'mhlo' Dialecto

Operaciones

mhlo.abs (mhlo::AbsOp)

operación de abdominales

Sintaxis:

operation ::= `mhlo.abs` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

Realiza una operación abs por elementos en el tensor operand y produce un tensor result .

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand tensor clasificado de entero sin signo de 4/8/16/32/64 bits o tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o 4/8/16/ Entero sin signo cuantificado uniforme de 32 bits o valores enteros sin signo cuantificados uniformemente de 4/8/16/32 bits por eje

Resultados:

Resultado Descripción
result tensor clasificado de entero sin signo de 4/8/16/32/64 bits o tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o Entero con signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero sin signo cuantificado uniforme de 4/8/16/32 bits o 4/8/16/ Valores enteros sin signo cuantificados uniformemente por eje de 32 bits

mhlo.add (mhlo::AddOp)

Agregar operación

Sintaxis:

operation ::= `mhlo.add` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

Realiza la suma por elementos de dos tensores lhs y rhs y produce un tensor result .

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4/8 /Entero sin signo de 16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o cuantificación uniforme de 4/8/16/32 bits Entero con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje valores enteros sin signo
rhs tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4/8 /Entero sin signo de 16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o cuantificación uniforme de 4/8/16/32 bits Entero con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje valores enteros sin signo

Resultados:

Resultado Descripción
result tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4/8 /Entero sin signo de 16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o cuantificación uniforme de 4/8/16/32 bits Entero con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje valores enteros sin signo

mhlo.add_dependency (mhlo::AddDependencyOp)

Operación Agregar Dependencia

Sintaxis:

operation ::= `mhlo.add_dependency` operands attr-dict `:` functional-type(operands, results)

Esta operación es privada del compilador XLA, por lo que aún no tiene una especificación.

De manera informal, esta operación tiene dos operandos: un operando de datos y un token. La salida de la operación es el operando de datos. Cuando se usa con AfterAll, esta operación permite ordenar operaciones sin efectos secundarios (aquellas que no producen valores simbólicos).

Ejemplo:

%1 = mhlo.add_dependency %arg0, %0 : (tensor<3x4xf32>, !mhlo.token) -> tensor<3x4xf32>

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4/8 /Entero sin signo de 16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o cuantificación uniforme de 4/8/16/32 bits entero con signo o valores enteros sin signo cuantificados uniformemente de 4/8/16/32 bits o tensor clasificado de cuantificación uniforme de 4/8/16/32 bits por eje entero con signo o cuantificación uniforme de 4/8/16/32 bits por eje valores enteros sin signo o token
token simbólico

Resultados:

Resultado Descripción
output tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4/8 /Entero sin signo de 16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o cuantificación uniforme de 4/8/16/32 bits entero con signo o valores enteros sin signo cuantificados uniformemente de 4/8/16/32 bits o tensor clasificado de cuantificación uniforme de 4/8/16/32 bits por eje entero con signo o cuantificación uniforme de 4/8/16/32 bits por eje valores enteros sin signo o token

mhlo.after_all (mhlo::AfterAllOp)

Operación después de todo

Sintaxis:

operation ::= `mhlo.after_all` $inputs attr-dict
              `:` custom<VariadicSameOperandsAndResultType>(ref($inputs), type($inputs), type($result))

Garantiza que las operaciones que producen las inputs se ejecuten antes que cualquier operación que dependa del result .

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
inputs variadic de token

Resultados:

Resultado Descripción
result simbólico

mhlo.all_gather (mhlo::AllGatherOp)

Operación AllGather

Dentro de cada grupo de procesos en la cuadrícula de procesos, concatena los valores del tensor de operando de cada proceso a lo largo de all_gather_dim y produce un tensor de resultado. El computation se aplica por separado para cada operando en operands , produciendo un resultado por operando.

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

Ejemplo:

%result = "mhlo.all_gather"(%operand) {
  all_gather_dim = 1 : i64,
  replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>
  // channel_id = 0
  channel_handle = #mhlo.channel_handle<handle = 0, type = 0>,
  // use_global_device_ids = false
} : (tensor<2x2xf32>) -> tensor<2x4xf32>

Rasgos: SameOperandsAndResultElementType

Atributos:

Atributo Tipo MLIR Descripción
all_gather_dim ::mlir::IntegerAttr Atributo entero sin signo de 64 bits
replica_groups ::mlir::DenseIntElementsAttr Atributo de elementos enteros sin signo de 64 bits
channel_handle ::mlir::mhlo::ChannelHandleAttr dos enteros de 64 bits 'identificar' y 'escribir'
use_global_device_ids ::mlir::AtributoUnidad atributo de unidad

Operandos:

Operando Descripción
operands variadic de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4 /Entero sin signo de 8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 4/8/16/32 bits Entero cuantificado uniforme con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero sin signo cuantificado uniforme de 4/8/16/32 bits por eje valores

Resultados:

Resultado Descripción
"sin nombre" variadic de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4 /Entero sin signo de 8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 4/8/16/32 bits Entero cuantificado uniforme con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero sin signo cuantificado uniforme de 4/8/16/32 bits por eje valores

mhlo.all_reduce (mhlo::AllReduceOp)

Operación AllReduce

Dentro de cada grupo de procesos en la cuadrícula de procesos, aplica un computation función de reducción a los valores de un tensor de operando de cada proceso y produce un tensor de resultado. El computation se aplica por separado para cada operando en operands , produciendo un resultado por operando.

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

Ejemplo:

%result = "mhlo.all_reduce"(%operand) ({
  ^bb0(%arg0: tensor<f32>, %arg1: tensor<f32>):
    %0 = mhlo.add %arg1, %arg2 : tensor<f32>
    mhlo.return %0 : tensor<f32>
}) {
  replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>
  // channel_id = 0
  channel_handle = #mhlo.channel_handle<handle = 0, type = 0>
  // use_global_device_ids = false
} : (tensor<4xf32>) -> tensor<4xf32>

Rasgos: InferTensorType , SingleBlockImplicitTerminator<ReturnOp> , SingleBlock

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
replica_groups ::mlir::DenseIntElementsAttr Atributo de elementos enteros sin signo de 64 bits
channel_handle ::mlir::mhlo::ChannelHandleAttr dos enteros de 64 bits 'identificar' y 'escribir'
use_global_device_ids ::mlir::AtributoUnidad atributo de unidad

Operandos:

Operando Descripción
operands variadic de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4 /Entero sin signo de 8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 4/8/16/32 bits Entero cuantificado uniforme con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero sin signo cuantificado uniforme de 4/8/16/32 bits por eje valores

Resultados:

Resultado Descripción
"sin nombre" variadic de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4 /Entero sin signo de 8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 4/8/16/32 bits Entero cuantificado uniforme con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero sin signo cuantificado uniforme de 4/8/16/32 bits por eje valores

mhlo.all_to_all (mhlo::AllToAllOp)

Operación todo a todo

Dentro de cada grupo de procesos en la cuadrícula de procesos, divide los valores del tensor operand a lo largo split_dimension en partes, dispersa las partes divididas entre los procesos, concatena las partes dispersas a lo largo de concat_dimension y produce un tensor result .

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

Ejemplo:

%result = "mhlo.all_to_all"(%operand) {
  split_dimension = 1 : i64,
  concat_dimension = 0 : i64,
  split_count = 2 : i64,
  replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>
} : (tensor<2x4xf32>) -> tensor<4x2xf32>

Rasgos: AlwaysSpeculatableImplTrait , InferTensorType , SameOperandsElementType , SameOperandsShape , SameVariadicOperandSize

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
split_dimension ::mlir::IntegerAttr Atributo entero sin signo de 64 bits
concat_dimension ::mlir::IntegerAttr Atributo entero sin signo de 64 bits
split_count ::mlir::IntegerAttr Atributo entero sin signo de 64 bits
replica_groups ::mlir::DenseIntElementsAttr Atributo de elementos enteros sin signo de 64 bits
channel_handle ::mlir::mhlo::ChannelHandleAttr dos enteros de 64 bits 'identificar' y 'escribir'

Operandos:

Operando Descripción
operand variadic de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4 /Entero sin signo de 8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 4/8/16/32 bits Entero cuantificado uniforme con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero sin signo cuantificado uniforme de 4/8/16/32 bits por eje valores

Resultados:

Resultado Descripción
"sin nombre" variadic de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4 /Entero sin signo de 8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 4/8/16/32 bits Entero cuantificado uniforme con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero sin signo cuantificado uniforme de 4/8/16/32 bits por eje valores

mhlo.and (mhlo::AndOp)

y operación

Sintaxis:

operation ::= `mhlo.and` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

Realiza AND por elementos de dos tensores lhs y rhs y produce un tensor result

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs tensor clasificado de pred (también conocido como booleano o entero de 1 bit) o ​​entero sin signo de 4/8/16/32/64 bits o valores enteros sin signo de 4/8/16/32/64 bits
rhs tensor clasificado de pred (también conocido como booleano o entero de 1 bit) o ​​entero sin signo de 4/8/16/32/64 bits o valores enteros sin signo de 4/8/16/32/64 bits

Resultados:

Resultado Descripción
result tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4/8 /Entero sin signo de 16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o cuantificación uniforme de 4/8/16/32 bits Entero con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje valores enteros sin signo

mhlo.async_done (mhlo::AsyncDoneOp)

Operación asíncrona

Esta operación es privada del compilador XLA, por lo que aún no tiene una especificación.

De manera informal, esta operación se bloquea hasta el final de un cálculo asincrónico. Devuelve el resultado final del cálculo asincrónico.

Consulte la documentación de AsyncStart para obtener más información.

Interfaces: InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
called_computation ::mlir::FlatSymbolRefAttr atributo de referencia de símbolo plano
execution_thread ::mlir::StringAttr atributo de cadena

Operandos:

Operando Descripción
bundle async_bundle con cualquier combinación de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) ) o entero sin signo de 4/8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 8/4/16/ Entero con signo cuantificado uniforme de 32 bits o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por valores enteros sin signo del eje o valores simbólicos

Resultados:

Resultado Descripción
"sin nombre" variadic de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4 /Entero sin signo de 8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 4/8/16/32 bits Entero cuantificado uniforme con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero sin signo cuantificado uniforme de 4/8/16/32 bits por eje valores o token o tupla anidada con cualquier combinación de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​entero sin signo de 4/8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 4 /Entero con signo cuantificado uniforme de 8/16/32 bits o valores enteros sin signo cuantificados uniformes de 4/8/16/32 bits o tensor clasificado de entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o 4/8 /Valores enteros sin signo o valores simbólicos cuantificados uniformemente de 16/32 bits por eje

mhlo.async_start (mhlo::AsyncStartOp)

Operación de inicio asíncrono

Esta operación es privada del compilador XLA, por lo que aún no tiene una especificación.

De manera informal, esta operación inicia un cálculo asincrónico.

Esto se utiliza cuando hay funciones que contienen esperas asincrónicas (como DMA) y cálculos en subprocesos. Por ejemplo, una función podría consistir en un cálculo, un DMA, otro cálculo, un segundo DMA y un cálculo final. Esto se representaría como async_start seguido de async_update y async_done. async_start haría el primer cálculo en el subproceso y luego iniciaría el DMA. async_update esperaría a que se complete el DMA si aún no se ha hecho, luego ejecutará el segundo cálculo en la función e iniciará el segundo DMA. Finalmente, async_done esperaría en este último DMA y luego ejecutaría el último cálculo que debe ejecutarse en el subproceso y devolvería el resultado de ese cálculo final.

operands se pasan al cálculo directamente called_computation es la función que se ejecutará de forma asincrónica execution_thread es el nombre del hilo en el que se ejecutará. El hilo principal se llama "principal". Todos los hilos tienen nombres.

Esto devuelve todo el estado necesario entre operaciones asíncronas. Después de la asignación del búfer, los valores devueltos representan el espacio necesario para contener la entrada, los resultados y cualquier bloc de notas necesario o editado por la operación asíncrona.

Atributos:

Atributo Tipo MLIR Descripción
called_computation ::mlir::FlatSymbolRefAttr atributo de referencia de símbolo plano
execution_thread ::mlir::StringAttr atributo de cadena

Operandos:

Operando Descripción
inputs variadic de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4 /Entero sin signo de 8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 4/8/16/32 bits Entero cuantificado uniforme con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero sin signo cuantificado uniforme de 4/8/16/32 bits por eje valores o token o tupla anidada con cualquier combinación de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​entero sin signo de 4/8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 4 /Entero con signo cuantificado uniforme de 8/16/32 bits o valores enteros sin signo cuantificados uniformes de 4/8/16/32 bits o tensor clasificado de entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o 4/8 /Valores enteros sin signo o valores simbólicos cuantificados uniformemente de 16/32 bits por eje

Resultados:

Resultado Descripción
"sin nombre" async_bundle con cualquier combinación de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) ) o entero sin signo de 4/8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 8/4/16/ Entero con signo cuantificado uniforme de 32 bits o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por valores enteros sin signo del eje o valores simbólicos

mhlo.async_update (mhlo::AsyncUpdateOp)

Operación de actualización asíncrona

Esta operación es privada del compilador XLA, por lo que aún no tiene una especificación.

