Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure
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onnx.Abs
(ONNXAbsOp)ONNX Abs operation
Absolute takes one input data (Tensor
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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{}
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
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 |
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 |
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{}
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 |
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 |
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{}
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 |
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{}
Operand | Description |
---|---|
A |
tensor of 1-bit signless integer values |
B |
tensor of 1-bit signless integer values |
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{}
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 |
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 |
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{}
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 |
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 |
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{}
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 |
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{}
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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)
or
output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)
if ceil_mode is enabled. pad_shape[i]
is the sum of pads along axis i
. Sliding windows that would start in the right padded region are ignored.
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{}
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 |
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
epsilon | ::mlir::FloatAttr | 32-bit float attribute |
momentum | ::mlir::FloatAttr | 32-bit float attribute |
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 |
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:
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{}
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
dtype | ::mlir::IntegerAttr | 64-bit signed integer attribute |
seed | ::mlir::FloatAttr | 32-bit float attribute |
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float 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{}
Attribute | MLIR Type | Description |
---|---|---|
threshold | ::mlir::FloatAttr | 32-bit float attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
direction | ::mlir::StringAttr | string attribute |
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 |
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{}
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 |
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{}
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 |
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{}
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 |
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{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
output_datatype | ::mlir::IntegerAttr | 64-bit signed integer attribute |
periodic | ::mlir::IntegerAttr | 64-bit signed integer attribute |
Operand | Description |
---|---|
size |
tensor of 32-bit signless integer values or tensor of 64-bit signless integer values |
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 or tensor of bfloat16 type values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
saturate | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
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 |
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 |
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.
{1.0, 0.0}
.{1, 0}
.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{}
Attribute | MLIR Type | Description |
---|---|---|
saturate | ::mlir::IntegerAttr | 64-bit signed integer attribute |
to | ::mlir::TypeAttr | any type attribute |
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 |
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.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{}
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 |
Operand | Description |
---|---|
X |
tensor of string type values or tensor of 64-bit signless integer values |
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
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
alpha | ::mlir::FloatAttr | 32-bit float attribute |
Operand | Description |
---|---|
X |
tensor of 32-bit float values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
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 |
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{}
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 |
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 or tensor of bfloat16 type values |
onnx.ClipV11
(ONNXClipV11Op)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{}
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 |
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
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{}
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 |
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.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{}
Attribute | MLIR Type | Description |
---|---|---|
max | ::mlir::FloatAttr | 32-bit float attribute |
min | ::mlir::FloatAttr | 32-bit float attribute |
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
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 |
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 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 |
condition |
tensor of 1-bit signless integer values |
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 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.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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
new_axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
value | ::mlir::Attribute | any attribute |
Operand | Description |
---|---|
input |
tensor of 64-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 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{}
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 |
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 |
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{}
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 |
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 |
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{}
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 |
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 |
Result | Description |
---|---|
Y |
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{}
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 |
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 |
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{}
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
exclusive | ::mlir::IntegerAttr | 64-bit signed integer attribute |
reverse | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
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 |
Operand | Description |
---|---|
inputs |
variadic of tensor of any type values or memref of any type values or none type |
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
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{}
Attribute | MLIR Type | Description |
---|---|---|
inverse | ::mlir::IntegerAttr | 64-bit signed integer attribute |
onesided | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
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 |
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 |
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{}
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
blocksize | ::mlir::IntegerAttr | 64-bit signed integer attribute |
mode | ::mlir::StringAttr | string attribute |
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
int64_vocabulary | ::mlir::ArrayAttr | 64-bit integer array attribute |
string_vocabulary | ::mlir::ArrayAttr | string array attribute |
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 |
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.
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
group_id | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
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 |
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.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{}
Attribute | MLIR Type | Description |
---|---|---|
seed | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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)
Zero point is calculated as:
intermediate_zero_point = qmin - min(x)/y_scale
y_zero_point = cast(round(saturate(itermediate_zero_point)))
Data quantization formula is:
y = saturate (round (x / y_scale) + y_zero_point)
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
Operand | Description |
---|---|
x |
tensor of 32-bit float values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
equation | ::mlir::StringAttr | string attribute |
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 |
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
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
Attribute | MLIR Type | Description |
---|---|---|
alpha | ::mlir::FloatAttr | 32-bit float attribute |
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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.
