onnx-mlir

`onnx.Abs` (ONNXAbsOp)

ONNX Abs operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

X tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Operand	Description
`X`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result Description

Y tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Result	Description
`Y`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.Acos` (ONNXAcosOp)

ONNX Acos operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Acosh` (ONNXAcoshOp)

ONNX Acosh operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Adagrad` (ONNXAdagradOp)

ONNX Adagrad operation

Compute one iteration of ADAGRAD, a stochastic gradient based optimization algorithm. This operator can conduct the optimization of multiple tensor variables.

Let's define the behavior of this operator. As you can imagine, ADAGRAD requires
some parameters:

 - The initial learning-rate \"R\".
 - The update count \"T\". That is, the number of training iterations conducted.
 - A L2-norm regularization coefficient \"norm_coefficient\".
 - A learning-rate decay factor \"decay_factor\".
 - A small constant \"epsilon\" to avoid dividing-by-zero.

At each ADAGRAD iteration, the optimized tensors are moved along a direction
computed based on their estimated gradient and accumulated squared gradient. Assume
that only a single tensor \"X\" is updated by this operator. We need the value of \"X\",
its gradient \"G\", and its accumulated squared gradient \"H\". Therefore, variables in
this operator's input list are sequentially \"R\", \"T\", \"X\", \"G\", and \"H\". Other
parameters are given as attributes because they are usually constants. Also, the
corresponding output tensors are the new value of \"X\" (called \"X_new\"), and then
the new accumulated squared gradient (called \"H_new\"). Those outputs are computed
from the given inputs following the pseudo code below.

Let \"+\", \"-\", \"*\", and \"/\" are all element-wise arithmetic operations with
numpy-style broadcasting support. The pseudo code to compute those outputs is:

  // Compute a scalar learning-rate factor. At the first update of X, T is generally
  // 0 (0-based update index) or 1 (1-based update index).
  r = R / (1 + T * decay_factor);

  // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm.
  G_regularized = norm_coefficient * X + G;

  // Compute new accumulated squared gradient.
  H_new = H + G_regularized * G_regularized;

  // Compute the adaptive part of per-coordinate learning rate. Note that Sqrt(...)
  // computes element-wise square-root.
  H_adaptive = Sqrt(H_new) + epsilon

  // Compute the new value of \"X\".
  X_new = X - r * G_regularized / H_adaptive;

If one assign this operators to optimize multiple inputs, for example, \"X_1\" and \"X_2\", the same
pseudo code may be extended to handle all tensors jointly. More specifically, we can view \"X\" as a
concatenation of \"X_1\" and \"X_2\" (of course, their gradient and accumulate gradient should
be concatenated too) and then just reuse the entire pseudo code.

Note that ADAGRAD was first proposed in http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf.
In that reference paper, this operator is a special case of the Figure 1's composite mirror
descent update.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`decay_factor`	::mlir::FloatAttr	32-bit float attribute
`epsilon`	::mlir::FloatAttr	32-bit float attribute
`norm_coefficient`	::mlir::FloatAttr	32-bit float attribute

Operands:

Operand	Description
`R`	tensor of 32-bit float values or tensor of 64-bit float values
`T`	tensor of 64-bit signless integer values
`inputs`	variadic of tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`outputs`	variadic of tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Adam` (ONNXAdamOp)

ONNX Adam operation

Compute one iteration of Adam, a stochastic gradient based optimization algorithm. This operator can conduct the optimization of multiple tensor variables.

Let's define the behavior of this operator. First of all, Adam requires
some parameters:

 - The learning-rate \"R\".
 - The update count \"T\". That is, the number of training iterations conducted.
 - A L2-norm regularization coefficient \"norm_coefficient\".
 - A small constant \"epsilon\" to avoid dividing-by-zero.
 - Two coefficients, \"alpha\" and \"beta\".

At each Adam iteration, the optimized tensors are moved along a direction
computed based on their exponentially-averaged historical gradient and
exponentially-averaged historical squared gradient. Assume that only a tensor
\"X\" is being optimized. The rest of required information is

 - the value of \"X\",
 - \"X\"'s gradient (denoted by \"G\"),
 - \"X\"'s exponentially-averaged historical gradient (denoted by \"V\"), and
 - \"X\"'s exponentially-averaged historical squared gradient (denoted by \"H\").

Some of those parameters are passed into this operator as input tensors and others
are stored as this operator's attributes. Specifically, this operator's input tensor
list is [\"R\", \"T\", \"X\", \"G\", \"V\", \"H\"]. That is, \"R\" is the first input, \"T\" is
the second input, and so on. Other parameters are given as attributes because they
are constants. Moreover, the corresponding output tensors are

 - the new value of \"X\" (called \"X_new\"),
 - the new exponentially-averaged historical gradient (denoted by \"V_new\"), and
 - the new exponentially-averaged historical squared gradient (denoted by \"H_new\").

Those outputs are computed following the pseudo code below.

Let \"+\", \"-\", \"*\", and \"/\" are all element-wise arithmetic operations with
numpy-style broadcasting support. The pseudo code to compute those outputs is:

  // Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm.
  G_regularized = norm_coefficient * X + G

  // Update exponentially-averaged historical gradient.
  V_new = alpha * V + (1 - alpha) * G_regularized

  // Update exponentially-averaged historical squared gradient.
  H_new = beta * H + (1 - beta) * G_regularized * G_regularized

  // Compute the element-wise square-root of H_new. V_new will be element-wisely
  // divided by H_sqrt for a better update direction.
  H_sqrt = Sqrt(H_new) + epsilon

  // Compute learning-rate. Note that \"alpha**T\"/\"beta**T\" is alpha's/beta's T-th power.
  R_adjusted = T > 0 ? R * Sqrt(1 - beta**T) / (1 - alpha**T) : R

  // Compute new value of \"X\".
  X_new = X - R_adjusted * V_new / H_sqrt

  // Post-update regularization.
  X_final = (1 - norm_coefficient_post) * X_new

If there are multiple inputs to be optimized, the pseudo code will be applied
independently to each of them.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`alpha`	::mlir::FloatAttr	32-bit float attribute
`beta`	::mlir::FloatAttr	32-bit float attribute
`epsilon`	::mlir::FloatAttr	32-bit float attribute
`norm_coefficient`	::mlir::FloatAttr	32-bit float attribute
`norm_coefficient_post`	::mlir::FloatAttr	32-bit float attribute

Operands:

Operand	Description
`R`	tensor of 32-bit float values or tensor of 64-bit float values
`T`	tensor of 64-bit signless integer values
`inputs`	variadic of tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`outputs`	variadic of tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Add` (ONNXAddOp)

ONNX Add operation

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.

(Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

A tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

B tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Operand	Description
`A`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`B`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result Description

C tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Result	Description
`C`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.And` (ONNXAndOp)

ONNX And operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`A`	tensor of 1-bit signless integer values
`B`	tensor of 1-bit signless integer values

Results:

Result	Description
`C`	tensor of 1-bit signless integer values

`onnx.ArgMax` (ONNXArgMaxOp)

ONNX ArgMax operation

Computes the indices of the max elements of the input tensor’s element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the max is selected if the max appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	64-bit signed integer attribute
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`select_last_index`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand Description

Operand	Description
`data`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`reduced`	tensor of 64-bit signless integer values

`onnx.ArgMin` (ONNXArgMinOp)

ONNX ArgMin operation

Computes the indices of the min elements of the input tensor’s element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the min is selected if the min appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	64-bit signed integer attribute
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`select_last_index`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand Description

Operand	Description
`data`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`reduced`	tensor of 64-bit signless integer values

`onnx.ArrayFeatureExtractor` (ONNXArrayFeatureExtractorOp)

ONNX ArrayFeatureExtractor operation

Select elements of the input tensor based on the indices passed.
The indices are applied to the last axes of the tensor.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values or tensor of string type values
`Y`	tensor of 64-bit signless integer values

Results:

Result	Description
`Z`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values or tensor of string type values

`onnx.Asin` (ONNXAsinOp)

ONNX Asin operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Asinh` (ONNXAsinhOp)

ONNX Asinh operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Atan` (ONNXAtanOp)

ONNX Atan operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Atanh` (ONNXAtanhOp)

ONNX Atanh operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.AveragePool` (ONNXAveragePoolOp)

ONNX AveragePool operation

AveragePool consumes an input tensor X and applies average pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. average pooling consisting of computing the average on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape is calculated differently depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized. With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d):

 output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)

 output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)

if ceil_mode is enabled. pad_shape[i] is the sum of pads along axis i.

auto_pad is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:

 VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])
 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])

or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D):

 VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1
 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1

And pad shape will be following if SAME_UPPER or SAME_LOWER:

 pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]

The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.BatchNormalizationInferenceMode` (ONNXBatchNormalizationInferenceModeOp)

ONNX BatchNormalization operation in test mode

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.

This operation is not part of the standard and was added to assist onnx-mlir.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`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

Results:

Result	Description
`o_Y`	memref of any type values or tensor of any type values

`onnx.BatchNormalization` (ONNXBatchNormalizationOp)

ONNX BatchNormalization operation

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 five required inputs ‘X’, ‘scale’, ‘B’, ‘input_mean’ and ‘input_var’. Note that ‘input_mean’ and ‘input_var’ are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below:

Output case #1: Y, running_mean, running_var (training_mode=True)
Output case #2: Y (training_mode=False)

When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True:

running_mean = input_mean * momentum + current_mean * (1 - momentum)
running_var = input_var * momentum + current_var * (1 - momentum)

Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B

where:

current_mean = ReduceMean(X, axis=all_except_channel_index)
current_var =  ReduceVar(X, axis=all_except_channel_index)

Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1).

The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs.

When training_mode=False:

Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B

For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * … * 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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`epsilon`	::mlir::FloatAttr	32-bit float attribute
`momentum`	::mlir::FloatAttr	32-bit float attribute
`training_mode`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`scale`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`B`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`input_mean`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`input_var`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`running_mean`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or none type
`running_var`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or none type

`onnx.Bernoulli` (ONNXBernoulliOp)

ONNX Bernoulli operation

Draws binary random numbers (0 or 1) from a Bernoulli distribution. The input tensor should be a tensor containing probabilities p (a value in the range [0,1]) to be used for drawing the binary random number, where an output of 1 is produced with probability p and an output of 0 is produced with probability (1-p).

This operator is non-deterministic and may not produce the same values in different implementations (even if a seed is specified).

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result Description

output tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 1-bit signless integer values

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 1-bit signless integer values

`onnx.Binarizer` (ONNXBinarizerOp)

ONNX Binarizer operation

Maps the values of the input tensor to either 0 or 1, element-wise, based on the outcome of a comparison against a threshold value.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`threshold`	::mlir::FloatAttr	32-bit float attribute

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values

Results:

Result	Description
`Y`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values

`onnx.BitShift` (ONNXBitShiftOp)

ONNX BitShift operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values
`Y`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values

Results:

Result	Description
`Z`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values

`onnx.BitwiseAnd` (ONNXBitwiseAndOp)

ONNX BitwiseAnd operation

Returns the tensor resulting from performing the bitwise and 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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`A`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`B`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result	Description
`C`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

`onnx.BitwiseNot` (ONNXBitwiseNotOp)

ONNX BitwiseNot operation

Returns the bitwise not of the input tensor element-wise.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result	Description
`Y`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

`onnx.BitwiseOr` (ONNXBitwiseOrOp)

ONNX BitwiseOr operation

Returns the tensor resulting from performing the bitwise or 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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`A`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`B`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result	Description
`C`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

`onnx.BitwiseXor` (ONNXBitwiseXorOp)

ONNX BitwiseXor operation

Returns the tensor resulting from performing the bitwise xor 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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`A`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`B`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result	Description
`C`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

`onnx.BlackmanWindow` (ONNXBlackmanWindowOp)

ONNX BlackmanWindow operation

Generates a Blackman window as described in the paper https://ieeexplore.ieee.org/document/1455106.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`output_datatype`	::mlir::IntegerAttr	64-bit signed integer attribute
`periodic`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`size`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result Description

`onnx.CastLike` (ONNXCastLikeOp)

ONNX CastLike operation

The operator casts the elements of a given input tensor (the first input) to the same data type as the elements of the second input tensor. See documentation of the Cast operator for further details.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`saturate`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand Description

input tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 1-bit signless integer values or tensor of string type values or tensor of bfloat16 type values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values

target_type tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 1-bit signless integer values or tensor of string type values or tensor of bfloat16 type values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values

Results:

Result Description

output tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 1-bit signless integer values or tensor of string type values or tensor of bfloat16 type values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values

`onnx.CastMap` (ONNXCastMapOp)

ONNX CastMap operation

Converts a map to a tensor.
The map key must be an int64 and the values will be ordered in ascending order based on this key.
The operator supports dense packing or sparse packing. If using sparse packing, the key cannot exceed the max_map-1 value.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`cast_to`	::mlir::StringAttr	string attribute
`map_form`	::mlir::StringAttr	string attribute
`max_map`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`X`	tuple with any combination of 64-bit signless integer or string type values or tuple with any combination of 64-bit signless integer or 32-bit float values

Results:

Result	Description
`Y`	tensor of string type values or tensor of 32-bit float values or tensor of 64-bit signless integer values

`onnx.Cast` (ONNXCastOp)

ONNX Cast operation

The operator casts the elements of a given input tensor to a data type specified by the ‘to’ argument and returns an output tensor of the same size in the converted type. The ‘to’ argument must be one of the data types specified in the ‘DataType’ enum field in the TensorProto message.

Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations (e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may yield result 100. There are some string literals reserved for special floating-point values; "+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively. Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly, this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors to string tensors, plain floating-point representation (such as "314.15926") would be used. Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior.

Conversion from a numerical type to any numerical type is always allowed. User must be aware of precision loss and value change caused by range difference between two types. For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting an integer 36 to Boolean may produce 1 because we truncate bits which can’t be stored in the targeted type.

In more detail, the conversion among numerical types should follow these rules if the destination type is not a float 8 type.

Casting from floating point to:
- floating point: +/- infinity if OOR (out of range).
- fixed point: undefined if OOR.
- bool: +/- 0.0 to False; all else to True.
Casting from fixed point to:
- floating point: +/- infinity if OOR. (+ infinity in the case of uint)
- fixed point: when OOR, discard higher bits and reinterpret (with respect to two’s complement representation for signed types). For example, 200 (int16) -> -56 (int8).
- bool: zero to False; nonzero to True.
Casting from bool to:
- floating point: {1.0, 0.0}.
- fixed point: {1, 0}.
- bool: no change.

Float 8 type were introduced to speed up the training of deep models. By default the conversion of a float x obeys to the following rules. [x] means the value rounded to the target mantissa width.

x	E4M3FN	E4M3FNUZ	E5M2	E5M2FNUZ
0	0	0	0	0
-0	-0	0	-0	0
NaN	NaN	NaN	NaN	NaN
+/- Inf	+/- FLT_MAX	NaN	FLT_MAX	NaN
[x] > FLT_MAX	FLT_MAX	FLT_MAX	FLT_MAX	FLT_MAX
[x] < -FLT_MAX	-FLT_MAX	-FLT_MAX	-FLT_MAX	-FLT_MAX
else	RNE	RNE	RNE	RNE

The behavior changes if the parameter ‘saturate’ is set to False. The rules then become:

x	E4M3FN	E4M3FNUZ	E5M2	E5M2FNUZ
0	0	0	0	0
-0	-0	0	-0	0
NaN	NaN	NaN	NaN	NaN
+/- Inf	NaN	NaN	+/- Inf	NaN
[x] > FLT_MAX	NaN	NaN	Inf	NaN
[x] < -FLT_MAX	NaN	NaN	-Inf	NaN
else	RNE	RNE	RNE	RNE

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ResultTypeInferenceOpInterface, ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`saturate`	::mlir::IntegerAttr	64-bit signed integer attribute
`to`	::mlir::TypeAttr	any type attribute

Operands:

Operand Description

Results:

Result Description

`onnx.CategoryMapper` (ONNXCategoryMapperOp)

ONNX CategoryMapper operation

Converts strings to integers and vice versa.
Two sequences of equal length are used to map between integers and strings, with strings and integers at the same index detailing the mapping.
Each operator converts either integers to strings or strings to integers, depending on which default value attribute is provided. Only one default value attribute should be defined.
If the string default value is set, it will convert integers to strings. If the int default value is set, it will convert strings to integers.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`cats_int64s`	::mlir::ArrayAttr	64-bit integer array attribute
`cats_strings`	::mlir::ArrayAttr	string array attribute
`default_int64`	::mlir::IntegerAttr	64-bit signed integer attribute
`default_string`	::mlir::StringAttr	string attribute

Operands:

Operand	Description
`X`	tensor of string type values or tensor of 64-bit signless integer values

Results:

Result	Description
`Y`	tensor of string type values or tensor of 64-bit signless integer values

`onnx.Ceil` (ONNXCeilOp)

ONNX Ceil operation

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. If x is integral, +0, -0, NaN, or infinite, x itself is returned.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.Celu` (ONNXCeluOp)

ONNX Celu operation

Continuously Differentiable Exponential Linear Units: Perform the linear unit element-wise on the input tensor X using formula:

max(0,x) + min(0,alpha*(exp(x/alpha)-1))

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 32-bit float values

Results:

Result	Description
`Y`	tensor of 32-bit float values

`onnx.CenterCropPad` (ONNXCenterCropPadOp)

ONNX CenterCropPad operation

Center crop or pad an input to given dimensions.

The crop/pad dimensions can be specified for a subset of the axes. Non-specified dimensions will not be cropped or padded.

If the input dimensions are bigger than the crop shape, a centered cropping window is extracted from the input. If the input dimensions are smaller than the crop shape, the input is padded on each side equally, so that the input is centered in the output.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

input_data tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

shape tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result Description

output_data tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.Clip` (ONNXClipOp)

ONNX Clip operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`min`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or none type
`max`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or none type

Results:

Result Description

`onnx.ClipV11` (ONNXClipV11Op)

ONNX Clip operation

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`min`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`max`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.ClipV12` (ONNXClipV12Op)

ONNX Clip operation

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`min`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`max`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

Results:

Result Description

`onnx.ClipV6` (ONNXClipV6Op)

ONNX Clip operation

Clip operator limits the given input within an interval. The interval is specified with arguments ‘min’ and ‘max’. They default to numeric_limits::lowest() and numeric_limits::max() respectively.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`max`	::mlir::FloatAttr	32-bit float attribute
`min`	::mlir::FloatAttr	32-bit float attribute

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Col2Im` (ONNXCol2ImOp)

ONNX Col2Im operation

The operator rearranges column blocks back into a multidimensional image

Col2Im behaves similarly to PyTorch’s fold https://pytorch.org/docs/stable/generated/torch.nn.Fold.html, but it only supports batched multi-dimensional image tensors. Another implementation in Python with N-dimension support can be found at https://github.com/f-dangel/unfoldNd/.