De manera informal, esta operación se bloquea en un cálculo asincrónico hasta una barrera de sincronización. Este bundle devuelve después de operarlo.

Consulte la documentación de AsyncStart para obtener más información.

Interfaces: InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
called_computation ::mlir::FlatSymbolRefAttr atributo de referencia de símbolo plano
execution_thread ::mlir::StringAttr atributo de cadena

Operandos:

Operando Descripción
bundle async_bundle con cualquier combinación de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) ) o entero sin signo de 4/8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 8/4/16/ Entero con signo cuantificado uniforme de 32 bits o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por valores enteros sin signo del eje o valores simbólicos

Resultados:

Resultado Descripción
"sin nombre" async_bundle con cualquier combinación de tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) ) o entero sin signo de 4/8/16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o 8/4/16/ Entero con signo cuantificado uniforme de 32 bits o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por valores enteros sin signo del eje o valores simbólicos

mhlo.atan2 (mhlo::Atan2Op)

operación atan2

Sintaxis:

operation ::= `mhlo.atan2` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

Realiza la operación atan2 por elementos en los tensores lhs y rhs y produce un tensor result .

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

Ejemplo:

%result = mhlo.atan2 %lhs, %rhs : tensor<3xf32>

Rasgos: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o Entero con signo cuantificado uniforme de 4/8/16/32 bits o valores enteros sin signo cuantificados uniformes de 4/8/16/32 bits
rhs tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o Entero con signo cuantificado uniforme de 4/8/16/32 bits o valores enteros sin signo cuantificados uniformes de 4/8/16/32 bits

Resultados:

Resultado Descripción
result tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o Entero con signo cuantificado uniforme de 4/8/16/32 bits o valores enteros sin signo cuantificados uniformes de 4/8/16/32 bits

mhlo.batch_norm_grad (mhlo::BatchNormGradOp)

Operación BatchNormGrad

Calcula los gradientes de varias entradas de BatchNormTrainingOp que se propagan hacia atrás desde grad_output y produce tensores grad_operand , grad_scale y grad_offset .

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

Ejemplo:

%grad_operand, %grad_scale, %grad_offset =
"mhlo.batch_norm_grad"(%operand, %scale, %mean, %variance, %grad_output) {
  epsilon = 0.0 : f32,
  feature_index = 2 : i64
} : (tensor<2x2x2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>,
    tensor<2x2x2xf32>) -> (tensor<2x2x2xf32>, tensor<2xf32>, tensor<2xf32>)

Rasgos: AlwaysSpeculatableImplTrait , InferTensorType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
epsilon ::mlir::FloatAttr atributo flotante de 32 bits
feature_index ::mlir::IntegerAttr Atributo entero sin signo de 64 bits

Operandos:

Operando Descripción
operand tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
scale Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
mean Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
variance Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
grad_output tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16

Resultados:

Resultado Descripción
grad_operand tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
grad_scale Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
grad_offset Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16

mhlo.batch_norm_inference (mhlo::BatchNormInferenceOp)

Operación BatchNormInference

Normaliza el tensor operand en todas las dimensiones excepto en la dimensión feature_index y produce un tensor result .

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

Ejemplo:

%result = "mhlo.batch_norm_inference"(%operand, %scale, %offset, %mean, %variance) {
  epsilon = 0.0 : f32,
  feature_index = 2 : i64
} : (tensor<2x2x2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> tensor<2x2x2xf32>

Rasgos: AlwaysSpeculatableImplTrait , InferTensorType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
epsilon ::mlir::FloatAttr atributo flotante de 32 bits
feature_index ::mlir::IntegerAttr Atributo entero sin signo de 64 bits

Operandos:

Operando Descripción
operand tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
scale Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
offset Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
mean Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
variance Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16

Resultados:

Resultado Descripción
result tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16

mhlo.batch_norm_training (mhlo::BatchNormTrainingOp)

Operación de entrenamiento BatchNorm

Calcula la media y la varianza entre dimensiones espaciales y por lotes y normaliza el tensor operand para cada característica en la dimensión feature_index y produce tensores output , batch_mean y batch_var .

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

Ejemplo:

%output, %batch_mean, %batch_var = "mhlo.batch_norm_training"(%operand, %scale, %offset) {
  epsilon = 0.0 : f32,
  feature_index = 2 : i64
} : (tensor<2x2x2xf32>, tensor<2xf32>, tensor<2xf32>) -> (tensor<2x2x2xf32>, tensor<2xf32>, tensor<2xf32>)

Rasgos: AlwaysSpeculatableImplTrait , InferTensorType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
epsilon ::mlir::FloatAttr atributo flotante de 32 bits
feature_index ::mlir::IntegerAttr Atributo entero sin signo de 64 bits

Operandos:

Operando Descripción
operand tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
scale Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
offset Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16

Resultados:

Resultado Descripción
output tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
batch_mean Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16
batch_var Tensor 1D de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o valores flotantes de 16 bits o flotantes de 32 bits o flotantes de 64 bits o bfloat16

mhlo.bitcast (mhlo::BitcastOp)

Operación de transmisión de bits

Sintaxis:

operation ::= `mhlo.bitcast` operands attr-dict `:` functional-type(operands, results)

Esta operación es privada del compilador XLA, por lo que aún no tiene una especificación.

De manera informal, esta operación cambia la forma de la entrada de la misma manera que la disposición física de los elementos no cambia.

Esta operación necesita información de diseño para entender la "disposición física de los elementos", y el soporte de diseño en MHLO es actualmente un trabajo en progreso.

Ejemplo:

%0 = mhlo.bitcast %arg0 : (tensor<3x4xf32>) -> tensor<3x4x1xf32>

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4/8 /Entero sin signo de 16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o cuantificación uniforme de 4/8/16/32 bits Entero con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje valores enteros sin signo

Resultados:

Resultado Descripción
"sin nombre" tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4/8 /Entero sin signo de 16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o cuantificación uniforme de 4/8/16/32 bits Entero con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje valores enteros sin signo

mhlo.bitcast_convert (mhlo::BitcastConvertOp)

Operación BitcastConvert

Sintaxis:

operation ::= `mhlo.bitcast_convert` operands attr-dict `:` functional-type(operands, results)

Realiza una operación de difusión de bits en el tensor operand y produce un tensor result donde los bits de todo el tensor operand se reinterpretan utilizando el tipo de tensor de result .

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

Ejemplo:

%result = mhlo.bitcast_convert %operand : (tensor<2xf32>) -> tensor<2x4xi8>

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4/8 /Entero sin signo de 16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o cuantificación uniforme de 4/8/16/32 bits Entero con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje valores enteros sin signo

Resultados:

Resultado Descripción
"sin nombre" tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4/8 /Entero sin signo de 16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o cuantificación uniforme de 4/8/16/32 bits Entero con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje valores enteros sin signo

mhlo.broadcast (mhlo::BroadcastOp)

Operación de transmisión

Esta operación está saliendo de StableHLO, por lo que no está incluida en la especificación: https://github.com/openxla/stablehlo/issues/3

De manera informal, esta operación hace lo mismo que la transmisión de XLA: https://www.tensorflow.org/xla/operation_semantics#broadcast

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait , InferTensorType , SameOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
broadcast_sizes ::mlir::DenseIntElementsAttr Atributo de elementos enteros sin signo de 64 bits

Operandos:

Operando Descripción
operand tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4/8 /Entero sin signo de 16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o cuantificación uniforme de 4/8/16/32 bits Entero con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje valores enteros sin signo

Resultados:

Resultado Descripción
"sin nombre" tensor clasificado de tipo f8E4M3B11FNUZ o tipo f8E4M3FN o tipo f8E4M3FNUZ o tipo f8E5M2 o tipo f8E5M2FNUZ o flotante de 16 bits o flotante de 32 bits o flotante de 64 bits o tipo bfloat16 o pred (también conocido como booleano o entero de 1 bit) o ​​4/8 /Entero sin signo de 16/32/64 bits o entero sin signo de 4/8/16/32/64 bits o tipo complejo con elementos flotantes de 32 bits o flotantes de 64 bits o cuantificación uniforme de 4/8/16/32 bits Entero con signo o entero sin signo cuantificado uniforme de 4/8/16/32 bits o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje o entero con signo cuantificado uniforme de 4/8/16/32 bits por eje valores enteros sin signo

mhlo.broadcast_in_dim (mhlo::BroadcastInDimOp)

Operación BroadcastInDim

Expande las dimensiones y/o rango de un tensor de entrada al duplicar los datos en el tensor operand y produce un tensor result .

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait , HLO_CompatibleOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
broadcast_dimensions :: mlir :: denseintelementsattr Atributo de elementos enteros de 64 bits Signless Integer

Operandos:

Operando Descripción
operand Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

Resultados:

Resultado Descripción
"sin nombre" statically shaped tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/ 8/16/32/64 bits Integer sin signos o 4/8/16/32/64 bits enteros sin firmar o tipo complejo con uniformes flotantes de 32 bits o elementos flotantes de 64 bits o 4/8/16/32 bits uniformes entero firmado cuantizado o 4/8/16/16/32 bits enteros cuantizados sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/16/32 bits cuantizados por eje sin signo sin firmar valores

mhlo.case (mhlo :: caseop)

Operación de caja

Produce la salida al ejecutar exactamente una function de branches dependiendo del valor del index .

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

Ejemplo:

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

Rasgos: RecursiveMemoryEffects , SingleBlockImplicitTerminator<ReturnOp> , SingleBlock

Interfaces: InferTypeOpInterface

Operandos:

Operando Descripción
index Tensor de valores enteros sin signos de 32 bits

Resultados:

Resultado Descripción
"sin nombre" Variadic de tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ Tipo o F8E5M2 Tipo o F8E5M2FNUZ Tipo o Tipo Float de 16 bítes /8/16/32/64 bits Integer sin signos o 4/8/16/32/64 bits enteros sin firmar o tipo complejo con flotador de 32 bits o elementos flotantes de 64 bits o 4/8/16/32 bits Uniforme entero cuantizado firmado o 4/8/8/16/32 bits valores enteros cuantificados sin firmar o tensor clasificado de 4/8/16/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizado por eje sin firmar valores o token de enteros sin firmar

mhlo.cbrt (mhlo :: cbrtop)

Operación CBRT

Sintaxis:

operation ::= `mhlo.cbrt` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

Realiza el funcionamiento de la raíz cúbica en forma de elemento en el tensor operand y produce un tensor result .

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

Ejemplo:

%result = mhlo.cbrt %operand : tensor<4xf32>

Rasgos: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ Tipo o F8E5M2 o F8E5M2FNUZ Tipo o Float de 16 bits o Float de 32 bits o Float de 64 bits o tipo BFLOAT16 Tipo o tipo complejo con 32 bits Float o 64 bit float o 64 bit o Float o 64 bit o tipo bfloat16 Tipo o tipo complejo de 32 bits o float de 32 bits o float de 64 bit 4/8/16/16/32 bits enteros firmados cuantizados o 4/8/16/16/32 bits valores de entero cuantificados uniformes sin firmar

Resultados:

Resultado Descripción
result Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ Tipo o F8E5M2 o F8E5M2FNUZ Tipo o Float de 16 bits o Float de 32 bits o Float de 64 bits o tipo BFLOAT16 Tipo o tipo complejo con 32 bits Float o 64 bit float o 64 bit o Float o 64 bit o tipo bfloat16 Tipo o tipo complejo de 32 bits o float de 32 bits o float de 64 bit 4/8/16/16/32 bits enteros firmados cuantizados o 4/8/16/16/32 bits valores de entero cuantificados uniformes sin firmar

mhlo.ceil (Mhlo :: Ceilop)

Operación de techo

Sintaxis:

operation ::= `mhlo.ceil` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

Realiza un techo de elementos de tensor de operand y produce un tensor result .

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o tipo F8E4M3FNUZ o tipo F8E5M2 o tipo F8E5M2FNUZ o Float de 16 bits o flotadores de 32 bit

Resultados:

Resultado Descripción
result Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o tipo F8E4M3FNUZ o tipo F8E5M2 o tipo F8E5M2FNUZ o Float de 16 bits o flotadores de 32 bit

mhlo.cholesky (mhlo :: choleskyop)

Operación de Cholesky

Calcula la descomposición de Cholesky de un lote de matrices.

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

Ejemplo:

%result = mhlo.cholesky %a, lower = true : tensor<3x3xf32>

Rasgos: AlwaysSpeculatableImplTrait , InferTensorType , SameOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
lower ::mlir::BoolAttr atributo booleano

Operandos:

Operando Descripción
a Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ Tipo o F8E5M2 o F8E5M2FNUZ Tipo o Float de 16 bits o Float de 32 bits o Float o 64 bit o BFLOAT16 Tipo o tipo complejo con un tipo de flotación de 32 bits o 64 bits Float o Float de 64 bit

Resultados:

Resultado Descripción
"sin nombre" Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ Tipo o F8E5M2 o F8E5M2FNUZ Tipo o Float de 16 bits o Float de 32 bits o Float o 64 bit o BFLOAT16 Tipo o tipo complejo con un tipo de flotación de 32 bits o 64 bits Float o Float de 64 bit

mhlo.clamp (Mhlo :: Clampop)

Operación de sujeción

Sintaxis:

operation ::= `mhlo.clamp` $min `,` $operand `,` $max attr-dict
              `:` custom<SameOperandsAndResultType>(type($min), type($operand), type($max), type($result))

Agua cada elemento del tensor operand entre un valor mínimo y máximo y produce un tensor result .

