StableHLO Dialect

Refer to the official documentation for more details.

Reactant.MLIR.Dialects.stablehlo.abs Method

abs

Performs element-wise abs operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#abs

Example

%result = stablehlo.abs %operand : tensor<3xi32>

source

Reactant.MLIR.Dialects.stablehlo.add Method

add

Performs element-wise addition of two tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#add

Example

%result = stablehlo.add %lhs, %rhs : tensor<2x2xi32>

source

Reactant.MLIR.Dialects.stablehlo.after_all Method

after_all

Ensures that the operations producing the inputs are executed before any operations that depend on result.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all

Example

%result = stablehlo.after_all %input0, %input1 : !stablehlo.token

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Reactant.MLIR.Dialects.stablehlo.all_gather Method

all_gather

Within each process group in the process grid, concatenates the values of the operand tensor from each process along all_gather_dim and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_gather

Example

%result:2 = "stablehlo.all_gather"(%operand0, %operand1) {
  all_gather_dim = 1 : i64,
  replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
  channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
} : (tensor<2x2xi64>, tensor<2x2xi64>) -> (tensor<2x4xi64>, tensor<2x4xi64>)

source

Reactant.MLIR.Dialects.stablehlo.all_reduce Method

all_reduce

Within each process group in the process grid, applies a reduction function computation to the values of the operand tensor from each process and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce

Example

%result:2 = "stablehlo.all_reduce"(%operand0, %operand0) ({
  ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
  %0 = "stablehlo.add"(%arg0, %arg1) : (tensor<i64>, tensor<i64>) -> tensor<i64>
  "stablehlo.return"(%0) : (tensor<i64>) -> ()
}) {
  replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
  channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
} : (tensor<4xi64>, tensor<4xi64>) -> (tensor<4xi64>, tensor<4xi64>)

source

Reactant.MLIR.Dialects.stablehlo.all_to_all Method

all_to_all

Within each process group in the process grid, splits the values of the operand tensor along split_dimension into parts, scatters the split parts between the processes, concatenates the scattered parts along concat_dimension and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_to_all

Example

%result:2 = "stablehlo.all_to_all"(%operand1, %operand2) {
  split_dimension = 1 : i64,
  concat_dimension = 0 : i64,
  split_count = 2 : i64,
  replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>
} : (tensor<2x4xi64>, tensor<2x4xi64>) -> (tensor<4x2xi64>, tensor<4x2xi64>)

source

Reactant.MLIR.Dialects.stablehlo.and Method

and

Performs element-wise AND of two tensors lhs and rhs and produces a result tensor

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#and

Example

%result = stablehlo.and %lhs, %rhs : tensor<2x2xi32>

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Reactant.MLIR.Dialects.stablehlo.atan2 Method

atan2

Performs element-wise atan2 operation on lhs and rhs tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#atan2

Example

%result = stablehlo.atan2 %lhs, %rhs : tensor<3xf64>

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Reactant.MLIR.Dialects.stablehlo.batch_norm_grad Method

batch_norm_grad

Computes gradients of several inputs of BatchNormTrainingOp backpropagating from grad_output, and produces grad_operand, grad_scale and grad_offset tensors.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_grad

Example

%grad_operand, %grad_scale, %grad_offset =
"stablehlo.batch_norm_grad"(%operand, %scale, %mean, %variance, %grad_output) {
  epsilon = 0.0 : f32,
  feature_index = 2 : i64
} : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>,
     tensor<2x2x2xf64>) -> (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)

source

Reactant.MLIR.Dialects.stablehlo.batch_norm_inference Method

batch_norm_inference

Normalizes the operand tensor across all dimensions except for the feature_index dimension and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_inference

Example

%result = "stablehlo.batch_norm_inference"(%operand, %scale, %offset, %mean, %variance) {
  epsilon = 0.0 : f32,
  feature_index = 2 : i64
} : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>, tensor<2xf64>) -> tensor<2x2x2xf64>

source

Reactant.MLIR.Dialects.stablehlo.batch_norm_training Method

batch_norm_training

Computes mean and variance across batch and spatial dimensions and normalizes the operand tensor, for each feature in the feature_index dimension and produces output, batch_mean and batch_var tensors.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#batch_norm_training

Example

%output, %batch_mean, %batch_var = "stablehlo.batch_norm_training"(%operand, %scale, %offset) {
  epsilon = 0.0 : f32,
  feature_index = 2 : i64
} : (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>) ->
    (tensor<2x2x2xf64>, tensor<2xf64>, tensor<2xf64>)

source

Reactant.MLIR.Dialects.stablehlo.bitcast_convert Method

bitcast_convert

Performs a bitcast operation on operand tensor and produces a result tensor where the bits of the entire operand tensor are reinterpreted using the type of the result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#bitcast_convert

Example

%result = stablehlo.bitcast_convert %operand : (tensor<f64>) -> tensor<4xf16>

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Reactant.MLIR.Dialects.stablehlo.broadcast Method

broadcast

This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.