NOTE: Although specifying image_shape looks redundant because it could be calculated from convolution formulas, it is required as input for more advanced scenarios as explained at PyTorch’s implementation (https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Col2Im.cpp#L10)

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`dilations`	::mlir::ArrayAttr	64-bit integer array attribute
`pads`	::mlir::ArrayAttr	64-bit integer array attribute
`strides`	::mlir::ArrayAttr	64-bit integer array attribute

Operands:

Operand	Description
`input`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`image_shape`	tensor of 64-bit signless integer values
`block_shape`	tensor of 64-bit signless integer values

Results:

Result Description

`onnx.Compress` (ONNXCompressOp)

ONNX Compress operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

condition tensor of 1-bit signless integer values

Results:

Result Description

`onnx.ConcatFromSequence` (ONNXConcatFromSequenceOp)

ONNX ConcatFromSequence operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

input_sequence SeqType of tensor of 8-bit unsigned integer values values or SeqType of tensor of 16-bit unsigned integer values values or SeqType of tensor of 32-bit unsigned integer values values or SeqType of tensor of 64-bit unsigned integer values values or SeqType of tensor of 8-bit signless integer values values or SeqType of tensor of 16-bit signless integer values values or SeqType of tensor of 32-bit signless integer values values or SeqType of tensor of 64-bit signless integer values values or SeqType of tensor of 16-bit float values values or SeqType of tensor of 32-bit float values values or SeqType of tensor of 64-bit float values values or SeqType of tensor of string type values values or SeqType of tensor of 1-bit signless integer values values or SeqType of tensor of complex type with 32-bit float elements values values or SeqType of tensor of complex type with 64-bit float elements values values

Results:

Result Description

concat_result tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.Concat` (ONNXConcatOp)

ONNX Concat operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

inputs variadic of tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

Results:

Result Description

concat_result tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.ConcatShapeTranspose` (ONNXConcatShapeTransposeOp)

ONNX merged operation

Merge the following sequence of ops into one op v1 = onnx.concat v2 = onnx.shape(v1) v3 = onnx.transpose(v1)

This operation is not part of the standard and was added to assist onnx-mlir.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	64-bit signed integer attribute
`end`	::mlir::IntegerAttr	64-bit signed integer attribute
`start`	::mlir::IntegerAttr	64-bit signed integer attribute
`perm`	::mlir::ArrayAttr	64-bit integer array attribute

Operands:

Operand Description

Results:

Result Description

shape tensor of 64-bit signless integer values

transposed tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.ConstantOfShape` (ONNXConstantOfShapeOp)

ONNX ConstantOfShape operation

Generate a tensor with given value and shape.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ResultTypeInferenceOpInterface, ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`input`	tensor of 64-bit signless integer values

Results:

Result Description

output tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 1-bit signless integer values or tensor of bfloat16 type values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values

`onnx.Constant` (ONNXConstantOp)

ONNX Constant operation

This operator produces a constant tensor. Exactly one of the provided attributes, either value, sparse_value, or value_* must be specified.

Traits: AlwaysSpeculatableImplTrait, ConstantLike

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ResultTypeInferenceOpInterface, ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`sparse_value`	::mlir::Attribute	any attribute
`value`	::mlir::Attribute	any attribute
`value_float`	::mlir::FloatAttr	32-bit float attribute
`value_floats`	::mlir::ArrayAttr	32-bit float array attribute
`value_int`	::mlir::IntegerAttr	64-bit signed integer attribute
`value_ints`	::mlir::ArrayAttr	64-bit integer array attribute
`value_string`	::mlir::StringAttr	string attribute
`value_strings`	::mlir::ArrayAttr	string array attribute

Results:

Result Description

`onnx.ConvInteger` (ONNXConvIntegerOp)

ONNX ConvInteger operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`x`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`w`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`x_zero_point`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values or none type
`w_zero_point`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values or none type

Results:

Result	Description
`y`	tensor of 32-bit signless integer values

`onnx.Conv` (ONNXConvOp)

ONNX Conv operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`W`	tensor of 16-bit float values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`B`	tensor of 16-bit float values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.ConvTranspose` (ONNXConvTransposeOp)

ONNX ConvTranspose operation

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).

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`W`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`B`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Cos` (ONNXCosOp)

ONNX Cos operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Cosh` (ONNXCoshOp)

ONNX Cosh operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.CumSum` (ONNXCumSumOp)

ONNX CumSum operation

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]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`x`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`axis`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result	Description
`y`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.Custom` (ONNXCustomOp)

ONNX Custom operation

CustomOp is not an Op defined in onnx standard and was added to support extention of Op that can be transformed or finally call a user-defined external function.”

It allows for calling a user-defined operation, with a single required attribute being a string that names the operation. Other inputs are passed to the user operation.

The number of inputs and outputs can vary.

NoneType is allowed for both input and output, as the CustomOp may require a fixed number of inputs/outputs for the external function call.

In addition to the values passed to the user-defined operation, certain attributes are introduced to facilitate the analysis and transformation of CustomOp.

Since the compiler does not define the semantics of CustomOp, onnx-mlir cannot infer the shape of its output. Consequently, specific attributes are introduced to specify how shape inference should be performed on a CustomOp. These attributes are: ‘inputs_for_infer’: Optional. The index of inputs used for shape inference. The value of index should be [0, the number of inputs). If not specified, all the inputs of the CustomOp will be used for shape inference. ‘shape_infer_pattern’: Optional. Specify how to propagate the shape info from the inputs (may be limited by inputs_for_infer) to output. Current supported patterns are SameAs, MDBroadcast. ‘output_element_type’: Optional. The element type for the output tensor. If not specified, follow the shape infer pattern behavior. Usually the element type of the first input is used. Each instance of CustomOp can have its own attributes for shape inference, allowing for customization. However, CustomOps with the same function_name typically behave similarly in terms of shape inference, and therefore have the same attributes.

The existing shape inference patterns for ONNX ops are reused for CustomOp, with the polymorphism in shape inference based on its attribute values. Due to the current implementation for ONNX Ops, a CustomOp with specified shape inference attributes supports only a single output, rather than variadic outputs.

When attributes for shape inference are not provided, the shape inference for CustomOp will simply pass through.

All of these additional attributes are optional, designed to be less intrusive. The .mlir file can remain the same when a new attribute is added.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`function_name`	::mlir::StringAttr	string attribute
`output_element_type`	::mlir::TypeAttr	any type attribute
`shape_infer_pattern`	::mlir::StringAttr	string attribute
`inputs_for_infer`	::mlir::ArrayAttr	64-bit integer array attribute

Operands:

Operand	Description
`inputs`	variadic of tensor of any type values or memref of any type values or none type

Results:

Result	Description
`outputs`	variadic of tensor of any type values or memref of any type values or none type

`onnx.DFT` (ONNXDFTOp)

ONNX DFT operation

Computes the discrete Fourier Transform (DFT) of the input.

Assuming the input has shape [M, N], where N is the dimension over which the DFT is computed and M denotes the conceptual "all other dimensions," the DFT y[m, k] of shape [M, N] is defined as

\[y[m, k] = \sum_{n=0}^{N-1} e^{-2 \pi j \frac{k n}{N} } x[m, n] ,\]

and the inverse transform is defined as

\[x[m, n] = \frac{1}{N} \sum_{k=0}^{N-1} e^{2 \pi j \frac{k n}{N} } y[m, k] ,\]

where $j$ is the imaginary unit.

The actual shape of the output is specified in the "output" section.

Reference: https://docs.scipy.org/doc/scipy/tutorial/fft.html

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`inverse`	::mlir::IntegerAttr	64-bit signed integer attribute
`onesided`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`input`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`dft_length`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or none type
`axis`	tensor of 64-bit signless integer values or none type

Results:

Result	Description
`output`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.DFTV17` (ONNXDFTV17Op)

ONNX DFT operation

Computes the discrete Fourier transform of input.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	64-bit signed integer attribute
`inverse`	::mlir::IntegerAttr	64-bit signed integer attribute
`onesided`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`dft_length`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or none type

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.DeformConv` (ONNXDeformConvOp)

ONNX DeformConv operation

Performs deformable convolution as described in https://arxiv.org/abs/1703.06211 and https://arxiv.org/abs/1811.11168. This operator specification supports the general N-D case. Note that most common use cases have 2D or 3D data.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`dilations`	::mlir::ArrayAttr	64-bit integer array attribute
`group`	::mlir::IntegerAttr	64-bit signed integer attribute
`kernel_shape`	::mlir::ArrayAttr	64-bit integer array attribute
`offset_group`	::mlir::IntegerAttr	64-bit signed integer attribute
`pads`	::mlir::ArrayAttr	64-bit integer array attribute
`strides`	::mlir::ArrayAttr	64-bit integer array attribute

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`W`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`offset`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`B`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`mask`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.DepthToSpace` (ONNXDepthToSpaceOp)

ONNX DepthToSpace operation

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 // (blocksize**2), h, w])
tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
y = np.reshape(tmp, [b, c // (blocksize**2), 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])

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

Results:

Result Description

`onnx.DequantizeLinear` (ONNXDequantizeLinearOp)

ONNX DequantizeLinear operation

The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor. The dequantization formula is y = (x - x_zero_point) * x_scale. x_scale and x_zero_point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. x_zero_point and x must have same type. x and y must have same shape. In the case of dequantizing int32, there’s no zero point (zero point is supposed to be 0). zero-point is usually not used in the case of float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz quantization, but the dequantization formula remains the same for consistency and ‘x_scale’ still determines the output type.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`x`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values
`x_scale`	tensor of 32-bit float values or tensor of 16-bit float values or tensor of bfloat16 type values
`x_zero_point`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values or none type

Results:

Result	Description
`y`	tensor of 32-bit float values or tensor of 16-bit float values or tensor of bfloat16 type values

`onnx.Det` (ONNXDetOp)

ONNX Det operation

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: []).

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.DictVectorizer` (ONNXDictVectorizerOp)

ONNX DictVectorizer operation

Uses an index mapping to convert a dictionary to an array.
Given a dictionary, each key is looked up in the vocabulary attribute corresponding to the key type. The index into the vocabulary array at which the key is found is then used to index the output 1-D tensor ‘Y’ and insert into it the value found in the dictionary ‘X’.
The key type of the input map must correspond to the element type of the defined vocabulary attribute. Therefore, the output array will be equal in length to the index mapping vector parameter. All keys in the input dictionary must be present in the index mapping vector. For each item in the input dictionary, insert its value in the output array. Any keys not present in the input dictionary, will be zero in the output array.
For example: if the string_vocabulary parameter is set to [\"a\", \"c\", \"b\", \"z\"], then an input of {\"a\": 4, \"c\": 8} will produce an output of [4, 8, 0, 0].

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`int64_vocabulary`	::mlir::ArrayAttr	64-bit integer array attribute
`string_vocabulary`	::mlir::ArrayAttr	string array attribute

Operands:

Operand Description

X tuple with any combination of string type or 64-bit signless integer values or tuple with any combination of 64-bit signless integer or string type values or tuple with any combination of 64-bit signless integer or 32-bit float values or tuple with any combination of 64-bit signless integer or 64-bit float values or tuple with any combination of string type or 32-bit float values or tuple with any combination of string type or 64-bit float values

Results:

Result	Description
`Y`	tensor of 64-bit signless integer values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values

`onnx.DimGroup` (ONNXDimGroupOp)

ONNX dimension group operation.

This operation is to link a compile-time unknown dimension of a Tensor to a group id. Two dimensions that have the same group id are expected to be equal at runtime.

"onnx.DimGroup"(%tensor) {axis = 0 : si64, group_id = 1: si64} : (tensor<?x3x5xf32>) -> ()

axis identifies the dimension position in the tensor.

group_id identifies the group id of the dimension. It is non-negative. Value -1 for group_id means the dimension does not belong to any group.

This operation is currently used in the pass --onnx-dim-analysis for testing the unknown dimension analysis class.

This operation is not part of the standard and was added to assist onnx-mlir.

Attributes:

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	64-bit signed integer attribute
`group_id`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand Description

data tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.Dim` (ONNXDimOp)

ONNX dimensions operation.

This operation is to obtain the dimension of a Tensor;

"onnx.Dim"(%tensor) {axis = 0 : si64} : (tensor<?x3x5xf32>) -> tensor<1xi64>

The axis identifies the dimension within the shape which is going to be obtained.

This operation is not part of the standard and was added to assist onnx-mlir.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

Results:

Result	Description
`dim`	tensor of 64-bit signless integer values

`onnx.Div` (ONNXDivOp)

ONNX Div operation

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.

(Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result Description

`onnx.Dropout` (ONNXDropoutOp)

ONNX Dropout operation

Dropout takes an input floating-point tensor, an optional input ratio (floating-point scalar) and an optional input training_mode (boolean scalar). It produces two tensor outputs, output (floating-point tensor) and mask (optional Tensor<bool>). If training_mode is true then the output Y will be a random dropout; Note that this Dropout scales the masked input data by the following equation, so to convert the trained model into inference mode, the user can simply not pass training_mode input or set it to false.

output = scale * data * mask,

where

scale = 1. / (1. - ratio).

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`seed`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`data`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`ratio`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`training_mode`	tensor of 1-bit signless integer values or none type

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`mask`	tensor of 1-bit signless integer values or none type

`onnx.DynamicQuantizeLinear` (ONNXDynamicQuantizeLinearOp)

ONNX DynamicQuantizeLinear operation

A Function to fuse calculation for Scale, Zero Point and FP32->8Bit conversion of FP32 Input data. Outputs Scale, ZeroPoint and Quantized Input for a given FP32 Input. Scale is calculated as:

y_scale = (maximum(0, max(x)) - minimum(0, 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)/y_scale
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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`x`	tensor of 32-bit float values

Results:

Result	Description
`y`	tensor of 8-bit unsigned integer values
`y_scale`	tensor of 32-bit float values
`y_zero_point`	tensor of 8-bit unsigned integer values

`onnx.Einsum` (ONNXEinsumOp)

ONNX Einsum operation

An einsum of the form term1, term2 -> output-term produces an output tensor using the following equation

output[output-term] = reduce-sum( input1[term1] * input2[term2] )

where the reduce-sum performs a summation over all the indices occurring in the input terms (term1, term2) that do not occur in the output-term.

The Einsum operator evaluates algebraic tensor operations on a sequence of tensors, using the Einstein summation convention. The equation string contains a comma-separated sequence of lower case letters. Each term corresponds to an operand tensor, and the characters within the terms correspond to operands dimensions.

This sequence may be followed by "->" to separate the left and right hand side of the equation. If the equation contains "->" followed by the right-hand side, the explicit (not classical) form of the Einstein summation is performed, and the right-hand side indices indicate output tensor dimensions. In other cases, output indices are (implicitly) set to the alphabetically sorted sequence of indices appearing exactly once in the equation.

When a dimension character is repeated in the left-hand side, it represents summation along the dimension.

The equation may contain ellipsis ("…") to enable broadcasting. Ellipsis must indicate a fixed number of dimensions. Specifically, every occurrence of ellipsis in the equation must represent the same number of dimensions. The right-hand side may contain exactly one ellipsis. In implicit mode, the ellipsis dimensions are set to the beginning of the output. The equation string may contain space (U+0020) character.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`equation`	::mlir::StringAttr	string attribute

Operands:

Operand Description

Inputs variadic of tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result Description

Output tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Elu` (ONNXEluOp)

ONNX Elu operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.EntryPoint` (ONNXEntryPointOp)

Indicate ONNX entry point

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

This operation is not part of the standard and was added to assist onnx-mlir.

Attributes:

Attribute	MLIR Type	Description
`func`	::mlir::SymbolRefAttr	symbol reference attribute

`onnx.Equal` (ONNXEqualOp)

ONNX Equal operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

A tensor of 1-bit signless integer values or tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of string type values

B tensor of 1-bit signless integer values or tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of string type values

Results:

Result	Description
`C`	tensor of 1-bit signless integer values

`onnx.Erf` (ONNXErfOp)

ONNX Erf operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result Description

`onnx.Exp` (ONNXExpOp)

ONNX Exp operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Expand` (ONNXExpandOp)

ONNX Expand operation

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 dimensions 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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

shape tensor of 64-bit signless integer values

Results:

Result Description

`onnx.EyeLike` (ONNXEyeLikeOp)

ONNX EyeLike operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

Results:

Result Description

`onnx.FeatureVectorizer` (ONNXFeatureVectorizerOp)

ONNX FeatureVectorizer operation

Concatenates input tensors into one continuous output.
All input shapes are 2-D and are concatenated along the second dimension. 1-D tensors are treated as [1,C]. Inputs are copied to the output maintaining the order of the input arguments.
All inputs must be integers or floats, while the output will be all floating point values.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`inputdimensions`	::mlir::ArrayAttr	64-bit integer array attribute

Operands:

Operand	Description
`X`	variadic of tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 32-bit float values

`onnx.Flatten` (ONNXFlattenOp)

ONNX Flatten operation

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).

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

Results:

Result Description

`onnx.Floor` (ONNXFloorOp)

ONNX Floor operation

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. If x is integral, +0, -0, NaN, or infinite, x itself is returned.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.GRU` (ONNXGRUOp)

ONNX GRU operation

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) - alpha * x + beta
LeakyRelu(x) - x if x >= 0 else alpha * x
ThresholdedRelu(x) - x if x >= alpha else 0
ScaledTanh(x) - alpha * Tanh(beta * x)
HardSigmoid(x) - min(max(alpha * x + 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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`W`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`R`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`B`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`sequence_lens`	tensor of 32-bit signless integer values or none type
`initial_h`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`Y_h`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

`onnx.GatherElements` (ONNXGatherElementsOp)

ONNX GatherElements operation

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],
]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

indices tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result Description

`onnx.GatherND` (ONNXGatherNDOp)

ONNX GatherND operation

Given data tensor of rank r >= 1, indices tensor of rank q >= 1, and batch_dims integer b, this operator gathers slices of data into an output tensor of rank q + r - indices_shape[-1] - 1 - b.

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

batch_dims (denoted as b) is an integer indicating the number of batch dimensions, i.e the leading b number of dimensions of data tensor and indices are representing the batches, and the gather starts from the b+1 dimension.

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 first b dimensions of the shape of indices tensor and data tensor must be equal.

3) b < min(q, r) is to be honored.