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait , HLO_BroadcastingElementwise , InferTensorType , SameOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
min Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar
operand Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar
max Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

Resultados:

Resultado Descripción
result Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

mhlo.collective_broadcast (mhlo :: colectiveBroadcastop)

Operación colectiva de transmisión

Dentro de cada grupo de proceso en la cuadrícula de proceso, envíe el valor del tensor operand desde el proceso de origen a los procesos de destino y produce un tensor result .

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

Ejemplo:

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

Rasgos: CompatibleOperandsAndResultType

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
replica_groups :: mlir :: denseintelementsattr Atributo de elementos enteros de 64 bits Signless Integer
channel_handle :: mlir :: mhlo :: ChannelHandLeattr Dos enteros de 64 bits 'mango' y 'tipo'

Operandos:

Operando Descripción
operand Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

Resultados:

Resultado Descripción
"sin nombre" Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

mhlo.collective_permute (mhlo :: colectivepermuteop)

Operación colectiva por periódico

Dentro de cada grupo de proceso en la cuadrícula de proceso, envía el valor del tensor operand desde el proceso de origen al proceso de destino y produce un tensor result .

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

Ejemplo:

%result = "mhlo.collective_permute"(%operand) {
  source_target_pairs = dense<[[0, 1], [1, 2]]> : tensor<2x2xi64>,
  // channel_id = 0
  channel_handle = #mhlo.channel_handle<handle = 0, type = 0>
} : (tensor<4x2xf32>) -> tensor<4x2xf32>

Rasgos: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
source_target_pairs :: mlir :: denseintelementsattr Atributo de elementos enteros de 64 bits Signless Integer
channel_handle :: mlir :: mhlo :: ChannelHandLeattr Dos enteros de 64 bits 'mango' y 'tipo'

Operandos:

Operando Descripción
operand Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

Resultados:

Resultado Descripción
"sin nombre" Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

mhlo.compare (mhlo :: compareop)

Comparar operación

Sintaxis:

operation ::= `mhlo.compare` $comparison_direction `,` $lhs `,` $rhs (`,` $compare_type^)?
              attr-dict `:` functional-type(operands, results)

Realiza la comparación de elementos de los tensores lhs y rhs de acuerdo con comparison_direction y compare_type , y produce un tensor result .

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait , Elementwise , InferTensorType , lo mismo ocurre SameOperandsAndResultShape SameOperandsElementType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
comparison_direction :: mlir :: mhlo :: comparisondirectionattr Qué operación de comparación realizar.
compare_type :: mlir :: mhlo :: comparación typeattr Que tipo de comparación usar.

Operandos:

Operando Descripción
lhs Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar
rhs Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

Resultados:

Resultado Descripción
"sin nombre" Tensor clasificado de los valores de Pred (también conocido como Boolean o 1 bit Integer)

mhlo.complex (mhlo :: compleP)

Operación compleja

Sintaxis:

operation ::= `mhlo.complex` operands attr-dict
              `:` custom<ComplexOpType>(type($lhs), type($rhs), type($result))

Realiza una conversión de elementos a un valor complejo de un par de valores reales e imaginarios, lhs y rhs , y produce un tensor result .

Ver: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#complex

Ejemplo:

%result = mhlo.complex %lhs, %rhs : tensor<2xcomplex<f32>>

Rasgos: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape , lo mismo SameOperandsElementType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs Tensor clasificado de 32 bits flotante o valores de flotación de 64 bits
rhs Tensor clasificado de 32 bits flotante o valores de flotación de 64 bits

Resultados:

Resultado Descripción
result Tensor clasificado de tipo complejo con valores de elementos flotantes de 32 bits o flotadores de 64 bits

mhlo.composite (mhlo :: compositelop)

operación compuesta

Sintaxis:

operation ::= `mhlo.composite` $name $inputs attr-dict `:` functional-type(operands, results)

Encapsula una operación compuesta (compuesta) de otras operaciones de Stablehlo, tomando inputs y composite_attributes y produciendo results . La semántica del OP se implementa mediante el atributo decomposition . El OP composite se puede reemplazar con su descomposición sin cambiar la semántica del programa. En los casos en que en la descomposición no proporciona la misma semántica OP, prefiera usar custom_call .

El campo version (predeterminado se usa a 0 ) se usa para denotar cuando cambia la semántica de un compuesto.

Ver: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#composit .

Ejemplo:

%results = mhlo.composite "my.op" %arg0, %arg1 {
  decomposition = @my_op,
  composite_attributes = { my_attribute = "my_value" },
  version = 1 : i32
} : (tensor<f32>, tensor<f32>) -> tensor<f32>

Interfaces: SymbolUserOpInterface

Atributos:

Atributo Tipo MLIR Descripción
name ::mlir::StringAttr atributo de cadena
composite_attributes :: mlir :: diccionaryattr Diccionario de valores de atributos nombrados
decomposition ::mlir::FlatSymbolRefAttr atributo de referencia de símbolo plano
version ::mlir::IntegerAttr Atributo entero sin signo de 32 bits

Operandos:

Operando Descripción
inputs Variadic de tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ Tipo o F8E5M2 Tipo o F8E5M2FNUZ Tipo o Tipo Float de 16 bítes /8/16/32/64 bits Integer sin signos o 4/8/16/32/64 bits enteros sin firmar o tipo complejo con flotador de 32 bits o elementos flotantes de 64 bits o 4/8/16/32 bits entero uniforme cuantizado firmado o 4/8/16/16/32 bits enteros cuantizados sin signo o 4/8/16/16/32 bits cuantificados por número entero firmado o 4/8/16/16/32 bits cuantizado por eje unsigned unsigned valores o token o tuple anidados con cualquier combinación de tensor clasificado de f8e4m3b11fnuz o tipo f8e4m3fn o f8e4m3fnuz tipo o tipo f8e5m2 o f8e5m2fnuz tipo o float de 16 bits o 32 bit float o 64 bit float o bfloat16 type o predet o número entero de 1 bits) o 4/8/16/32/64 bits entero sin signos o 4/8/16/32/64 bits entero sin signo o tipo complejo con 32 bits flotante o elementos flotantes de 64 bits o 4 /8/16/32 bits Uniformes cuantizados Integer o 4/8/16/16/32 bits valores de entero sin firmar uniforme sin signo o tensor clasificado de 4/8/16/32 bits cuantificados por eje firmado o 4/8 /16/32 bits cuantificados por eje sin firmar valores enteros o valores de token

Resultados:

Resultado Descripción
"sin nombre" Variadic de tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ Tipo o F8E5M2 Tipo o F8E5M2FNUZ Tipo o Tipo Float de 16 bítes /8/16/32/64 bits Integer sin signos o 4/8/16/32/64 bits enteros sin firmar o tipo complejo con flotador de 32 bits o elementos flotantes de 64 bits o 4/8/16/32 bits entero uniforme cuantizado firmado o 4/8/16/16/32 bits enteros cuantizados sin signo o 4/8/16/16/32 bits cuantificados por número entero firmado o 4/8/16/16/32 bits cuantizado por eje unsigned unsigned valores o token o tuple anidados con cualquier combinación de tensor clasificado de f8e4m3b11fnuz o tipo f8e4m3fn o f8e4m3fnuz tipo o tipo f8e5m2 o f8e5m2fnuz tipo o float de 16 bits o 32 bit float o 64 bit float o bfloat16 type o predet o número entero de 1 bits) o 4/8/16/32/64 bits entero sin signos o 4/8/16/32/64 bits entero sin signo o tipo complejo con 32 bits flotante o elementos flotantes de 64 bits o 4 /8/16/32 bits Uniformes cuantizados Integer o 4/8/16/16/32 bits valores de entero sin firmar uniforme sin signo o tensor clasificado de 4/8/16/32 bits cuantificados por eje firmado o 4/8 /16/32 bits cuantificados por eje sin firmar valores enteros o valores de token

mhlo.compute_reshape_shape (mhlo :: computereshapeShapeop)

Operación de compensación

Sintaxis:

operation ::= `mhlo.compute_reshape_shape` operands attr-dict `:` functional-type(operands, results)

Esta operación es un trabajo en progreso, por lo que aún no se incluye en la especificación: https://github.com/openxla/stablehlo/issues/8

Informalmente, esta operación calcula una salida_shape para DynamicReshapeop a partir del número de elementos num_elements en un operando de DynamicReshapeop y la forma dynamic_shape proporcionada a la reashape de TF: https://www.tensorflow.org/api_docs/pythontt/reshape

Por ejemplo, para num_elements = 12 y dynamic_shape = [2, -1] , el result será [2, 6] . Si los operandos no son válidos (por ejemplo, si las dimensiones no dividen uniformemente el número de elementos, o si hay múltiples valores de -1 en las dimensiones), esto conduce a un comportamiento indefinido.

Ejemplo:

%result = mhlo.compute_reshape_shape %num_elements, %dynamic_shape
       : (index, tensor<2xi32>) -> tensor<2xi32>

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
num_elements índice
dynamic_shape 1D tensor de valores enteros o de índice

Resultados:

Resultado Descripción
result 1D tensor de valores enteros o de índice

mhlo.concatenate (mhlo :: concatenateop)

Concatenato de operación

Concatena un número variádico de tensores en inputs a lo largo de la dimensión dimension en el mismo orden que los argumentos dados y produce un tensor result .

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait , SameOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
dimension ::mlir::IntegerAttr Atributo entero sin signo de 64 bits

Operandos:

Operando Descripción
val Variadic de tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ Tipo o F8E5M2 Tipo o F8E5M2FNUZ Tipo o Tipo Float de 16 bítes /8/16/32/64 bits Integer sin signos o 4/8/16/32/64 bits enteros sin firmar o tipo complejo con flotador de 32 bits o elementos flotantes de 64 bits o 4/8/16/32 bits entero uniforme cuantizado firmado o 4/8/16/16/32 bits enteros cuantizados sin signo o 4/8/16/16/32 bits cuantificados por número entero firmado o 4/8/16/16/32 bits cuantizado por eje unsigned unsigned valores

Resultados:

Resultado Descripción
"sin nombre" Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

mhlo.constant (mhlo :: constantop)

Operación constante

Produce un tensor output a partir de un value constante.

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait , ConstantLike

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
value :: mlir :: Elementsattr atributo de vector constante/tensor

Resultados:

Resultado Descripción
output statically shaped tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/ 8/16/32/64 bits Integer sin signos o 4/8/16/32/64 bits enteros sin firmar o tipo complejo con uniformes flotantes de 32 bits o elementos flotantes de 64 bits o 4/8/16/32 bits uniformes entero firmado cuantizado o 4/8/16/16/32 bits enteros cuantizados sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/16/32 bits cuantizados por eje sin signo sin firmar valores

mhlo.convert (mhlo :: convertop)

Convertir operación

Sintaxis:

operation ::= `mhlo.convert` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

Realiza una conversión de elemento de un tipo de elemento a otro en el tensor operand y produce un tensor result .

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

Ejemplo:

%result = mhlo.convert %operand : (tensor<3xi32>) -> tensor<3xcomplex<f32>>

Rasgos: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

Resultados:

Resultado Descripción
result Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

mhlo.convolution (mhlo :: convolución)

operación de convolución

Sintaxis:

operation ::= `mhlo.convolution` `(`operands`)`
              `dim_numbers` `=` custom<ConvolutionDimensions>($dimension_numbers) `,`
              `window` `=` `{` custom<WindowAttributes>($window_strides, $padding,
              $lhs_dilation, $rhs_dilation,
              $window_reversal) `}`
              attr-dict `:` functional-type(operands, results)

Calcula productos DOT entre ventanas de lhs y rodajas de rhs y produce result .

