'mhlo' Lehçe

Operasyonlar

mhlo.abs (mhlo::AbsOp)

Abs operasyonu

Sözdizimi:

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

operand tensöründe eleman bazında mutlak değer alma işlemi gerçekleştirir ve result tensörünü üretir.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

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

Etkiler: MemoryEffects::Effect{}

İşlenenler:

İşlenen

Tanım

operand

2/4/8/16/32/64 bit işaretsiz tamsayı veya 4/6/8/16/32/64 bit kayan noktalı sayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tip veya 2/4/8/16/32 bit tekdüze nicelenmiş işaretli tamsayı veya 2/4/8/16/32 bit eksen başına tekdüze nicelenmiş işaretli tamsayı veya 2/4/8/16/32 bit tekdüze nicelenmiş işaretsiz tamsayı veya 2/4/8/16/32 bit eksen başına tekdüze nicelenmiş işaretsiz tamsayı değerlerine sahip sıralı tensör

Sonuçlar:

Sonuç	Tanım
result	2/4/8/16/32/64 bit işaretsiz tamsayı veya 4/6/8/16/32/64 bit kayan noktalı sayı veya 2/4/8/16/32 bit tekdüze nicelenmiş işaretli tamsayı veya 2/4/8/16/32 bit eksen başına tekdüze nicelenmiş işaretli tamsayı veya 2/4/8/16/32 bit tekdüze nicelenmiş işaretsiz tamsayı veya 2/4/8/16/32 bit eksen başına tekdüze nicelenmiş işaretsiz tamsayı değerlerinden oluşan sıralı tensör.

mhlo.acos (mhlo::AcosOp)

Acos operasyonu

Sözdizimi:

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

operand tensörüne eleman bazında ACOS işlemi uygular ve result tensörünü üretir.

Örnek:

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

Özellikler: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Arayüzler: InferShapedTypeOpInterface , InferTypeOpInterface

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı eleman değerlerine sahip tensör.

Sonuçlar:

Sonuç	Tanım
result	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı eleman değerlerine sahip tensör.

mhlo.acosh (mhlo::AcoshOp)

Acosh operasyonu

Sözdizimi:

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

operand tensörüne eleman bazında acosh işlemi uygular ve result tensörünü üretir.

Örnek:

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

Özellikler: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Arayüzler: InferShapedTypeOpInterface , InferTypeOpInterface

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı eleman değerlerine sahip tensör.

Sonuçlar:

Sonuç	Tanım
result	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı eleman değerlerine sahip tensör.

mhlo.add (mhlo::AddOp)

İşlem ekle

Sözdizimi:

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

İki tensörün lhs ve rhs eleman bazında toplamasını gerçekleştirir ve result tensörünü üretir.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Etkiler: MemoryEffects::Effect{}

İşlenenler:

İşlenen	Tanım
lhs	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.
rhs	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

Sonuçlar:

Sonuç	Tanım
result	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

mhlo.add_dependency (mhlo::AddDependencyOp)

AddDependency işlemi

Sözdizimi:

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

Bu işlem XLA derleyicisine özeldir, bu nedenle henüz bir spesifikasyonu bulunmamaktadır.

Gayriresmi olarak, bu işlem iki işlenen alır: bir veri işleneni ve bir belirteç. İşlemin çıktısı veri işlenenidir. AfterAll ile birlikte kullanıldığında, bu işlem yan etki yaratmayan işlemleri (belirteç değerleri üretmeyen işlemleri) sıralamayı sağlar.

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait

Arayüzler: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Etkiler: MemoryEffects::Effect{}

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensör veya tensör başına tamsayı nicelleştirilmiş değerler veya eksen başına tamsayı nicelleştirilmiş değerlerden oluşan sıralı tensör veya belirteç veya stablehlo belirteci
token	token veya stablehlo token

Sonuçlar:

Sonuç	Tanım
output	4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensör veya tensör başına tamsayı nicelleştirilmiş değerler veya eksen başına tamsayı nicelleştirilmiş değerlerden oluşan sıralı tensör veya belirteç veya stablehlo belirteci

mhlo.after_all (mhlo::AfterAllOp)

AfterAll operasyonu

Sözdizimi:

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

inputs üreten işlemlerin, result bağlı olan tüm işlemlerden önce yürütülmesini sağlar.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait

Arayüzler: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Etkiler: MemoryEffects::Effect{}

İşlenenler:

İşlenen	Tanım
inputs	belirtecin değişkenliği

Sonuçlar:

Sonuç	Tanım
result	belirteç

mhlo.all_gather (mhlo::AllGatherOp)

AllGather operasyonu

İşlem ızgarasındaki her işlem grubu içinde, her işlemden gelen işlenen tensörünün değerlerini all_gather_dim boyunca birleştirir ve bir sonuç tensörü üretir. computation , operands her işlenen için ayrı ayrı uygulanır ve her işlenen için bir sonuç üretir.