4) The indices_shape[-1] should have a value between 1 (inclusive) and rank r-b (inclusive)

5) 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-b => error condition

2) If indices_shape[-1] == r-b, since the rank of indices is q, indices can be thought of as N (q-b-1)-dimensional tensors containing 1-D tensors of dimension r-b, where N is an integer equals to the product of 1 and all the elements in the batch dimensions of the indices_shape. Let us think of each such r-b ranked tensor as indices_slice. Each scalar value corresponding to data[0:b-1,indices_slice] is filled into the corresponding location of the (q-b-1)-dimensional tensor to form the output tensor (Example 1 below)

3) If indices_shape[-1] < r-b, since the rank of indices is q, indices can be thought of as N (q-b-1)-dimensional tensor containing 1-D tensors of dimension < r-b. Let us think of each such tensors as indices_slice. Each tensor slice corresponding to data[0:b-1, indices_slice , :] is filled into the corresponding location of the (q-b-1)-dimensional tensor to form the output tensor (Examples 2, 3, 4 and 5 below)

This operator is the inverse of ScatterND.

Example 1

batch_dims = 0
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

batch_dims = 0
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

batch_dims = 0
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

batch_dims = 0
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]

Example 5

batch_dims = 1
data    = [[[0,1],[2,3]],[[4,5],[6,7]]] # data_shape    = [2, 2, 2]
indices = [[1],[0]]                     # indices_shape = [2, 1]
output  = [[2,3],[4,5]]                 # output_shape  = [2, 2]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`batch_dims`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand Description

indices tensor of 64-bit signless integer values

Results:

Result Description

`onnx.Gather` (ONNXGatherOp)

ONNX Gather operation

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).

If 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],
    ],
]

If axis = 1, let k = indices[i_{0}, ..., i_{q-1\}\] then output[j_{0}, i_{0}, ..., i_{q-1}, j_{1}, ..., 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]],
]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

indices tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result Description

`onnx.Gelu` (ONNXGeluOp)

ONNX Gelu operation

Gelu takes one input data (Tensor) and produces one output data (Tensor) where the gaussian error linear units function, $y = 0.5 * x * (1 + erf(x/sqrt(2)))$ is applied to the tensor elementwise. If the attribute \"approximate\" is set to \"tanh\", the function estimation, $y = 0.5 * x * (1 + Tanh(sqrt(2/\pi) * (x + 0.044715 * x^3)))$ is used and applied to the tensor elementwise.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`approximate`	::mlir::StringAttr	string attribute

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.Gemm` (ONNXGemmOp)

ONNX Gemm operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`A`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values
`B`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values
`C`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or none type

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values

`onnx.GlobalAveragePool` (ONNXGlobalAveragePoolOp)

ONNX GlobalAveragePool operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.GlobalLpPool` (ONNXGlobalLpPoolOp)

ONNX GlobalLpPool operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.GlobalMaxPool` (ONNXGlobalMaxPoolOp)

ONNX GlobalMaxPool operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Gradient` (ONNXGradientOp)

ONNX Gradient operation

Gradient operator computes the partial derivatives of a specific tensor w.r.t. some other tensors. This operator is widely used in gradient-based training algorithms. To illustrate its use, let’s consider a computation graph,

X -----.
       |
       v
W --> Conv --> H --> Gemm --> Y
                      ^
                      |
                      Z

, where W and Z are trainable tensors. Note that operators’ attributes are omitted for the sake of simplicity. Let dY/dW (dY/dZ) be the gradient of Y with respect to W (Z). The user can compute gradient by inserting Gradient operator to form another graph shown below.

W --> Conv --> H --> Gemm --> Y
|      ^              ^
|      |              |
|      X              Z
|      |              |
|      |   .----------'
|      |   |  (W/Z/X is the 1st/2nd/3rd input of Gradient as shown in
|      |   |   \"xs\" followed by \"zs\")
|      v   v
'---> Gradient(xs=[\"W\", \"Z\"], zs=[\"X\"], y=\"Y\")
       |   |
       |   '-----------------------------------> dY/dW (1st output of Gradient)
       |
       '---------------------------------------> dY/dZ (2nd output of Gradient)

By definition, the tensor "y" is a function of independent variables in "xs" and "zs". Since we only compute the gradient of "y" w.r.t. the differentiable variables in "xs", this Gradient only outputs dY/dW and dY/dZ. Note that "H" cannot appear in "xs" and "zs". The reason is that "H" can be determined by tensors "W" and "X" and therefore "H" is not an independent variable.

All outputs are optional. If needed, for example, user can assign an empty string to the 1st output name of that Gradient to skip the generation of dY/dW. Note that the concept of optional outputs can also be found in ONNX’s RNN, GRU, and LSTM.

Gradient operator can compute derivative against intermediate tensors. For example, the gradient of Y with respect to H can be done via

W --> Conv --> H --> Gemm --> Y
       ^       |      ^
       |       |      |
       X       |      Z
       .-------'      |
       |   .----------'
       |   | (H/Z is the 1st/2nd input of Gradient as shown in \"xs\")
       v   v
      Gradient(xs=[\"H\", \"Z\"], y=\"Y\")
       |   |
       |   '-----------------------------------> dY/dH (1st output of Gradient)
       |
       '---------------------------------------> dY/dZ (2nd output of Gradient)

It is possible to represent high-order differentiation using Gradient operators. For example, given the following linear model:

W --> Gemm --> Y --> Loss --> O
       ^              ^
       |              |
       X              L

To compute the 2nd order derivative of O with respect to W (denoted by d^2O/dW^2), one can do

W --> Gemm --> Y --> Loss --> O
|      ^              ^
|      |              |
|      X .------------L
|      | |            |
|      | |            v
+------+-+> Gradient(xs=[\"X\", \"W\"], zs=[\"L\"], y=\"O\") ---> dO/dX (1st output of Gradient)
|      | |    |
|      | |    '---> dO/dW (2nd output of Gradient)
|      v v
'---> Gradient(xs=[\"X\", \"W\"], zs=[\"L\"], y=\"dO/dW\") ---> d(dO/dW)dX (1st output of
       |                                                  Gradient)
       |
       |
       '---> d^2O/dW^2 (2nd output of Gradient)

The tensors named in attributes "xs", "zs", and "y" define the differentiated computation graph, and the inputs to Gradient node define the values at which the gradient is computed. We can feed different tensors to the identified graph. For example, one can compute the gradient of Y with respect to H at a specific value of H, H_1, by providing that value as an input to the Gradient node.

W --> Conv --> H --> Gemm --> Y
       ^              ^
       |              |
       X              Z

          Z_1 (2nd input of Gradient)
           |
           v
H_1 --> Gradient(xs=[\"H\", \"Z\"], y=\"Y\") ---> dY/dH when H = H_1 and Y = Y_1.
           |
           '------------------------------> dY/dZ (2nd output of Gradient)

When the inputs of Gradient are the tensors named in "xs" and "zs", the computation can be optimized. More specifically, intermediate variables in forward pass can be reused if the gradient is computed via reverse-mode auto-differentiation.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`xs`	::mlir::ArrayAttr	string array attribute
`y`	::mlir::StringAttr	string attribute
`zs`	::mlir::ArrayAttr	string array attribute

Operands:

Operand Description

Results:

Result	Description
`Outputs`	variadic of tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Greater` (ONNXGreaterOp)

ONNX Greater operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result	Description
`C`	tensor of 1-bit signless integer values

`onnx.GreaterOrEqual` (ONNXGreaterOrEqualOp)

ONNX GreaterOrEqual operation

Returns the tensor resulted from performing the greater_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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result	Description
`C`	tensor of 1-bit signless integer values

`onnx.GridSample` (ONNXGridSampleOp)

ONNX GridSample operation

Given an input X and a flow-field grid, computes the output Y using X values and pixel locations from grid. Currently, only spatial (4-D) inputs are supported. For input X with shape (N, C, H, W) and grid with shape (N, H_out, W_out, 2), the output Y will have shape (N, C, H_out, W_out).

The tensor X contains values at centers of square pixels in a H by W 2-dimensional image. The tensor grid describes normalized positions where the output Y is to be computed using a specified interpolation method (the mode) and a padding mode (for grid positions falling outside the 2-dimensional image).

Elements in grid[N, H_out, W_out] are size-2 vectors specifying positions in the 2-dimensional space of X. They are used to interpolate output values of Y[N, C, H_out, W_out].

The GridSample operator is often used in doing grid generator and sampler in the Spatial Transformer Networks. See also in torch.nn.functional.grid_sample.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`align_corners`	::mlir::IntegerAttr	64-bit signed integer attribute
`mode`	::mlir::StringAttr	string attribute
`padding_mode`	::mlir::StringAttr	string attribute

Operands:

Operand Description

grid tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result Description

`onnx.GroupNormalization` (ONNXGroupNormalizationOp)

ONNX GroupNormalization operation

A GroupNormalization function. Carries out group normalization as described in the paper https://arxiv.org/abs/1803.08494

This operator transforms input according to

y = scale * (x - mean) / sqrt(variance + epsilon) + bias,

where the mean and variance are computed per instance per group of channels, and scale and bias should be specified for each group of channels. The number of groups num_groups should be divisible by the number of channels so that there are an equal number of channels per group.

The overall computation has two stages: the first stage normalizes the elements to have zero mean and unit variance for each instance in each group, and the second stage scales and shifts the results of the first stage. The floating-point precision used in the first stage is determined by the stash_type attribute. For example, if stash_type is 1, the operator casts all input variables to 32-bit float, performs the computation, and finally casts the normalized results back to the original type of X. The second stage does not depend on stash_type.

When the number of groups is the same as the number of channels, this operator is equivalent to InstanceNormalization. When there is only one group, this operator is equivalent to LayerNormalization.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`epsilon`	::mlir::FloatAttr	32-bit float attribute
`num_groups`	::mlir::IntegerAttr	64-bit signed integer attribute
`stash_type`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`X`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`scale`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`bias`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.GroupNormalizationV18` (ONNXGroupNormalizationV18Op)

ONNX GroupNormalization operation

A GroupNormalization function. Carries out group normalization as described in the paper https://arxiv.org/abs/1803.08494

This operator transforms input according to

y = scale * (x - mean) / sqrt(variance + epsilon) + bias,

When the number of groups is the same as the number of channels, this operator is equivalent to InstanceNormalization. When there is only one group, this operator is equivalent to LayerNormalization.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`epsilon`	::mlir::FloatAttr	32-bit float attribute
`num_groups`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`scale`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`bias`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.HammingWindow` (ONNXHammingWindowOp)

ONNX HammingWindow operation

Generates a Hamming window as described in the paper https://ieeexplore.ieee.org/document/1455106.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`output_datatype`	::mlir::IntegerAttr	64-bit signed integer attribute
`periodic`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`size`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result Description

`onnx.HannWindow` (ONNXHannWindowOp)

ONNX HannWindow operation

Generates a Hann window as described in the paper https://ieeexplore.ieee.org/document/1455106.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`output_datatype`	::mlir::IntegerAttr	64-bit signed integer attribute
`periodic`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`size`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result Description

`onnx.HardSigmoid` (ONNXHardSigmoidOp)

ONNX HardSigmoid operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.HardSwish` (ONNXHardSwishOp)

ONNX HardSwish operation

HardSwish takes one input data (Tensor) and produces one output data (Tensor) where the HardSwish function, y = x * max(0, min(1, alpha * x + beta)) = x * HardSigmoid<alpha, beta>(x), where alpha = 1/6 and beta = 0.5, is applied to the tensor elementwise.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Hardmax` (ONNXHardmaxOp)

ONNX Hardmax operation

The operator computes the hardmax values for the given input:

Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwise

The "axis" attribute indicates the dimension along which Hardmax will be performed. The output tensor has the same shape and contains the Hardmax values of the corresponding input.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.Identity` (ONNXIdentityOp)

ONNX Identity operation

Identity operator

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

input tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values or SeqType of tensor of 8-bit unsigned integer values values or SeqType of tensor of 16-bit unsigned integer values values or SeqType of tensor of 32-bit unsigned integer values values or SeqType of tensor of 64-bit unsigned integer values values or SeqType of tensor of 8-bit signless integer values values or SeqType of tensor of 16-bit signless integer values values or SeqType of tensor of 32-bit signless integer values values or SeqType of tensor of 64-bit signless integer values values or SeqType of tensor of 16-bit float values values or SeqType of tensor of 32-bit float values values or SeqType of tensor of 64-bit float values values or SeqType of tensor of string type values values or SeqType of tensor of 1-bit signless integer values values or SeqType of tensor of complex type with 32-bit float elements values values or SeqType of tensor of complex type with 64-bit float elements values values or OptType of SeqType of tensor of 8-bit unsigned integer values values values or OptType of SeqType of tensor of 16-bit unsigned integer values values values or OptType of SeqType of tensor of 32-bit unsigned integer values values values or OptType of SeqType of tensor of 64-bit unsigned integer values values values or OptType of SeqType of tensor of 8-bit signless integer values values values or OptType of SeqType of tensor of 16-bit signless integer values values values or OptType of SeqType of tensor of 32-bit signless integer values values values or OptType of SeqType of tensor of 64-bit signless integer values values values or OptType of SeqType of tensor of 16-bit float values values values or OptType of SeqType of tensor of 32-bit float values values values or OptType of SeqType of tensor of 64-bit float values values values or OptType of SeqType of tensor of string type values values values or OptType of SeqType of tensor of 1-bit signless integer values values values or OptType of SeqType of tensor of complex type with 32-bit float elements values values values or OptType of SeqType of tensor of complex type with 64-bit float elements values values values or OptType of tensor of 8-bit unsigned integer values values or OptType of tensor of 16-bit unsigned integer values values or OptType of tensor of 32-bit unsigned integer values values or OptType of tensor of 64-bit unsigned integer values values or OptType of tensor of 8-bit signless integer values values or OptType of tensor of 16-bit signless integer values values or OptType of tensor of 32-bit signless integer values values or OptType of tensor of 64-bit signless integer values values or OptType of tensor of 16-bit float values values or OptType of tensor of 32-bit float values values or OptType of tensor of 64-bit float values values or OptType of tensor of string type values values or OptType of tensor of 1-bit signless integer values values or OptType of tensor of complex type with 32-bit float elements values values or OptType of tensor of complex type with 64-bit float elements values values

Results:

Result Description

output tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values or SeqType of tensor of 8-bit unsigned integer values values or SeqType of tensor of 16-bit unsigned integer values values or SeqType of tensor of 32-bit unsigned integer values values or SeqType of tensor of 64-bit unsigned integer values values or SeqType of tensor of 8-bit signless integer values values or SeqType of tensor of 16-bit signless integer values values or SeqType of tensor of 32-bit signless integer values values or SeqType of tensor of 64-bit signless integer values values or SeqType of tensor of 16-bit float values values or SeqType of tensor of 32-bit float values values or SeqType of tensor of 64-bit float values values or SeqType of tensor of string type values values or SeqType of tensor of 1-bit signless integer values values or SeqType of tensor of complex type with 32-bit float elements values values or SeqType of tensor of complex type with 64-bit float elements values values or OptType of SeqType of tensor of 8-bit unsigned integer values values values or OptType of SeqType of tensor of 16-bit unsigned integer values values values or OptType of SeqType of tensor of 32-bit unsigned integer values values values or OptType of SeqType of tensor of 64-bit unsigned integer values values values or OptType of SeqType of tensor of 8-bit signless integer values values values or OptType of SeqType of tensor of 16-bit signless integer values values values or OptType of SeqType of tensor of 32-bit signless integer values values values or OptType of SeqType of tensor of 64-bit signless integer values values values or OptType of SeqType of tensor of 16-bit float values values values or OptType of SeqType of tensor of 32-bit float values values values or OptType of SeqType of tensor of 64-bit float values values values or OptType of SeqType of tensor of string type values values values or OptType of SeqType of tensor of 1-bit signless integer values values values or OptType of SeqType of tensor of complex type with 32-bit float elements values values values or OptType of SeqType of tensor of complex type with 64-bit float elements values values values or OptType of tensor of 8-bit unsigned integer values values or OptType of tensor of 16-bit unsigned integer values values or OptType of tensor of 32-bit unsigned integer values values or OptType of tensor of 64-bit unsigned integer values values or OptType of tensor of 8-bit signless integer values values or OptType of tensor of 16-bit signless integer values values or OptType of tensor of 32-bit signless integer values values or OptType of tensor of 64-bit signless integer values values or OptType of tensor of 16-bit float values values or OptType of tensor of 32-bit float values values or OptType of tensor of 64-bit float values values or OptType of tensor of string type values values or OptType of tensor of 1-bit signless integer values values or OptType of tensor of complex type with 32-bit float elements values values or OptType of tensor of complex type with 64-bit float elements values values

`onnx.If` (ONNXIfOp)

ONNX If operation

If conditional

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, HasOnnxSubgraphOpInterface, NoMemoryEffect (MemoryEffectOpInterface), ResultTypeInferenceOpInterface, ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`cond`	tensor of 1-bit signless integer values

Results:

Result Description

outputs variadic of tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values or SeqType of tensor of 8-bit unsigned integer values values or SeqType of tensor of 16-bit unsigned integer values values or SeqType of tensor of 32-bit unsigned integer values values or SeqType of tensor of 64-bit unsigned integer values values or SeqType of tensor of 8-bit signless integer values values or SeqType of tensor of 16-bit signless integer values values or SeqType of tensor of 32-bit signless integer values values or SeqType of tensor of 64-bit signless integer values values or SeqType of tensor of bfloat16 type values values or SeqType of tensor of 16-bit float values values or SeqType of tensor of 32-bit float values values or SeqType of tensor of 64-bit float values values or SeqType of tensor of string type values values or SeqType of tensor of 1-bit signless integer values values or SeqType of tensor of complex type with 32-bit float elements values values or SeqType of tensor of complex type with 64-bit float elements values values or SeqType of tensor of f8E4M3FN type values values or SeqType of tensor of f8E4M3FNUZ type values values or SeqType of tensor of f8E5M2 type values values or SeqType of tensor of f8E5M2FNUZ type values values or OptType of SeqType of tensor of 8-bit unsigned integer values values values or OptType of SeqType of tensor of 16-bit unsigned integer values values values or OptType of SeqType of tensor of 32-bit unsigned integer values values values or OptType of SeqType of tensor of 64-bit unsigned integer values values values or OptType of SeqType of tensor of 8-bit signless integer values values values or OptType of SeqType of tensor of 16-bit signless integer values values values or OptType of SeqType of tensor of 32-bit signless integer values values values or OptType of SeqType of tensor of 64-bit signless integer values values values or OptType of SeqType of tensor of bfloat16 type values values values or OptType of SeqType of tensor of 16-bit float values values values or OptType of SeqType of tensor of 32-bit float values values values or OptType of SeqType of tensor of 64-bit float values values values or OptType of SeqType of tensor of string type values values values or OptType of SeqType of tensor of 1-bit signless integer values values values or OptType of SeqType of tensor of complex type with 32-bit float elements values values values or OptType of SeqType of tensor of complex type with 64-bit float elements values values values or OptType of tensor of 8-bit unsigned integer values values or OptType of tensor of 16-bit unsigned integer values values or OptType of tensor of 32-bit unsigned integer values values or OptType of tensor of 64-bit unsigned integer values values or OptType of tensor of 8-bit signless integer values values or OptType of tensor of 16-bit signless integer values values or OptType of tensor of 32-bit signless integer values values or OptType of tensor of 64-bit signless integer values values or OptType of tensor of bfloat16 type values values or OptType of tensor of 16-bit float values values or OptType of tensor of 32-bit float values values or OptType of tensor of 64-bit float values values or OptType of tensor of string type values values or OptType of tensor of 1-bit signless integer values values or OptType of tensor of complex type with 32-bit float elements values values or OptType of tensor of complex type with 64-bit float elements values values or OptType of tensor of f8E4M3FN type values values or OptType of tensor of f8E4M3FNUZ type values values or OptType of tensor of f8E5M2 type values values or OptType of tensor of f8E5M2FNUZ type values values