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

Ejemplo:

%result = "mhlo.convolution"(%lhs, %rhs) {
  window_strides = dense<4> : tensor<2xi64>,
  padding = dense<0> : tensor<2x2xi64>,
  lhs_dilation = dense<2> : tensor<2xi64>,
  rhs_dilation = dense<1> : tensor<2xi64>,
  window_reversal = dense<false> : tensor<2xi1>,
  dimension_numbers = #mhlo.conv<[b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f]>,
  feature_group_count = 1 : i64,
  batch_group_count = 1 : i64,
  precision_config = [#stablehlo<precision DEFAULT>, #stablehlo<precision DEFAULT>]
} : (tensor<1x4x4x1xi32>, tensor<3x3x1x1xi32>) -> tensor<1x2x2x1xi32>

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
window_strides :: mlir :: denseintelementsattr Atributo de elementos enteros de 64 bits Signless Integer
padding :: mlir :: denseintelementsattr Atributo de elementos enteros de 64 bits Signless Integer
lhs_dilation :: mlir :: denseintelementsattr Atributo de elementos enteros de 64 bits Signless Integer
rhs_dilation :: mlir :: denseintelementsattr Atributo de elementos enteros de 64 bits Signless Integer
window_reversal :: mlir :: denseElementsattr Atributo de vector booleano/tensor constante
dimension_numbers :: mlir :: mhlo :: convdimensionnumbersattr Estructura de la información de dimensión para convivir
feature_group_count ::mlir::IntegerAttr Atributo entero sin signo de 64 bits
batch_group_count ::mlir::IntegerAttr Atributo entero sin signo de 64 bits
precision_config ::mlir::ArrayAttr Atributo de configuración de precisión

Operandos:

Operando Descripción
lhs Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar
rhs Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

Resultados:

Resultado Descripción
"sin nombre" Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

mhlo.copy (mhlo :: copyop)

Operación de copia

Sintaxis:

operation ::= `mhlo.copy` operands attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

Esta operación es privada para el compilador XLA, por lo que aún no tiene una especificación.

Informalmente, esta operación es una copia de operand . Dependiendo de los metadatos adjuntos a la operación, puede comportarse de manera muy diferente a una OP de OP.

Ejemplo:

%0 = mhlo.copy %arg0 : tensor<f32>

Rasgos: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
cross_program_prefetch_index ::mlir::IntegerAttr Atributo entero sin signo de 32 bits

Operandos:

Operando Descripción
operand Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantificados por eje entero sin firmar o valores de entero sin firmar o sin firmar Token o anidada token o tipo de tipo con cualquier combinación de tensor clasificado de tipo f8e4m3b11fnuz o tipo f8e4m3fn o f8e4m3fnuz tipo o tipo f8e5m2 o f8e5m2M2fnuz tipo o float de 16 bit -bit entero) o 4/8/16/32/64 bits entero sin signos o 4/8/16/32/64 bits entero sin signo o tipo complejo con 32 bits flotante o elementos flotantes de 64 bits o 4/8 /16/32 bits Uniformes cuantizados Integer o 4/8/16/32 bits Valores de entero sin signo cuantificado o tensor clasificado de 4/8/16/32 bits cuantificados por eje firmado o 4/8/16 /32 bits cuantificados por eje sin firmar valores enteros o valores de token

Resultados:

Resultado Descripción
result Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantificados por eje entero sin firmar o valores de entero sin firmar o sin firmar Token o anidada token o tipo de tipo con cualquier combinación de tensor clasificado de tipo f8e4m3b11fnuz o tipo f8e4m3fn o f8e4m3fnuz tipo o tipo f8e5m2 o f8e5m2M2fnuz tipo o float de 16 bit -bit entero) o 4/8/16/32/64 bits entero sin signos o 4/8/16/32/64 bits entero sin signo o tipo complejo con 32 bits flotante o elementos flotantes de 64 bits o 4/8 /16/32 bits Uniformes cuantizados Integer o 4/8/16/32 bits Valores de entero sin signo cuantificado o tensor clasificado de 4/8/16/32 bits cuantificados por eje firmado o 4/8/16 /32 bits cuantificados por eje sin firmar valores enteros o valores de token

mhlo.cosine (Mhlo :: Cosineop)

Operación coseno

Sintaxis:

operation ::= `mhlo.cosine` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

Realiza la operación de coseno en forma de elemento en el tensor operand y produce un tensor result .

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ Tipo o F8E5M2 o F8E5M2FNUZ Tipo o Float de 16 bits o Float de 32 bits o Float de 64 bits o tipo BFLOAT16 Tipo o tipo complejo con 32 bits Float o 64 bit float o 64 bit o Float o 64 bit o tipo bfloat16 Tipo o tipo complejo de 32 bits o float de 32 bits o float de 64 bit 4/8/16/16/32 bits enteros firmados cuantizados o 4/8/16/16/32 bits valores de entero cuantificados uniformes sin firmar

Resultados:

Resultado Descripción
result Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ Tipo o F8E5M2 o F8E5M2FNUZ Tipo o Float de 16 bits o Float de 32 bits o Float de 64 bits o tipo BFLOAT16 Tipo o tipo complejo con 32 bits Float o 64 bit float o 64 bit o Float o 64 bit o tipo bfloat16 Tipo o tipo complejo de 32 bits o float de 32 bits o float de 64 bit 4/8/16/16/32 bits enteros firmados cuantizados o 4/8/16/16/32 bits valores de entero cuantificados uniformes sin firmar

mhlo.count_leading_zeros (mhlo :: clzop)

Operación CLZ

Sintaxis:

operation ::= `mhlo.count_leading_zeros` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

Realiza un recuento de elementos del número de bits cero principales en el tensor operand y produce un tensor result .

Ver: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#count_leading_zeross

Ejemplo:

%result = mhlo.count_leading_zeros %operand : tensor<2x2xi8>

Rasgos: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand Tensor clasificado de 4/8/16/32/64 bits Integer sin signos o 4/8/16/32/64 bits Valores de Integer Unsigned

Resultados:

Resultado Descripción
result Tensor clasificado de 4/8/16/32/64 bits Integer sin signos o 4/8/16/32/64 bits Valores de Integer Unsigned

mhlo.create_token (mhlo :: createTokenop)

Operación de createToken

Sintaxis:

operation ::= `mhlo.create_token` attr-dict `:` type(results)

Esta operación está saliendo de Stablehlo, por lo que no está incluida en la especificación: https://github.com/openxla/stablehlo/issues/3

Informalmente, esta operación hace lo mismo que AfterAllop con 0 entradas: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all

Ejemplo:

%output = mhlo.create_token : !mhlo.token

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Resultados:

Resultado Descripción
output simbólico

mhlo.cross-replica-sum (Mhlo :: CrossReplicAsumop)

Operación CrossReplicAsum

Esta operación está saliendo de Stablehlo, por lo que no está incluida en la especificación: https://github.com/openxla/stablehlo/issues/3

Informalmente, esta operación hace lo mismo que Allreduceop con channel_id = 0 , use_global_device_ids = false e implementación de computation Adición: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
replica_groups :: mlir :: denseintelementsattr Atributo de elementos enteros de 64 bits Signless Integer

Operandos:

Operando Descripción
operand Tensor clasificado de tipo F8E4M3B11FNUZ o tipo F8E4M3FN o F8E4M3FNUZ o tipo F8E5M2 o F8E5M2FNUZ TIPO /16/32/64 bits Integer sin signos o 4/8/16/32/64 bits entero sin firmar o tipo complejo con flotación de 32 bits o elementos flotantes de 64 bits o 4/8/16/16/32 bits cuantizados entero firmado o 4/8/16/16/32 bits enteros uniformes sin signo o 4/8/16/32 bits cuantificados por eje entero firmado o 4/8/16/32 bits cuantizados por eje sin firmar valores de entero sin firmar

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.cstr_reshapable (mhlo::CstrReshapableOp)

CstrReshapable operation

Sintaxis:

operation ::= `mhlo.cstr_reshapable` operands attr-dict `:` functional-type(operands, results)

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

Informally, this operation creates a witness on the constraint that ComputeReshapeShape would succeed with the provided operands.

Ejemplo:

%result = mhlo.cstr_reshapable %num_elements, %dynamic_shape
       : (index, tensor<3xi32>) -> !shape.witness

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
num_elements índice
dynamic_shape 1D tensor of integer or index values

Resultados:

Resultado Descripción
result

mhlo.custom_call (mhlo::CustomCallOp)

CustomCall operation

Sintaxis:

operation ::= `mhlo.custom_call` custom<CustomCallTarget>($call_target_name) `(` $inputs `)`
              attr-dict `:` functional-type(operands, results)

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

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

Ejemplo:

%results = "mhlo.custom_call"(%input0) {
  call_target_name = "foo",
  has_side_effect = false,
  backend_config = "bar",
  api_version = 1 : i32,
  called_computations = [@foo]
} : (tensor<f32>) -> tensor<f32>

A custom call invokes code external to XLA. The `inputs` are passed to the
external code, and the external code is expected to produce a result of the
given type. The exact mechanism is backend-specific. For example, in the CPU
backend, a call instruction is emitted which targets a symbol with the name
`call_target_name`.

If XLA runtime is enabled for a backend, then custom calls use the runtime
custom call calling convention to call into the external functions. This
calling convention defines an ABI for encoding arguments, attributes and
results.

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

1. For `API_VERSION_TYPED_FFI` custom calls `backend_config` must be a
   dictionary attribute, that will be encoded according to the custom call
   calling convention and passed to the external function as the attributes
   argument. External code is expected to use declarative bindings (see
   `xla/runtime/custom_call.h`) to decode them at run time. These custom
   calls are only supported if XLA uses XLA runtime.

2. For previous API versions it is the user responsibility to encode extra
   bits of static information as a string `backend_config` attribute, and
   decode it at run time.

Interfaces: MemoryEffectOpInterface

Atributos:

Atributo Tipo MLIR Descripción
call_target_name ::mlir::StringAttr atributo de cadena
has_side_effect ::mlir::BoolAttr atributo booleano
backend_config ::mlir::Atributo string attribute or dictionary of named attribute values
api_version ::mlir::mhlo::CustomCallApiVersionAttr Custom call API version
called_computations ::mlir::ArrayAttr flat symbol ref array attribute
custom_call_schedule ::mlir::mhlo::CustomCallScheduleAttr Specifies the desired schedule for the custom-call.
operand_layouts ::mlir::ArrayAttr Array of layout (1D tensor of index type) attributes
result_layouts ::mlir::ArrayAttr Array of layout (1D tensor of index type) attributes
output_operand_aliases ::mlir::ArrayAttr Aliasing attribute for outputs and operands of CustomCall

Operandos:

Operando Descripción
inputs variadic of tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token or nested tuple with any combination of tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token values

Resultados:

Resultado Descripción
"sin nombre" variadic of tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token or nested tuple with any combination of tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token values

mhlo.divide (mhlo::DivOp)

operación división

Sintaxis:

operation ::= `mhlo.divide` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
rhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.domain (mhlo::DomainOp)

Domain operation

This operation is private to the XLA compiler, so it is does not yet have a specification.

Informally, these operations are used to group instructions with the same DomainMetadata property. ShardingMetadata is the main use case today to group instructions on the same device. Domain instructions provide two major benefits:

  • Prevent unintentionally optimizing instructions across domains.
  • Automatically assign the metadata of the instructions created in the domain. Without domain instructions, each HLO optimization pass would have to check and propagate the metadata, which would be easy to miss and also adds complexity to the compiler. Since domain instructions connect two different domains, each domain instruction is associated with two DomainMetadata -- one on the operand side and one on the user side of the domain.

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
kind ::mlir::mhlo::DomainKindAttr Kind of domain metatdata attached to an HLO domain.
entry_metadata ::mlir::StringAttr atributo de cadena
exit_metadata ::mlir::StringAttr atributo de cadena

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token

mhlo.dot (mhlo::DotOp)

Dot operation

This operation is on its way out of StableHLO, so it is not included in the 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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
precision_config ::mlir::ArrayAttr Precision Config attribute

Operandos:

Operando Descripción
lhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
rhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.dot_general (mhlo::DotGeneralOp)

DotGeneral operation

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

Ejemplo:

%result = "mhlo.dot_general"(%lhs, %rhs) {
  dot_dimension_numbers = #mhlo.dot<
    lhs_batching_dimensions = [0],
    rhs_batching_dimensions = [0],
    lhs_contracting_dimensions = [2],
    rhs_contracting_dimensions = [1]
  >,
  precision_config = [#stablehlo<precision DEFAULT>, #stablehlo<precision DEFAULT>]
} : (tensor<2x2x2xi32>, tensor<2x2x2xi32>) -> tensor<2x2x2xi32>

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
dot_dimension_numbers ::mlir::mhlo::DotDimensionNumbersAttr Attribute that models the dimension information for dot.
precision_config ::mlir::ArrayAttr Precision Config attribute

Operandos:

Operando Descripción
lhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
rhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.dynamic_broadcast_in_dim (mhlo::DynamicBroadcastInDimOp)

DynamicBroadcastInDim operation

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

Informally, this operation does the same thing as BroadcastInDimOp except that the result shape is specified dynamically via output_dimensions : https://github.com/openxla/stablehlo/blob/main/docs/spec.md#broadcast_in_dim

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.