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{}
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 |
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{}
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 |
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 or tensor of bfloat16 type values |
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{}
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 |
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.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{}
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 |
shape |
tensor of 64-bit signless integer values |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
dtype | ::mlir::IntegerAttr | 64-bit signed integer attribute |
k | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
inputdimensions | ::mlir::ArrayAttr | 64-bit integer array attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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 |
onnx.Floor
(ONNXFloorOp)ONNX Floor operation
Floor takes one input data (Tensor
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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 tensorz
- update gater
- reset gateh
- hidden gatet
- time step (t-1 means previous time step)W[zrh]
- W parameter weight matrix for update, reset, and hidden gatesR[zrh]
- R recurrence weight matrix for update, reset, and hidden gatesWb[zrh]
- W bias vectors for update, reset, and hidden gatesRb[zrh]
- R bias vectors for update, reset, and hidden gatesWB[zrh]
- W parameter weight matrix for backward update, reset, and hidden gatesRB[zrh]
- R recurrence weight matrix for backward update, reset, and hidden gatesWBb[zrh]
- W bias vectors for backward update, reset, and hidden gatesRBb[zrh]
- R bias vectors for backward update, reset, and hidden gatesH
- Hidden statenum_directions
- 2 if direction == bidirectional else 1Activation functions:
NOTE: Below are optional
Softsign(x) - x/(1 + | x | ) |
Equations (Default: f=Sigmoid, g=Tanh):
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
batch_dims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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 |
onnx.Gelu
(ONNXGeluOp)ONNX Gelu operation
Gelu takes one input data (Tensor
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
Attribute | MLIR Type | Description |
---|---|---|
approximate | ::mlir::StringAttr | string attribute |
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 |
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
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{}
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 |
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 |
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{}
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
p | ::mlir::IntegerAttr | 64-bit signed integer attribute |
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
Result | Description |
---|---|
Y |
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{}
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
xs | ::mlir::ArrayAttr | string array attribute |
y | ::mlir::StringAttr | string attribute |
zs | ::mlir::ArrayAttr | string array attribute |
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 |
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{}
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 |
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{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
align_corners | ::mlir::IntegerAttr | 64-bit signed integer attribute |
mode | ::mlir::StringAttr | string attribute |
padding_mode | ::mlir::StringAttr | string attribute |
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 |
grid |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float 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 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.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.
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{}
Attribute | MLIR Type | Description |
---|---|---|
epsilon | ::mlir::FloatAttr | 32-bit float attribute |
num_groups | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
output_datatype | ::mlir::IntegerAttr | 64-bit signed integer attribute |
periodic | ::mlir::IntegerAttr | 64-bit signed integer attribute |
Operand | Description |
---|---|
size |
tensor of 32-bit signless integer values or tensor of 64-bit signless integer values |
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 or tensor of bfloat16 type values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
output_datatype | ::mlir::IntegerAttr | 64-bit signed integer attribute |
periodic | ::mlir::IntegerAttr | 64-bit signed integer attribute |
Operand | Description |
---|---|
size |
tensor of 32-bit signless integer values or tensor of 64-bit signless integer values |
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 or tensor of bfloat16 type values |
onnx.HardSigmoid
(ONNXHardSigmoidOp)ONNX HardSigmoid operation
HardSigmoid takes one input data (Tensor
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
Attribute | MLIR Type | Description |
---|---|---|
alpha | ::mlir::FloatAttr | 32-bit float attribute |
beta | ::mlir::FloatAttr | 32-bit float attribute |
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
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 |
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{}
Operand | Description |
---|---|
cond |
tensor of 1-bit signless integer values |
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{}
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
epsilon | ::mlir::FloatAttr | 32-bit float attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
detect_negative | ::mlir::IntegerAttr | 64-bit signed integer attribute |
detect_positive | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
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 |
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{}
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 |
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 |
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 tensori
- input gateo
- output gatef
- forget gatec
- cell gatet
- time step (t-1 means previous time step)W[iofc]
- W parameter weight matrix for input, output, forget, and cell gatesR[iofc]
- R recurrence weight matrix for input, output, forget, and cell gatesWb[iofc]
- W bias vectors for input, output, forget, and cell gatesRb[iofc]
- R bias vectors for input, output, forget, and cell gatesP[iof]
- P peephole weight vector for input, output, and forget gatesWB[iofc]
- W parameter weight matrix for backward input, output, forget, and cell gatesRB[iofc]
- R recurrence weight matrix for backward input, output, forget, and cell gatesWBb[iofc]
- W bias vectors for backward input, output, forget, and cell gatesRBb[iofc]
- R bias vectors for backward input, output, forget, and cell gatesPB[iof]
- P peephole weight vector for backward input, output, and forget gatesH
- Hidden statenum_directions
- 2 if direction == bidirectional else 1Activation functions:
NOTE: Below are optional
Softsign(x) - x/(1 + | x | ) |
Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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 |
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{}
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 |
Operand | Description |
---|---|
X |
tensor of string type values or tensor of 64-bit signless integer values or tensor of 32-bit float values |
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{}
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
