, 0, 0], [2, 3, 4" " output_indices = [0, 2]" " output_inverse_indices = [0, 0, 1]" " output_counts = [2, 1]" "" "Example 4:" " input_x = [1., 1.], [0., 1.], [2., 1.], [0., 1., " " 1., 1.], [0., 1.], [2., 1.], [0., 1.]" " attribute_sorted = 1" " attribute_axis = 1" "" " intermediate data are presented below for better understanding: " " " " there are 4 subtensors sliced along axis 1 of input_x (shape = (2, 4, 2)):" " A: 1, 1], [1, 1, " " 0, 1], [0, 1, " " 2, 1], [2, 1, " " 0, 1], [0, 1." " " " there are 3 unique subtensors: " " 1, 1], [1, 1, " " 0, 1], [0, 1, " " 2, 1], [2, 1." " " " sorted unique subtensors:" " B: 0, 1], [0, 1, " " 1, 1], [1, 1, " " 2, 1], [2, 1." " " " output_Y is constructed from B:" " [0. 1.], [1. 1.], [2. 1., " " 0. 1.], [1. 1.], [2. 1.]" "" " output_indices is to map from B to A:" " [1, 0, 2]" " " " output_inverse_indices is to map from A to B:" " [1, 0, 2, 0]" "" " output_counts = [2 1 1]"

Operands:

X: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axis`	`IntegerAttr`	64-bit integer attribute attribute
`sorted`	`IntegerAttr`	64-bit integer attribute attribute

Results:

Y: memref of any type values or tensor of any type values
indices: memref of any type values or tensor of any type values or none type
inverse_indices: memref of any type values or tensor of any type values or none type
counts: memref of any type values or tensor of any type values or none type

onnx.Unsqueeze (ONNXUnsqueezeOp)

ONNX Unsqueeze operation

Description:

"Insert single-dimensional entries to the shape of an input tensor (data)." "Takes one required argument axes - which contains a list of dimension indices and this operator will insert a dimension of value 1 into the corresponding index of the output tensor (expanded)." "" "For example:" " Given an input tensor (data) of shape [3, 4, 5], then" " Unsqueeze(data, axes=[0, 4]) outputs a tensor (expanded) containing same data as data but with shape [1, 3, 4, 5, 1]." "" "The attribute axes should not contain any duplicate entries. It is an error if it contains duplicates." "The rank of the output tensor (output_rank) is the rank of the input tensor (data) plus the number of values in axes." "Each value in axes should be within the (inclusive) range [-output_rank , output_rank - 1]. " "The order of values in axes does not matter and can come in any order. " ""

Operands:

data: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`axes`	`ArrayAttr`	64-bit integer array attribute attribute

Results:

expanded: memref of any type values or tensor of any type values

onnx.Upsample (ONNXUpsampleOp)

ONNX Upsample operation

Description:

"Upsample the input tensor." "Each dimension value of the output tensor is:" " output_dimension = floor(input_dimension * scale)."

Operands:

X: memref of any type values or tensor of any type values
scales: memref of any type values or tensor of any type values

Attributes:

Attribute	MLIR Type	Description
`mode`	`StringAttr`	string attribute attribute

Results:

Y: memref of any type values or tensor of any type values

onnx.Where (ONNXWhereOp)

ONNX Where operation

Description:

"Return elements, either from X or Y, depending on condition" " (with Numpy-style broadcasting support)." " Where behaves like numpy.where with three parameters:" " https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html"

Operands:

condition: memref of any type values or tensor of any type values
X: memref of any type values or tensor of any type values
Y: memref of any type values or tensor of any type values

Attributes:

Results:

output: memref of any type values or tensor of any type values

onnx.Xor (ONNXXorOp)

ONNX Xor operation

Description:

"Returns the tensor resulted from performing the xor 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 the doc."

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Dialect 'onnx' definition

Operation definition

onnx.Abs (ONNXAbsOp)

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onnx.Acos (ONNXAcosOp)

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onnx.Acosh (ONNXAcoshOp)

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onnx.Add (ONNXAddOp)

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onnx.And (ONNXAndOp)

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onnx.ArgMax (ONNXArgMaxOp)

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onnx.ArgMin (ONNXArgMinOp)

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onnx.Asin (ONNXAsinOp)

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onnx.Asinh (ONNXAsinhOp)

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onnx.Atan (ONNXAtanOp)

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onnx.Atanh (ONNXAtanhOp)

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onnx.AveragePool (ONNXAveragePoolOp)

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onnx.BatchNormalization (ONNXBatchNormalizationOp)

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onnx.BatchNormalizationTestMode (ONNXBatchNormalizationTestModeOp)

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onnx.BitShift (ONNXBitShiftOp)

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onnx.Cast (ONNXCastOp)

Description:

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