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
broadcast_dimensions ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
known_expanding_dimensions ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
known_nonexpanding_dimensions ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
output_dimensions 1D tensor of index or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.dynamic_conv (mhlo::DynamicConvOp)

DynamicConv operation

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

Informally, this operation does the same thing as ConvolutionOp except that padding is specified dynamically via d_padding : https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convolution

Ejemplo:

%result = "mhlo.dynamic_conv"(%lhs, %rhs, %d_padding) {
  window_strides = dense<4> : tensor<2xi64>,
  lhs_dilation = dense<2> : tensor<2xi64>,
  rhs_dilation = dense<1> : tensor<2xi64>,
  window_reversal = dense<false> : tensor<2xi1>,
  dimension_numbers = #mhlo.conv<[b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f]>,
  feature_group_count = 1 : i64,
  batch_group_count = 1 : i64,
  precision_config = [#stablehlo<precision DEFAULT>, #stablehlo<precision DEFAULT>]
} : (tensor<1x4x4x1xi32>, tensor<3x3x1x1xi32>, tensor<2x2xi64>) -> tensor<1x2x2x1xi32>

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
window_strides ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
padding ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
lhs_dilation ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
rhs_dilation ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
window_reversal ::mlir::DenseElementsAttr constant boolean vector/tensor attribute
dimension_numbers ::mlir::mhlo::ConvDimensionNumbersAttr Structure of dimension information for conv op
feature_group_count ::mlir::IntegerAttr Atributo entero sin signo de 64 bits
batch_group_count ::mlir::IntegerAttr Atributo entero sin signo de 64 bits
precision_config ::mlir::ArrayAttr Precision Config attribute

Operandos:

Operando Descripción
lhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
rhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
d_padding ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.dynamic_gather (mhlo::DynamicGatherOp)

DynamicGather operation

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

Informally, this operation does the same thing as GatherOp except that slice_sizes are specified dynamically: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather

Ejemplo:

%result = "mhlo.dynamic_gather"(%operand, %start_indices, %slice_sizes) {
  dimension_numbers = #mhlo.gather<
    offset_dims = [2, 3],
    collapsed_slice_dims = [0],
    start_index_map = [0, 2],
    index_vector_dim = 2>,
  indices_are_sorted = false
} : (tensor<3x4x2xi32>, tensor<2x3x2xi64>, tensor<3xi64>) -> tensor<2x3x2x2xi32>

Traits: AlwaysSpeculatableImplTrait , InferTensorType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
dimension_numbers ::mlir::mhlo::GatherDimensionNumbersAttr Attribute that models the dimension information for gather
indices_are_sorted ::mlir::BoolAttr atributo booleano

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
start_indices ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values
slice_sizes ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.dynamic_iota (mhlo::DynamicIotaOp)

DynamicIota operation

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

Informally, this operation does the same thing as IotaOp except that the result shape is specified dynamically via output_shape : https://github.com/openxla/stablehlo/blob/main/docs/spec.md#iota

Ejemplo:

%0 = mhlo.dynamic_iota %arg0, dim = 0 : (tensor<1xindex>) -> tensor<4xi32>

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
iota_dimension ::mlir::IntegerAttr Atributo entero sin signo de 64 bits

Operandos:

Operando Descripción
output_shape 1D tensor of index or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.dynamic_pad (mhlo::DynamicPadOp)

DynamicPad operation

Sintaxis:

operation ::= `mhlo.dynamic_pad` operands attr-dict `:` functional-type(operands, results)

Dynamically Pads the operand , with amount of padding added at low-end/high-end/interior is passed through input tensors.

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
padding_value ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
edge_padding_low 1D tensor of index or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values
edge_padding_high 1D tensor of index or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values
interior_padding 1D tensor of index or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.dynamic_reshape (mhlo::DynamicReshapeOp)

DynamicReshape operation

Sintaxis:

operation ::= `mhlo.dynamic_reshape` operands attr-dict `:` functional-type(operands, results)

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

Informally, this operation does the same thing as ReshapeOp except that the result shape is specified dynamically via output_shape : https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reshape

Ejemplo:

%0 = mhlo.dynamic_reshape %arg0, %shape : (tensor<?xf32>, tensor<2xindex>) -> tensor<?x?xf32>

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
output_shape 1D tensor of index or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.dynamic_slice (mhlo::DynamicSliceOp)

DynamicSlice operation

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , InferTensorType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
slice_sizes ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
start_indices variadic of 0D tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.dynamic_update_slice (mhlo::DynamicUpdateSliceOp)

DynamicUpdateSlice operation

Sintaxis:

operation ::= `mhlo.dynamic_update_slice` operands attr-dict `:` functional-type(operands, results)

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , InferTensorType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
update ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
start_indices variadic of 0D tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.einsum (mhlo::EinsumOp)

Einsum operation

This operation is on its way out of StableHLO, so it is not included in the 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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
einsum_config ::mlir::StringAttr atributo de cadena

Operandos:

Operando Descripción
lhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
rhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.erf (mhlo::ErfOp)

Erf operation

Sintaxis:

operation ::= `mhlo.erf` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

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

Ejemplo:

%result = mhlo.erf %operand : tensor<2x2xf32>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

mhlo.exponential (mhlo::ExpOp)

Exp operation

Sintaxis:

operation ::= `mhlo.exponential` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

mhlo.exponential_minus_one (mhlo::Expm1Op)

Expm1 operation

Sintaxis:

operation ::= `mhlo.exponential_minus_one` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

%result = mhlo.exponential_minus_one %operand : tensor<2xf32>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

mhlo.fft (mhlo::FftOp)

Fft operation

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , InferTensorType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
fft_type ::mlir::mhlo::FftTypeAttr XLA fast fourier transform type.
fft_length ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.floor (mhlo::FloorOp)

Floor operation

Sintaxis:

operation ::= `mhlo.floor` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

mhlo.fusion (mhlo::FusionOp)

Fusion operation

This operation is private to the XLA compiler, so it is does not yet have a specification.

Informally, this operation consists of a group of basic ops (represented as a region attached to it). It serves as a hint to the backend that it is beneficial to emit the contained ops into a single loop nest or kernel.

Atributos:

Atributo Tipo MLIR Descripción
fusion_kind ::mlir::mhlo::FusionKindAttr fusion kind
output_operand_aliases ::mlir::ArrayAttr Aliasing attribute for outputs and operands of Fusion

Operandos:

Operando Descripción
inputs variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token

Resultados:

Resultado Descripción
results variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or nested tuple with any combination of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token values

mhlo.gather (mhlo::GatherOp)

Gather operation

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

Ejemplo:

%result = "mhlo.gather"(%operand, %start_indices) {
  dimension_numbers = #mhlo.gather<
    offset_dims = [2, 3],
    collapsed_slice_dims = [0],
    start_index_map = [0, 2],
    index_vector_dim = 2>,
  slice_sizes = dense<[0, 2, 2]> : tensor<3xi64>,
  indices_are_sorted = false
} : (tensor<3x4x2xi32>, tensor<2x3x2xi64>) -> tensor<2x3x2x2xi32>

Traits: AlwaysSpeculatableImplTrait , InferTensorType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
dimension_numbers ::mlir::mhlo::GatherDimensionNumbersAttr Attribute that models the dimension information for gather
slice_sizes ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
indices_are_sorted ::mlir::BoolAttr atributo booleano

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
start_indices ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.get_dimension_size (mhlo::GetDimensionSizeOp)

GetDimensionSize operation

Produces the size of the given dimension of the operand .

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

Ejemplo:

%result = mhlo.get_dimension_size %operand, dim = 1 : (tensor<2x3xf32>) -> tensor<i32>

Traits: AlwaysSpeculatableImplTrait , InferTensorType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
dimension ::mlir::IntegerAttr Atributo entero sin signo de 64 bits

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" tensor of 32-bit signless integer values

mhlo.get_tuple_element (mhlo::GetTupleElementOp)

GetTupleElement operation

Sintaxis:

operation ::= `mhlo.get_tuple_element` $operand `[` $index `]` attr-dict `:` functional-type(operands, results)

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

Ejemplo:

%result = mhlo.get_tuple_element %operand[0] : (tuple<tensor<2xf32>, tuple<tensor<i32>>>) -> tensor<2xf32>

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
index ::mlir::IntegerAttr Atributo entero sin signo de 32 bits

Operandos:

Operando Descripción
operand nested tuple with any combination of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token or nested tuple with any combination of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token values

mhlo.if (mhlo::IfOp)

If operation

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 = "mhlo.if"(%pred) ({ "mhlo.return"(%result_true_branch) : (tensor ) -> () }, { "mhlo.return"(%result_false_branch) : (tensor ) -> () }) : (tensor ) -> tensor

Traits: RecursiveMemoryEffects , SingleBlockImplicitTerminator<ReturnOp> , SingleBlock

Interfaces: InferTypeOpInterface

Operandos:

Operando Descripción
pred ranked tensor of pred (AKA boolean or 1-bit integer) values

Resultados:

Resultado Descripción
"sin nombre" variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token

mhlo.imag (mhlo::ImagOp)

Imag operation

Sintaxis:

operation ::= `mhlo.imag` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

mhlo.infeed (mhlo::InfeedOp)

Infeed operation

Reads data from the infeed and produces results .

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

Ejemplo:

%results:2 = "mhlo.infeed"(%token) {
  infeed_config = ""
} : (!mhlo.token) -> (tensor<3x3x3xi32>, !mhlo.token)

Atributos:

Atributo Tipo MLIR Descripción
infeed_config ::mlir::StringAttr atributo de cadena
layout ::mlir::ArrayAttr atributo de matriz

Operandos:

Operando Descripción
token simbólico

Resultados:

Resultado Descripción
"sin nombre" variadic of statically shaped tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or statically shaped tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token

mhlo.iota (mhlo::IotaOp)

Iota operation

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
iota_dimension ::mlir::IntegerAttr Atributo entero sin signo de 64 bits

Resultados:

Resultado Descripción
output statically shaped tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements values

mhlo.is_finite (mhlo::IsFiniteOp)

IsFinite operation

Sintaxis:

operation ::= `mhlo.is_finite` $x attr-dict `:` functional-type(operands, results)

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

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

Ejemplo:

%y = mhlo.is_finite %x : (tensor<7xf32>) -> tensor<7xi1>

Traits: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
x ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

Resultados:

Resultado Descripción
y ranked tensor of pred (AKA boolean or 1-bit integer) values

mhlo.log (mhlo::LogOp)

Log operation

Sintaxis:

operation ::= `mhlo.log` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

mhlo.log_plus_one (mhlo::Log1pOp)

Log1p operation

Sintaxis:

operation ::= `mhlo.log_plus_one` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

%result = mhlo.log_plus_one %operand : tensor<6xf32>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

mhlo.logistic (mhlo::LogisticOp)

Logistic operation

Sintaxis:

operation ::= `mhlo.logistic` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

%result = mhlo.logistic %operand : tensor<2x2xf32>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

mhlo.map (mhlo::MapOp)

Map operation

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

Ejemplo:

%result = "mhlo.map"(%input0, %input1) ({
  ^bb0(%arg0: tensor<i32>, %arg1: tensor<i32>):
    %0 = mhlo.multiply %arg0, %arg1 : tensor<i32>
    mhlo.return %0 : tensor<i32>
}) {
  dimensions = dense<[0, 1]> : tensor<2xi64>
} : (tensor<2x2xi32>, tensor<2x2xi32>) -> tensor<2x2xi32>

Traits: InferTensorType , RecursiveMemoryEffects , SameOperandsAndResultShape , SingleBlockImplicitTerminator<ReturnOp> , SingleBlock

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
dimensions ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute

Operandos:

Operando Descripción
inputs variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.maximum (mhlo::MaxOp)

Max operation

Sintaxis:

operation ::= `mhlo.maximum` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
rhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.minimum (mhlo::MinOp)

Min operation

Sintaxis:

operation ::= `mhlo.minimum` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
rhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.multiply (mhlo::MulOp)

Mul operation

Sintaxis:

operation ::= `mhlo.multiply` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
rhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.negate (mhlo::NegOp)

Neg operation

Sintaxis:

operation ::= `mhlo.negate` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.not (mhlo::NotOp)

Not operation

Sintaxis:

operation ::= `mhlo.not` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

mhlo.optimization_barrier (mhlo::OptimizationBarrierOp)

OptimizationBarrier operation

Sintaxis:

operation ::= `mhlo.optimization_barrier` attr-dict ($operand^ `:` custom<PairwiseOpType>(type($operand), type($result))):(`(` `)`)?

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, ie result = operand .

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , HLO_PairwiseSameOperandAndResultType

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token

Resultados:

Resultado Descripción
result variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token

mhlo.or (mhlo::OrOp)

Or operation

Sintaxis:

operation ::= `mhlo.or` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs ranked tensor of pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values
rhs ranked tensor of pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.outfeed (mhlo::OutfeedOp)

Outfeed operation

Writes inputs to the outfeed and produces a result token.

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

Ejemplo:

%result = "mhlo.outfeed"(%input0, %token) {
  outfeed_config = ""
} : (tensor<3x3x3xi32>, !mhlo.token) -> !mhlo.token

Interfaces: InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
outfeed_config ::mlir::StringAttr atributo de cadena

Operandos:

Operando Descripción
inputs variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores
token simbólico

Resultados:

Resultado Descripción
"sin nombre" simbólico

mhlo.pad (mhlo::PadOp)

Pad operation

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , SameOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
edge_padding_low ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
edge_padding_high ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
interior_padding ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
padding_value ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.partition_id (mhlo::PartitionIdOp)

PartitionId operation

Sintaxis:

operation ::= `mhlo.partition_id` attr-dict `:` type(results)

Produces partition_id of the current process.