`onnx.Imputer` (ONNXImputerOp)

ONNX Imputer operation

Replaces inputs that equal one value with another, leaving all other elements alone.
This operator is typically used to replace missing values in situations where they have a canonical representation, such as -1, 0, NaN, or some extreme value.
One and only one of imputed_value_floats or imputed_value_int64s should be defined – floats if the input tensor holds floats, integers if the input tensor holds integers. The imputed values must all fit within the width of the tensor element type. One and only one of the replaced_value_float or replaced_value_int64 should be defined, which one depends on whether floats or integers are being processed.
The imputed_value attribute length can be 1 element, or it can have one element per input feature.
In other words, if the input tensor has the shape [*,F], then the length of the attribute array may be 1 or F. If it is 1, then it is broadcast along the last dimension and applied to each feature.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`imputed_value_floats`	::mlir::ArrayAttr	32-bit float array attribute
`imputed_value_int64s`	::mlir::ArrayAttr	64-bit integer array attribute
`replaced_value_float`	::mlir::FloatAttr	32-bit float attribute
`replaced_value_int64`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values

Results:

Result	Description
`Y`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values

`onnx.InstanceNormalization` (ONNXInstanceNormalizationOp)

ONNX InstanceNormalization operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`scale`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`B`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.IsInf` (ONNXIsInfOp)

ONNX IsInf operation

Map infinity to true and other values to false.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values

Results:

Result	Description
`Y`	tensor of 1-bit signless integer values

`onnx.IsNaN` (ONNXIsNaNOp)

ONNX IsNaN operation

Returns which elements of the input are NaN.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values

Results:

Result	Description
`Y`	tensor of 1-bit signless integer values

`onnx.LRN` (ONNXLRNOp)

ONNX LRN operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.LSTM` (ONNXLSTMOp)

ONNX LSTM operation

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) - alpha*x + 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(alpha*x + 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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`W`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`R`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`B`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`sequence_lens`	tensor of 32-bit signless integer values or none type
`initial_h`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`initial_c`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`P`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`Y_h`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`Y_c`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

`onnx.LabelEncoder` (ONNXLabelEncoderOp)

ONNX LabelEncoder operation

Maps each element in the input tensor to another value.
The mapping is determined by the two parallel attributes, ‘keys_’ and ‘values_’ attribute. The i-th value in the specified ‘keys_’ attribute would be mapped to the i-th value in the specified ‘values_’ attribute. It implies that input’s element type and the element type of the specified ‘keys_’ should be identical while the output type is identical to the specified ‘values_’ attribute. If an input element can not be found in the specified ‘keys_’ attribute, the ‘default_’ that matches the specified ‘values_’ attribute may be used as its output value.
Let’s consider an example which maps a string tensor to an integer tensor. Assume and ‘keys_strings’ is ["Amy", "Sally"], ‘values_int64s’ is [5, 6], and ‘default_int64’ is ‘-1’. The input ["Dori", "Amy", "Amy", "Sally", "Sally"] would be mapped to [-1, 5, 5, 6, 6].
Since this operator is an one-to-one mapping, its input and output shapes are the same. Notice that only one of ‘keys_’/’values_’ can be set.
For key look-up, bit-wise comparison is used so even a float NaN can be mapped to a value in ‘values_’ attribute.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`default_float`	::mlir::FloatAttr	32-bit float attribute
`default_int64`	::mlir::IntegerAttr	64-bit signed integer attribute
`default_string`	::mlir::StringAttr	string attribute
`keys_floats`	::mlir::ArrayAttr	32-bit float array attribute
`keys_int64s`	::mlir::ArrayAttr	64-bit integer array attribute
`keys_strings`	::mlir::ArrayAttr	string array attribute
`values_floats`	::mlir::ArrayAttr	32-bit float array attribute
`values_int64s`	::mlir::ArrayAttr	64-bit integer array attribute
`values_strings`	::mlir::ArrayAttr	string array attribute

Operands:

Operand	Description
`X`	tensor of string type values or tensor of 64-bit signless integer values or tensor of 32-bit float values

Results:

Result	Description
`Y`	tensor of string type values or tensor of 64-bit signless integer values or tensor of 32-bit float values

`onnx.LayerNormalization` (ONNXLayerNormalizationOp)

ONNX LayerNormalization operation

This is layer normalization defined in ONNX as function. The overall computation can be split into two stages. The first stage is standardization, which makes the normalized elements have zero mean and unit variances. The computation required by standardization can be described by the following equations. Mean = ReduceMean<axes=normalized_axes>(X) D = Sub(X, Mean) DD = Mul(D, D) Var = ReduceMean<axes=normalized_axes>(DD) VarEps = Add(Var, epsilon) StdDev = Sqrt(VarEps) InvStdDev = Reciprocal(StdDev) Normalized = Mul(D, InvStdDev) where normalized_axes is [axis, ..., rank of X - 1]. The variables Var and StdDev stand for variance and standard deviation, respectively. The second output is Mean and the last one is InvStdDev. Depending on stash_type attribute, the actual computation must happen in different floating-point precision. For example, if stash_type is 1, this operator casts all input variables to 32-bit float, perform the computation, and finally cast Normalized back to the original type of X. The second stage then scales and shifts the outcome of the first stage using NormalizedScaled = Mul(Normalized, Scale) Y = Add(NormalizedScaled, B) The second stage doesn’t depends on stash_type. All equations are in this syntax. The same variable (i.e., input, output, and attribute) uses the same name in the equations above and this operator’s definition. Let d[i] indicate the i-th dimension of X. If X’s shape is [d[0], ..., d[axis-1], d[axis], ..., d[rank-1]], the shape of Mean and InvStdDev is [d[0], ..., d[axis-1], 1, ..., 1]. Y and X have the same shape. This operator supports unidirectional broadcasting (tensors Scale and B should be unidirectional broadcastable to tensor X); for more details please check the doc.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	64-bit signed integer attribute
`epsilon`	::mlir::FloatAttr	32-bit float attribute
`stash_type`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`Scale`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`B`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or none type

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`Mean`	tensor of 32-bit float values or tensor of bfloat16 type values or none type
`InvStdDev`	tensor of 32-bit float values or tensor of bfloat16 type values or none type

`onnx.LayoutTransform` (ONNXLayoutTransformOp)

An operation that transforms data between different layout formats

An operation that transforms a tensor from a layout to another layout. A layout is defined by an attribute, i.e. target_layout, which allows this operation work with an arbitrary layout (e.g. a layout used for accelerators).

target_layout is optional. If it is not given, the input tensor will be transformed to a normal tensor that does not have layout.

If target_layout is the same as the input’s layout, this operation will become an no-op by canonicalization.

The input and output tensors must have the same shape.

This operation is not part of the standard and was added to assist onnx-mlir.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`target_layout`	::mlir::Attribute	layout attribute

Operands:

Operand	Description
`data`	tensor of 16-bit float or 32-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float or 32-bit float values

`onnx.LeakyRelu` (ONNXLeakyReluOp)

ONNX LeakyRelu operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Less` (ONNXLessOp)

ONNX Less operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result	Description
`C`	tensor of 1-bit signless integer values

`onnx.LessOrEqual` (ONNXLessOrEqualOp)

ONNX LessOrEqual operation

Returns the tensor resulted from performing the less_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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result	Description
`C`	tensor of 1-bit signless integer values

`onnx.LinearClassifier` (ONNXLinearClassifierOp)

ONNX LinearClassifier operation

Linear classifier

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`classlabels_ints`	::mlir::ArrayAttr	64-bit integer array attribute
`classlabels_strings`	::mlir::ArrayAttr	string array attribute
`coefficients`	::mlir::ArrayAttr	32-bit float array attribute
`intercepts`	::mlir::ArrayAttr	32-bit float array attribute
`multi_class`	::mlir::IntegerAttr	64-bit signed integer attribute
`post_transform`	::mlir::StringAttr	string attribute

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values

Results:

Result	Description
`Y`	tensor of string type values or tensor of 64-bit signless integer values
`Z`	tensor of 32-bit float values

`onnx.LinearRegressor` (ONNXLinearRegressorOp)

ONNX LinearRegressor operation

Generalized linear regression evaluation.
If targets is set to 1 (default) then univariate regression is performed.
If targets is set to M then M sets of coefficients must be passed in as a sequence and M results will be output for each input n in N.
The coefficients array is of length n, and the coefficients for each target are contiguous. Intercepts are optional but if provided must match the number of targets.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`coefficients`	::mlir::ArrayAttr	32-bit float array attribute
`intercepts`	::mlir::ArrayAttr	32-bit float array attribute
`post_transform`	::mlir::StringAttr	string attribute
`targets`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values

Results:

Result	Description
`Y`	tensor of 32-bit float values

`onnx.Log` (ONNXLogOp)

ONNX Log operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.LogSoftmax` (ONNXLogSoftmaxOp)

ONNX LogSoftmax operation

The operator computes the log of softmax values for the given input:

LogSoftmax(input, axis) = Log(Softmax(input, axis=axis))

The "axis" attribute indicates the dimension along which LogSoftmax will be performed. The output tensor has the same shape and contains the LogSoftmax values of the corresponding input.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.Loop` (ONNXLoopOp)

ONNX Loop operation

Generic Looping construct. This loop has multiple termination conditions:

1) Trip count. Iteration count specified at runtime. Set by specifying the input M. Optional. Set to empty string to omit. Note that a static trip count (specified at graph construction time) can be specified by passing in a constant node for input M. 2) Loop termination condition. This is an input to the op that determines whether to run the first iteration and also a loop-carried dependency for the body graph. The body graph must yield a value for the condition variable, whether this input is provided or not.

This table summarizes the operating modes of this operator with equivalent C-style code:

Operator inputs defined as (max_trip_count, condition_var).

input ("", ""): for (int i=0; ; ++i) { cond = … // Note this value is ignored, but is required in the body }
input ("", cond) // Note this is analogous to a while loop bool cond = …; for (int i=0; cond; ++i) { cond = …; }
input ("", 1) // Note this is analogous to a do-while loop bool cond = true for (int i=0; cond; ++i) { cond = …; }
input (trip_count, "") // Note this is analogous to a for loop int trip_count = … for (int i=0; i < trip_count; ++i) { cond = …; // ignored }
input (trip_count, cond) int trip_count = …; bool cond = …; for (int i=0; i < trip_count && cond; ++i) { cond = …; }

Sample usage - cond as well as trip count

graph predict-net {
  %a = Constant[value = <Scalar Tensor [3]>]()
  %b = Constant[value = <Scalar Tensor [6]>]()
  %keepgoing = Constant[value = <Scalar Tensor [1]>]()
  %max_trip_count = Constant[value = <Scalar Tensor [10]>]()
  %keepgoing_out, %b_out, %user_defined_vals = Loop[body = <graph body-net>](%max_trip_count, %keepgoing, %b)
  return
}

graph body-net (
  %i[INT32, scalar]           // iteration number
  %keepgoing_in[BOOL, scalar] // incoming loop-termination-condition; not used
  %b_in[INT32, scalar]        // incoming value of loop-carried-dependency b
) {
  %my_local = Add(%a, %b_in)
  %b_out = Sub(%a, %b_in) // outgoing value of loop-carried-dependency b
  %keepgoing_out = Greater(%my_local, %b_out) // outgoing loop-termination-condition
  %user_defined_val = Add(%b_in, %b_in) // scan-output value to be accumulated
  return %keepgoing_out, %b_out, %user_defined_val
}

Sample equivalent C code

{
  /* User-defined code (enclosing scope) */
  int a = 3, b = 6;
  bool keepgoing = true; // Analogous to input cond
  /* End user-defined code */

  /* Implicitly-defined code */
  const int max_trip_count = 10; // Analogous to input M
  int user_defined_vals[]; // Imagine this is resizable
  /* End implicitly-defined code */
  /* initialize loop-carried variables and scan-output variables */
  bool keepgoing_out = keepgoing
  int b_out = b

  for (int i=0; i < max_trip_count && keepgoing_out; ++i) {
    /* Implicitly-defined code: bind actual parameter values
       to formal parameter variables of loop-body */
    bool keepgoing_in = keepgoing_out;
    bool b_in = b_out;

    /* User-defined code (loop body) */
    int my_local = a + b_in; // Reading value \"a\" from the enclosing scope is fine
    b_out = a - b_in;
    keepgoing_out = my_local > b_out;
    user_defined_val = b_in + b_in; // b_in and b_out are different variables
    /* End user-defined code */

    /* Implicitly defined-code */
    user_defined_vals[i] = user_defined_val // accumulate scan-output values
  }
  // int t = my_local; // Can't do this. my_local is not accessible here.

  // The values below are bound to the output variables of the loop and therefore accessible
  // b_out; user_defined_vals; keepgoing_out;
}

There are several things of note in this code snippet:

1) Values from the enclosing scope (i.e. variable "a" here) are in scope and can be referenced in the inputs of the loop. 2) Any values computed in the loop body that needs to be used in a subsequent iteration or after the loop are modelled using a pair of variables in the loop-body, consisting of an input variable (eg., b_in) and an output variable (eg., b_out). These are referred to as loop-carried dependences. The loop operation node supplies the input value of the input variable for the first iteration, and returns the output value of the output variable produced by the final iteration. 3) Scan_output variables are used to implicitly concatenate values computed across all the iterations. In the above example, the value of user_defined_val computed over all iterations are concatenated and returned as the value of user_defined_vals after the loop. 4) Values created in the body cannot be accessed in the enclosing scope, except using the mechanism described above.

Note that the semantics of this op support "diagonal" or "wavefront" execution. (See Step 3 here for an example: https://devblogs.nvidia.com/optimizing-recurrent-neural-networks-cudnn-5/). Frontends should emit multi-layer RNNs as a series of While operators (with time being the inner looping dimension), with each successive layer consuming the scan_outputs from the previous layer, possibly going through several point-wise operators (e.g. dropout, residual connections, linear layer).

The input/output of subgraph (produced by loop node) matching is based on order instead of name. The implementation will figure out the names based on this order.

Traits: AlwaysSpeculatableImplTrait

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`M`	tensor of 64-bit signless integer values or none type
`cond`	tensor of 1-bit signless integer values or none type
`v_initial`	variadic of tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values or SeqType of tensor of 8-bit unsigned integer values values or SeqType of tensor of 16-bit unsigned integer values values or SeqType of tensor of 32-bit unsigned integer values values or SeqType of tensor of 64-bit unsigned integer values values or SeqType of tensor of 8-bit signless integer values values or SeqType of tensor of 16-bit signless integer values values or SeqType of tensor of 32-bit signless integer values values or SeqType of tensor of 64-bit signless integer values values or SeqType of tensor of bfloat16 type values values or SeqType of tensor of 16-bit float values values or SeqType of tensor of 32-bit float values values or SeqType of tensor of 64-bit float values values or SeqType of tensor of string type values values or SeqType of tensor of 1-bit signless integer values values or SeqType of tensor of complex type with 32-bit float elements values values or SeqType of tensor of complex type with 64-bit float elements values values or SeqType of tensor of f8E4M3FN type values values or SeqType of tensor of f8E4M3FNUZ type values values or SeqType of tensor of f8E5M2 type values values or SeqType of tensor of f8E5M2FNUZ type values values or OptType of SeqType of tensor of 8-bit unsigned integer values values values or OptType of SeqType of tensor of 16-bit unsigned integer values values values or OptType of SeqType of tensor of 32-bit unsigned integer values values values or OptType of SeqType of tensor of 64-bit unsigned integer values values values or OptType of SeqType of tensor of 8-bit signless integer values values values or OptType of SeqType of tensor of 16-bit signless integer values values values or OptType of SeqType of tensor of 32-bit signless integer values values values or OptType of SeqType of tensor of 64-bit signless integer values values values or OptType of SeqType of tensor of bfloat16 type values values values or OptType of SeqType of tensor of 16-bit float values values values or OptType of SeqType of tensor of 32-bit float values values values or OptType of SeqType of tensor of 64-bit float values values values or OptType of SeqType of tensor of string type values values values or OptType of SeqType of tensor of 1-bit signless integer values values values or OptType of SeqType of tensor of complex type with 32-bit float elements values values values or OptType of SeqType of tensor of complex type with 64-bit float elements values values values or OptType of tensor of 8-bit unsigned integer values values or OptType of tensor of 16-bit unsigned integer values values or OptType of tensor of 32-bit unsigned integer values values or OptType of tensor of 64-bit unsigned integer values values or OptType of tensor of 8-bit signless integer values values or OptType of tensor of 16-bit signless integer values values or OptType of tensor of 32-bit signless integer values values or OptType of tensor of 64-bit signless integer values values or OptType of tensor of bfloat16 type values values or OptType of tensor of 16-bit float values values or OptType of tensor of 32-bit float values values or OptType of tensor of 64-bit float values values or OptType of tensor of string type values values or OptType of tensor of 1-bit signless integer values values or OptType of tensor of complex type with 32-bit float elements values values or OptType of tensor of complex type with 64-bit float elements values values or OptType of tensor of f8E4M3FN type values values or OptType of tensor of f8E4M3FNUZ type values values or OptType of tensor of f8E5M2 type values values or OptType of tensor of f8E5M2FNUZ type values values

Results:

Result Description

v_final_and_scan_outputs variadic of tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values or SeqType of tensor of 8-bit unsigned integer values values or SeqType of tensor of 16-bit unsigned integer values values or SeqType of tensor of 32-bit unsigned integer values values or SeqType of tensor of 64-bit unsigned integer values values or SeqType of tensor of 8-bit signless integer values values or SeqType of tensor of 16-bit signless integer values values or SeqType of tensor of 32-bit signless integer values values or SeqType of tensor of 64-bit signless integer values values or SeqType of tensor of bfloat16 type values values or SeqType of tensor of 16-bit float values values or SeqType of tensor of 32-bit float values values or SeqType of tensor of 64-bit float values values or SeqType of tensor of string type values values or SeqType of tensor of 1-bit signless integer values values or SeqType of tensor of complex type with 32-bit float elements values values or SeqType of tensor of complex type with 64-bit float elements values values or SeqType of tensor of f8E4M3FN type values values or SeqType of tensor of f8E4M3FNUZ type values values or SeqType of tensor of f8E5M2 type values values or SeqType of tensor of f8E5M2FNUZ type values values or OptType of SeqType of tensor of 8-bit unsigned integer values values values or OptType of SeqType of tensor of 16-bit unsigned integer values values values or OptType of SeqType of tensor of 32-bit unsigned integer values values values or OptType of SeqType of tensor of 64-bit unsigned integer values values values or OptType of SeqType of tensor of 8-bit signless integer values values values or OptType of SeqType of tensor of 16-bit signless integer values values values or OptType of SeqType of tensor of 32-bit signless integer values values values or OptType of SeqType of tensor of 64-bit signless integer values values values or OptType of SeqType of tensor of bfloat16 type values values values or OptType of SeqType of tensor of 16-bit float values values values or OptType of SeqType of tensor of 32-bit float values values values or OptType of SeqType of tensor of 64-bit float values values values or OptType of SeqType of tensor of string type values values values or OptType of SeqType of tensor of 1-bit signless integer values values values or OptType of SeqType of tensor of complex type with 32-bit float elements values values values or OptType of SeqType of tensor of complex type with 64-bit float elements values values values or OptType of tensor of 8-bit unsigned integer values values or OptType of tensor of 16-bit unsigned integer values values or OptType of tensor of 32-bit unsigned integer values values or OptType of tensor of 64-bit unsigned integer values values or OptType of tensor of 8-bit signless integer values values or OptType of tensor of 16-bit signless integer values values or OptType of tensor of 32-bit signless integer values values or OptType of tensor of 64-bit signless integer values values or OptType of tensor of bfloat16 type values values or OptType of tensor of 16-bit float values values or OptType of tensor of 32-bit float values values or OptType of tensor of 64-bit float values values or OptType of tensor of string type values values or OptType of tensor of 1-bit signless integer values values or OptType of tensor of complex type with 32-bit float elements values values or OptType of tensor of complex type with 64-bit float elements values values or OptType of tensor of f8E4M3FN type values values or OptType of tensor of f8E4M3FNUZ type values values or OptType of tensor of f8E5M2 type values values or OptType of tensor of f8E5M2FNUZ type values values

`onnx.LpNormalization` (ONNXLpNormalizationOp)

ONNX LpNormalization operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.LpPool` (ONNXLpPoolOp)

ONNX LpPool operation

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. The output spatial shape will be following:

 output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)

 output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1)

if ceil_mode is enabled pad_shape[i] is the sum of pads along axis i.

auto_pad is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:

 VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 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] + {kernelSpatialShape} - input_spatial_shape[i]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.MatMulInteger` (ONNXMatMulIntegerOp)

ONNX MatMulInteger operation

Matrix product that behaves like numpy.matmul. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`A`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`B`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`a_zero_point`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values or none type
`b_zero_point`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values or none type

Results:

Result	Description
`Y`	tensor of 32-bit signless integer values

`onnx.MatMul` (ONNXMatMulOp)

ONNX MatMul operation

Matrix product that behaves like numpy.matmul.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`A`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values
`B`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values

`onnx.Max` (ONNXMaxOp)

ONNX Max operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

data_0 variadic of tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result Description

max tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.MaxPool` (ONNXMaxPoolOp)

ONNX MaxPool operation

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 is calculated differently depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized. With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d):

 output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)

 output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)

if ceil_mode is enabled. pad_shape[i] is the sum of pads along axis i.

auto_pad is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:

 VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])
 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])

or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D):

 VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1
 SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1

And pad shape will be following if SAME_UPPER or SAME_LOWER:

 pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]

The output of each pooling window is maximum number of elements exclude pad.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`Indices`	tensor of 64-bit signless integer values or none type

`onnx.MaxPoolSingleOut` (ONNXMaxPoolSingleOutOp)

ONNX MaxPool operation with a single output.

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

This operation is not part of the standard and was added to assist onnx-mlir.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	memref of any type values or tensor of any type values

Results:

Result	Description
`o_Y`	memref of any type values or tensor of any type values

`onnx.MaxRoiPool` (ONNXMaxRoiPoolOp)

ONNX MaxRoiPool operation

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]).

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`rois`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.MaxUnpool` (ONNXMaxUnpoolOp)

ONNX MaxUnpool operation

MaxUnpool essentially computes the partial inverse of the MaxPool op. The input information to this op is typically 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 corresponding 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 corresponding pooling op that the unpooling op is trying to invert.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`I`	tensor of 64-bit signless integer values
`output_shape`	tensor of 64-bit signless integer values or none type

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Mean` (ONNXMeanOp)

ONNX Mean operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`data_0`	variadic of tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`mean`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.MeanVarianceNormalization` (ONNXMeanVarianceNormalizationOp)

ONNX MeanVarianceNormalization operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.MelWeightMatrix` (ONNXMelWeightMatrixOp)

ONNX MelWeightMatrix operation

Generate a MelWeightMatrix that can be used to re-weight a Tensor containing a linearly sampled frequency spectra (from DFT or STFT) into num_mel_bins frequency information based on the [lower_edge_hertz, upper_edge_hertz] range on the mel scale. This function defines the mel scale in terms of a frequency in hertz according to the following formula:

mel(f) = 2595 * log10(1 + f/700)

In the returned matrix, all the triangles (filterbanks) have a peak value of 1.0.

The returned MelWeightMatrix can be used to right-multiply a spectrogram S of shape [frames, num_spectrogram_bins] of linear scale spectrum values (e.g. STFT magnitudes) to generate a "mel spectrogram" M of shape [frames, num_mel_bins].

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`output_datatype`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`num_mel_bins`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`dft_length`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`sample_rate`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`lower_edge_hertz`	tensor of 32-bit float values or tensor of 16-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`upper_edge_hertz`	tensor of 32-bit float values or tensor of 16-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result Description

`onnx.Min` (ONNXMinOp)

ONNX Min operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result Description

min tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.Mish` (ONNXMishOp)

ONNX Mish operation

Mish: A Self Regularized Non-Monotonic Neural Activation Function.

Perform the linear unit element-wise on the input tensor X using formula:

mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + e^{x}))

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Mod` (ONNXModOp)

ONNX Mod operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

Results:

Result Description

`onnx.Momentum` (ONNXMomentumOp)

ONNX Momentum operation

Compute one iteration of stochastic gradient update with momentum. This operator can conduct the optimization of multiple tensor variables.

Let's define the behavior of this operator. As you can imagine, SG with momentum requires
several parameters:

 - The learning-rate \"R\".
 - The update count \"T\". That is, the number of conducted training iterations. It should
   be zero in the first training iteration.
 - A L2-norm regularization coefficient \"norm_coefficient\".
 - A decay coefficient of previous accumulated gradient (i.e., momentum) \"alpha\".
 - The scaling coefficient of current gradient \"beta\".
 - An attribute to choose either standard momentum or Nesterov's momentum \"mode\" should
   be used.

For the sake of simplicity, assume that there is only one tensor (called \"X\") to be optimized.
Other necessary inputs are \"X\"'s gradient (called \"G\") and \"X\"'s momentum (called \"V\"). This
Momentum operator maps all these inputs to the new value of \"X\" (called \"X_new\") and its new
momentum (called \"V_new\").

This operator supports two different momentum algorithms. Set the attribute \"mode\" to
\"nesterov\" if Nesterov's momentum is desired. Otherwise, set the attribute \"model\" to
\"standard\" to use standard momentum. Computation details are described subsequently.

Let \"+\", \"-\", \"*\", and \"/\" are all element-wise operations with numpy-style broadcasting.

Pseudo code for SG with standard momentum:

  // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared
  // values of all elements in X.
  G_regularized = norm_coefficient * X + G

  // In the first training iteration, beta should always be 1.
  beta_adjusted = T > 0 ? beta : 1

  // Compute the current momentum based on previous momentum and the current gradient.
  V_new = alpha * V + beta_adjusted * G_regularized

  // Update X.
  X_new = X - R * V_new

Pseudo code for SG with Nesterov's momentum:

  // Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared
  // values of all elements in X.
  G_regularized = norm_coefficient * X + G;

  // In the first training iteration, beta should always be 1.
  beta_adjusted = T > 0 ? beta : 1

  // Compute the current momentum based on previous momentum and the current gradient.
  V_new = alpha * V + beta_adjusted * G_regularized;

  // Compute final update direction and then update X.
  X_new = X - R * (G_regularized + alpha * V_new)

If one assign this operators to optimize multiple inputs, for example, \"X_1\" and \"X_2\". The same
pseudo code would be extended to handle all tensors jointly. More specifically, we can view \"X\" as a
concatenation of \"X_1\" and \"X_2\" (of course, their gradient and accumulate gradient should
be concatenated too) and then our pseudo code becomes applicable.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`alpha`	::mlir::FloatAttr	32-bit float attribute
`beta`	::mlir::FloatAttr	32-bit float attribute
`mode`	::mlir::StringAttr	string attribute
`norm_coefficient`	::mlir::FloatAttr	32-bit float attribute

Operands:

Operand	Description
`R`	tensor of 32-bit float values or tensor of 64-bit float values
`T`	tensor of 64-bit signless integer values
`inputs`	variadic of tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`outputs`	variadic of tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Mul` (ONNXMulOp)

ONNX Mul operation

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.

(Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result Description

`onnx.Multinomial` (ONNXMultinomialOp)

ONNX Multinomial operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

`onnx.Neg` (ONNXNegOp)

ONNX Neg operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 32-bit signless integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 32-bit float values or tensor of 32-bit signless integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.NegativeLogLikelihoodLoss` (ONNXNegativeLogLikelihoodLossOp)

ONNX NegativeLogLikelihoodLoss operation

A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. Its "input" tensor has the shape of (N, C, d1, d2, …, dk) where k >= 0. The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,…, d_k] being in a class of [0, C). The operator’s "target" input tensor has the shape of (N, d1, d2, …, dk). It encodes class labels (one of C classes) or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x … x dk samples. The loss value for input[n, :, d_1, d_2,…d_k] being classified as class c = target[n][d_1][d_2]…[d_k] is computed as:

loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k].

When an optional "weight" is provided, the sample loss is calculated as:

loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c].

loss is zero for the case when target-value equals ignore_index.

loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index

If "reduction" attribute is set to "none", the operator’s output will be the above loss with shape (N, d1, d2, …, dk). If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged:

mean(loss), if \"weight\" is not provided,

or if weight is provided,

sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples.

If "reduction" attribute is set to "sum", the output is a scalar: sum(loss).

See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss.

Example 1:

// negative log likelihood loss, \"none\" reduction
N, C, d1 = 2, 3, 2
input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
          [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
target = [[2, 1], [0, 2]]

loss = np.zeros((N, d1))
for n in range(N):
    for d_1 in range(d1):
        c = target[n][d_1]
        loss[n][d_1] = -input[n][c][d_1]

// print(loss)
// [[-3. -2.]
//  [-0. -2.]]

Example 2:

// weighted negative log likelihood loss, sum reduction
N, C, d1 = 2, 3, 2
input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
        [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
target = [[2, 1], [0, 2]]
weight = [0.2, 0.3, 0.1]
loss = np.zeros((N, d1))
for n in range(N):
    for d_1 in range(d1):
        c = target[n][d_1]
        loss[n][d_1] = -input[n][c][d_1] * weight[c]

loss = np.sum(loss)
// print(loss)
// -1.1

Example 3:

// weighted negative log likelihood loss, mean reduction
N, C, d1 = 2, 3, 2
input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],
        [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]
target = [[2, 1], [0, 2]]
weight = [0.2, 0.3, 0.1]
loss = np.zeros((N, d1))
weight_total = 0
for n in range(N):
    for d_1 in range(d1):
        c = target[n][d_1]
        loss[n][d_1] = -input[n][c][d_1] * weight[c]
        weight_total = weight_total + weight[c]

loss = np.sum(loss) / weight_total
// print(loss)
// -1.57

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`ignore_index`	::mlir::IntegerAttr	64-bit signed integer attribute
`reduction`	::mlir::StringAttr	string attribute

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`target`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`weight`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

Results:

Result	Description
`loss`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.NonMaxSuppression` (ONNXNonMaxSuppressionOp)

ONNX NonMaxSuppression operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`boxes`	tensor of 32-bit float values
`scores`	tensor of 32-bit float values
`max_output_boxes_per_class`	tensor of 64-bit signless integer values or none type
`iou_threshold`	tensor of 32-bit float values or none type
`score_threshold`	tensor of 32-bit float values or none type

Results:

Result	Description
`selected_indices`	tensor of 64-bit signless integer values

`onnx.NonZero` (ONNXNonZeroOp)

ONNX NonZero operation

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, but for scalar input, NonZero produces output shape (0, N) instead of (1, N), which is different from Numpy’s behavior.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

X tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

Results:

Result	Description
`Y`	tensor of 64-bit signless integer values

`onnx.NoValue` (ONNXNoneOp)

An operation representing the absence of a value.

This operation can be used to represent the absence of a value. It is typically used as an argument to operators that have optional parameters.

Example:

  %cst = "onnx.NoValue"() {value} : () -> none
  %0, %1 = "onnx.Split"(%arg0, %cst) { axis=1 : si64 } : (tensor<?xf32>, none) -> (tensor<*xf32>, tensor<*xf32>)

This operation is not part of the standard and was added to assist onnx-mlir.

Traits: AlwaysSpeculatableImplTrait, ConstantLike

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`value`	::mlir::UnitAttr	unit attribute

Results:

Result	Description
`none_val`	none type

`onnx.Normalizer` (ONNXNormalizerOp)

ONNX Normalizer operation

Normalize the input. There are three normalization modes, which have the corresponding formulas, defined using element-wise infix operators ‘/’ and ‘^’ and tensor-wide functions ‘max’ and ‘sum’:

Max: Y = X / max(X)
L1: Y = X / sum(X)
L2: Y = sqrt(X^2 / sum(X^2)}
In all modes, if the divisor is zero, Y == X.
For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row of the batch is normalized independently.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`norm`	::mlir::StringAttr	string attribute

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values

Results:

Result	Description
`Y`	tensor of 32-bit float values

`onnx.Not` (ONNXNotOp)

ONNX Not operation

Returns the negation of the input tensor element-wise.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 1-bit signless integer values

Results:

Result	Description
`Y`	tensor of 1-bit signless integer values

`onnx.OneHotEncoder` (ONNXOneHotEncoderOp)

ONNX OneHotEncoder operation

Replace each input element with an array of ones and zeros, where a single one is placed at the index of the category that was passed in. The total category count will determine the size of the extra dimension of the output array Y.
For example, if we pass a tensor with a single value of 4, and a category count of 8, the output will be a tensor with [0,0,0,0,1,0,0,0].
This operator assumes every input feature is from the same set of categories.
If the input is a tensor of float, int32, or double, the data will be cast to integers and the cats_int64s category list will be used for the lookups.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`cats_int64s`	::mlir::ArrayAttr	64-bit integer array attribute
`cats_strings`	::mlir::ArrayAttr	string array attribute
`zeros`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`X`	tensor of string type values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 32-bit float values

`onnx.OneHot` (ONNXOneHotOp)

ONNX OneHot operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`indices`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`depth`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`values`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

Results:

Result Description

`onnx.OptionalGetElement` (ONNXOptionalGetElementOp)

ONNX OptionalGetElement operation

If the input is a tensor or sequence type, it returns the input. If the input is an optional type, it outputs the element in the input. It is an error if the input is an empty optional-type (i.e. does not have an element) and the behavior is undefined in this case.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

input OptType of SeqType of tensor of 8-bit unsigned integer values values values or OptType of SeqType of tensor of 16-bit unsigned integer values values values or OptType of SeqType of tensor of 32-bit unsigned integer values values values or OptType of SeqType of tensor of 64-bit unsigned integer values values values or OptType of SeqType of tensor of 8-bit signless integer values values values or OptType of SeqType of tensor of 16-bit signless integer values values values or OptType of SeqType of tensor of 32-bit signless integer values values values or OptType of SeqType of tensor of 64-bit signless integer values values values or OptType of SeqType of tensor of 16-bit float values values values or OptType of SeqType of tensor of 32-bit float values values values or OptType of SeqType of tensor of 64-bit float values values values or OptType of SeqType of tensor of string type values values values or OptType of SeqType of tensor of 1-bit signless integer values values values or OptType of SeqType of tensor of complex type with 32-bit float elements values values values or OptType of SeqType of tensor of complex type with 64-bit float elements values values values or OptType of tensor of 8-bit unsigned integer values values or OptType of tensor of 16-bit unsigned integer values values or OptType of tensor of 32-bit unsigned integer values values or OptType of tensor of 64-bit unsigned integer values values or OptType of tensor of 8-bit signless integer values values or OptType of tensor of 16-bit signless integer values values or OptType of tensor of 32-bit signless integer values values or OptType of tensor of 64-bit signless integer values values or OptType of tensor of 16-bit float values values or OptType of tensor of 32-bit float values values or OptType of tensor of 64-bit float values values or OptType of tensor of string type values values or OptType of tensor of 1-bit signless integer values values or OptType of tensor of complex type with 32-bit float elements values values or OptType of tensor of complex type with 64-bit float elements values values or tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or SeqType of tensor of 8-bit unsigned integer values values or SeqType of tensor of 16-bit unsigned integer values values or SeqType of tensor of 32-bit unsigned integer values values or SeqType of tensor of 64-bit unsigned integer values values or SeqType of tensor of 8-bit signless integer values values or SeqType of tensor of 16-bit signless integer values values or SeqType of tensor of 32-bit signless integer values values or SeqType of tensor of 64-bit signless integer values values or SeqType of tensor of 16-bit float values values or SeqType of tensor of 32-bit float values values or SeqType of tensor of 64-bit float values values or SeqType of tensor of string type values values or SeqType of tensor of 1-bit signless integer values values or SeqType of tensor of complex type with 32-bit float elements values values or SeqType of tensor of complex type with 64-bit float elements values values

Results:

Result Description

`onnx.OptionalHasElement` (ONNXOptionalHasElementOp)

ONNX OptionalHasElement operation

Returns true if (1) the input is an optional-type and contains an element, or, (2) the input is a tensor or sequence type. If the input is not provided or is an empty optional-type, this op returns false.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result	Description
`output`	tensor of 1-bit signless integer values

`onnx.Optional` (ONNXOptionalOp)

ONNX Optional operation

Constructs an optional-type value containing either an empty optional of a certain type specified by the attribute, or a non-empty value containing the input element.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`type`	::mlir::TypeAttr	any type attribute

Operands:

Operand Description

Results:

Result Description

output OptType of SeqType of tensor of 8-bit unsigned integer values values values or OptType of SeqType of tensor of 16-bit unsigned integer values values values or OptType of SeqType of tensor of 32-bit unsigned integer values values values or OptType of SeqType of tensor of 64-bit unsigned integer values values values or OptType of SeqType of tensor of 8-bit signless integer values values values or OptType of SeqType of tensor of 16-bit signless integer values values values or OptType of SeqType of tensor of 32-bit signless integer values values values or OptType of SeqType of tensor of 64-bit signless integer values values values or OptType of SeqType of tensor of 16-bit float values values values or OptType of SeqType of tensor of 32-bit float values values values or OptType of SeqType of tensor of 64-bit float values values values or OptType of SeqType of tensor of string type values values values or OptType of SeqType of tensor of 1-bit signless integer values values values or OptType of SeqType of tensor of complex type with 32-bit float elements values values values or OptType of SeqType of tensor of complex type with 64-bit float elements values values values or OptType of tensor of 8-bit unsigned integer values values or OptType of tensor of 16-bit unsigned integer values values or OptType of tensor of 32-bit unsigned integer values values or OptType of tensor of 64-bit unsigned integer values values or OptType of tensor of 8-bit signless integer values values or OptType of tensor of 16-bit signless integer values values or OptType of tensor of 32-bit signless integer values values or OptType of tensor of 64-bit signless integer values values or OptType of tensor of 16-bit float values values or OptType of tensor of 32-bit float values values or OptType of tensor of 64-bit float values values or OptType of tensor of string type values values or OptType of tensor of 1-bit signless integer values values or OptType of tensor of complex type with 32-bit float elements values values or OptType of tensor of complex type with 64-bit float elements values values

`onnx.Or` (ONNXOrOp)

ONNX Or operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`A`	tensor of 1-bit signless integer values
`B`	tensor of 1-bit signless integer values

Results:

Result	Description
`C`	tensor of 1-bit signless integer values

`onnx.PRelu` (ONNXPReluOp)

ONNX PRelu operation

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](Broadcasting.md).