Informally, this operation does the same thing as XLA's Broadcast: https://www.tensorflow.org/xla/operation_semantics#broadcast

Example

%result = stablehlo.broadcast %operand, sizes = [1, 2] : (tensor<3xi32>) -> tensor<1x2x3xi32>

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Reactant.MLIR.Dialects.stablehlo.broadcast_in_dim Method

broadcast_in_dim

Expands the dimensions and/or rank of an input tensor by duplicating the data in the operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#broadcast_in_dim

Example

%result = stablehlo.broadcast_in_dim %operand, dims = [2, 1] : (tensor<1x3xi32>) -> tensor<2x3x2xi32>

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Reactant.MLIR.Dialects.stablehlo.case Method

case

Produces the output from executing exactly one function from branches depending on the value of index.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#case

Example

%result0, %result1 = "stablehlo.case"(%index) ({
  stablehlo.return %result_branch0, %result_branch0 : tensor<2xi64>, tensor<2xi64>
}, {
  stablehlo.return %result_branch1, %result_branch1 : tensor<2xi64>, tensor<2xi64>
}) : (tensor<i32>) -> (tensor<2xi64>, tensor<2xi64>)

source

Reactant.MLIR.Dialects.stablehlo.cbrt Method

cbrt

Performs element-wise cubic root operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cbrt

Example

%result = stablehlo.cbrt %operand : tensor<4xf64>

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Reactant.MLIR.Dialects.stablehlo.ceil Method

ceil

Performs element-wise ceil of operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#ceil

Example

%result = stablehlo.ceil %operand : tensor<5xf32>

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Reactant.MLIR.Dialects.stablehlo.cholesky Method

cholesky

Computes the Cholesky decomposition of a batch of matrices.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cholesky

Example

%result = stablehlo.cholesky %a, lower = true : tensor<3x3xf64>

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Reactant.MLIR.Dialects.stablehlo.clamp Method

clamp

Clamps every element of the operand tensor between a minimum and maximum value and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#clamp

Example

%result = stablehlo.clamp %min, %operand, %max : tensor<3xi32>

source

Reactant.MLIR.Dialects.stablehlo.collective_broadcast Method

collective_broadcast

Within each process group in the process grid, send the value of the operand tensor from the source process to the target processes and produce a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_broadcast

Example

%result = "stablehlo.collective_broadcast"(%operand) {
  replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
  channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
} : (tensor<1x2xi64>) -> tensor<1x2xi64>

source

Reactant.MLIR.Dialects.stablehlo.collective_permute Method

collective_permute

Within each process group in the process grid, sends the value of the operand tensor from the source process to the target process and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#collective_permute

Example

%result = "stablehlo.collective_permute"(%operand) {
  source_target_pairs = dense<[[0, 1], [1, 2]]> : tensor<2x2xi64>,
  channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
} : (tensor<2x2xi64>) -> tensor<2x2xi64>

source

Reactant.MLIR.Dialects.stablehlo.compare Method

compare

Performs element-wise comparison of lhs and rhs tensors according to comparison_direction and compare_type, and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#compare

Example

%result = stablehlo.compare LT, %lhs, %rhs, FLOAT : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xi1>

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Reactant.MLIR.Dialects.stablehlo.complex Method

complex

Performs element-wise conversion to a complex value from a pair of real and imaginary values, lhs and rhs, and produces a result tensor. See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#complex

Example

%result = stablehlo.complex %lhs, %rhs : tensor<2xcomplex<f64>>

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Reactant.MLIR.Dialects.stablehlo.composite Method

composite

Encapsulates an operation made up (composed) of other StableHLO operations, taking inputs and composite_attributes and producing results. The semantics of the op are implemented by the decomposition attribute. The composite op can be replaced with its decomposition without changing program semantics. In cases where inlining the decomposition does not provide the same op semantics, prefer using custom_call.

The version field (defaults to 0) is used to denote when a composite's semantics change.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#composite

Example

%results = stablehlo.composite "my.op" %input0, %input1 {
  composite_attributes = {
    my_attribute = "my_value"
  },
  decomposition = @my_op,
  version = 1 : i32
} : (tensor<f32>, tensor<f32>) -> tensor<f32>

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Reactant.MLIR.Dialects.stablehlo.concatenate Method

concatenate

Concatenates a variadic number of tensors in inputs along dimension dimension in the same order as the given arguments and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#concatenate

Example

%result = stablehlo.concatenate %input0, %input1, dim = 0 : (tensor<3x2xi64>, tensor<1x2xi64>) -> tensor<4x2xi64>

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Reactant.MLIR.Dialects.stablehlo.constant Method

constant

Produces an output tensor from a constant value.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#constant

Example

%output = stablehlo.constant dense<[[0.0, 1.0], [2.0, 3.0]]> : tensor<2x2xf32>

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Reactant.MLIR.Dialects.stablehlo.convert Method

convert

Performs an element-wise conversion from one element type to another on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convert

Example

%result = stablehlo.convert %operand : (tensor<3xi64>) -> tensor<3xcomplex<f64>>

source

Reactant.MLIR.Dialects.stablehlo.convolution Method

convolution

Computes dot products between windows of lhs and slices of rhs and produces result.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convolution

Example

%result = stablehlo.convolution(%lhs, %rhs)
  dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f],
  window = {
    stride = [4, 4],
    pad = [[0, 0], [0, 0]],
    lhs_dilate = [2, 2],
    rhs_dilate = [1, 1],
    reverse = [0, 0]
  } {
    feature_group_count = 1 : i64,
    batch_group_count = 1 : i64,
    precision_config = [#stablehlo<precision DEFAULT>, #stablehlo<precision DEFAULT>]
  } :
(tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>) -> tensor<1x2x2x1xi64>

source

Reactant.MLIR.Dialects.stablehlo.cosine Method

cosine

Performs element-wise cosine operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#cosine

Example

%result = stablehlo.cosine %operand : tensor<2xf32>

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Reactant.MLIR.Dialects.stablehlo.count_leading_zeros Method

count_leading_zeros

Performs element-wise count of the number of leading zero bits in the operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#count_leading_zeros

Example

%result = stablehlo.count_leading_zeros %operand : tensor<2x2xi64>

source

Reactant.MLIR.Dialects.stablehlo.create_token Method

create_token

This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.