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

Ejemplo:

%result = mhlo.partition_id : tensor<ui32>

Interfaces: InferTypeOpInterface

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of 32-bit unsigned integer values

mhlo.popcnt (mhlo::PopulationCountOp)

PopulationCount operation

Sintaxis:

operation ::= `mhlo.popcnt` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

%result = mhlo.popcnt %operand : tensor<4xi8>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

mhlo.power (mhlo::PowOp)

operación de potencia

Sintaxis:

operation ::= `mhlo.power` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

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

Ejemplo:

%result = mhlo.power %lhs, %rhs : tensor<6xf32>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
rhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.real (mhlo::RealOp)

Real operation

Sintaxis:

operation ::= `mhlo.real` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

mhlo.real_dynamic_slice (mhlo::RealDynamicSliceOp)

RealDynamicSlice operation

Sintaxis:

operation ::= `mhlo.real_dynamic_slice` operands attr-dict `:` functional-type(operands, results)

This operation is a work in progress, so it is not yet included in the 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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
start_indices 1D tensor of index or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values
limit_indices 1D tensor of index or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values
strides 1D tensor of index or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.recv (mhlo::RecvOp)

Recv operation

Receives data from a channel with channel_id and produces results .

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

Ejemplo:

%results:2 = "mhlo.recv"(%token) {
  // channel_id = 5 : i64,
  // channel_type = #stablehlo<channel_type HOST_TO_DEVICE>,
  channel_handle = #mhlo.channel_handle<handle = 5, type = 3>,
  is_host_transfer = true
} : (!mhlo.token) -> (tensor<3x4xi32>, !mhlo.token)

Atributos:

Atributo Tipo MLIR Descripción
channel_handle ::mlir::mhlo::ChannelHandleAttr two 64-bit integers 'handle' and 'type'
is_host_transfer ::mlir::BoolAttr atributo booleano

Operandos:

Operando Descripción
token simbólico

Resultados:

Resultado Descripción
"sin nombre" variadic of statically shaped tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or statically shaped tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token

mhlo.reduce (mhlo::ReduceOp)

Reduce operation

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

Ejemplo:

%result = "mhlo.reduce"(%input, %init_value) ({
  ^bb0(%arg0: tensor<i32>, %arg1: tensor<i32>):
    %0 = "mhlo.add"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    "mhlo.return"(%0) : (tensor<i32>) -> ()
}) {
  dimensions = dense<1> : tensor<1xi64>
} : (tensor<1x6xi32>, tensor<i32>) -> tensor<1xi32>

Traits: InferTensorType , RecursiveMemoryEffects , SameVariadicOperandSize , SingleBlockImplicitTerminator<ReturnOp> , SingleBlock

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
dimensions ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute

Operandos:

Operando Descripción
inputs variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores
init_values variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores

Resultados:

Resultado Descripción
"sin nombre" variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores

mhlo.reduce_precision (mhlo::ReducePrecisionOp)

ReducePrecision operation

Sintaxis:

operation ::= `mhlo.reduce_precision` $operand `,` `format` `=` custom<ExponentMantissa>($exponent_bits, $mantissa_bits)
              attr-dict `:` custom<SameOperandsAndResultType>(type($operand), type($output))

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

Ejemplo:

%output = mhlo.reduce_precision %operand, format = e5m2 : tensor<6xf32>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
exponent_bits ::mlir::IntegerAttr Atributo entero sin signo de 32 bits
mantissa_bits ::mlir::IntegerAttr Atributo entero sin signo de 32 bits

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

Resultados:

Resultado Descripción
output ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

mhlo.reduce_scatter (mhlo::ReduceScatterOp)

Operación de reducción de dispersión

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.md#reduce_scatter

Ejemplo:

%result = "mhlo.reduce_scatter"(%operand) ({
  ^bb0(%arg0: tensor<f32>, %arg1: tensor<f32>):
  %0 = mhlo.add %arg0, %arg1 : tensor<f32>
  mhlo.return %0 : tensor<f32>
}) {
  scatter_dimension = 1 : i64,
  replica_groups = dense<[[0, 1]]> : tensor<1x2xi64>,
  // channel_id = 0
  channel_handle = #mhlo.channel_handle<handle = 0, type = 0>
  // use_global_device_ids = false
} : (tensor<2x4xf32>) -> tensor<2x2xf32>

Atributos:

Atributo Tipo MLIR Descripción
scatter_dimension ::mlir::IntegerAttr Atributo entero sin signo de 64 bits
replica_groups ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
channel_handle ::mlir::mhlo::ChannelHandleAttr two 64-bit integers 'handle' and 'type'
use_global_device_ids ::mlir::AtributoUnidad atributo de unidad

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.reduce_window (mhlo::ReduceWindowOp)

ReduceWindow operation

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

Ejemplo:

%result = "mhlo.reduce_window"(%input, %init_value) ({
  ^bb0(%arg0: tensor<i32>, %arg1: tensor<i32>):
    %0 = mhlo.add %arg0, %arg1 : tensor<i32>
    mhlo.return %0 : tensor<i32>
}) {
  window_dimensions = dense<[2, 1]> : tensor<2xi64>,
  window_strides = dense<[4, 1]> : tensor<2xi64>,
  base_dilations = dense<[2, 1]> : tensor<2xi64>,
  window_dilations = dense<[3, 1]> : tensor<2xi64>,
  padding = dense<[[2, 1], [0, 0]]> : tensor<2x2xi64>
} : (tensor<3x2xi32>, tensor<i32>) -> tensor<2x2xi32>

Traits: InferTensorType , RecursiveMemoryEffects , SameVariadicOperandSize , SingleBlockImplicitTerminator<ReturnOp> , SingleBlock

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
window_dimensions ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
window_strides ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
base_dilations ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
window_dilations ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
padding ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute

Operandos:

Operando Descripción
inputs variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores
init_values variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores

Resultados:

Resultado Descripción
"sin nombre" variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores

mhlo.remainder (mhlo::RemOp)

Rem operation

Sintaxis:

operation ::= `mhlo.remainder` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
rhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.replica_id (mhlo::ReplicaIdOp)

ReplicaId operation

Sintaxis:

operation ::= `mhlo.replica_id` attr-dict `:` type(results)

Produces replica_id of the current process.

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

Ejemplo:

%result = mhlo.replica_id : tensor<ui32>

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of 32-bit unsigned integer values

mhlo.reshape (mhlo::ReshapeOp)

Reshape operation

Sintaxis:

operation ::= `mhlo.reshape` operands attr-dict `:` functional-type(operands, results)

Performs reshape of operand tensor to a result tensor.

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , HLO_CompatibleOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" statically shaped tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.return (mhlo::ReturnOp)

_This operation is a work in progress, so it is not yet included in the specification: https://github.com/openxla/stablehlo/issues/425

Informally, this operation serves as a terminator for regions defined by
the StableHLO ops. Non-StableHLO ops, e.g. `func.func`, have their own
terminators, e.g. `func.return`.

Example:

    ```mlir
    %result = "mhlo.reduce"(%input, %init_value) ({
      ^bb0(%arg0: tensor<i32>, %arg1: tensor<i32>):
        %0 = "mhlo.add"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
        "mhlo.return"(%0) : (tensor<i32>) -> ()
    }) {
      dimensions = dense<1> : tensor<1xi64>
    } : (tensor<1x6xi32>, tensor<i32>) -> tensor<1xi32>
    ```_


Syntax:

```

operation ::= mhlo.return $results attr-dict ( : type($results)^)?



Traits: `AlwaysSpeculatableImplTrait`, `Terminator`

Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`

Effects: `MemoryEffects::Effect{}`

#### Operands:

| Operand | Description |
| :-----: | ----------- |
| `results` | variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token or nested tuple with any combination of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token values


### `mhlo.reverse` (mhlo::ReverseOp)

_Reverse operation_

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

See:
<a href="https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reverse">https://github.com/openxla/stablehlo/blob/main/docs/spec.md#reverse</a>

Example:
```mlir
%result = mhlo.reverse %operand, dims = [1] : tensor<3x2xi32>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
dimensions ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.rng (mhlo::RngOp)

Rng operation

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

Ejemplo:

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

Traits: InferTensorType

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
rng_distribution ::mlir::mhlo::RngDistributionAttr XLA PRNG distribution to be used.

Operandos:

Operando Descripción
a 0D tensor of pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values
b 0D tensor of pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values
shape 1D tensor of index or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

mhlo.rng_bit_generator (mhlo::RngBitGeneratorOp)

RngBitGenerator operation

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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
rng_algorithm ::mlir::mhlo::RngAlgorithmAttr XLA PRNG algorithm to be used.

Operandos:

Operando Descripción
initial_state ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

Resultados:

Resultado Descripción
output_state ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values
output statically shaped tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

mhlo.round_nearest_afz (mhlo::RoundOp)

Round operation

Sintaxis:

operation ::= `mhlo.round_nearest_afz` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

%result = mhlo.round_nearest_afz %operand : tensor<5xf32>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

mhlo.round_nearest_even (mhlo::RoundNearestEvenOp)

RoundNearestEven operation

Sintaxis:

operation ::= `mhlo.round_nearest_even` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

%result = mhlo.round_nearest_even %operand : tensor<5xf32>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

mhlo.rsqrt (mhlo::RsqrtOp)

Rsqrt operation

Sintaxis:

operation ::= `mhlo.rsqrt` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

mhlo.scatter (mhlo::ScatterOp)

Scatter operation

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

Ejemplo:

%result = "mhlo.scatter"(%input, %scatter_indices, %update) ({
  ^bb0(%arg0: tensor<i32>, %arg1: tensor<i32>):
    %0 = mhlo.add %arg0, %arg1 : tensor<i32>
    mhlo.return %0 : tensor<i32>
}) {
  scatter_dimension_numbers = #mhlo.scatter<
    update_window_dims = [2,3],
    inserted_window_dims = [0],
    scatter_dims_to_operand_dims = [1, 0],
    index_vector_dim = 2>,
  indices_are_sorted = false,
  unique_indices = false
} : (tensor<3x4x2xi32>, tensor<2x3x2xi64>, tensor<2x3x2x2xi32>) -> tensor<3x4x2xi32>

Traits: RecursiveMemoryEffects , SameVariadicOperandSize

Interfaces: InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
scatter_dimension_numbers ::mlir::mhlo::ScatterDimensionNumbersAttr Attribute that models the dimension information for scatter
indices_are_sorted ::mlir::BoolAttr atributo booleano
unique_indices ::mlir::BoolAttr atributo booleano

Operandos:

Operando Descripción
inputs variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores
scatter_indices ranked tensor of integer or index values
updates variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores

Resultados:

Resultado Descripción
"sin nombre" variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores

mhlo.select (mhlo::SelectOp)

Seleccionar operación

Sintaxis:

operation ::= `mhlo.select` operands attr-dict `:`
              custom<SelectOpType>(type($pred), type($on_true), type($on_false), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , HLO_BroadcastingElementwise , InferTensorType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
pred ranked tensor of pred (AKA boolean or 1-bit integer) values
on_true ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
on_false ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.select_and_scatter (mhlo::SelectAndScatterOp)

SelectAndScatter operation

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

Ejemplo:

%result = "mhlo.select_and_scatter"(%operand, %source, %init_value) ({
  ^bb0(%arg0: tensor<i32>, %arg1: tensor<i32>):
    %0 = "mhlo.compare"(%arg0, %arg1) {
      comparison_direction = #stablehlo<comparison_direction GE>
    } : (tensor<i32>, tensor<i32>) -> tensor<i1>
    "mhlo.return"(%0) : (tensor<i1>) -> ()
}, {
  ^bb0(%arg0: tensor<i32>, %arg1: tensor<i32>):
    %0 = "mhlo.add"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    "mhlo.return"(%0) : (tensor<i32>) -> ()
}) {
  window_dimensions = dense<[3, 1]> : tensor<2xi64>,
  window_strides = dense<[2, 1]> : tensor<2xi64>,
  padding = dense<[[0, 1], [0, 0]]> : tensor<2x2xi64>
} : (tensor<4x2xi32>, tensor<2x2xi32>, tensor<i32>) -> tensor<4x2xi32>

Traits: RecursiveMemoryEffects

Interfaces: InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
window_dimensions ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
window_strides ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
padding ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
source ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
init_value ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.send (mhlo::SendOp)

Send operation

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

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

Ejemplo:

%result = "mhlo.send"(%operand, %token) {
  // channel_id = 5 : i64,
  // channel_type = #stablehlo<channel_type DEVICE_TO_HOST>,
  channel_handle = #mhlo.channel_handle<handle = 5, type = 2>,
  is_host_transfer = true
} : (tensor<3x4xi32>, !mhlo.token) -> !mhlo.token

Interfaces: InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
channel_handle ::mlir::mhlo::ChannelHandleAttr two 64-bit integers 'handle' and 'type'
is_host_transfer ::mlir::BoolAttr atributo booleano