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

Örnek:

%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>

Özellikler: SameOperandsAndResultElementType

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
all_gather_dim	::mlir::IntegerAttr	Değeri negatif olmayan 64 bitlik işaretsiz tamsayı özniteliği
replica_groups	::mlir::DenseIntElementsAttr	64 bitlik işaretsiz tamsayı elemanları özniteliği
channel_handle	::mlir::mhlo::ChannelHandleAttr	iki adet 64 bit tamsayı: 'handle' ve 'type'
use_global_device_ids	::mlir::UnitAttr	birim özelliği

İşlenenler:

İşlenen	Tanım
operands	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörün değişken sayıda elemanı veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerler.

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörün değişken sayıda elemanı veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerler.

mhlo.all_reduce (mhlo::AllReduceOp)

AllReduce işlemi

İşlem ızgarasındaki her işlem grubu içinde, her işlemden gelen bir işlenen tensörünün değerlerine bir indirgeme fonksiyonu computation uygulanır ve bir sonuç tensörü üretilir. computation , operands içindeki her işlenen için ayrı ayrı uygulanır ve her işlenen için bir sonuç üretilir.

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

Örnek:

%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>

Özellikler: InferTensorType , SingleBlockImplicitTerminator<ReturnOp> , SingleBlock

Arayüzler: InferShapedTypeOpInterface , InferTypeOpInterface

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
replica_groups	::mlir::DenseIntElementsAttr	64 bitlik işaretsiz tamsayı elemanları özniteliği
channel_handle	::mlir::mhlo::ChannelHandleAttr	iki adet 64 bit tamsayı: 'handle' ve 'type'
use_global_device_ids	::mlir::UnitAttr	birim özelliği

İşlenenler:

İşlenen	Tanım
operands	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörün değişken sayıda elemanı veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerler.

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörün değişken sayıda elemanı veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerler.

mhlo.all_to_all (mhlo::AllToAllOp)

AllToAll operasyonu

İşlem ızgarasındaki her işlem grubu içinde, operand tensörün değerlerini split_dimension boyunca parçalara ayırır, ayrılan parçaları işlemler arasında dağıtır, dağıtılan parçaları concat_dimension boyunca birleştirir ve bir result tensörü üretir.

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

Örnek:

%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>

Özellikler: AlwaysSpeculatableImplTrait , InferTensorType , SameOperandsElementType , SameOperandsShape , SameVariadicOperandSize

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

Etkiler: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
split_dimension	::mlir::IntegerAttr	Değeri negatif olmayan 64 bitlik işaretsiz tamsayı özniteliği
concat_dimension	::mlir::IntegerAttr	Değeri negatif olmayan 64 bitlik işaretsiz tamsayı özniteliği
split_count	::mlir::IntegerAttr	Değeri pozitif olan 64 bitlik işaretsiz tamsayı özniteliği
replica_groups	::mlir::DenseIntElementsAttr	64 bitlik işaretsiz tamsayı elemanları özniteliği
channel_handle	::mlir::mhlo::ChannelHandleAttr	iki adet 64 bit tamsayı: 'handle' ve 'type'

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörün değişken sayıda elemanı veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerler.

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörün değişken sayıda elemanı veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerler.

mhlo.and (mhlo::AndOp)

Ve operasyon

Sözdizimi:

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

İki tensörün lhs ve rhs eleman bazında AND işlemini gerçekleştirir ve result tensörünü üretir.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Etkiler: MemoryEffects::Effect{}

İşlenenler:

İşlenen	Tanım
lhs	Mantıksal veya 2/4/8/16/32/64 bit tamsayı değerlerinden oluşan sıralı tensör.
rhs	Mantıksal veya 2/4/8/16/32/64 bit tamsayı değerlerinden oluşan sıralı tensör.

Sonuçlar:

Sonuç	Tanım
result	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

mhlo.asin (mhlo::AsinOp)

Asin operasyonu

Sözdizimi:

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

operand tensörüne eleman bazında asin işlemi uygular ve result tensörünü üretir.

Örnek:

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

Özellikler: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Arayüzler: InferShapedTypeOpInterface , InferTypeOpInterface

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı eleman değerlerine sahip tensör.