Informally, this operation does the same thing as AfterAllOp with 0 inputs: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all

Example

%output = stablehlo.create_token : !stablehlo.token

source

Reactant.MLIR.Dialects.stablehlo.cross_replica_sum Method

cross_replica_sum

This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.

Informally, this operation does the same thing as AllReduceOp with channel_id = 0, use_global_device_ids = false and computation implementing addition: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce

Example

%result = "stablehlo.cross-replica-sum"(%operand) {
  replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>
} : (tensor<4xf32>) -> tensor<4xf32>

source

Reactant.MLIR.Dialects.stablehlo.custom_call Method

custom_call

Encapsulates an implementation-defined operation call_target_name that takes inputs and called_computations and produces results.

Depending on the API version there are two ways to pass extra bits of static information to the external function:

1. Use API_VERSION_TYPED_FFI which allows passing a dictionary attribute.

2. Use a previous API version with a StringAttr to encode backend config.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#custom_call

Example

%results = stablehlo.custom_call @foo(%input0) {
  backend_config = {bar = 42 : i32},
  api_version = 4 : i32,
  called_computations = [@foo]
} : (tensor<f64>) -> tensor<f64>

source

Reactant.MLIR.Dialects.stablehlo.divide Method

divide

Performs element-wise division of dividend lhs and divisor rhs tensors and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#divide

Example

%result = stablehlo.divide %lhs, %rhs : tensor<4xf32>

source

Reactant.MLIR.Dialects.stablehlo.dot Method

dot

This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.

Informally, this operation does the same thing as XLA's Dot: https://www.tensorflow.org/xla/operation_semantics#dot

Example

%0 = stablehlo.dot %arg0, %arg1 : (tensor<1x2xi32>, tensor<2x1xi32>) -> tensor<1x1xi32>

source

Reactant.MLIR.Dialects.stablehlo.dot_general Method

dot_general

Computes dot products between slices of lhs and slices of rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dot_general

Example

%result = stablehlo.dot_general %lhs, %rhs,
  batching_dims = [0] x [0],
  contracting_dims = [2] x [1],
  precision = [DEFAULT, DEFAULT],
  algorithm = <lhs_precision_type = tf32, rhs_precision_type = tf32, accumulation_type = f32, lhs_component_count = 1, rhs_component_count = 1, num_primitive_operations = 1, allow_imprecise_accumulation = false>
  : (tensor<2x2x2xi64>, tensor<2x2x2xi64>) -> tensor<2x2x2xi64>

source

Reactant.MLIR.Dialects.stablehlo.dynamic_broadcast_in_dim Method

dynamic_broadcast_in_dim

This operation is functionally identical to broadcast_in_dim op, but the result shape is specified dynamically via output_dimensions.

It also accepts optional attributes to express static knowledge about the expanding behavior of dimensions. If not specified, all dimensions are assumed to be possibly expanding. The sets of dimensions that are known to be expanding and the set of dimensions that are known to be non-expanding must be disjoint and they must be a subset of the operand's dimensions.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_broadcast_in_dim

Example

%operand = stablehlo.constant dense<[[1, 2, 3]]> : tensor<1x3xi64>
%output_dimensions = stablehlo.constant dense<[2, 3, 2]> : tensor<3xi64>
%result = "stablehlo.dynamic_broadcast_in_dim"(%operand, %output_dimensions) {
  broadcast_dimensions = array<i64: 2, 1>,
  known_expanding_dimensions = array<i64: 0>,
  known_nonexpanding_dimensions = array<i64: 1>
} : (tensor<1x3xi64>, tensor<3xi64>) -> tensor<2x3x2xi64>

source

Reactant.MLIR.Dialects.stablehlo.dynamic_conv Method

dynamic_conv

This operation is functionally identical to convolution op, but the padding is specified dynamically via padding.

Example

%padding = stablehlo.constant dense<2> : tensor<2x2xi64>
%result = "stablehlo.dynamic_conv"(%lhs, %rhs, %padding) {
  window_strides = array<i64: 4, 4>,
  lhs_dilation = array<i64: 2, 2>,
  rhs_dilation = array<i64: 1, 1>,
  window_reversal = array<i1: false, false>,
  dimension_numbers = #stablehlo.conv<[b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f]>,
  batch_group_count = 1 : i64,
  feature_group_count = 1 : i64,
  precision_config = [#stablehlo<precision DEFAULT>, #stablehlo<precision DEFAULT>]
} : (tensor<1x4x4x1xi64>, tensor<3x3x1x1xi64>, tensor<2x2xi64>) -> tensor<1x2x2x1xi64>

source

Reactant.MLIR.Dialects.stablehlo.dynamic_gather Method

dynamic_gather

This operation is functionally identical to gather op, with the slice_sizes specified dynamically as an operand.