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`slope`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result	Description
`Y`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

`onnx.Pad` (ONNXPadOp)

ONNX Pad operation

Given a tensor containing the data to be padded (data), a tensor containing the number of start and end pad values for axis (pads), (optionally) a mode, and (optionally) constant_value, a padded tensor (output) is generated.

The three supported modes are (similar to corresponding modes supported by numpy.pad):

1) constant(default) - pads with a given constant value as specified by constant_value (which defaults to 0, empty string, or False)

2) reflect - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis

3) edge - pads with the edge values of array

4) wrap - wrap-around padding as if the data tensor forms a torus

Example 1 (constant mode):

Insert 0 pads to the beginning of the second dimension.

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'constant'

constant_value = 0.0

output = [
    [0.0, 0.0, 1.0, 1.2],
    [0.0, 0.0, 2.3, 3.4],
    [0.0, 0.0, 4.5, 5.7],
]

Example 2 (reflect mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'reflect'

output = [
    [1.0, 1.2, 1.0, 1.2],
    [2.3, 3.4, 2.3, 3.4],
    [4.5, 5.7, 4.5, 5.7],
]

Example 3 (edge mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [0, 2, 0, 0]

mode = 'edge'

output = [
    [1.0, 1.0, 1.0, 1.2],
    [2.3, 2.3, 2.3, 3.4],
    [4.5, 4.5, 4.5, 5.7],
]

Example 4 (wrap mode):

data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]

pads = [2, 1, 1, 1]

mode = 'wrap'

output = [
    [3.4, 2.3, 3.4, 2.3],
    [5.7, 4.5, 5.7, 4.5],
    [1.2, 1.0, 1.2, 1.0],
    [3.4, 2.3, 3.4, 2.3],
    [5.7, 4.5, 5.7, 4.5],
    [1.2, 1.0, 1.2, 1.0],
]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`pads`	tensor of 64-bit signless integer values
`constant_value`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or none type
`axes`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or none type

Results:

Result Description

`onnx.PadV11` (ONNXPadV11Op)

ONNX Pad operation

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], ]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`pads`	tensor of 64-bit signless integer values
`constant_value`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

Results:

Result Description

`onnx.PadV13` (ONNXPadV13Op)

ONNX Pad operation

The three supported modes are (similar to corresponding modes supported by numpy.pad):

1) constant(default) - pads with a given constant value as specified by constant_value (which defaults to 0, empty string, or False)

2) reflect - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis

3) edge - pads with the edge values of array

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], ]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`pads`	tensor of 64-bit signless integer values
`constant_value`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or none type

Results:

Result Description

`onnx.PadV18` (ONNXPadV18Op)

ONNX Pad operation

The three supported modes are (similar to corresponding modes supported by numpy.pad):

1) constant(default) - pads with a given constant value as specified by constant_value (which defaults to 0, empty string, or False)

2) reflect - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis

3) edge - pads with the edge values of array

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],
]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`pads`	tensor of 64-bit signless integer values
`constant_value`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or none type
`axes`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or none type

Results:

Result Description

`onnx.PadV2` (ONNXPadV2Op)

ONNX Pad operation

Given data tensor, pads, mode, and value. Example: 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] 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], ], ]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Pow` (ONNXPowOp)

ONNX Pow operation

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](Broadcasting.md).

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

X tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Z`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.PrintSignature` (ONNXPrintSignatureOp)

ONNX Op to print type signature of its input operands

Print type signature of the op’s input operands. This operation is introduced early so as to preserve the name of the original ONNX op.

This operation is not part of the standard and was added to assist onnx-mlir.

Attributes:

Attribute	MLIR Type	Description
`op_name`	::mlir::StringAttr	string attribute

Operands:

Operand	Description
`input`	variadic of tensor of any type values or none type

`onnx.QLinearConv` (ONNXQLinearConvOp)

ONNX QLinearConv operation

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. When bias is present it must be quantized using scale = input scale * weight scale and zero point as 0.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`x`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`x_scale`	tensor of 32-bit float values
`x_zero_point`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`w`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`w_scale`	tensor of 32-bit float values
`w_zero_point`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`y_scale`	tensor of 32-bit float values
`y_zero_point`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`B`	tensor of 32-bit signless integer values or none type

Results:

Result	Description
`y`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values

`onnx.QLinearMatMul` (ONNXQLinearMatMulOp)

ONNX QLinearMatMul operation

Matrix product that behaves like numpy.matmul. 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 N-D tensor (per row for ‘a’ and per column for ‘b’). Scalar refers to per tensor quantization whereas N-D refers to per row or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be an M element vector [v_1, v_2, …, v_M] for per row quantization and K element vector of shape [v_1, v_2, …, v_K] for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization. Production must never overflow, and accumulation may overflow if and only if in 32 bits.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`a`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`a_scale`	tensor of 32-bit float values
`a_zero_point`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`b`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`b_scale`	tensor of 32-bit float values
`b_zero_point`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values
`y_scale`	tensor of 32-bit float values
`y_zero_point`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values

Results:

Result	Description
`y`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values

`onnx.QuantizeLinear` (ONNXQuantizeLinearOp)

ONNX QuantizeLinear operation

The linear quantization operator. It consumes a high precision tensor, a scale, and a zero point to compute the low precision / quantized tensor. The scale factor and zero point must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it’s uint8, or [-128, 127] if it’s int8. For (x / y_scale), it’s rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. ‘y_zero_point’ and ‘y’ must have same type. ‘y_zero_point’ is usually not used for quantization to float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz, but the quantization formula remains the same for consistency and the type of the attribute ‘y_zero_point’ still determines the quantization type.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	64-bit signed integer attribute
`saturate`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`x`	tensor of 32-bit float values or tensor of 16-bit float values or tensor of bfloat16 type values or tensor of 32-bit signless integer values
`y_scale`	tensor of 32-bit float values or tensor of 16-bit float values or tensor of bfloat16 type values or tensor of 32-bit signless integer values
`y_zero_point`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values or none type

Results:

Result	Description
`y`	tensor of 8-bit signless integer values or tensor of 8-bit unsigned integer values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values

`onnx.RMSLayerNormalization` (ONNXRMSLayerNormalizationOp)

ONNX RMSLayerNormalization operation

This is RMS layer normalization defined in ONNX as function. The overall computation can be split into two stages. The first stage is an approximate standardization, which makes the normalized elements have zero mean and unit variances. See Equation (4) in this paper. The computation required by standardization can be described by the following equations. DD = Mul(X, X) Var = ReduceMean<axes=normalized_axes>(DD) VarEps = Add(Var, epsilon) StdDev = Sqrt(VarEps) InvStdDev = Reciprocal(StdDev) Normalized = Mul(X, InvStdDev) where normalized_axes is [axis, ..., rank of X - 1]. The variables Var and StdDev stand for approximate variance and standard deviation, respectively. Depending on stash_type attribute, the actual computation must happen in different floating-point precision. For example, if stash_type is 1, this operator casts all input variables to 32-bit float, perform the computation, and finally cast Normalized back to the original type of X. The second stage then scales and shifts the outcome of the first stage using NormalizedScaled = Mul(Normalized, Scale) Y = Add(NormalizedScaled, B) The second stage doesn’t depends on stash_type. All equations are in this syntax. The same variable (i.e., input, output, and attribute) uses the same name in the equations above and this operator’s definition. Let d[i] indicate the i-th dimension of X. If X’s shape is [d[0], ..., d[axis-1], d[axis], ..., d[rank-1]], the shape of Mean and InvStdDev is [d[0], ..., d[axis-1], 1, ..., 1]. Y and X have the same shape.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	64-bit signed integer attribute
`epsilon`	::mlir::FloatAttr	32-bit float attribute
`stash_type`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`Scale`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`B`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or none type

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`InvStdDev`	tensor of 32-bit float values or tensor of bfloat16 type values or none type

`onnx.RNN` (ONNXRNNOp)

ONNX RNN operation

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) - alpha*x + 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(alpha*x + 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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`W`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`R`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`B`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`sequence_lens`	tensor of 32-bit signless integer values or none type
`initial_h`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`Y_h`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type

`onnx.RandomNormalLike` (ONNXRandomNormalLikeOp)

ONNX RandomNormalLike operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.RandomNormal` (ONNXRandomNormalOp)

ONNX RandomNormal operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ResultTypeInferenceOpInterface, ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.RandomUniformLike` (ONNXRandomUniformLikeOp)

ONNX RandomUniformLike operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.RandomUniform` (ONNXRandomUniformOp)

ONNX RandomUniform operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Range` (ONNXRangeOp)

ONNX Range operation

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]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`start`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`limit`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`delta`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result	Description
`output`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

`onnx.Reciprocal` (ONNXReciprocalOp)

ONNX Reciprocal operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceL1` (ONNXReduceL1Op)

ONNX ReduceL1 operation

Computes the L1 norm of the input tensor’s elements along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`noop_with_empty_axes`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`axes`	tensor of 64-bit signless integer values or none type

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceL1V13` (ONNXReduceL1V13Op)

ONNX ReduceL1 operation

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceL2` (ONNXReduceL2Op)

ONNX ReduceL2 operation

Computes the L2 norm of the input tensor’s elements along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`noop_with_empty_axes`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`axes`	tensor of 64-bit signless integer values or none type

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceL2V13` (ONNXReduceL2V13Op)

ONNX ReduceL2 operation

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceLogSumExp` (ONNXReduceLogSumExpOp)

ONNX ReduceLogSumExp operation

Computes the log sum exponent of the input tensor’s elements along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`noop_with_empty_axes`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`axes`	tensor of 64-bit signless integer values or none type

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceLogSumExpV13` (ONNXReduceLogSumExpV13Op)

ONNX ReduceLogSumExp operation

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceLogSum` (ONNXReduceLogSumOp)

ONNX ReduceLogSum operation

Computes the log sum of the input tensor’s elements along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or undefined otherwise.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`noop_with_empty_axes`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`axes`	tensor of 64-bit signless integer values or none type

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceLogSumV13` (ONNXReduceLogSumV13Op)

ONNX ReduceLogSum operation

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceMax` (ONNXReduceMaxOp)

ONNX ReduceMax operation

Computes the max of the input tensor’s elements along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields minus infinity (if supported by the datatype) or the minimum value of the data type otherwise.

If the input data type is Boolean, the comparison should consider False < True.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`noop_with_empty_axes`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand Description

data tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of 8-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 1-bit signless integer values

axes tensor of 64-bit signless integer values or none type

Results:

Result Description

reduced tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of 8-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 1-bit signless integer values

`onnx.ReduceMaxV13` (ONNXReduceMaxV13Op)

ONNX ReduceMax operation

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of 8-bit unsigned integer values or tensor of 8-bit signless integer values

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of 8-bit unsigned integer values or tensor of 8-bit signless integer values

`onnx.ReduceMaxV18` (ONNXReduceMaxV18Op)

ONNX ReduceMax operation

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`noop_with_empty_axes`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of 8-bit unsigned integer values or tensor of 8-bit signless integer values
`axes`	tensor of 64-bit signless integer values or none type

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of 8-bit unsigned integer values or tensor of 8-bit signless integer values

`onnx.ReduceMean` (ONNXReduceMeanOp)

ONNX ReduceMean operation

Computes the mean of the input tensor’s elements along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields undefined.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`noop_with_empty_axes`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`axes`	tensor of 64-bit signless integer values or none type

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceMeanV13` (ONNXReduceMeanV13Op)

ONNX ReduceMean operation

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceMin` (ONNXReduceMinOp)

ONNX ReduceMin operation

Computes the min of the input tensor’s elements along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields plus infinity (if supported by the datatype) or the maximum value of the data type otherwise.

If the input data type is Boolean, the comparison should consider False < True.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`noop_with_empty_axes`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand Description

axes tensor of 64-bit signless integer values or none type

Results:

Result Description

`onnx.ReduceMinV13` (ONNXReduceMinV13Op)

ONNX ReduceMin operation

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of 8-bit unsigned integer values or tensor of 8-bit signless integer values

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of 8-bit unsigned integer values or tensor of 8-bit signless integer values

`onnx.ReduceMinV18` (ONNXReduceMinV18Op)

ONNX ReduceMin operation

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`noop_with_empty_axes`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of 8-bit unsigned integer values or tensor of 8-bit signless integer values
`axes`	tensor of 64-bit signless integer values or none type

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or tensor of 8-bit unsigned integer values or tensor of 8-bit signless integer values

`onnx.ReduceProd` (ONNXReduceProdOp)

ONNX ReduceProd operation

Computes the product of the input tensor’s elements along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 1.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`noop_with_empty_axes`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`axes`	tensor of 64-bit signless integer values or none type

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceProdV13` (ONNXReduceProdV13Op)

ONNX ReduceProd operation

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceSum` (ONNXReduceSumOp)

ONNX ReduceSum operation

Computes the sum of the input tensor’s elements along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`noop_with_empty_axes`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`axes`	tensor of 64-bit signless integer values or none type

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceSumSquare` (ONNXReduceSumSquareOp)

ONNX ReduceSumSquare operation

Computes the sum square of the input tensor’s elements along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields 0.

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`keepdims`	::mlir::IntegerAttr	64-bit signed integer attribute
`noop_with_empty_axes`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`axes`	tensor of 64-bit signless integer values or none type

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceSumSquareV13` (ONNXReduceSumSquareV13Op)

ONNX ReduceSumSquare operation

The above behavior is similar to numpy, with the exception that numpy defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.ReduceSumV11` (ONNXReduceSumV11Op)

ONNX ReduceSum operation

Computes the sum of the input tensor’s element along the provided axes. The resulting tensor has the same rank as the input if keepdims equals 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 defaults keepdims to False instead of True.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`reduced`	tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Relu` (ONNXReluOp)

ONNX Relu operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 32-bit signless integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 32-bit float values or tensor of 32-bit signless integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.Reshape` (ONNXReshapeOp)

ONNX Reshape operation

Reshape the input tensor similar to numpy.reshape. First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor. At most one dimension of the new shape can be -1. In this case, the value is inferred from the size of the tensor and the remaining dimensions. A dimension could also be 0, in which case the actual dimension value is unchanged (i.e. taken from the input tensor). If ‘allowzero’ is set, and the new shape includes 0, the dimension will be set explicitly to zero (i.e. not taken from input tensor). Shape (second input) could be an empty shape, which means converting to a scalar. The input tensor’s shape and the output tensor’s shape are required to have the same number of elements.

If the attribute ‘allowzero’ is set, it is invalid for the specified shape to contain both a zero value and -1, as the value of the dimension corresponding to -1 cannot be determined uniquely.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`allowzero`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand Description

shape tensor of 64-bit signless integer values

Results:

Result Description

reshaped tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values

`onnx.Resize` (ONNXResizeOp)

ONNX Resize operation

output_dimension = floor(input_dimension * (roi_end - roi_start) * scale)

if input \“sizes\” is not specified.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`antialias`	::mlir::IntegerAttr	64-bit signed integer attribute
`axes`	::mlir::ArrayAttr	64-bit integer array attribute
`coordinate_transformation_mode`	::mlir::StringAttr	string attribute
`cubic_coeff_a`	::mlir::FloatAttr	32-bit float attribute
`exclude_outside`	::mlir::IntegerAttr	64-bit signed integer attribute
`extrapolation_value`	::mlir::FloatAttr	32-bit float attribute
`keep_aspect_ratio_policy`	::mlir::StringAttr	string attribute
`mode`	::mlir::StringAttr	string attribute
`nearest_mode`	::mlir::StringAttr	string attribute

Operands:

Operand	Description
`X`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`roi`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`scales`	tensor of 32-bit float values or none type
`sizes`	tensor of 64-bit signless integer values or none type

Results:

Result Description

Y tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.ResizeV10` (ONNXResizeV10Op)

ONNX Resize operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

scales tensor of 32-bit float values

Results:

Result Description

`onnx.ResizeV11` (ONNXResizeV11Op)

ONNX Resize operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`roi`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`scales`	tensor of 32-bit float values
`sizes`	tensor of 64-bit signless integer values or none type

Results:

Result Description

`onnx.ResizeV13` (ONNXResizeV13Op)

ONNX Resize operation

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`roi`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`scales`	tensor of 32-bit float values or none type
`sizes`	tensor of 64-bit signless integer values or none type

Results:

Result Description

Y tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.ResizeV18` (ONNXResizeV18Op)

ONNX Resize operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`antialias`	::mlir::IntegerAttr	64-bit signed integer attribute
`axes`	::mlir::ArrayAttr	64-bit integer array attribute
`coordinate_transformation_mode`	::mlir::StringAttr	string attribute
`cubic_coeff_a`	::mlir::FloatAttr	32-bit float attribute
`exclude_outside`	::mlir::IntegerAttr	64-bit signed integer attribute
`extrapolation_value`	::mlir::FloatAttr	32-bit float attribute
`keep_aspect_ratio_policy`	::mlir::StringAttr	string attribute
`mode`	::mlir::StringAttr	string attribute
`nearest_mode`	::mlir::StringAttr	string attribute

Operands:

Operand	Description
`X`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`roi`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or none type
`scales`	tensor of 32-bit float values or none type
`sizes`	tensor of 64-bit signless integer values or none type

Results:

Result Description

Y tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.Return` (ONNXReturnOp)

Function return operation

Syntax:

operation ::= `onnx.Return` attr-dict ($operands^ `:` type($operands))?