Sonuçlar:

Sonuç	Tanım
result	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı eleman değerlerine sahip tensör.

mhlo.asinh (mhlo::AsinhOp)

Asinh operasyonu

Sözdizimi:

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

operand tensörüne eleman bazında asinh işlemi uygular ve result tensörünü üretir.

Örnek:

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

Özellikler: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Arayüzler: InferShapedTypeOpInterface , InferTypeOpInterface

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı eleman değerlerine sahip tensör.

Sonuçlar:

Sonuç	Tanım
result	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı eleman değerlerine sahip tensör.

mhlo.async_done (mhlo::AsyncDoneOp)

AsyncDone işlemi

Bu işlem XLA derleyicisine özeldir, bu nedenle henüz bir spesifikasyonu bulunmamaktadır.

Gayriresmi olarak, bu işlem eşzamansız bir hesaplamanın sonuna kadar bloke eder. Eşzamansız hesaplamanın nihai sonucunu döndürür.

Daha fazla bilgi için AsyncStart dokümantasyonuna bakın.

Arayüzler: InferTypeOpInterface

İşlenenler:

İşlenen	Tanım
bundle	4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörlerin veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerin veya belirteç veya stablehlo belirteç değerlerinin herhangi bir kombinasyonuyla async_bundle

Sonuçlar:

Sonuç

Tanım

«adı belirtilmemiş»

4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörün değişken sayıda elemanı veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerler veya belirteç veya stablehlo belirteci veya 4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörün herhangi bir kombinasyonuyla iç içe geçmiş demet veya 4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte memref veya tensör başına tamsayı Eksen başına tamsayı nicelleştirilmiş değerlerin veya belirteç değerlerinin nicelleştirilmiş değerleri veya sıralı tensörü

mhlo.async_start (mhlo::AsyncStartOp)

AsyncStart işlemi

Bu işlem XLA derleyicisine özeldir, bu nedenle henüz bir spesifikasyonu bulunmamaktadır.

Gayriresmi olarak, bu işlem eşzamansız bir hesaplamayı başlatır.

Bu, hem eşzamansız beklemeler (örneğin DMA'lar) hem de iş parçacığı üzerinde hesaplama içeren fonksiyonlar olduğunda kullanılır. Örneğin, bir fonksiyon bir hesaplama, bir DMA, başka bir hesaplama, ikinci bir DMA ve son bir hesaplamadan oluşabilir. Bu, async_start, ardından async_update ve async_done ile temsil edilir. async_start, ilk hesaplamayı iş parçacığı üzerinde yapar ve ardından DMA'yı başlatır. async_update, DMA henüz tamamlanmadıysa tamamlanmasını bekler, ardından fonksiyondaki ikinci hesaplamayı yürütür ve ikinci DMA'yı başlatır. Son olarak, async_done bu son DMA'yı bekler ve ardından iş parçacığı üzerinde çalıştırılması gereken son hesaplamayı çalıştırır ve bu son hesaplamanın sonucunu döndürür.

operands doğrudan hesaplamaya iletilir. called_computation eşzamansız olarak çalıştırılacak fonksiyondur. execution_thread çalıştırılacağı iş parçacığının adıdır. Ana iş parçacığına "main" adı verilir. Tüm iş parçacıklarının isimleri vardır.

Bu, eşzamansız işlemler arasında ihtiyaç duyulan tüm durumu döndürür. Tampon atamasından sonra, dönüş değerleri, girişi, sonuçları ve eşzamansız işlem tarafından ihtiyaç duyulan veya düzenlenen tüm geçici bellek alanlarını tutmak için gereken alanı temsil eder.

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
called_computation	::mlir::FlatSymbolRefAttr	düz sembol referans özniteliği
execution_thread	::mlir::StringAttr	dize özniteliği

İşlenenler:

İşlenen

Tanım

inputs

4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörün değişken sayıda elemanı veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerler veya belirteç veya stablehlo belirteci veya 4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörün herhangi bir kombinasyonuyla iç içe geçmiş demet veya 4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte memref veya tensör başına tamsayı Eksen başına tamsayı nicelleştirilmiş değerlerin veya belirteç değerlerinin nicelleştirilmiş değerleri veya sıralı tensörü

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörlerin veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerin veya belirteç veya stablehlo belirteç değerlerinin herhangi bir kombinasyonuyla async_bundle

mhlo.async_update (mhlo::AsyncUpdateOp)

AsyncUpdate işlemi

Bu işlem XLA derleyicisine özeldir, bu nedenle henüz bir spesifikasyonu bulunmamaktadır.

Gayriresmi olarak, bu işlem bir senkronizasyon bariyerine ulaşılana kadar eşzamansız bir hesaplamayı bloke eder. Bu işlem, üzerinde işlem yaptıktan sonra bundle döndürür.