Example

%slice_sizes = stablehlo.constant dense<[1, 2, 2]> : tensor<3xi64>
%result = "stablehlo.dynamic_gather"(%operand, %start_indices, %slice_sizes) {
  dimension_numbers = #stablehlo.gather<
    offset_dims = [2, 3],
    collapsed_slice_dims = [0],
    start_index_map = [0, 2],
    index_vector_dim = 2>,
  indices_are_sorted = false
} : (tensor<3x4x2xi64>, tensor<2x3x2xi64>, tensor<3xi64>) -> tensor<2x3x2x2xi64>

source

Reactant.MLIR.Dialects.stablehlo.dynamic_iota Method

dynamic_iota

This operation is functionally identical to iota op, but the result shape is specified dynamically via output_shape.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_iota

Example

%output_shape = stablehlo.constant dense<[4, 5]> : tensor<2xi64>
%0 = stablehlo.dynamic_iota %output_shape, dim = 0 : (tensor<2xi64>) -> tensor<4x5xi64>

source

Reactant.MLIR.Dialects.stablehlo.dynamic_pad Method

dynamic_pad

This operation is functionally identical to pad https://github.com/openxla/stablehlo/pull/2306#discussion_r1595669709 op, but with edge_padding_low,edge_padding_highandinterior_padding specified dynamically as values.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_pad

Example

%edge_padding_low = stablehlo.constant dense<[0, 1]> : tensor<2xi32>
%edge_padding_high = stablehlo.constant dense<[2, 1]> : tensor<2xi32>
%interior_padding = stablehlo.constant dense<[1, 2]> : tensor<2xi32>
%result = stablehlo.dynamic_pad %operand, %padding_value,
            %edge_padding_low, %edge_padding_high, %interior_padding
            : (tensor<2x3xi64>, tensor<i64>, tensor<2xi64>, tensor<2xi64>, tensor<2xi64>) -> tensor<5x9xi64>

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Reactant.MLIR.Dialects.stablehlo.dynamic_reshape Method

dynamic_reshape

This operation is functionally identical to reshape op, but the result shape is specified dynamically via output_shape.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_reshape

Example

%output_shape = stablehlo.constant dense<[3, 2]> : tensor<2xi64>
%result = stablehlo.dynamic_reshape %operand, %output_shape : (tensor<2x3xi64>, tensor<2xi64>) -> tensor<3x2xi64>

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Reactant.MLIR.Dialects.stablehlo.dynamic_slice Method

dynamic_slice

Extracts a slice from the operand using dynamically-computed starting indices and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_slice

Example

%result = stablehlo.dynamic_slice %operand, %start_indices0, %start_indices1, sizes = [2, 2]
  : (tensor<4x4xi32>, tensor<i64>, tensor<i64>) -> tensor<2x2xi32>

source

Reactant.MLIR.Dialects.stablehlo.dynamic_update_slice Method

dynamic_update_slice

Produces a result tensor which is equal to the operand tensor except that the slice starting at start_indices is updated with the values in update.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#dynamic_update_slice

Example

%result = stablehlo.dynamic_update_slice %operand, %update, %start_indices0, %start_indices1
  : (tensor<4x4xi32>, tensor<2x2xi32>, tensor<i64>, tensor<i64>) -> tensor<4x4xi32>

source

Reactant.MLIR.Dialects.stablehlo.einsum Method

einsum

This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.

Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum

Example

%result = "stablehlo.einsum"(%lhs, %rhs) {
  einsum_config = "ab,bc->ac"
} : (tensor<4x16xf32>, tensor<16x4xf32>) -> tensor<4x4xf32>

source

Reactant.MLIR.Dialects.stablehlo.exponential Method

exponential

Performs element-wise exponential operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential

Example

%result = stablehlo.exponential %operand : tensor<2x2xf64>

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Reactant.MLIR.Dialects.stablehlo.exponential_minus_one Method

exponential_minus_one

Performs element-wise exponential minus one operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential_minus_one

Example

%result = stablehlo.exponential_minus_one %operand : tensor<2xf64>

source

Reactant.MLIR.Dialects.stablehlo.fft Method

fft

Performs the forward and inverse Fourier transforms for real and complex inputs/outputs.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#fft

Example

%result = stablehlo.fft %operand, type = FFT, length = [4] : (tensor<4xcomplex<f32>>) -> tensor<4xcomplex<f32>>

source

Reactant.MLIR.Dialects.stablehlo.floor Method

floor

Performs element-wise floor of operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#floor

Example

%result = stablehlo.floor %operand : tensor<2xf32>

source

Reactant.MLIR.Dialects.stablehlo.gather Method

gather

Gathers slices from operand tensor from offsets specified in start_indices and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather

Example

%result = "stablehlo.gather"(%operand, %start_indices) {
  dimension_numbers = #stablehlo.gather<
    offset_dims = [3, 4],
    collapsed_slice_dims = [1],
    operand_batching_dims = [0],
    start_indices_batching_dims = [1],
    start_index_map = [2, 1],
    index_vector_dim = 3>,
  slice_sizes = array<i64: 1, 1, 2, 2>,
  indices_are_sorted = false
} : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>) -> tensor<2x2x3x2x2xi64>

source

Reactant.MLIR.Dialects.stablehlo.get_dimension_size Method

get_dimension_size

Produces the size of the given dimension of the operand.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_dimension_size

Example

%result = stablehlo.get_dimension_size %operand, dim = 1 : (tensor<2x3xi64>) -> tensor<i32>

source

Reactant.MLIR.Dialects.stablehlo.get_tuple_element Method

get_tuple_element

Extracts element at index position of the operand tuple and produces a result.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#get_tuple_element

Example

%result = stablehlo.get_tuple_element %operand[0] : (tuple<tensor<2xf64>, tuple<tensor<i64>>>) -> tensor<2xf64>

source

Reactant.MLIR.Dialects.stablehlo.if_ Method

if_

Produces the output from executing exactly one branch from true_branch or false_branch depending on the value of pred.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#if