target_layout | ::mlir::Attribute | layout attribute |
Operand | Description |
---|---|
data |
tensor of 16-bit float or 32-bit float values |
Result | Description |
---|---|
output |
tensor of 16-bit float or 32-bit float values |
onnx.LeakyRelu
(ONNXLeakyReluOp)ONNX LeakyRelu operation
LeakyRelu takes input data (Tensor
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
Attribute | MLIR Type | Description |
---|---|---|
alpha | ::mlir::FloatAttr | 32-bit float attribute |
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 |
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{}
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 |
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{}
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 |
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{}
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 |
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 |
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{}
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 |
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 |
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{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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
Interfaces: ConditionallySpeculatable
, HasOnnxSubgraphOpInterface
, NoMemoryEffect (MemoryEffectOpInterface)
, ResultTypeInferenceOpInterface
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
p | ::mlir::IntegerAttr | 64-bit signed integer attribute |
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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)
or
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{}
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 |
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
Result | Description |
---|---|
Y |
tensor of 32-bit signless integer values |
onnx.MatMul
(ONNXMatMulOp)ONNX MatMul operation
Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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{}
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 |
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)
or
output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)
if ceil_mode is enabled. pad_shape[i]
is the sum of pads along axis i
. Sliding windows that would start in the right padded region are ignored.
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{}
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 |
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 |
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{}
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 |
Operand | Description |
---|---|
X |
memref of any type values or tensor of any type values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
pooled_shape | ::mlir::ArrayAttr | 64-bit integer array attribute |
spatial_scale | ::mlir::FloatAttr | 32-bit float attribute |
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 |
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{}
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 |
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 |
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{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
output_datatype | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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 or tensor of bfloat16 type values |
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{}
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 |
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{}
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
fmod | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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.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{}
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 |
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 |
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{}
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 |
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.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{}
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 |
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
ignore_index | ::mlir::IntegerAttr | 64-bit signed integer attribute |
reduction | ::mlir::StringAttr | string attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
center_point_box | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
value | ::mlir::UnitAttr | unit attribute |
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{}
Attribute | MLIR Type | Description |
---|---|---|
norm | ::mlir::StringAttr | string attribute |
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 |
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{}
Operand | Description |
---|---|
X |
tensor of 1-bit signless integer values |
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{}
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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 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.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{}
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 |
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 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 |
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{}
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 or none type |
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{}
Attribute | MLIR Type | Description |
---|---|---|
type | ::mlir::TypeAttr | any type attribute |
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 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 or none type |
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{}
Operand | Description |
---|---|
A |
tensor of 1-bit signless integer values |
B |
tensor of 1-bit signless integer values |
Result | Description |
---|---|
C |
tensor of 1-bit signless integer values |
onnx.PRelu
(ONNXPReluOp)ONNX PRelu operation
PRelu takes input data (Tensor
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
mode | ::mlir::StringAttr | string attribute |
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 |
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 |
onnx.PadV11
(ONNXPadV11Op)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)
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{}
Attribute | MLIR Type | Description |
---|---|---|
mode | ::mlir::StringAttr | string attribute |
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 |
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.PadV13
(ONNXPadV13Op)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
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{}
Attribute | MLIR Type | Description |
---|---|---|
mode | ::mlir::StringAttr | string attribute |
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 |
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 |
onnx.PadV18
(ONNXPadV18Op)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
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{}
Attribute | MLIR Type | Description |
---|---|---|
mode | ::mlir::StringAttr | string attribute |
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
mode | ::mlir::StringAttr | string attribute |
pads | ::mlir::ArrayAttr | 64-bit integer array attribute |
value | ::mlir::FloatAttr | 32-bit float attribute |
Operand | Description |
---|---|
data |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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 |
---|---|
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.