Daha fazla bilgi için AsyncStart dokümantasyonuna bakın.

Arayüzler: InferTypeOpInterface

İşlenenler:

İşlenen	Tanım
bundle	4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörlerin veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerin veya belirteç veya stablehlo belirteç değerlerinin herhangi bir kombinasyonuyla async_bundle

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörlerin veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerin veya belirteç veya stablehlo belirteç değerlerinin herhangi bir kombinasyonuyla async_bundle

mhlo.atan2 (mhlo::Atan2Op)

Atan2 operasyonu

Sözdizimi:

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

lhs ve rhs tensörler üzerinde eleman bazında atan2 işlemini gerçekleştirir ve result tensörünü üretir.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Etkiler: MemoryEffects::Effect{}

İşlenenler:

İşlenen	Tanım
lhs	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı elemanlara veya tensör başına tamsayı nicelleştirilmiş değerlere sahip sıralı tensör.
rhs	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı elemanlara veya tensör başına tamsayı nicelleştirilmiş değerlere sahip sıralı tensör.

Sonuçlar:

Sonuç	Tanım
result	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı elemanlara veya tensör başına tamsayı nicelleştirilmiş değerlere sahip sıralı tensör.

mhlo.atanh (mhlo::AtanhOp)

Atanh operasyonu

Sözdizimi:

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

operand tensörüne eleman bazında atama işlemi uygular ve result tensörünü üretir.

Örnek:

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

Özellikler: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Arayüzler: InferShapedTypeOpInterface , InferTypeOpInterface

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı eleman değerlerine sahip tensör.

Sonuçlar:

Sonuç	Tanım
result	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı eleman değerlerine sahip tensör.

mhlo.batch_norm_grad (mhlo::BatchNormGradOp)

BatchNormGrad işlemi

BatchNormTrainingOp'un çeşitli girdilerinin gradyanlarını grad_output geri yayılım yaparak hesaplar ve grad_operand , grad_scale ve grad_offset tensörlerini üretir.

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

Örnek:

%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>)

Özellikler: AlwaysSpeculatableImplTrait , InferTensorType

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

Etkiler: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
epsilon	::mlir::FloatAttr	32 bit kayan nokta özelliği
feature_index	::mlir::IntegerAttr	Değeri negatif olmayan 64 bitlik işaretsiz tamsayı özniteliği

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı değerlerin sıralı tensörü
scale	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör
mean	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör
variance	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör
grad_output	4/6/8/16/32/64 bit kayan noktalı değerlerin sıralı tensörü

Sonuçlar:

Sonuç	Tanım
grad_operand	4/6/8/16/32/64 bit kayan noktalı değerlerin sıralı tensörü
grad_scale	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör
grad_offset	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör

mhlo.batch_norm_inference (mhlo::BatchNormInferenceOp)

TopluNormÇıkarım işlemi

operand tensörü, feature_index boyutu hariç tüm boyutlarda normalleştirir ve bir result tensörü üretir.

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

Örnek:

%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>

Özellikler: AlwaysSpeculatableImplTrait , InferTensorType

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

Etkiler: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
epsilon	::mlir::FloatAttr	32 bit kayan nokta özelliği
feature_index	::mlir::IntegerAttr	Değeri negatif olmayan 64 bitlik işaretsiz tamsayı özniteliği

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı değerlerin sıralı tensörü
scale	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör
offset	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör
mean	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör
variance	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör

Sonuçlar:

Sonuç	Tanım
result	4/6/8/16/32/64 bit kayan noktalı değerlerin sıralı tensörü

mhlo.batch_norm_training (mhlo::BatchNormTrainingOp)

Toplu NormEğitim işlemi

Bu fonksiyon, feature_index boyutundaki her bir özellik için toplu işlem ve uzamsal boyutlar boyunca ortalama ve varyansı hesaplar ve operand tensörü normalleştirir; batch_mean ve batch_var tensörlerini output üretir.

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

Örnek:

%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>)

Özellikler: AlwaysSpeculatableImplTrait , InferTensorType

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

Etkiler: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
epsilon	::mlir::FloatAttr	32 bit kayan nokta özelliği
feature_index	::mlir::IntegerAttr	Değeri negatif olmayan 64 bitlik işaretsiz tamsayı özniteliği

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı değerlerin sıralı tensörü
scale	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör
offset	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör

Sonuçlar:

Sonuç	Tanım
output	4/6/8/16/32/64 bit kayan noktalı değerlerin sıralı tensörü
batch_mean	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör
batch_var	4/6/8/16/32/64 bit kayan noktalı değerlerden oluşan 1 boyutlu tensör

mhlo.bitcast (mhlo::BitcastOp)

Bitcast operasyonu

Sözdizimi:

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

Bu işlem XLA derleyicisine özeldir, bu nedenle henüz bir spesifikasyonu bulunmamaktadır.