Example

%result = "stablehlo.if"(%pred) ({ "stablehlo.return"(%result_true_branch) : (tensor<i32>) -> () }, { "stablehlo.return"(%result_false_branch) : (tensor<i32>) -> () }) : (tensor<i1>) -> tensor<i32>

source

Reactant.MLIR.Dialects.stablehlo.imag Method

imag

Extracts the imaginary part, element-wise, from the operand and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#imag

Example

%result = stablehlo.imag %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>

source

Reactant.MLIR.Dialects.stablehlo.infeed Method

infeed

Reads data from the infeed and produces results.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#infeed

Example

%results0:2 = "stablehlo.infeed"(%token) :
    (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)

source

Reactant.MLIR.Dialects.stablehlo.iota Method

iota

Fills an output tensor with values in increasing order starting from zero along the iota_dimension dimension.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#iota

Example

%output = stablehlo.iota dim = 0 : tensor<4x5xi32>

source

Reactant.MLIR.Dialects.stablehlo.is_finite Method

is_finite

Performs element-wise check whether the value in x is finite (i.e. is neither +Inf, -Inf, nor NaN) and produces a y tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#is_finite

Example

%y = stablehlo.is_finite %x : (tensor<7xf64>) -> tensor<7xi1>

source

Reactant.MLIR.Dialects.stablehlo.log Method

log

Performs element-wise logarithm operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log

Example

%result = stablehlo.log %operand : tensor<2x2xf64>

source

Reactant.MLIR.Dialects.stablehlo.log_plus_one Method

log_plus_one

Performs element-wise logarithm plus one operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#log_plus_one

Example

%result = stablehlo.log_plus_one %operand : tensor<5xf64>

source

Reactant.MLIR.Dialects.stablehlo.logistic Method

logistic

Performs element-wise logistic operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#logistic

Example

%result = stablehlo.logistic %operand : tensor<2x2xf64>

source

Reactant.MLIR.Dialects.stablehlo.map Method

map

Applies a map function computation to inputs along the dimensions and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#map

Example

%result = "stablehlo.map"(%input0, %input1) ({
  ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
    %0 = stablehlo.multiply %arg0, %arg1 : tensor<i64>
    stablehlo.return %0 : tensor<i64>
}) {
  dimensions = array<i64: 0, 1>
} : (tensor<2x2xi64>, tensor<2x2xi64>) -> tensor<2x2xi64>

source

Reactant.MLIR.Dialects.stablehlo.maximum Method

maximum

Performs element-wise max operation on tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#maximum

Example

%result = stablehlo.maximum %lhs, %rhs : tensor<4xf32>

source

Reactant.MLIR.Dialects.stablehlo.minimum Method

minimum

Performs element-wise min operation on tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#minimum

Example

%result = stablehlo.minimum %lhs, %rhs : tensor<4xf32>

source

Reactant.MLIR.Dialects.stablehlo.multiply Method

multiply

Performs element-wise product of two tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#multiply

Example

%result = stablehlo.multiply %lhs, %rhs : tensor<2xi32>

source

Reactant.MLIR.Dialects.stablehlo.negate Method

negate

Performs element-wise negation of operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#negate

Example

%result = stablehlo.negate %operand : tensor<2x3xi32>

source

Reactant.MLIR.Dialects.stablehlo.not Method

not

Performs element-wise NOT of tensor operand of type integer and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#not

Example

%result = stablehlo.not %operand : tensor<5x3x1xi1>

source

Reactant.MLIR.Dialects.stablehlo.optimization_barrier Method

optimization_barrier

Ensures that the operations that produce the operand are executed before any operations that depend on the result and prevents compiler transformations from moving operations across the barrier. Other than that, the operation is an identity, i.e. result = operand.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#optimization_barrier

Example

%result0, %result1 = stablehlo.optimization_barrier %operand0, %operand1 : tensor<f32>, tensor<f32>

source

Reactant.MLIR.Dialects.stablehlo.or Method

or

Performs element-wise OR of two tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#or

Example

%result = stablehlo.or %lhs, %rhs : tensor<2xi1>

source

Reactant.MLIR.Dialects.stablehlo.outfeed Method

outfeed

Writes inputs to the outfeed and produces a result token.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#outfeed

Example

%result = "stablehlo.outfeed"(%input0, %token) :
    (tensor<2x2x2xi64>, !stablehlo.token) -> !stablehlo.token

source

Reactant.MLIR.Dialects.stablehlo.pad Method

pad

Expands operand by padding around the tensor as well as between the elements of the tensor with the given padding_value.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#pad

Example

%0 = stablehlo.pad %arg0, %arg1, low = [0, 1], high = [2, 1], interior = [1, 2]
  : (tensor<2x3xi32>, tensor<i32>) -> tensor<5x9xi32>

source

Reactant.MLIR.Dialects.stablehlo.partition_id Method

partition_id

Produces partition_id of the current process.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#partition_id

Example

%result = stablehlo.partition_id : tensor<ui32>

source

Reactant.MLIR.Dialects.stablehlo.popcnt Method

popcnt

Performs element-wise count of the number of bits set in the operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#popcnt

Example

%result = stablehlo.popcnt %operand : tensor<4xi64>

source

Reactant.MLIR.Dialects.stablehlo.power Method

power

Performs element-wise exponentiation of lhs tensor by rhs tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#power

Example

%result = stablehlo.power %lhs, %rhs : tensor<6xf64>

source

Reactant.MLIR.Dialects.stablehlo.real Method

real

Extracts the real part, element-wise, from the operand and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#real

Example

%result = stablehlo.real %operand : (tensor<2xcomplex<f32>>) -> tensor<2xf32>

source

Reactant.MLIR.Dialects.stablehlo.real_dynamic_slice Method

real_dynamic_slice

This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.