Operandos:

Operando Descripción
inputs variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores
token simbólico

Resultados:

Resultado Descripción
"sin nombre" simbólico

mhlo.set_dimension_size (mhlo::SetDimensionSizeOp)

SetDimensionSize operation

This operation is a work in progress, so it is not yet included in the 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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , InferTensorType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
dimension ::mlir::IntegerAttr Atributo entero sin signo de 64 bits

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
size tensor of 32-bit signless integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.shift_left (mhlo::ShiftLeftOp)

ShiftLeft operation

Sintaxis:

operation ::= `mhlo.shift_left` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

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

Ejemplo:

%result = mhlo.shift_left %lhs, %rhs : tensor<6xi8>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values
rhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

mhlo.shift_right_arithmetic (mhlo::ShiftRightArithmeticOp)

ShiftRightArithmetic operation

Sintaxis:

operation ::= `mhlo.shift_right_arithmetic` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

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

Ejemplo:

%result = mhlo.shift_right_arithmetic %lhs, %rhs : tensor<6xi8>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values
rhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

mhlo.shift_right_logical (mhlo::ShiftRightLogicalOp)

ShiftRightLogical operation

Sintaxis:

operation ::= `mhlo.shift_right_logical` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

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

Ejemplo:

%result = mhlo.shift_right_logical %lhs, %rhs : tensor<6xi8>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values
rhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

mhlo.sign (mhlo::SignOp)

Sign operation

Sintaxis:

operation ::= `mhlo.sign` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

%result = mhlo.sign %operand : tensor<7xf32>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of 4/8/16/32/64-bit signless integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of 4/8/16/32/64-bit signless integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

mhlo.sine (mhlo::SineOp)

Sine operation

Sintaxis:

operation ::= `mhlo.sine` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

mhlo.slice (mhlo::SliceOp)

Operación de corte

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

Ejemplo:

%result = "mhlo.slice" (%operand) {
  start_indices = dense<[1, 2]> : tensor<2xi64>,
  limit_indices = dense<[3, 4]> : tensor<2xi64>,
  strides = dense<1> : tensor<2xi64>
} : (tensor<3x4xi64>) -> tensor<2x2xi64>

Traits: AlwaysSpeculatableImplTrait , SameOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
start_indices ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
limit_indices ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute
strides ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.sort (mhlo::SortOp)

Sort operation

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

Ejemplo:

%result0, %result1 = "mhlo.sort"(%input0, %input1) ({
  ^bb0(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>):
    %predicate = "mhlo.compare"(%arg0, %arg1) {
      comparison_direction = #stablehlo<comparison_direction GT>
      } : (tensor<i32>, tensor<i32>) -> tensor<i1>
    "mhlo.return"(%predicate) : (tensor<i1>) -> ()
}) {
  dimension = 0 : i64,
  is_stable = true
} : (tensor<2x3xi32>, tensor<2x3xi32>) -> (tensor<2x3xi32>, tensor<2x3xi32>)

Traits: InferTensorType , RecursiveMemoryEffects , SameOperandsAndResultShape

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
dimension ::mlir::IntegerAttr Atributo entero sin signo de 64 bits
is_stable ::mlir::BoolAttr atributo booleano

Operandos:

Operando Descripción
inputs variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores

Resultados:

Resultado Descripción
"sin nombre" variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores

mhlo.sparse_dot (mhlo::SparseDotOp)

Sparse dot operation

Similar to dot_general operation, with one or both of the operands being sparse. An additional argument provides sparsity meta information. Disclaimer: this op is experimental / a work in progress.

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
lhs_sparsity ::mlir::mhlo::SparsityDescriptorAttr Describes structured (N:M) sparsity configuration
rhs_sparsity ::mlir::mhlo::SparsityDescriptorAttr Describes structured (N:M) sparsity configuration
dot_dimension_numbers ::mlir::mhlo::DotDimensionNumbersAttr Attribute that models the dimension information for dot.
precision_config ::mlir::ArrayAttr Precision Config attribute

Operandos:

Operando Descripción
lhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
rhs ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
meta variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4 /8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer valores

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.sqrt (mhlo::SqrtOp)

Sqrt operation

Sintaxis:

operation ::= `mhlo.sqrt` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

%result = mhlo.sqrt %operand : tensor<2x2xf32>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

mhlo.stochastic_convert (mhlo::StochasticConvertOp)

StochasticConvert operation

This operation is a work in progress, so it is not yet included in the specification: https://github.com/openxla/stablehlo/issues/295

Informally, this operation performs element-wise conversion of values from a bigger type to a smaller one with stochastic rounding using the random number passed in.

Traits: AlwaysSpeculatableImplTrait , Elementwise

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values
random ranked tensor of 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.subtract (mhlo::SubtractOp)

Subtract operation

Sintaxis:

operation ::= `mhlo.subtract` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
rhs ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.tan (mhlo::TanOp)

Tan operation

Sintaxis:

operation ::= `mhlo.tan` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

This operation is a work in progress, so it is not yet included in the specification: https://github.com/openxla/stablehlo/issues/954

Informally, this operation returns Tan(operand) element-wise.

Ejemplo:

%0 = mhlo.tan %arg0 : tensor<2xf32>

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements values

mhlo.tanh (mhlo::TanhOp)

Tanh operation

Sintaxis:

operation ::= `mhlo.tanh` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values

mhlo.topk (mhlo::TopKOp)

TopK operation

Sintaxis:

operation ::= `mhlo.topk` `(`$operand `,` `k` `=` $k (`,` `largest` `=` $largest^)? `)` attr-dict `:`
              type($operand) `->` `(`type($values)`,` type($indices)`)`

Returns top k values and their indices, along the last dimension of the operand if largest=true or the bottom k values if largest=false .

See: https://www.tensorflow.org/xla/operation_semantics#top-k

Ejemplo:

%values, %indices = mhlo.topk(%operand, k=5, largest=true)
  : tensor<100xf32> -> (tensor<5xf32>, tensor<5xi32>)

Traits: InferTensorType , RecursiveMemoryEffects

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
k ::mlir::IntegerAttr Atributo entero sin signo de 64 bits
largest ::mlir::BoolAttr atributo booleano

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
values ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
indices ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.torch_index_select (mhlo::TorchIndexSelectOp)

TorchIndexSelect operation

This operation is on its way out of StableHLO, so it is not included in the 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.

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
dim ::mlir::IntegerAttr Atributo entero sin signo de 64 bits
batch_dims ::mlir::IntegerAttr Atributo entero sin signo de 64 bits

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values
index ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.trace (mhlo::TraceOp)

Trace operation

Sintaxis:

operation ::= `mhlo.trace` $operand `,` $tag attr-dict `:` type($operand)

This operation is on its way out of StableHLO, so it is not included in the specification: https://github.com/openxla/stablehlo/issues/604

It is not used by JAX, PyTorch or TensorFlow, so it looks like we should've classified it as "Private to XLA" and not included it in StableHLO in the first place. With that in mind, its semantics will not be documented here.

Ejemplo:

mhlo.trace %arg0, "In test code." : tensor<5x1x5xi32>

Atributos:

Atributo Tipo MLIR Descripción
tag ::mlir::StringAttr atributo de cadena

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.transpose (mhlo::TransposeOp)

Transpose operation

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , HLO_CompatibleOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
permutation ::mlir::DenseIntElementsAttr 64-bit signless integer elements attribute

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.triangular_solve (mhlo::TriangularSolveOp)

TriangularSolve operation

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , InferTensorType , SameOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
left_side ::mlir::BoolAttr atributo booleano
lower ::mlir::BoolAttr atributo booleano
unit_diagonal ::mlir::BoolAttr atributo booleano
transpose_a ::mlir::mhlo::TransposeAttr Transpose options

Operandos:

Operando Descripción
a ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements values
b ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or complex type with 32-bit float or 64-bit float elements values

mhlo.tuple (mhlo::TupleOp)

Tuple operation

Sintaxis:

operation ::= `mhlo.tuple` $val attr-dict `:` custom<TupleOpType>(type($val), type($result))

Produces a result tuple from values val .

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

Ejemplo:

%result = mhlo.tuple %val0, %val1 : tuple<tensor<2xf32>, tuple<tensor<i32>>>

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
val variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token or nested tuple with any combination of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token values

Resultados:

Resultado Descripción
result nested tuple with any combination of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token values

mhlo.unary_einsum (mhlo::UnaryEinsumOp)

UnaryEinsum operation

This operation is on its way out of StableHLO, so it is not included in the 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

Ejemplo:

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

Rasgos: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Atributos:

Atributo Tipo MLIR Descripción
einsum_config ::mlir::StringAttr atributo de cadena

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
"sin nombre" ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.uniform_dequantize (mhlo::UniformDequantizeOp)

UniformDequantize operation

Sintaxis:

operation ::= `mhlo.uniform_dequantize` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

%result = mhlo.uniform_dequantize %operand : (tensor<16x16x!quant.uniform<i8:f32, 34.0:16>>) -> tensor<16x16xf32>

Traits: AlwaysSpeculatableImplTrait , Elementwise , InferTensorType , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type values

mhlo.uniform_quantize (mhlo::UniformQuantizeOp)

UniformQuantize operation

Sintaxis:

operation ::= `mhlo.uniform_quantize` $operand attr-dict
              `:` custom<SameOperandsAndResultType>(type($operand), type($result))

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

Ejemplo:

%result = mhlo.uniform_quantize %operand : (tensor<16x16xf32>) -> tensor<16x16x!quant.uniform<ui8:f32, 34.0:16>>

Traits: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
operand ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

mhlo.while (mhlo::WhileOp)

While operation

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

Ejemplo:

%results0, %results1 = "mhlo.while"(%operand0, %operand1) ({
  ^bb0(%arg0: tensor<i32>, %arg1: tensor<i32>):
    %0 = "mhlo.compare"(%arg0, %arg1) {
      comparison_direction = #stablehlo<comparison_direction LT>
    } : (tensor<i32>, tensor<i32>) -> tensor<i1>
    "mhlo.return"(%0) : (tensor<i1>) -> ()
}, {
  ^bb0(%arg0: tensor<i32>, %arg1: tensor<i32>):
    %0 = "mhlo.add"(%arg0, %constant0) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    "mhlo.return"(%0, %arg1) : (tensor<i32>, tensor<i32>) -> ()
}) : (tensor<i32>, tensor<i32>) -> (tensor<i32>, tensor<i32>)

Traits: RecursiveMemoryEffects , SingleBlockImplicitTerminator<ReturnOp> , SingleBlock

Interfaces: InferTypeOpInterface , OpAsmOpInterface

Operandos:

Operando Descripción
operand variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token

Resultados:

Resultado Descripción
"sin nombre" variadic of ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer values or ranked tensor of 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values or token

mhlo.xla.rng_get_and_update_state (mhlo::XlaRngGetAndUpdateStateOp)

XlaRngGetAndUpdateState operation

Sintaxis:

operation ::= `mhlo.xla.rng_get_and_update_state` attr-dict

This operation is private to the XLA compiler, so it is does not yet have a specification.

Informally, this operation represents the change of the global random number generator state for rng instructions. The global state is incremented by delta and the old state is returned.

The output is currently defined for a single output type. If this changes in the future to support multiple types, lowering to use of a global memref must ensure that a single memref is still used and updated appropriately.

Interfaces: InferTypeOpInterface

Atributos:

Atributo Tipo MLIR Descripción
delta ::mlir::IntegerAttr Atributo entero sin signo de 64 bits

Resultados:

Resultado Descripción
"sin nombre" statically shaped tensor of 64-bit unsigned integer values

mhlo.xor (mhlo::XorOp)

Xor operation

Sintaxis:

operation ::= `mhlo.xor` $lhs `,` $rhs attr-dict
              `:` custom<SameOperandsAndResultType>(type($lhs), type($rhs), type($result))

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

Ejemplo:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efectos: MemoryEffects::Effect{}

Operandos:

Operando Descripción
lhs ranked tensor of pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values
rhs ranked tensor of pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer values

Resultados:

Resultado Descripción
result ranked tensor of f8E4M3B11FNUZ type or f8E4M3FN type or f8E4M3FNUZ type or f8E5M2 type or f8E5M2FNUZ type or 16-bit float or 32-bit float or 64-bit float or bfloat16 type or pred (AKA boolean or 1-bit integer) or 4/8/16/32/64-bit signless integer or 4/8/16/32/64-bit unsigned integer or complex type with 32-bit float or 64-bit float elements or 4/8/16/32-bit uniform quantized signed integer or 4/8/16/32-bit uniform quantized unsigned integer or 4/8/16/32-bit uniform quantized per axis signed integer or 4/8/16/32-bit uniform quantized per axis unsigned integer values

Atributos

ArgResultAliasAttr

Attribute that models the alias relationship of entry function argument

This attribute captures the alias relationship of an MHLO main function argument to one of the results, denoted by resultIndex . The argTupleIndices and resultTupleIndices are used to index into nested tuples in operand and result respectively. If isMustAlias is true then the operand-result pair must alias.