Gayriresmi olarak, bu işlem, elemanların fiziksel düzenlemesi değişmeden girişin şeklini değiştirir.

Bu işlem, "elemanların fiziksel düzenini" anlamlandırmak için yerleşim bilgisine ihtiyaç duyar ve MHLO'daki yerleşim desteği şu anda geliştirme aşamasındadır.

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait

Arayüzler: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Etkiler: MemoryEffects::Effect{}

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

mhlo.bitcast_convert (mhlo::BitcastConvertOp)

BitcastConvert işlemi

Sözdizimi:

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

operand tensör üzerinde bit dönüştürme işlemi gerçekleştirir ve operand tensörün tamamının bitlerinin, result tensörünün türü kullanılarak yeniden yorumlandığı bir result tensörü üretir.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait

Arayüzler: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Etkiler: MemoryEffects::Effect{}

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

mhlo.broadcast (mhlo::BroadcastOp)

Yayın operasyonu

Bu işlem StableHLO'dan çıkarılmak üzere olduğundan, spesifikasyona dahil edilmemiştir: https://github.com/openxla/stablehlo/issues/3

Gayriresmi olarak, bu işlem XLA'nın Broadcast işlemiyle aynı işlevi görür: https://www.tensorflow.org/xla/operation_semantics#broadcast

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait , InferTensorType , SameOperandsAndResultElementType

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

Etkiler: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
broadcast_sizes	::mlir::DenseIntElementsAttr	64 bitlik işaretsiz tamsayı elemanları özniteliği

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

mhlo.broadcast_in_dim (mhlo::BroadcastInDimOp)

BroadcastInDim işlemi

Giriş tensörünün boyutlarını ve/veya derecesini, operand tensördeki verileri kopyalayarak genişletir ve bir result tensörü üretir.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait , HLO_CompatibleOperandsAndResultElementType

Arayüzler: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Etkiler: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
broadcast_dimensions	::mlir::DenseIntElementsAttr	64 bitlik işaretsiz tamsayı elemanları özniteliği

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte, statik olarak şekillendirilmiş veya tek sınırlı boyutlu tensör; 32/64 bit kayan noktalı elemanlara sahip veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlere sahip.

mhlo.case (mhlo::CaseOp)

Vaka operasyonu

index değerine bağlı olarak branches yalnızca bir function çalıştırılmasıyla elde edilen çıktıyı üretir.

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

Örnek:

%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>)

Özellikler: RecursiveMemoryEffects , SingleBlockImplicitTerminator<ReturnOp> , SingleBlock

Arayüzler: InferTypeOpInterface

İşlenenler:

İşlenen	Tanım
index	32 bitlik işaretsiz tamsayı değerlerinden oluşan tensör

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya boolean veya 2/4/8/16/32/64 bit tamsayı veya 32/64 bit kayan noktalı elemanlara sahip karmaşık tipte sıralı tensörün değişken sayıda elemanı veya tensör başına tamsayı nicelleştirilmiş değerler veya eksen başına tamsayı nicelleştirilmiş değerlere sahip sıralı tensör veya belirteç

mhlo.cbrt (mhlo::CbrtOp)

CBRT operasyonu

Sözdizimi:

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

operand tensörüne eleman bazında kübik kök alma işlemi uygular ve result tensörünü üretir.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Etkiler: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	Tekli işlemler için istenen doğruluk oranı.

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı elemanlara veya tensör başına tamsayı nicelleştirilmiş değerlere sahip sıralı tensör.

Sonuçlar:

Sonuç	Tanım
result	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı elemanlara veya tensör başına tamsayı nicelleştirilmiş değerlere sahip sıralı tensör.

mhlo.ceil (mhlo::CeilOp)

Tavan operasyonu

Sözdizimi:

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

operand tensörünün eleman bazında ceil işlemini gerçekleştirir ve result tensörünü üretir.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Etkiler: MemoryEffects::Effect{}

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya tensör başına tamsayı nicelleştirilmiş değerlerden oluşan sıralı tensör

Sonuçlar:

Sonuç	Tanım
result	4/6/8/16/32/64 bit kayan noktalı veya tensör başına tamsayı nicelleştirilmiş değerlerden oluşan sıralı tensör

mhlo.cholesky (mhlo::CholeskyOp)

Cholesky operasyonu

Bir matris grubunun Cholesky ayrıştırmasını hesaplar.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait , InferTensorType , SameOperandsAndResultElementType

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

Etkiler: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
lower	::mlir::BoolAttr	bool özniteliği

İşlenenler:

İşlenen	Tanım
a	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı eleman değerlerine sahip sıralı tensör.