Attribute | MLIR Type | Description |
---|---|---|
op_name | ::mlir::StringAttr | string attribute |
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{}
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 |
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 |
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: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or 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{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
saturate | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
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 |
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 |
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 tensori
- input gatet
- time step (t-1 means previous time step)Wi
- W parameter weight matrix for input gateRi
- R recurrence weight matrix for input gateWbi
- W parameter bias vector for input gateRbi
- R parameter bias vector for input gateWBi
- W parameter weight matrix for backward input gateRBi
- R recurrence weight matrix for backward input gateWBbi
- WR bias vectors for backward input gateRBbi
- RR bias vectors for backward input gateH
- Hidden statenum_directions
- 2 if direction == bidirectional else 1Activation functions:
NOTE: Below are optional
Softsign(x) - x/(1 + | x | ) |
Equations (Default: f=Tanh):
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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 |
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{}
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 |
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 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 |
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{}
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 |
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
.
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{}
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 |
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 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 |
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{}
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 |
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{}
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 |
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
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
noop_with_empty_axes | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
noop_with_empty_axes | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
noop_with_empty_axes | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
noop_with_empty_axes | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
noop_with_empty_axes | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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
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.
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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
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.
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{}
Attribute | MLIR Type | Description |
---|---|---|
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
noop_with_empty_axes | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
noop_with_empty_axes | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
noop_with_empty_axes | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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.ReduceMinV13
(ONNXReduceMinV13Op)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.
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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
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.
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{}
Attribute | MLIR Type | Description |
---|---|---|
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
noop_with_empty_axes | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
noop_with_empty_axes | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
noop_with_empty_axes | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
noop_with_empty_axes | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
allowzero | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values |
shape |
tensor of 64-bit signless integer values |
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
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{}
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
mode | ::mlir::StringAttr | string attribute |
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 |
scales |
tensor of 32-bit float 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 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.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{}
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 |
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 |
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 |
onnx.ResizeV13
(ONNXResizeV13Op)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{}
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 |
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 |
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{}
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 |
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 |
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{}
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{}
Attribute | MLIR Type | Description |
---|---|---|
batch_axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
time_axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 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 |
sequence_lens |
tensor of 64-bit signless integer 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 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.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{}
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 |
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 |
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{}
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
onesided | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
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 |
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 |
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{}
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
offset | ::mlir::ArrayAttr | 32-bit float array attribute |
scale | ::mlir::ArrayAttr | 32-bit float array attribute |
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 |
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
Interfaces: ConditionallySpeculatable
, HasOnnxSubgraphOpInterface
, NoMemoryEffect (MemoryEffectOpInterface)
, ResultTypeInferenceOpInterface
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
reduction | ::mlir::StringAttr | string attribute |
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
reduction | ::mlir::StringAttr | string attribute |
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 |
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 |
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 each entry in updates
, the target index in data
is obtained by combining
the corresponding entry in indices
with the index of the entry itself: the
index-value for dimension = axis is obtained from the value of the corresponding
entry in indices
and the index-value for dimension != axis is obtained from the
index of the entry itself.
For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry is performed as below:
output[indices[i][j]][j] = updates[i][j] if axis = 0,
output[i][indices[i][j]] = updates[i][j] if axis = 1,
This operator is the inverse of GatherElements. It is similar to Torch’s Scatter operation.
Example 1:
data = [
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
]
indices = [
[1, 0, 2],
[0, 2, 1],
]
updates = [
[1.0, 1.1, 1.2],
[2.0, 2.1, 2.2],
]
output = [
[2.0, 1.1, 0.0]
[1.0, 0.0, 2.2]
[0.0, 2.1, 1.2]
]
Example 2:
data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
indices = [[1, 3]]
updates = [[1.1, 2.1]]
axis = 1
output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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 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.Selu
(ONNXSeluOp)ONNX Selu operation
Selu takes one input data (Tensor
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
Attribute | MLIR Type | Description |
---|---|---|
alpha | ::mlir::FloatAttr | 32-bit float attribute |
gamma | ::mlir::FloatAttr | 32-bit float attribute |
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
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 |
position |
tensor of 32-bit signless integer values or tensor of 64-bit signless integer values |
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{}
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
dtype | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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{}
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 |
position |
tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or none type |
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.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{}
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 |
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.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{}
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 |
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{}
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
end | ::mlir::IntegerAttr | 64-bit signed integer attribute |
start | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
index_map | ::mlir::AffineMapAttr | AffineMap attribute |
Operand | Description |
---|---|
input |
tensor of 32-bit float values |
Result | Description |
---|---|
output |
tensor of 32-bit float values |
onnx.Shrink
(ONNXShrinkOp)ONNX Shrink operation
Shrink takes one input data (Tensor
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
Attribute | MLIR Type | Description |
---|---|---|
bias | ::mlir::FloatAttr | 32-bit float attribute |
lambd | ::mlir::FloatAttr | 32-bit float attribute |
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 |
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.Sigmoid
(ONNXSigmoidOp)ONNX Sigmoid operation
Sigmoid takes one input data (Tensor
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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{}
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 |
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 or tensor of bfloat16 type values |
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{}
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
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 or tensor of f8E4M3FN type values or tensor of f8E4M3FNUZ type values or tensor of f8E5M2 type values or tensor of f8E5M2FNUZ type values |
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{}
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 |
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 |
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.