Informally, this operation does the same thing as SliceOp except that start_indices, limit_indices and strides are specified dynamically: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice

Example

%result = stablehlo.real_dynamic_slice %operand,
            %start_indices, %limit_indices, %strides
       : (tensor<256x?xf32>, tensor<2xindex>, tensor<2xindex>, tensor<2xindex>) -> tensor<256x?xf32>

source

Reactant.MLIR.Dialects.stablehlo.recv Method

recv

Receives data from a channel with channel_id and produces results.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#recv

Example

%results:2 = "stablehlo.recv"(%token) {
  channel_handle = #stablehlo.channel_handle<handle = 0, type = 1>,
  is_host_transfer = false,
  source_target_pairs = dense<[[0, 1], [1, 2]]> : tensor<2x2xi64>
} : (!stablehlo.token) -> (tensor<2x2xi64>, !stablehlo.token)

source

Reactant.MLIR.Dialects.stablehlo.reduce Method

reduce

Applies a reduction function body to inputs and init_values along the dimensions and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce

Example

%result = "stablehlo.reduce"(%input, %init_value) ({
  ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
    %0 = stablehlo.add %arg0, %arg1 : tensor<i64>
    stablehlo.return %0 : tensor<i64>
}) {
  dimensions = array<i64: 1>
} : (tensor<1x6xi64>, tensor<i64>) -> tensor<1xi64>

source

Reactant.MLIR.Dialects.stablehlo.reduce_precision Method

reduce_precision

Performs element-wise conversion of operand to another floating-point type that uses exponent_bits and mantissa_bits and back to the original floating-point type and produces an output tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_precision

Example

%output = stablehlo.reduce_precision %operand, format = e5m10 : tensor<6xf64>

source

Reactant.MLIR.Dialects.stablehlo.reduce_scatter Method

reduce_scatter

Within each process group in the process grid, performs reduction, using computations, over the values of the operand tensor from each process, splits the reduction result along scatter_dimension into parts, and scatters the split parts between the processes to produce the result.