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya karmaşık tipte, 32/64 bit kayan noktalı eleman değerlerine sahip sıralı tensör.

mhlo.clamp (mhlo::ClampOp)

Sıkıştırma işlemi

Sözdizimi:

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

operand tensörün her elemanını minimum ve maksimum değer arasında sınırlandırır ve bir result tensörü üretir.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait , HLO_BroadcastingElementwise , InferTensorType , SameOperandsAndResultElementType

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

Etkiler: MemoryEffects::Effect{}

İşlenenler:

İşlenen	Tanım
min	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.
operand	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.
max	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

Sonuçlar:

Sonuç	Tanım
result	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

mhlo.collective_broadcast (mhlo::CollectiveBroadcastOp)

Toplu Yayın operasyonu

İşlem ızgarasındaki her işlem grubu içinde, kaynak işlemden hedef işlemlere operand tensörünün değerini gönderin ve bir result tensörü oluşturun.

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

Örnek:

%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>

Özellikler: CompatibleOperandsAndResultType

Arayüzler: InferShapedTypeOpInterface , InferTypeOpInterface

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
replica_groups	::mlir::DenseIntElementsAttr	64 bitlik işaretsiz tamsayı elemanları özniteliği
channel_handle	::mlir::mhlo::ChannelHandleAttr	iki adet 64 bit tamsayı: 'handle' ve 'type'

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

mhlo.collective_permute (mhlo::CollectivePermuteOp)

Toplu Değişim operasyonu

Süreç ızgarasındaki her bir süreç grubu içinde, kaynak süreçten hedef sürece operand tensörünün değerini gönderir ve bir result tensörü üretir.

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

Örnek:

%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>

Özellikler: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType

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

Etkiler: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
source_target_pairs	::mlir::DenseIntElementsAttr	64 bitlik işaretsiz tamsayı elemanları özniteliği
channel_handle	::mlir::mhlo::ChannelHandleAttr	iki adet 64 bit tamsayı: 'handle' ve 'type'

İşlenenler:

İşlenen	Tanım
operand	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

mhlo.compare (mhlo::CompareOp)

Karşılaştırma işlemi

Sözdizimi:

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

comparison_direction ve compare_type parametrelerine göre lhs ve rhs tensörlerin eleman bazında karşılaştırmasını yapar ve result tensörünü üretir.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait , Elementwise , InferTensorType , SameOperandsAndResultShape , SameOperandsElementType

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

Etkiler: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Tipi	Tanım
comparison_direction	::mlir::mhlo::ComparisonDirectionAttr	Hangi karşılaştırma işlemini gerçekleştireceğiz?
compare_type	::mlir::mhlo::ComparisonTypeAttr	Hangi karşılaştırma türünü kullanmalıyız?

İşlenenler:

İşlenen	Tanım
lhs	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.
rhs	4/6/8/16/32/64 bit kayan noktalı veya mantıksal veya 2/4/8/16/32/64 bit tamsayı veya karmaşık tipte sıralı tensör, 32/64 bit kayan noktalı elemanlarla veya tensör başına tamsayı nicelleştirilmiş veya eksen başına tamsayı nicelleştirilmiş değerlerle.

Sonuçlar:

Sonuç	Tanım
«adı belirtilmemiş»	Mantıksal değerlerin sıralı tensörü

mhlo.complex (mhlo::ComplexOp)

Karmaşık operasyon

Sözdizimi:

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

Gerçek ve sanal değerlerden oluşan bir çift olan lhs ve rhs eleman bazında karmaşık bir değere dönüştürür ve result bir tensör üretir.