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.
or
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:
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{}
Attribute | MLIR Type | Description |
---|---|---|
ignore_index | ::mlir::IntegerAttr | 64-bit signed integer attribute |
reduction | ::mlir::StringAttr | string attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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+ | x | )) of the given input tensor element-wise. |
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
blocksize | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
num_outputs | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
split |
tensor of 64-bit signless integer values or none type |
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 |
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 of
split’.
‘split’ must contain only positive numbers.
‘split’ is either a scalar (tensor of empty shape), or a 1-D tensor.
If ‘split’ is a scalar, then ‘input’ will be split into 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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
keepdims | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 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 |
split |
tensor of 32-bit signless integer values or tensor of 64-bit signless integer values or none type |
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.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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
split | ::mlir::ArrayAttr | 64-bit integer array attribute |
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 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 |
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 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.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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
split |
tensor of 64-bit signless integer values or none type |
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 |
onnx.Sqrt
(ONNXSqrtOp)ONNX Sqrt operation
Square root takes one input data (Tensor
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
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 |
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{}
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 |
axes |
tensor of 64-bit signless integer values or none type |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
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 |
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{}
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 |
Operand | Description |
---|---|
X |
tensor of string type values |
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{}
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 |
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.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{}
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 |
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{}
Operand | Description |
---|---|
input |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
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 |
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.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{}
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 |
Operand | Description |
---|---|
X |
tensor of string type values or tensor of 32-bit signless integer values or tensor of 64-bit signless integer values |
Result | Description |
---|---|
Y |
tensor of 32-bit float values |
onnx.ThresholdedRelu
(ONNXThresholdedReluOp)ONNX ThresholdedRelu operation
ThresholdedRelu takes one input data (Tensor
Traits: AlwaysSpeculatableImplTrait
Interfaces: ConditionallySpeculatable
, NoMemoryEffect (MemoryEffectOpInterface)
, ShapeHelperOpInterface
, ShapeInferenceOpInterface
Effects: MemoryEffects::Effect{}
Attribute | MLIR Type | Description |
---|---|---|
alpha | ::mlir::FloatAttr | 32-bit float attribute |
Operand | Description |
---|---|
X |
tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values |
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{}
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 |
repeats |
tensor of 64-bit signless integer values |
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 |
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:
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).
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{}
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 |
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 |
K |
tensor of 64-bit signless integer values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
perm | ::mlir::ArrayAttr | 64-bit integer array attribute |
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 |
Result | Description |
---|---|
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.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{}
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 |
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 |
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{}
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 |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
upper | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
k |
tensor of 64-bit signless integer values or none type |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axis | ::mlir::IntegerAttr | 64-bit signed integer attribute |
sorted | ::mlir::IntegerAttr | 64-bit signed integer attribute |
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 |
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{}
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 |
axes |
tensor of 64-bit signless integer values |
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{}
Attribute | MLIR Type | Description |
---|---|---|
axes | ::mlir::ArrayAttr | 64-bit integer array attribute |
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 |
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{}
Attribute | MLIR Type | Description |
---|---|---|
mode | ::mlir::StringAttr | string attribute |
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 |
scales |
tensor of 32-bit float 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 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.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{}
Attribute | MLIR Type | Description |
---|---|---|
mode | ::mlir::StringAttr | string attribute |
scales | ::mlir::ArrayAttr | 32-bit float array attribute |
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 |
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 |
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{}
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 |
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 |
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{}
Operand | Description |
---|---|
A |
tensor of 1-bit signless integer values |
B |
tensor of 1-bit signless integer values |
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{}
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{}
Attribute | MLIR Type | Description |
---|---|---|
classlabels_int64s | ::mlir::ArrayAttr | 64-bit integer array attribute |
classlabels_strings | ::mlir::ArrayAttr | string array attribute |
Operand | Description |
---|---|
X |
tensor of 32-bit float values |
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 |