See:
https://github.com/openxla/stablehlo/blob/main/docs/spec#reduce_scatter

Example:
```mlir
%result = "stablehlo.reduce_scatter"(%operand) ({

^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension = 1 : i64, replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>, channel_handle = #stablehlo.channel_handle<handle = 0, type = 0> } : (tensor<2x4xi64>) -> tensor<2x2xi64> ```

source

Reactant.MLIR.Dialects.stablehlo.reduce_window Method

reduce_window

Applies a reduction function body to windows of inputs and init_values and produces results.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reduce_window

Example

%result = "stablehlo.reduce_window"(%input, %init_value) ({
  ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
    %0 = stablehlo.add %arg0, %arg1 : tensor<i64>
    stablehlo.return %0 : tensor<i64>
}) {
  window_dimensions = array<i64: 2, 1>,
  window_strides = array<i64: 4, 1>,
  base_dilations = array<i64: 2, 1>,
  window_dilations = array<i64: 3, 1>,
  padding = dense<[[2, 1], [0, 0]]> : tensor<2x2xi64>
} : (tensor<3x2xi64>, tensor<i64>) -> tensor<2x2xi64>

source

Reactant.MLIR.Dialects.stablehlo.remainder Method

remainder

Performs element-wise remainder of dividend lhs and divisor rhs tensors and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#remainder

Example

%result = stablehlo.remainder %lhs, %rhs : tensor<4xi64>

source

Reactant.MLIR.Dialects.stablehlo.replica_id Method

replica_id

Produces replica_id of the current process.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#replica_id

Example

%result = stablehlo.replica_id : tensor<ui32>

source

Reactant.MLIR.Dialects.stablehlo.reshape Method

reshape

Performs reshape of operand tensor to a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reshape

Example

%result = stablehlo.reshape %operand : (tensor<2xf32>) -> tensor<1x2xf32>

source

Reactant.MLIR.Dialects.stablehlo.reverse Method

reverse

Reverses the order of elements in the operand along the specified dimensions and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reverse

Example

%result = stablehlo.reverse %operand, dims = [1] : tensor<3x2xi32>

source

Reactant.MLIR.Dialects.stablehlo.rng Method

rng

Generates random numbers using the rng_distribution algorithm and produces a result tensor of a given shape shape.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng

Example

%result = stablehlo.rng %a, %b, %shape, distribution = NORMAL : (tensor<i32>, tensor<i32>, tensor<2xi64>) -> tensor<3x3xi32>

source

Reactant.MLIR.Dialects.stablehlo.rng_bit_generator Method

rng_bit_generator

Returns an output filled with uniform random data and an updated output state output_state given an initial state initial_state using the pseudorandom number generator algorithm rng_algorithm.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rng_bit_generator

Example

%output_state, %output = stablehlo.rng_bit_generator %initial_state, algorithm = THREE_FRY : (tensor<2xui64>) -> (tensor<2xui64>, tensor<2x2xui64>)

source

Reactant.MLIR.Dialects.stablehlo.round_nearest_afz Method

round_nearest_afz

Performs element-wise rounding towards the nearest integer, breaking ties away from zero, on the operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_afz

Example

%result = stablehlo.round_nearest_afz %operand : tensor<5xf64>

source

Reactant.MLIR.Dialects.stablehlo.round_nearest_even Method

round_nearest_even

Performs element-wise rounding towards the nearest integer, breaking ties towards the even integer, on the operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#round_nearest_even

Example

%result = stablehlo.round_nearest_even %operand : tensor<5xf64>

source

Reactant.MLIR.Dialects.stablehlo.rsqrt Method

rsqrt

Performs element-wise reciprocal square root operation on operand tensor and produces a result tensor, implementing the rSqrt operation from the IEEE-754 specification.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#rsqrt

Example

%result = stablehlo.rsqrt %operand : tensor<2x2xf32>

source

Reactant.MLIR.Dialects.stablehlo.scatter Method

scatter

Produces results tensors which are equal to inputs tensors except that several slices specified by scatter_indices are updated with the values updates using update_computation.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#scatter

Example: mlir %result = "stablehlo.scatter"(%input, %scatter_indices, %update) ({ ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>): %0 = stablehlo.add %arg0, %arg1 : tensor<i64> stablehlo.return %0 : tensor<i64> }) { scatter_dimension_numbers = #stablehlo.scatter< update_window_dims = [3, 4], inserted_window_dims = [1], input_batching_dims = [0], scatter_indices_batching_dims = [1], scatter_dims_to_operand_dims = [2, 1], index_vector_dim = 3>, indices_are_sorted = false, unique_indices = false } : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>, tensor<2x2x3x2x2xi64>) -> tensor<2x3x4x2xi64>

source

Reactant.MLIR.Dialects.stablehlo.select Method

select

Produces a result tensor where each element is selected from on_true or on_false tensor based on the value of the corresponding element of pred.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select

Example

%result = stablehlo.select %pred, %on_true, %on_false : tensor<2x2xi1>, tensor<2x2xi32>

source

Reactant.MLIR.Dialects.stablehlo.select_and_scatter Method

select_and_scatter

Scatters the values from the source tensor using scatter based on the outcome of reduce_window of the input tensor using select and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#select_and_scatter

Example

%result = "stablehlo.select_and_scatter"(%operand, %source, %init_value) ({
  ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
    %0 = stablehlo.compare GE, %arg0, %arg1 : (tensor<i64>, tensor<i64>) -> tensor<i1>
    stablehlo.return %0 : tensor<i1>
}, {
  ^bb0(%arg0: tensor<i64>, %arg1: tensor<i64>):
    %0 = stablehlo.add %arg0, %arg1 : tensor<i64>
    stablehlo.return %0 : tensor<i64>
}) {
  window_dimensions = array<i64: [3, 1]>,
  window_strides = array<i64: [2, 1]>,
  padding = dense<[[0, 1], [0, 0]]> : tensor<2x2xi64>
} : (tensor<4x2xi64>, tensor<2x2xi64>, tensor<i64>) -> tensor<4x2xi64>

source

Reactant.MLIR.Dialects.stablehlo.send Method

send

Sends inputs to a channel channel_id and produces a result token.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#send

Example

%result = "stablehlo.send"(%operand, %token) {
  channel_handle = #stablehlo.channel_handle<handle = 0, type = 1>,
  is_host_transfer = false,
  source_target_pairs = dense<[[0, 1], [1, 2]]> : tensor<2x2xi64>
} : (tensor<2x2xi64>, !stablehlo.token) -> !stablehlo.token

source

Reactant.MLIR.Dialects.stablehlo.set_dimension_size Method

set_dimension_size

This operation is a work in progress, so it is not yet included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/8.

Informally, this operation does the same thing as XLA's SetDimensionSize: https://www.tensorflow.org/xla/operation_semantics#setdimensionsize

Example

%0 = stablehlo.set_dimension_size %arg0, %arg1, dim = 1 : (tensor<4x2xf32>, tensor<i32>) -> tensor<4x2xf32>

source

Reactant.MLIR.Dialects.stablehlo.shift_left Method

shift_left

Performs element-wise left-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_left

Example

%result = stablehlo.shift_left %lhs, %rhs : tensor<3xi64>

source

Reactant.MLIR.Dialects.stablehlo.shift_right_arithmetic Method

shift_right_arithmetic

Performs element-wise arithmetic right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_arithmetic

Example

%result = stablehlo.shift_right_arithmetic %lhs, %rhs : tensor<3xi64>

source

Reactant.MLIR.Dialects.stablehlo.shift_right_logical Method

shift_right_logical

Performs element-wise logical right-shift operation on the lhs tensor by rhs number of bits and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#shift_right_logical

Example

%result = stablehlo.shift_right_logical %lhs, %rhs : tensor<3xi64>

source

Reactant.MLIR.Dialects.stablehlo.sign Method

sign

Returns the sign of the operand element-wise and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sign

Example

%result = stablehlo.sign %operand : tensor<5xf64>

source

Reactant.MLIR.Dialects.stablehlo.sine Method

sine

Performs element-wise sine operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sine

Example

%result = stablehlo.sine %operand : tensor<2xf32>

source

Reactant.MLIR.Dialects.stablehlo.slice Method

slice

Extracts a slice from the operand using statically-computed starting indices and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#slice

Example

%result = stablehlo.slice %operand [1:3, 4:8:2]
   : (tensor<3x8xi64>) -> tensor<2x2xi64>

// Same in generic form: the `1:3` above is mapped to the first entry in
// `start_indices` and `limit_indices`, while `strides` is implicitly 1.
// The `4:8:2` above is parsed into the second entry of `start_indices`,
// `limit_indices` and `strides` respectively.
%result = "stablehlo.slice" (%operand) {
  start_indices = array<i64: 1, 4>,
  limit_indices = array<i64: 3, 8>,
  strides = array<i64: 1, 2>
} : (tensor<3x8xi64>) -> tensor<2x2xi64>

source

Reactant.MLIR.Dialects.stablehlo.sort Method

sort

Sorts a variadic number of tensors in inputs together, according to a custom comparator, along the given dimension and produces a variadic number of tensors as results.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sort

Example

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</details>

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<summary><a id='Reactant.MLIR.Dialects.stablehlo.sqrt-Tuple{Reactant.MLIR.IR.Value}' href='#Reactant.MLIR.Dialects.stablehlo.sqrt-Tuple{Reactant.MLIR.IR.Value}'><span class="jlbinding">Reactant.MLIR.Dialects.stablehlo.sqrt</span></a> <Badge type="info" class="jlObjectType jlMethod" text="Method" /></summary>



`sqrt`

Performs element-wise square root operation on `operand` tensor and produces a `result` tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#sqrt

**Example**