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

Örnek:

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

Özellikler: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape , SameOperandsElementType

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

Etkiler: MemoryEffects::Effect{}

İşlenenler:

İşlenen	Tanım
lhs	ranked tensor of 32/64-bit float values
rhs	ranked tensor of 32/64-bit float values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of complex type with 32/64-bit float elements values

mhlo.composite (mhlo::CompositeOp)

Kompozit operasyon

Sözdizimi:

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

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

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

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

Örnek:

%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

Özellikler:

Bağlanmak	MLIR Type	Tanım
name	::mlir::StringAttr	string attribute
composite_attributes	::mlir::DictionaryAttr	dictionary of named attribute values
decomposition	::mlir::FlatSymbolRefAttr	flat symbol reference attribute
version	::mlir::IntegerAttr	32-bit signless integer attribute

Operands:

İşlenen

Tanım

inputs

variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values or token or nested tuple with any combination of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token values

Sonuçlar:

Sonuç

Tanım

«unnamed»

variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values or token or nested tuple with any combination of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token values

mhlo.concatenate (mhlo::ConcatenateOp)

Concatenate operation

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

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , SameOperandsAndResultElementType

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
dimension	::mlir::IntegerAttr	64-bit signless integer attribute whose value is non-negative

Operands:

İşlenen	Tanım
val	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.constant (mhlo::ConstantOp)

Constant operation

Produces an output tensor from a constant value .

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , ConstantLike

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
value	::mlir::ElementsAttr	constant vector/tensor attribute

Sonuçlar:

Sonuç	Tanım
output	statically shaped tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.convert (mhlo::ConvertOp)

Convert operation

Sözdizimi:

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

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

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.convolution (mhlo::ConvolutionOp)

Evrişim işlemi

Sözdizimi:

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)

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

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

Örnek:

%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>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
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	64-bit signless integer attribute whose value is positive
batch_group_count	::mlir::IntegerAttr	64-bit signless integer attribute whose value is positive
precision_config	::mlir::ArrayAttr	Precision Config attribute

Operands:

İşlenen	Tanım
lhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.copy (mhlo::CopyOp)

Copy operation

Sözdizimi:

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

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

Informally, this operation a copy of operand . Depending on the metadata attached to the operation, it can behave quite differently from a no-op.

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
cross_program_prefetch_index	::mlir::IntegerAttr	32-bit signless integer attribute

Operands:

İşlenen

Tanım

operand

ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values or token or nested tuple with any combination of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token values

Sonuçlar:

Sonuç

Tanım

result

ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values or token or nested tuple with any combination of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token values

mhlo.cosh (mhlo::CoshOp)

Cosh operation

Sözdizimi:

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

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

Örnek:

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

Traits: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Operands:

İşlenen	Tanım
operand	tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements values

Sonuçlar:

Sonuç	Tanım
result	tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements values

mhlo.cosine (mhlo::CosineOp)

Cosine operation

Sözdizimi:

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

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

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

mhlo.count_leading_zeros (mhlo::ClzOp)

Clz operation

Sözdizimi:

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

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

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 2/4/8/16/32/64-bit integer values

mhlo.create_token (mhlo::CreateTokenOp)

CreateToken operation

Sözdizimi:

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

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 AfterAllOp with 0 inputs: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all

Örnek:

%output = mhlo.create_token : !mhlo.token

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Sonuçlar:

Sonuç	Tanım
output	belirteç

mhlo.cross-replica-sum (mhlo::CrossReplicaSumOp)

CrossReplicaSum 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 AllReduceOp with channel_id = 0 , use_global_device_ids = false and computation implementing addition: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
replica_groups	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.custom_call (mhlo::CustomCallOp)

CustomCall operation

Sözdizimi:

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

Örnek:

%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

Özellikler:

Bağlanmak	MLIR Type	Tanım
call_target_name	::mlir::StringAttr	string attribute
has_side_effect	::mlir::BoolAttr	bool attribute
backend_config	::mlir::Attribute	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

Operands:

İşlenen

Tanım

inputs

variadic of tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or token or nested tuple with any combination of tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or token values

Sonuçlar:

Sonuç

Tanım

«unnamed»

variadic of tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or token or nested tuple with any combination of tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or token values

mhlo.divide (mhlo::DivOp)

Div operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
lhs	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
kind	::mlir::mhlo::DomainKindAttr	Kind of domain metatdata attached to an HLO domain.
entry_metadata	::mlir::StringAttr	string attribute
exit_metadata	::mlir::StringAttr	string attribute

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized 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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
precision_config	::mlir::ArrayAttr	Precision Config attribute

Operands:

İşlenen	Tanım
lhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%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>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
dot_dimension_numbers	::mlir::mhlo::DotDimensionNumbersAttr	Attribute that models the dimension information for dot.
precision_config	::mlir::ArrayAttr	Precision Config attribute
algorithm	::mlir::mhlo::DotAlgorithmAttr	Attribute that models the algorithm constraints to use for computing dot.