This is meant to be used as an attribute on a function argument in MHLO. For example, in the following code it expresses that %arg1 may alias 0-th result.

func @main(%arg0: tensor<2xf32>, %arg1: tensor<3xf32> {mhlo.result_alias =
    mhlo.result_alias<result_index = [2], ...>}
  ) -> tensor<2xf32>, tensor<3xf32> {
  // function body ...
}

Parámetros:

Parámetro tipo C ++ Descripción
argTupleIndices ::llvm::ArrayRef<int64_t> Dimensión
resultIndex int64_t
resultTupleIndices ::llvm::ArrayRef<int64_t> Dimensión
isMustAlias bool

ChannelHandleAttr

two 64-bit integers 'handle' and 'type'

Sintaxis:

#mhlo.channel_handle<
  int64_t,   # handle
  int64_t   # type
>

Parámetros:

Parámetro tipo C ++ Descripción
manejar int64_t
tipo int64_t

ComparisonDirectionAttr

Which comparison operation to perform.

Sintaxis:

#mhlo.comparison_direction<
  ::mlir::mhlo::ComparisonDirection   # value
>

Casos de enumeración:

  • EQ ( EQ )
  • NE ( NE )
  • GE ( GE )
  • GT ( GT )
  • LE ( LE )
  • LT ( LT ) #### Parameters:
Parámetro tipo C ++ Descripción
valor ::mlir::mhlo::ComparisonDirection an enum of type ComparisonDirection

ComparisonTypeAttr

Which comparison type to use.

Sintaxis:

#mhlo.comparison_type<
  ::mlir::mhlo::ComparisonType   # value
>

Casos de enumeración:

  • NOTYPE ( NOTYPE )
  • FLOAT ( FLOAT )
  • TOTALORDER ( TOTALORDER )
  • SIGNED ( SIGNED )
  • UNSIGNED ( UNSIGNED ) #### Parameters:
Parámetro tipo C ++ Descripción
valor ::mlir::mhlo::ComparisonType an enum of type ComparisonType

ConvDimensionNumbersAttr

Structure of dimension information for conv op

Parámetros:

Parámetro tipo C ++ Descripción
inputBatchDimension int64_t
inputFeatureDimension int64_t
inputSpatialDimensions ::llvm::ArrayRef<int64_t> Dimensión
kernelInputFeatureDimension int64_t
kernelOutputFeatureDimension int64_t
kernelSpatialDimensions ::llvm::ArrayRef<int64_t> Dimensión
outputBatchDimension int64_t
outputFeatureDimension int64_t
outputSpatialDimensions ::llvm::ArrayRef<int64_t> Dimensión

CrossProgramPrefetchAttr

Argument that is prefetched from another program

Sintaxis:

#mhlo.cross_program_prefetch<
  int64_t,   # parameter
  ::llvm::ArrayRef<int64_t>,   # indices
  std::optional<int64_t>   # offset
>

This attribute captures an argument that is prefetched from another program. For a given CrossProgramPrefetchAttr , parameter tells us which argument of the main function of the module is prefetched, and indices is a shape index telling us what subshape of that argument is prefetched.

A shape has a subshape iff it is a tuple. In that case, the subshape of the tuple by indices is the shape achieved after indexing by each element of indices in turn. For example, the [1,0] subshape of tuple<tuple<token, token>, tuple<tensor<i32>, token>> is tensor<i32> .

An empty value for indices means the whole shape is prefetched.

Por ejemplo,

module attributes { mhlo.cross_program_prefetch = [ #mhlo.cross_program_prefetch< parameter = 0, indices = [0]> ]} {
  func.func @copy(%arg0 : tuple<tensor<2x3xi32>, tensor<i32>>) -> tuple<tensor<2x3xi32>, tensor<i32>> {
    %0 = "mhlo.copy"(%arg0) {is_cross_program_prefetch}
    return %0 : tuple<tensor<2x3xi32>, tensor<i32>>
  }
  func.func @main(%arg0 : tuple<tensor<2x3xi32>, tensor<i32>>) -> tuple<tensor<2x3xi32>, tensor<i32>> {
    %1 = "mhlo.async_start"(%arg0) {called_computation=@copy}
    %2 = "mhlo.async_done"(%1) {called_computation=@copy}
    return %2 : tuple<tensor<2x3xi32>, tensor<i32>>
  }
}

The parameter = 0 tells us that the async copy of the 0 th parameter is a cross_program_prefetch , while the index of [0] tells us that the 0 th element of the tuple is prefetched while the other element of the tuple is not.

Parámetros:

Parámetro tipo C ++ Descripción
parámetro int64_t
índices ::llvm::ArrayRef<int64_t> Dimensión
compensar std::optional<int64_t>

CustomCallScheduleAttr

Specifies the desired schedule for the custom-call.

Sintaxis:

#mhlo.custom_call_schedule<
  ::mlir::mhlo::CustomCallSchedule   # value
>

Casos de enumeración:

  • NINGUNO NONE )
  • LATEST ( LATEST )
  • EARLIEST ( EARLIEST ) #### Parameters:
Parámetro tipo C ++ Descripción
valor ::mlir::mhlo::CustomCallSchedule an enum of type CustomCallSchedule

DequantizeModeAttr

Dequantization mode. Only MIN_COMBINED is supported.

Sintaxis:

#mhlo.dequantize_mode<
  ::mlir::mhlo::DequantizeMode   # value
>

Casos de enumeración:

  • MIN_COMBINED ( MIN_COMBINED ) #### Parameters:
Parámetro tipo C ++ Descripción
valor ::mlir::mhlo::DequantizeMode an enum of type DequantizeMode

DomainKindAttr

Kind of domain metatdata attached to an HLO domain.

Sintaxis:

#mhlo.kind<
  ::mlir::mhlo::DomainKind   # value
>

Casos de enumeración:

  • sharding ( sharding ) #### Parameters:
Parámetro tipo C ++ Descripción
valor ::mlir::mhlo::DomainKind an enum of type DomainKind

DotDimensionNumbersAttr

Attribute that models the dimension information for dot.

Parámetros:

Parámetro tipo C ++ Descripción
lhsBatchingDimensions ::llvm::ArrayRef<int64_t> Dimensión
rhsBatchingDimensions ::llvm::ArrayRef<int64_t> Dimensión
lhsContractingDimensions ::llvm::ArrayRef<int64_t> Dimensión
rhsContractingDimensions ::llvm::ArrayRef<int64_t> Dimensión

FftTypeAttr

XLA fast fourier transform type.

Sintaxis:

#mhlo.fft_type<
  ::mlir::mhlo::FftType   # value
>

Casos de enumeración:

  • FFT ( FFT )
  • IFFT ( IFFT )
  • RFFT ( RFFT )
  • IRFFT ( IRFFT ) #### Parameters:
Parámetro tipo C ++ Descripción
valor ::mlir::mhlo::FftType an enum of type FftType

FusionKindAttr

fusion kind

Sintaxis:

#mhlo.fusion_kind<
  ::mlir::mhlo::FusionKind   # value
>

Casos de enumeración:

  • kLoop ( kLoop )
  • kInput ( kInput )
  • kOutput ( kOutput )
  • kCustom ( kCustom ) #### Parameters:
Parámetro tipo C ++ Descripción
valor ::mlir::mhlo::FusionKind an enum of type FusionKind

GatherDimensionNumbersAttr

Attribute that models the dimension information for gather

Parámetros:

Parámetro tipo C ++ Descripción
offsetDims ::llvm::ArrayRef<int64_t> Dimensión
collapsedSliceDims ::llvm::ArrayRef<int64_t> Dimensión
startIndexMap ::llvm::ArrayRef<int64_t> Dimensión
indexVectorDim int64_t

OutputOperandAliasAttr

Attribute that models the alias relationship of output and operand of a CustomCall op

Sintaxis:

#mhlo.output_operand_alias<
  ::llvm::ArrayRef<int64_t>,   # outputTupleIndices
  int64_t,   # operandIndex
  ::llvm::ArrayRef<int64_t>   # operandTupleIndices
>

This attribute captures the alias relationship of the output to one of the operands for a CustomCall op, denoted by operand_index . The output_tuple_indices and operand_tuple_indices are used to index into output and operand types. These indices lists are empty if the corresponding types are not tuple types, and can be arbitrarily long in case of arbitrarily nested tuple types.

See https://www.tensorflow.org/xla/aliasing

Example when used as array with in mhlo.custom-call:

%0 = "mhlo.custom_call"(%arg0, %arg1) {
  // other attributes
  output_operand_alias = [
    #mhlo.output_operand_alias<output_tuple_indices = [0],
                               operand_index = 0,
                               operand_tuple_indices = [1]>
  ]
} : (tuple<tensor<1x1xf32>, tensor<2x3xf32>>, tensor<5x5xf32>) -> tuple<tensor<2x3xf32>>

The output and the 0th operand are both tuples. The aliasing shows the
relationship between the 0th element in output tuple with the 1st element in
the 0th operand. And both of them are of the same type: tensor<2x3xf32>.

Parámetros:

Parámetro tipo C ++ Descripción
outputTupleIndices ::llvm::ArrayRef<int64_t> Dimensión
operandIndex int64_t
operandTupleIndices ::llvm::ArrayRef<int64_t> Dimensión

PrecisionAttr

XLA precision for an operand. Has backend specific meaning.

Sintaxis:

#mhlo.precision<
  ::mlir::mhlo::Precision   # value
>

Casos de enumeración:

  • DEFAULT ( DEFAULT )
  • ALTA HIGH )
  • HIGHEST ( HIGHEST )
  • PACKED_NIBBLE ( PACKED_NIBBLE ) #### Parameters:
Parámetro tipo C ++ Descripción
valor ::mlir::mhlo::Precision an enum of type Precision

RngAlgorithmAttr

XLA PRNG algorithm to be used.

Sintaxis:

#mhlo.rng_algorithm<
  ::mlir::mhlo::RngAlgorithm   # value
>

Casos de enumeración:

  • DEFAULT ( DEFAULT )
  • THREE_FRY ( THREE_FRY )
  • PHILOX ( PHILOX ) #### Parameters:
Parámetro tipo C ++ Descripción
valor ::mlir::mhlo::RngAlgorithm an enum of type RngAlgorithm

RngDistributionAttr

XLA PRNG distribution to be used.

Sintaxis:

#mhlo.rng_distribution<
  ::mlir::mhlo::RngDistribution   # value
>

Casos de enumeración:

  • UNIFORM ( UNIFORM )
  • NORMAL ( NORMAL ) #### Parameters:
Parámetro tipo C ++ Descripción
valor ::mlir::mhlo::RngDistribution an enum of type RngDistribution

ScatterDimensionNumbersAttr

Attribute that models the dimension information for scatter

Parámetros:

Parámetro tipo C ++ Descripción
updateWindowDims ::llvm::ArrayRef<int64_t> Dimensión
insertedWindowDims ::llvm::ArrayRef<int64_t> Dimensión
scatterDimsToOperandDims ::llvm::ArrayRef<int64_t> Dimensión
indexVectorDim int64_t

SparsityDescriptorAttr

Describes structured (N:M) sparsity configuration

Sintaxis:

#mhlo.sparsity<
  int64_t,   # dimension
  int64_t,   # n
  int64_t   # m
>

This attribute is defined for a sparse dot operation with a structured sparse input tensor. With (N=2,M=4), every 4 consecutive logical elements have exactly 2 non-zero physical elements in the input tensor.

$dimension defines the index of the contracting dimension that is sparse (it has to be the most minor dimension). The additional metadata operand in the sparse dot operation defines which logical elements are zeroed out.

Parámetros:

Parámetro tipo C ++ Descripción
dimensión int64_t
norte int64_t
metro int64_t

TransposeAttr

Transpose options

Sintaxis:

#mhlo.transpose<
  ::mlir::mhlo::Transpose   # value
>

Casos de enumeración:

  • TRANSPOSE_INVALID ( TRANSPOSE_INVALID )
  • NO_TRANSPOSE ( NO_TRANSPOSE )
  • TRANSPONER ( TRANSPOSE )
  • ADJOINT ( ADJOINT ) #### Parameters:
Parámetro tipo C ++ Descripción
valor ::mlir::mhlo::Transpose an enum of type Transpose

TypeExtensionsAttr

Attribute that extends tensor type with MHLO type properties.

Sintaxis:

#mhlo.type_extensions<
  ::llvm::ArrayRef<int64_t>   # bounds
>

This attribute is used to extend MLIR tensor type with MHLO tensor specific properties. These properties aren't modeled in the MLIR type. This attribute is set in the encoding field of the tensor type.

See HLO_BoundedAttrInterface for documentation for bounds .

Parámetros:

Parámetro tipo C ++ Descripción
límites ::llvm::ArrayRef<int64_t>

Tipos

AsyncBundleType

Opaque collection of other types

Sintaxis:

!mhlo.async_bundle<
  ::llvm::ArrayRef<Type>   # types
>

Parámetros:

Parámetro tipo C ++ Descripción
tipos ::llvm::ArrayRef<Type>