The onnx.Return operation represents a return operation within a function. The operation takes variable number of operands and produces no results. The operand number and types must match the signature of the function that contains the operation, with the exception that shaped types may have more specific shapes than the function signature result types, which allows rewrites of defining ops of operands to make their result shapes more specific. This operation terminates a func::FuncOp in the ONNX dialect and is replaced by func::ReturnOp in StandardFuncReturnPass before lowering to Krnl or other dialects.

Traits: AlwaysSpeculatableImplTrait, HasParent<func::FuncOp>, ReturnLike, Terminator

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), RegionBranchTerminatorOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`operands`	variadic of any type

`onnx.ReverseSequence` (ONNXReverseSequenceOp)

ONNX ReverseSequence operation

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]]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

sequence_lens tensor of 64-bit signless integer values

Results:

Result Description

`onnx.RoiAlign` (ONNXRoiAlignOp)

ONNX RoiAlign operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`rois`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
`batch_indices`	tensor of 64-bit signless integer values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Round` (ONNXRoundOp)

ONNX Round operation

Round takes one input Tensor and rounds the values, element-wise, meaning it finds the nearest integer for each value. In case of halves, the rule is to round them to the nearest even integer. If input x is integral, +0, -0, NaN, or infinite, x itself is returned. 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]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.STFT` (ONNXSTFTOp)

ONNX STFT operation

Computes the Short-time Fourier Transform of the signal.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`onesided`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand	Description
`signal`	tensor of 32-bit float values or tensor of 16-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`frame_step`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`window`	tensor of 32-bit float values or tensor of 16-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or none type
`frame_length`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or none type

Results:

Result	Description
`output`	tensor of 32-bit float values or tensor of 16-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.SVMClassifier` (ONNXSVMClassifierOp)

ONNX SVMClassifier operation

Support Vector Machine classifier

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`classlabels_ints`	::mlir::ArrayAttr	64-bit integer array attribute
`classlabels_strings`	::mlir::ArrayAttr	string array attribute
`coefficients`	::mlir::ArrayAttr	32-bit float array attribute
`kernel_params`	::mlir::ArrayAttr	32-bit float array attribute
`kernel_type`	::mlir::StringAttr	string attribute
`post_transform`	::mlir::StringAttr	string attribute
`prob_a`	::mlir::ArrayAttr	32-bit float array attribute
`prob_b`	::mlir::ArrayAttr	32-bit float array attribute
`rho`	::mlir::ArrayAttr	32-bit float array attribute
`support_vectors`	::mlir::ArrayAttr	32-bit float array attribute
`vectors_per_class`	::mlir::ArrayAttr	64-bit integer array attribute

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values

Results:

Result	Description
`Y`	tensor of string type values or tensor of 64-bit signless integer values
`Z`	tensor of 32-bit float values

`onnx.SVMRegressor` (ONNXSVMRegressorOp)

ONNX SVMRegressor operation

Support Vector Machine regression prediction and one-class SVM anomaly detection.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`coefficients`	::mlir::ArrayAttr	32-bit float array attribute
`kernel_params`	::mlir::ArrayAttr	32-bit float array attribute
`kernel_type`	::mlir::StringAttr	string attribute
`n_supports`	::mlir::IntegerAttr	64-bit signed integer attribute
`one_class`	::mlir::IntegerAttr	64-bit signed integer attribute
`post_transform`	::mlir::StringAttr	string attribute
`rho`	::mlir::ArrayAttr	32-bit float array attribute
`support_vectors`	::mlir::ArrayAttr	32-bit float array attribute

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values

Results:

Result	Description
`Y`	tensor of 32-bit float values

`onnx.Scaler` (ONNXScalerOp)

ONNX Scaler operation

Rescale input data, for example to standardize features by removing the mean and scaling to unit variance.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`offset`	::mlir::ArrayAttr	32-bit float array attribute
`scale`	::mlir::ArrayAttr	32-bit float array attribute

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values

Results:

Result	Description
`Y`	tensor of 32-bit float values

`onnx.Scan` (ONNXScanOp)

ONNX Scan operation

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 = <graph rnn-cell-1>, 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
}

Traits: AlwaysSpeculatableImplTrait

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`num_scan_inputs`	::mlir::IntegerAttr	64-bit signed integer attribute
`scan_input_axes`	::mlir::ArrayAttr	64-bit integer array attribute
`scan_input_directions`	::mlir::ArrayAttr	64-bit integer array attribute
`scan_output_axes`	::mlir::ArrayAttr	64-bit integer array attribute
`scan_output_directions`	::mlir::ArrayAttr	64-bit integer array attribute

Operands:

Operand Description

initial_state_and_scan_inputs variadic of tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values

Results:

Result Description

final_state_and_scan_outputs variadic of tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values

`onnx.ScatterElements` (ONNXScatterElementsOp)

ONNX ScatterElements operation

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.

reduction allows specification of an optional reduction operation, which is applied to all values in updates tensor into output at the specified indices. In cases where reduction is set to "none", indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. 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,

When reduction is set to some reduction function f, the update corresponding to the [i][j] entry is performed as below:

output[indices[i][j]][j] = f(output[indices[i][j]][j], updates[i][j]) if axis = 0,
output[i][indices[i][j]] = f(output[i][indices[i][j]], updates[i][j]) if axis = 1,

where the f is +, *, max or min as specified.

This operator is the inverse of GatherElements. It is similar to Torch’s Scatter operation.

(Opset 18 change): Adds max/min to the set of allowed reduction ops.

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]]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	64-bit signed integer attribute
`reduction`	::mlir::StringAttr	string attribute

Operands:

Operand	Description
`data`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`indices`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`updates`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

Results:

Result Description

`onnx.ScatterND` (ONNXScatterNDOp)

ONNX ScatterND operation

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.

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. Index values are allowed to be negative, as per the usual convention for counting backwards from the end, but are expected in the valid range.

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.

reduction allows specification of an optional reduction operation, which is applied to all values in updates tensor into output at the specified indices. In cases where reduction is set to "none", 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. When reduction is set to some reduction function f, output is calculated as follows:

output = np.copy(data)
update_indices = indices.shape[:-1]
for idx in np.ndindex(update_indices):
    output[indices[idx]] = f(output[indices[idx]], updates[idx])

where the f is +, *, max or min as specified.

This operator is the inverse of GatherND.

(Opset 18 change): Adds max/min to the set of allowed reduction ops.

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]]]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`reduction`	::mlir::StringAttr	string attribute

Operands:

Operand	Description
`data`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`indices`	tensor of 64-bit signless integer values
`updates`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

Results:

Result Description

`onnx.Scatter` (ONNXScatterOp)

ONNX Scatter operation

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 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]]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`data`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`indices`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`updates`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

Results:

Result Description

`onnx.Selu` (ONNXSeluOp)

ONNX Selu operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.SequenceAt` (ONNXSequenceAtOp)

ONNX SequenceAt operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

position tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result Description

tensor tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.SequenceConstruct` (ONNXSequenceConstructOp)

ONNX SequenceConstruct operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

inputs variadic of tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

Results:

Result Description

output_sequence SeqType of tensor of 8-bit unsigned integer values values or SeqType of tensor of 16-bit unsigned integer values values or SeqType of tensor of 32-bit unsigned integer values values or SeqType of tensor of 64-bit unsigned integer values values or SeqType of tensor of 8-bit signless integer values values or SeqType of tensor of 16-bit signless integer values values or SeqType of tensor of 32-bit signless integer values values or SeqType of tensor of 64-bit signless integer values values or SeqType of tensor of 16-bit float values values or SeqType of tensor of 32-bit float values values or SeqType of tensor of 64-bit float values values or SeqType of tensor of string type values values or SeqType of tensor of 1-bit signless integer values values or SeqType of tensor of complex type with 32-bit float elements values values or SeqType of tensor of complex type with 64-bit float elements values values

`onnx.SequenceEmpty` (ONNXSequenceEmptyOp)

ONNX SequenceEmpty operation

Construct an empty tensor sequence, with given data type.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Results:

Result Description

output SeqType of tensor of 8-bit unsigned integer values values or SeqType of tensor of 16-bit unsigned integer values values or SeqType of tensor of 32-bit unsigned integer values values or SeqType of tensor of 64-bit unsigned integer values values or SeqType of tensor of 8-bit signless integer values values or SeqType of tensor of 16-bit signless integer values values or SeqType of tensor of 32-bit signless integer values values or SeqType of tensor of 64-bit signless integer values values or SeqType of tensor of 16-bit float values values or SeqType of tensor of 32-bit float values values or SeqType of tensor of 64-bit float values values or SeqType of tensor of string type values values or SeqType of tensor of 1-bit signless integer values values or SeqType of tensor of complex type with 32-bit float elements values values or SeqType of tensor of complex type with 64-bit float elements values values

`onnx.SequenceErase` (ONNXSequenceEraseOp)

ONNX SequenceErase operation

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’.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

position tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or none type

Results:

Result Description

`onnx.SequenceInsert` (ONNXSequenceInsertOp)

ONNX SequenceInsert operation

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’.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input_sequence`	SeqType of tensor of 8-bit unsigned integer values values or SeqType of tensor of 16-bit unsigned integer values values or SeqType of tensor of 32-bit unsigned integer values values or SeqType of tensor of 64-bit unsigned integer values values or SeqType of tensor of 8-bit signless integer values values or SeqType of tensor of 16-bit signless integer values values or SeqType of tensor of 32-bit signless integer values values or SeqType of tensor of 64-bit signless integer values values or SeqType of tensor of 16-bit float values values or SeqType of tensor of 32-bit float values values or SeqType of tensor of 64-bit float values values or SeqType of tensor of string type values values or SeqType of tensor of 1-bit signless integer values values or SeqType of tensor of complex type with 32-bit float elements values values or SeqType of tensor of complex type with 64-bit float elements values values
`tensor`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`position`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or none type

Results:

Result Description

`onnx.SequenceLength` (ONNXSequenceLengthOp)

ONNX SequenceLength operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result	Description
`length`	tensor of 64-bit signless integer values

`onnx.SequenceMap` (ONNXSequenceMapOp)

ONNX SequenceMap operation

Applies a sub-graph to each sample in the input sequence(s).

Inputs can be either tensors or sequences, with the exception of the first input which must be a sequence. The length of the first input sequence will determine the number of samples in the outputs. Any other sequence inputs should have the same number of samples. The number of inputs and outputs, should match the one of the subgraph.

For each i-th element in the output, a sample will be extracted from the input sequence(s) at the i-th position and the sub-graph will be applied to it. The outputs will contain the outputs of the sub-graph for each sample, in the same order as in the input.

This operator assumes that processing each sample is independent and could executed in parallel or in any order. Users cannot expect any specific ordering in which each subgraph is computed.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, HasOnnxSubgraphOpInterface, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

additional_inputs variadic of tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values or SeqType of tensor of 8-bit unsigned integer values values or SeqType of tensor of 16-bit unsigned integer values values or SeqType of tensor of 32-bit unsigned integer values values or SeqType of tensor of 64-bit unsigned integer values values or SeqType of tensor of 8-bit signless integer values values or SeqType of tensor of 16-bit signless integer values values or SeqType of tensor of 32-bit signless integer values values or SeqType of tensor of 64-bit signless integer values values or SeqType of tensor of 16-bit float values values or SeqType of tensor of 32-bit float values values or SeqType of tensor of 64-bit float values values or SeqType of tensor of string type values values or SeqType of tensor of 1-bit signless integer values values or SeqType of tensor of complex type with 32-bit float elements values values or SeqType of tensor of complex type with 64-bit float elements values values

Results:

Result Description

out_sequence variadic of SeqType of tensor of 8-bit unsigned integer values values or SeqType of tensor of 16-bit unsigned integer values values or SeqType of tensor of 32-bit unsigned integer values values or SeqType of tensor of 64-bit unsigned integer values values or SeqType of tensor of 8-bit signless integer values values or SeqType of tensor of 16-bit signless integer values values or SeqType of tensor of 32-bit signless integer values values or SeqType of tensor of 64-bit signless integer values values or SeqType of tensor of 16-bit float values values or SeqType of tensor of 32-bit float values values or SeqType of tensor of 64-bit float values values or SeqType of tensor of string type values values or SeqType of tensor of 1-bit signless integer values values or SeqType of tensor of complex type with 32-bit float elements values values or SeqType of tensor of complex type with 64-bit float elements values values

`onnx.Shape` (ONNXShapeOp)

ONNX Shape operation

Takes a tensor as input and outputs an 1D int64 tensor containing the shape of the input tensor. Optional attributes start and end can be used to compute a slice of the input tensor’s shape. If start axis is omitted, the slice starts from axis 0. The end axis, if specified, is exclusive (and the returned value will not include the size of that axis). If the end axis is omitted, the axes upto the last one will be included. Negative axes indicate counting back from the last axis. Note that axes will be clamped to the range [0, r-1], where r is the rank of the input tensor if they are out-of-range (after adding r in the case of negative axis). Thus, specifying any end value > r is equivalent to specifying an end value of r, and specifying any start value < -r is equivalent to specifying a start value of 0.

Examples:

Input tensor with shape: [2, 3, 4]
No attributes specified.
Output: [2, 3, 4]

Input tensor with shape: [2, 3, 4]
start: -1
Output: [4]

Input tensor with shape: [2, 3, 4]
end: -1
Output: [2, 3]

Input tensor with shape: [2, 3, 4]
start: 1
end: 2
Output: [3]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`end`	::mlir::IntegerAttr	64-bit signed integer attribute
`start`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand Description

Results:

Result	Description
`shape`	tensor of 64-bit signless integer values

`onnx.ShapeTransform` (ONNXShapeTransformOp)

ONNX Element-wise shape transformation operation

This operator transforms a tensor into another tensor whose shape is changed by a given affine map. This is elemement-wise transformation, so each element in the input will be copied to an element in the output via the affine map. The affine map must be bijective.

For example, the following code is using onnx.ShapeTransform to reshape a tensor from 2D to 4D.

#reshape = affine_map(d0, d1) -> (d0/32, d0%32, d1/64, d1%64)
%Y = onnx.ShapeTransform(%arg0) {index_map = #reshape} :  (tensor<128x128xf32>) -> tensor<4x32x2x64xf32>

onnx.ShapeTransform will be finally materialized into an affine.for via lowering to krnl dialect, e.g.

%alloc = memref.alloc() {alignment = 16 : i64} : memref<4x32x2x64xf32>
affine.for %arg1 = 0 to 128 {
  affine.for %arg2 = 0 to 128 {
    %0 = affine.load %arg0[%arg1, %arg2] : memref< 128x128xf32 >
    affine.store %0, %alloc[%arg1 / 32, %arg1 % 32, %arg2 / 64, %arg2 % 64] : memref<4x32x2x64xf32>
  }
}

When being canonicalized, ShapeTransform operations are composed into a new ShapeTransform operation by composing their affine maps.

At this moment, this operation only supports static dimensions.

This operation is not part of the standard and was added to assist onnx-mlir.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`index_map`	::mlir::AffineMapAttr	AffineMap attribute

Operands:

Operand	Description
`input`	tensor of 32-bit float values

Results:

Result	Description
`output`	tensor of 32-bit float values

`onnx.Shrink` (ONNXShrinkOp)

ONNX Shrink operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

Results:

Result Description

`onnx.Sigmoid` (ONNXSigmoidOp)

ONNX Sigmoid operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.Sign` (ONNXSignOp)

ONNX Sign operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result Description

`onnx.Sin` (ONNXSinOp)

ONNX Sin operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Sinh` (ONNXSinhOp)

ONNX Sinh operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Size` (ONNXSizeOp)

ONNX Size operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result	Description
`size`	tensor of 64-bit signless integer values

`onnx.Slice` (ONNXSliceOp)

ONNX Slice operation

Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/user/basics.indexing.html?highlight=slice#slicing-and-striding

Slice uses the starts, ends, axes and steps inputs to select a sub-tensor of its input data tensor.

An effective starts[i], ends[i], and steps[i] must be computed for each i in [0, ... r-1] where r = rank(input) as follows:

If axes are omitted, they are set to [0, ..., r-1]. If steps are omitted, they are set to [1, ..., 1] of length len(starts)

The effective values are initialized as start[i] = 0, ends[i] = dims[i] where dims are the dimensions of input and steps[i] = 1.

All negative elements of axes are made non-negative by adding r to them, where r =rank(input).

All negative values in starts[i] and ends[i] have dims[axes[i]] added to them, where dims are the dimensions of input. Then start[axes[i]] is the adjusted starts[i] is clamped into the range [0, dims[axes[i]]] for positive stepping and [0, dims[axes[i]]-1] for negative stepping.

The clamping for the adjusted ends[i] depends on the sign of steps[i] and must accommodate copying 0 through dims[axes[i]] elements, so for positive stepping ends[axes[i]] is clamped to [0, dims[axes[i]]], while for negative stepping it is clamped to [-1, dims[axes[i]]-1].

Finally, steps[axes[i]] = steps[i].

For slicing to the end of a dimension with unknown size, it is recommended to pass in INT_MAX when slicing forward and ‘INT_MIN’ when slicing backward.

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],
]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`data`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`starts`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`ends`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`axes`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or none type
`steps`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or none type

Results:

Result Description

`onnx.SoftmaxCrossEntropyLoss` (ONNXSoftmaxCrossEntropyLossOp)

ONNX SoftmaxCrossEntropyLoss operation

Loss function that measures the softmax cross entropy between ‘scores’ and ‘labels’. This operator first computes a loss tensor whose shape is identical to the labels input. If the input is 2-D with shape (N, C), the loss tensor may be a N-element vector L = (l_1, l_2, …, l_N). If the input is N-D tensor with shape (N, C, D1, D2, …, Dk), the loss tensor L may have (N, D1, D2, …, Dk) as its shape and L[i,][j_1][j_2]…[j_k] denotes a scalar element in L. After L is available, this operator can optionally do a reduction operator.

shape(scores): (N, C) where C is the number of classes, or (N, C, D1, D2,…, Dk), with K >= 1 in case of K-dimensional loss.
shape(labels): (N) where each value is 0 <= labels[i] <= C-1, or (N, D1, D2,…, Dk), with K >= 1 in case of K-dimensional loss.