```mlir
%result = stablehlo.sqrt %operand : tensor<2x2xf32>

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Reactant.MLIR.Dialects.stablehlo.subtract Method

subtract

Performs element-wise subtraction of two tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#subtract

Example

%result = stablehlo.subtract %lhs, %rhs : tensor<2xi32>

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Reactant.MLIR.Dialects.stablehlo.tan Method

tan

Performs element-wise tangent operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tan

Example

%result = stablehlo.tan %operand : tensor<2x2xf64>

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Reactant.MLIR.Dialects.stablehlo.tanh Method

tanh

Performs element-wise hyperbolic tangent operation on operand tensor and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tanh

Example

%result = stablehlo.tanh %operand : tensor<2xf32>

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Reactant.MLIR.Dialects.stablehlo.torch_index_select Method

torch_index_select

This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.

Informally, this operation does the same thing as PyTorch's index_select, augmented with support for batch dimensions: https://pytorch.org/docs/stable/generated/torch.index_select.html.

The batch_dims attribute specifies the number of major batch dimensions (0 or more) that act like a multidimensional loop over both the operand and the index.

Example

%result = "stablehlo.torch_index_select"(%operand, %index) {
  dim = 2 : i64,
  batch_dims = 1 : i64
} : (tensor<8x128x3072x64xf32>, tensor<8x16x1024xi32>) -> tensor<8x128x16x1024x64xf32>

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Reactant.MLIR.Dialects.stablehlo.transpose Method

transpose

Permutes the dimensions of operand tensor using permutation and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#transpose

Example

%0 = stablehlo.transpose %arg0, dims = [2, 1, 0] : (tensor<1x2x3xi32>) -> tensor<3x2x1xi32>

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Reactant.MLIR.Dialects.stablehlo.triangular_solve Method

triangular_solve

Solves batches of systems of linear equations with lower or upper triangular coefficient matrices.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#triangular_solve

Example

%result = "stablehlo.triangular_solve"(%a, %b) {
  left_side = true,
  lower = true,
  unit_diagonal = false,
  transpose_a = #stablehlo<transpose NO_TRANSPOSE>
} : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>

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Reactant.MLIR.Dialects.stablehlo.tuple Method

tuple

Produces a result tuple from values val.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#tuple

Example

%result = stablehlo.tuple %val0, %val1 : tuple<tensor<2xf64>, tuple<tensor<i64>>>

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Reactant.MLIR.Dialects.stablehlo.unary_einsum Method

unary_einsum

This operation is on its way out of StableHLO, so it is not included in the StableHLO specification: https://github.com/openxla/stablehlo/issues/3.

Informally, this operation does the same thing as TF's einsum: https://www.tensorflow.org/api_docs/python/tf/einsum

Example

%result = "stablehlo.unary_einsum"(%operand) {
  einsum_config = "ab->a"
} : (tensor<4x16xf32>) -> tensor<4xf32>

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Reactant.MLIR.Dialects.stablehlo.uniform_dequantize Method

uniform_dequantize

Performs element-wise conversion of quantized tensor operand to a floating-point tensor result according to the quantization parameters defined by the operand type.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_dequantize

Example

%result = stablehlo.uniform_dequantize %operand : (tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>) -> tensor<2xf32>

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Reactant.MLIR.Dialects.stablehlo.uniform_quantize Method

uniform_quantize

Performs element-wise conversion of floating-point tensor or quantized tensor operand to a quantized tensor result according to the quantization parameters defined by the result type.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#uniform_quantize

Example

%result = stablehlo.uniform_quantize %operand : (tensor<2xf32>) -> tensor<2x!quant.uniform<i8:f32:0, {0.1:-30,0.5:-20}>>

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Reactant.MLIR.Dialects.stablehlo.while_ Method

while_

Produces the output from executing body function 0 or more times while the cond function outputs true.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#while

Example

%results0, %results1 = stablehlo.while(%arg0 = %init_i, %arg1 = %init_sum) : tensor<i64>, tensor<i64>
cond {
  %cond = stablehlo.compare LT, %arg0, %ten : (tensor<i64>, tensor<i64>) -> tensor<i1>
  stablehlo.return %cond : tensor<i1>
} do {
  %new_sum = stablehlo.add %arg1, %one : tensor<i64>
  %new_i = stablehlo.add %arg0, %one : tensor<i64>
  stablehlo.return %new_i, %new_sum : tensor<i64>, tensor<i64>
}

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Reactant.MLIR.Dialects.stablehlo.xor Method

xor

Performs element-wise XOR of two tensors lhs and rhs and produces a result tensor.

See: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#xor

Example

%result = stablehlo.xor %lhs, %rhs : tensor<2xi32>

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