The loss for one sample, l_i, can calculated as follows:

l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk], where i is the index of classes.

l[i][d1][d2]...[dk] = -y[i][c][d1][d2]..[dk] * weights[c], if 'weights' is provided.

loss is zero for the case when label-value equals ignore_index.

l[i][d1][d2]...[dk]  = 0, when labels[n][d1][d2]...[dk] = ignore_index

where:

p = Softmax(scores)
y = Log(p)
c = labels[i][d1][d2]...[dk]

Finally, L is optionally reduced:

If reduction = ‘none’, the output is L with shape (N, D1, D2, …, Dk).
If reduction = ‘sum’, the output is scalar: Sum(L).
If reduction = ‘mean’, the output is scalar: ReduceMean(L), or if weight is provided: ReduceSum(L) / ReduceSum(W), where tensor W is of shape (N, D1, D2, ..., Dk) and W[n][d1][d2]...[dk] = weights[labels[i][d1][d2]...[dk]].

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`ignore_index`	::mlir::IntegerAttr	64-bit signed integer attribute
`reduction`	::mlir::StringAttr	string attribute

Operands:

Operand	Description
`scores`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`labels`	tensor of 32-bit signless integer values or tensor of 64-bit signless integer values
`weights`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or none type

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values
`log_prob`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values or none type

`onnx.Softmax` (ONNXSoftmaxOp)

ONNX Softmax operation

The operator computes the normalized exponential values for the given input:

Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1)

The "axis" attribute indicates the dimension along which Softmax will be performed. The output tensor has the same shape and contains the Softmax values of the corresponding input.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.SoftmaxV11` (ONNXSoftmaxV11Op)

ONNX Softmax operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Softplus` (ONNXSoftplusOp)

ONNX Softplus operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Softsign` (ONNXSoftsignOp)

ONNX Softsign operation

Calculates the softsign (x/(1+

)) of the given input tensor element-wise.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.SpaceToDepth` (ONNXSpaceToDepthOp)

ONNX SpaceToDepth operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

Results:

Result Description

`onnx.Split` (ONNXSplitOp)

ONNX Split operation

Split a tensor into a list of tensors, along the specified ‘axis’. Either input ‘split’ or the attribute ‘num_outputs’ should be specified, but not both. If the attribute ‘num_outputs’ is specified, then the tensor is split into equal sized parts. If the tensor is not evenly splittable into num_outputs, the last chunk will be smaller. If the input ‘split’ is specified, it indicates the sizes of each output in the split.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	64-bit signed integer attribute
`num_outputs`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand Description

split tensor of 64-bit signless integer values or none type

Results:

Result Description

`onnx.SplitToSequence` (ONNXSplitToSequenceOp)

ONNX SplitToSequence operation

Split a tensor into a sequence of tensors, along the specified ‘axis’. Lengths of the parts can be specified using the optional argument ‘split’. If the argument split' is not specified, a default scalar value of 1 is used as the value ofsplit’. ‘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 chunks all of size ‘split’ if possible. The last chunk alone may be smaller than ‘split’ if the ‘input’ size along the given axis ‘axis’ is not divisible by ‘split’. If ‘split’ is a 1-dimensional tensor, the input 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’.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

split tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or none type

Results:

Result Description

`onnx.SplitV11` (ONNXSplitV11Op)

ONNX Split operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

Results:

Result Description

`onnx.SplitV13` (ONNXSplitV13Op)

ONNX Split operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

split tensor of 64-bit signless integer values or none type

Results:

Result Description

`onnx.Sqrt` (ONNXSqrtOp)

ONNX Sqrt operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.Squeeze` (ONNXSqueezeOp)

ONNX Squeeze operation

Remove single-dimensional entries from the shape of a tensor. Takes an input 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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

axes tensor of 64-bit signless integer values or none type

Results:

Result Description

squeezed tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.SqueezeV11` (ONNXSqueezeV11Op)

ONNX Squeeze operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

data tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

Results:

Result Description

squeezed tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.StringNormalizer` (ONNXStringNormalizerOp)

ONNX StringNormalizer operation

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].

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of string type values

Results:

Result	Description
`Y`	tensor of string type values

`onnx.Sub` (ONNXSubOp)

ONNX Sub operation

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.

(Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

Results:

Result Description

`onnx.Sum` (ONNXSumOp)

ONNX Sum operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`data_0`	variadic of tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

Results:

Result	Description
`sum`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of bfloat16 type values

`onnx.Tan` (ONNXTanOp)

ONNX Tan operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Tanh` (ONNXTanhOp)

ONNX Tanh operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`input`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`output`	tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.TfIdfVectorizer` (ONNXTfIdfVectorizerOp)

ONNX TfIdfVectorizer operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of string type values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values

Results:

Result	Description
`Y`	tensor of 32-bit float values

`onnx.ThresholdedRelu` (ONNXThresholdedReluOp)

ONNX ThresholdedRelu operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand	Description
`X`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Results:

Result	Description
`Y`	tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

`onnx.Tile` (ONNXTileOp)

ONNX Tile operation

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]]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

repeats tensor of 64-bit signless integer values

Results:

Result Description

`onnx.TopK` (ONNXTopKOp)

ONNX TopK operation

Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of shape [a_0, a_1, …, a_{n-1}] and integer argument k, return two outputs:

Value tensor of shape [a_0, a_1, …, a_{axis-1}, k, a_{axis+1}, … a_{n-1}] which contains the values of the top k elements along the specified axis
Index tensor of shape [a_0, a_1, …, a_{axis-1}, k, a_{axis+1}, … a_{n-1}] 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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

K tensor of 64-bit signless integer values

Results:

Result Description

Values tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values

Indices tensor of 64-bit signless integer values

`onnx.Transpose` (ONNXTransposeOp)

ONNX Transpose operation

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).

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

Results:

Result Description

`onnx.TreeEnsembleClassifier` (ONNXTreeEnsembleClassifierOp)

ONNX TreeEnsembleClassifier operation

Tree Ensemble classifier. Returns the top class for each of N inputs.
The attributes named ‘nodes_X’ form a sequence of tuples, associated by index into the sequences, which must all be of equal length. These tuples define the nodes.
Similarly, all fields prefixed with ‘class_’ are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated class_weights index.
One and only one of classlabels_strings or classlabels_int64s will be defined. The class_ids are indices into this list.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`base_values`	::mlir::ArrayAttr	32-bit float array attribute
`class_ids`	::mlir::ArrayAttr	64-bit integer array attribute
`class_nodeids`	::mlir::ArrayAttr	64-bit integer array attribute
`class_treeids`	::mlir::ArrayAttr	64-bit integer array attribute
`class_weights`	::mlir::ArrayAttr	32-bit float array attribute
`classlabels_int64s`	::mlir::ArrayAttr	64-bit integer array attribute
`classlabels_strings`	::mlir::ArrayAttr	string array attribute
`nodes_falsenodeids`	::mlir::ArrayAttr	64-bit integer array attribute
`nodes_featureids`	::mlir::ArrayAttr	64-bit integer array attribute
`nodes_hitrates`	::mlir::ArrayAttr	32-bit float array attribute
`nodes_missing_value_tracks_true`	::mlir::ArrayAttr	64-bit integer array attribute
`nodes_modes`	::mlir::ArrayAttr	string array attribute
`nodes_nodeids`	::mlir::ArrayAttr	64-bit integer array attribute
`nodes_treeids`	::mlir::ArrayAttr	64-bit integer array attribute
`nodes_truenodeids`	::mlir::ArrayAttr	64-bit integer array attribute
`nodes_values`	::mlir::ArrayAttr	32-bit float array attribute
`post_transform`	::mlir::StringAttr	string attribute

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values

Results:

Result	Description
`Y`	tensor of string type values or tensor of 64-bit signless integer values
`Z`	tensor of 32-bit float values

`onnx.TreeEnsembleRegressor` (ONNXTreeEnsembleRegressorOp)

ONNX TreeEnsembleRegressor operation

Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and it is assumed they are the same length, and an index i will decode the tuple across these inputs. Each node id can appear only once for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by the associated target_weights index.
All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`aggregate_function`	::mlir::StringAttr	string attribute
`base_values`	::mlir::ArrayAttr	32-bit float array attribute
`n_targets`	::mlir::IntegerAttr	64-bit signed integer attribute
`nodes_falsenodeids`	::mlir::ArrayAttr	64-bit integer array attribute
`nodes_featureids`	::mlir::ArrayAttr	64-bit integer array attribute
`nodes_hitrates`	::mlir::ArrayAttr	32-bit float array attribute
`nodes_missing_value_tracks_true`	::mlir::ArrayAttr	64-bit integer array attribute
`nodes_modes`	::mlir::ArrayAttr	string array attribute
`nodes_nodeids`	::mlir::ArrayAttr	64-bit integer array attribute
`nodes_treeids`	::mlir::ArrayAttr	64-bit integer array attribute
`nodes_truenodeids`	::mlir::ArrayAttr	64-bit integer array attribute
`nodes_values`	::mlir::ArrayAttr	32-bit float array attribute
`post_transform`	::mlir::StringAttr	string attribute
`target_ids`	::mlir::ArrayAttr	64-bit integer array attribute
`target_nodeids`	::mlir::ArrayAttr	64-bit integer array attribute
`target_treeids`	::mlir::ArrayAttr	64-bit integer array attribute
`target_weights`	::mlir::ArrayAttr	32-bit float array attribute

Operands:

Operand	Description
`X`	tensor of 32-bit float values or tensor of 64-bit float values or tensor of 64-bit signless integer values or tensor of 32-bit signless integer values

Results:

Result	Description
`Y`	tensor of 32-bit float values

`onnx.Trilu` (ONNXTriluOp)

ONNX Trilu operation

Given a 2-D matrix or batches of 2-D matrices, returns the upper or lower triangular part of the tensor(s). The attribute "upper" determines whether the upper or lower part is retained. If set to true, the upper triangular matrix is retained. Lower triangular matrix is retained otherwise. Default value for the "upper" attribute is true. Trilu takes one input tensor of shape [*, N, M], where * is zero or more batch dimensions. The upper triangular part consists of the elements on and above the given diagonal (k). The lower triangular part consists of elements on and below the diagonal. All other elements in the matrix are set to zero. If k = 0, the triangular part on and above/below the main diagonal is retained. If upper is set to true, a positive k retains the upper triangular matrix excluding the main diagonal and (k-1) diagonals above it. A negative k value retains the main diagonal and |k| diagonals below it. If upper is set to false, a positive k retains the lower triangular matrix including the main diagonal and k diagonals above it. A negative k value excludes the main diagonal and (|k|-1) diagonals below it.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`upper`	::mlir::IntegerAttr	64-bit signed integer attribute

Operands:

Operand Description

k tensor of 64-bit signless integer values or none type

Results:

Result Description

`onnx.Unique` (ONNXUniqueOp)

ONNX Unique operation

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 occurrence 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]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

Results:

Result	Description
`Y`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`indices`	tensor of 64-bit signless integer values or none type
`inverse_indices`	tensor of 64-bit signless integer values or none type
`counts`	tensor of 64-bit signless integer values or none type

`onnx.Unsqueeze` (ONNXUnsqueezeOp)

ONNX Unsqueeze operation

Insert single-dimensional entries to the shape of an input tensor (data). Takes one required input 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 input 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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand Description

axes tensor of 64-bit signless integer values

Results:

Result Description

expanded tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.UnsqueezeV11` (ONNXUnsqueezeV11Op)

ONNX Unsqueeze operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

data tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

Results:

Result Description

expanded tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

`onnx.Upsample` (ONNXUpsampleOp)

ONNX Upsample operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

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

Operands:

Operand Description

scales tensor of 32-bit float values

Results:

Result Description

`onnx.UpsampleV7` (ONNXUpsampleV7Op)

ONNX Upsample operation

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`mode`	::mlir::StringAttr	string attribute
`scales`	::mlir::ArrayAttr	32-bit float array attribute

Operands:

Operand Description

Results:

Result Description

`onnx.Where` (ONNXWhereOp)

ONNX Where operation

Return elements, either from X or Y, depending on condition. Where behaves like numpy.where with three parameters.

This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check the doc.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`condition`	tensor of 1-bit signless integer values
`X`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values
`Y`	tensor of 8-bit unsigned integer values or tensor of 16-bit unsigned integer values or tensor of 32-bit unsigned integer values or tensor of 64-bit unsigned integer values or tensor of 8-bit signless integer values or tensor of 16-bit signless integer values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values or tensor of string type values or tensor of 1-bit signless integer values or tensor of complex type with 32-bit float elements values or tensor of complex type with 64-bit float elements values

Results:

Result Description

`onnx.Xor` (ONNXXorOp)

ONNX Xor operation

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`A`	tensor of 1-bit signless integer values
`B`	tensor of 1-bit signless integer values

Results:

Result	Description
`C`	tensor of 1-bit signless integer values

`onnx.Yield` (ONNXYieldOp)

ONNX yield operation

Syntax:

operation ::= `onnx.Yield` attr-dict ($operands^ `:` type($operands))?

The onnx.Yield operation represents a yield operation within an ONNX subgraph. The operation takes variable number of operands and produces no results.

This operation is not part of the standard and was added to assist onnx-mlir. It terminates a ONNXLoop/Scan/IfOp region.

Traits: AlwaysSpeculatableImplTrait, ReturnLike, Terminator

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), RegionBranchTerminatorOpInterface

Effects: MemoryEffects::Effect{}

Operands:

Operand	Description
`operands`	variadic of any type

`onnx.ZipMap` (ONNXZipMapOp)

ONNX ZipMap operation

Creates a map from the input and the attributes.
The values are provided by the input tensor, while the keys are specified by the attributes. Must provide keys in either classlabels_strings or classlabels_int64s (but not both).
The columns of the tensor correspond one-by-one to the keys specified by the attributes. There must be as many columns as keys.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), ShapeHelperOpInterface, ShapeInferenceOpInterface

Effects: MemoryEffects::Effect{}

Attributes:

Attribute	MLIR Type	Description
`classlabels_int64s`	::mlir::ArrayAttr	64-bit integer array attribute
`classlabels_strings`	::mlir::ArrayAttr	string array attribute

Operands:

Operand	Description
`X`	tensor of 32-bit float values

Results:

Result	Description
`Z`	SeqType of tuple with any combination of string type or 32-bit float values values or SeqType of tuple with any combination of 64-bit signless integer or 32-bit float values values

onnx.Abs (ONNXAbsOp)

Operands:

Results:

onnx.Acos (ONNXAcosOp)

Operands:

Results:

onnx.Acosh (ONNXAcoshOp)

Operands:

Results:

onnx.Adagrad (ONNXAdagradOp)

Attributes:

Operands:

Results:

onnx.Adam (ONNXAdamOp)

Attributes:

Operands:

Results:

onnx.Add (ONNXAddOp)

Operands:

Results:

onnx.And (ONNXAndOp)

Operands:

Results:

onnx.ArgMax (ONNXArgMaxOp)

Attributes:

Operands:

Results:

onnx.ArgMin (ONNXArgMinOp)

Attributes:

Operands:

Results:

onnx.ArrayFeatureExtractor (ONNXArrayFeatureExtractorOp)

Operands:

Results:

onnx.Asin (ONNXAsinOp)

Operands:

Results:

onnx.Asinh (ONNXAsinhOp)

Operands:

Results:

onnx.Atan (ONNXAtanOp)

Operands:

Results:

onnx.Atanh (ONNXAtanhOp)

Operands:

Results:

onnx.AveragePool (ONNXAveragePoolOp)

Attributes:

Operands:

Results:

onnx.BatchNormalizationInferenceMode (ONNXBatchNormalizationInferenceModeOp)

Attributes:

Operands:

Results:

onnx.BatchNormalization (ONNXBatchNormalizationOp)

Attributes:

Operands:

Results:

onnx.Bernoulli (ONNXBernoulliOp)

Attributes:

Operands:

Results:

onnx.Binarizer (ONNXBinarizerOp)

Attributes:

Operands:

Results:

onnx.BitShift (ONNXBitShiftOp)

Attributes:

Operands:

Results:

onnx.BitwiseAnd (ONNXBitwiseAndOp)

Operands:

Results:

onnx.BitwiseNot (ONNXBitwiseNotOp)

Operands:

Results:

onnx.BitwiseOr (ONNXBitwiseOrOp)

Operands:

Results:

onnx.BitwiseXor (ONNXBitwiseXorOp)

`onnx.Abs` (ONNXAbsOp)

`onnx.Acos` (ONNXAcosOp)

`onnx.Acosh` (ONNXAcoshOp)

`onnx.Adagrad` (ONNXAdagradOp)

`onnx.Adam` (ONNXAdamOp)

`onnx.Add` (ONNXAddOp)

`onnx.And` (ONNXAndOp)

`onnx.ArgMax` (ONNXArgMaxOp)

`onnx.ArgMin` (ONNXArgMinOp)

`onnx.ArrayFeatureExtractor` (ONNXArrayFeatureExtractorOp)

`onnx.Asin` (ONNXAsinOp)

`onnx.Asinh` (ONNXAsinhOp)

`onnx.Atan` (ONNXAtanOp)

`onnx.Atanh` (ONNXAtanhOp)

`onnx.AveragePool` (ONNXAveragePoolOp)

`onnx.BatchNormalizationInferenceMode` (ONNXBatchNormalizationInferenceModeOp)

`onnx.BatchNormalization` (ONNXBatchNormalizationOp)

`onnx.Bernoulli` (ONNXBernoulliOp)

`onnx.Binarizer` (ONNXBinarizerOp)

`onnx.BitShift` (ONNXBitShiftOp)

`onnx.BitwiseAnd` (ONNXBitwiseAndOp)

`onnx.BitwiseNot` (ONNXBitwiseNotOp)

`onnx.BitwiseOr` (ONNXBitwiseOrOp)

`onnx.BitwiseXor` (ONNXBitwiseXorOp)

`onnx.BlackmanWindow` (ONNXBlackmanWindowOp)

`onnx.CastLike` (ONNXCastLikeOp)

`onnx.CastMap` (ONNXCastMapOp)

`onnx.Cast` (ONNXCastOp)

`onnx.CategoryMapper` (ONNXCategoryMapperOp)

`onnx.Ceil` (ONNXCeilOp)

`onnx.Celu` (ONNXCeluOp)

`onnx.CenterCropPad` (ONNXCenterCropPadOp)

`onnx.Clip` (ONNXClipOp)

`onnx.ClipV11` (ONNXClipV11Op)

`onnx.ClipV12` (ONNXClipV12Op)

`onnx.ClipV6` (ONNXClipV6Op)

`onnx.Col2Im` (ONNXCol2ImOp)

`onnx.Compress` (ONNXCompressOp)

`onnx.ConcatFromSequence` (ONNXConcatFromSequenceOp)

`onnx.Concat` (ONNXConcatOp)

`onnx.ConcatShapeTranspose` (ONNXConcatShapeTransposeOp)