Operands:

İşlenen	Tanım
lhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.dynamic_broadcast_in_dim (mhlo::DynamicBroadcastInDimOp)

DynamicBroadcastInDim operation

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

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

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
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

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
output_dimensions	1D tensor of index or 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%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>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
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	64-bit signless integer attribute whose value is positive
batch_group_count	::mlir::IntegerAttr	64-bit signless integer attribute whose value is positive
precision_config	::mlir::ArrayAttr	Precision Config attribute

Operands:

İşlenen	Tanım
lhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
d_padding	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%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)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
dimension_numbers	::mlir::mhlo::GatherDimensionNumbersAttr	Attribute that models the dimension information for gather
indices_are_sorted	::mlir::BoolAttr	bool attribute

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
start_indices	ranked tensor of 2/4/8/16/32/64-bit integer values
slice_sizes	statically shaped 1-dimensional integer tensor of 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.dynamic_iota (mhlo::DynamicIotaOp)

DynamicIota operation

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

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
iota_dimension	::mlir::IntegerAttr	64-bit signless integer attribute whose value is non-negative

Operands:

İşlenen	Tanım
output_shape	1D tensor of index or 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.dynamic_pad (mhlo::DynamicPadOp)

DynamicPad operation

Sözdizimi:

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.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
padding_value	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
edge_padding_low	1D tensor of index or 2/4/8/16/32/64-bit integer values
edge_padding_high	1D tensor of index or 2/4/8/16/32/64-bit integer values
interior_padding	1D tensor of index or 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.dynamic_reshape (mhlo::DynamicReshapeOp)

DynamicReshape operation

Sözdizimi:

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

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

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
output_shape	1D tensor of index or 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%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)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
slice_sizes	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
start_indices	variadic of 0D tensor of 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.dynamic_update_slice (mhlo::DynamicUpdateSliceOp)

DynamicUpdateSlice operation

Sözdizimi:

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

Örnek:

%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)

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
update	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
start_indices	variadic of 0D tensor of 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
einsum_config	::mlir::StringAttr	string attribute

Operands:

İşlenen	Tanım
lhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.erf (mhlo::ErfOp)

Erf operation

Sözdizimi:

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.

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float values

mhlo.exponential (mhlo::ExpOp)

Exp operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

mhlo.exponential_minus_one (mhlo::Expm1Op)

Expm1 operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized 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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , InferTensorType

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
fft_type	::mlir::mhlo::FftTypeAttr	XLA fast fourier transform type.
fft_length	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.floor (mhlo::FloorOp)

Floor operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or per-tensor integer quantized 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.

Özellikler:

Bağlanmak	MLIR Type	Tanım
fusion_kind	::mlir::mhlo::FusionKindAttr	fusion kind
output_operand_aliases	::mlir::ArrayAttr	Aliasing attribute for outputs and operands of Fusion

Operands:

İşlenen	Tanım
inputs	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token

Sonuçlar:

Sonuç

Tanım

results

variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values or nested tuple with any combination of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized 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

Örnek:

%result = "mhlo.gather"(%operand, %start_indices) {
  dimension_numbers = #stablehlo.gather<
    offset_dims = [3, 4],
    collapsed_slice_dims = [1],
    operand_batching_dims = [0],
    start_indices_batching_dims = [1],
    start_index_map = [2, 1],
    index_vector_dim = 3>,
  slice_sizes = dense<[0, 2, 2]> : tensor<3xi64>,
  indices_are_sorted = false
} : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>) -> tensor<2x2x3x2x2xi64>

Traits: AlwaysSpeculatableImplTrait , InferTensorType

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
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	bool attribute

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
start_indices	ranked tensor of 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , InferTensorType

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
dimension	::mlir::IntegerAttr	64-bit signless integer attribute whose value is non-negative

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	tensor of 32-bit signless integer values

mhlo.get_tuple_element (mhlo::GetTupleElementOp)

GetTupleElement operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
index	::mlir::IntegerAttr	32-bit signless integer attribute whose value is non-negative

Operands:

İşlenen

Tanım

operand

nested tuple with any combination of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token values

Sonuçlar:

Sonuç

Tanım

«unnamed»

ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values or token or nested tuple with any combination of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized 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

Operands:

İşlenen	Tanım
pred	ranked tensor of bool values

Sonuçlar:

Sonuç	Tanım
«unnamed»	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token

mhlo.imag (mhlo::ImagOp)

Imag operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float 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

Örnek:

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

Özellikler:

Bağlanmak	MLIR Type	Tanım
infeed_config	::mlir::StringAttr	string attribute
layout	::mlir::ArrayAttr	array attribute

Operands:

İşlenen	Tanım
token	belirteç

Sonuçlar:

Sonuç

Tanım

«unnamed»

variadic of statically shaped tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or statically shaped tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
iota_dimension	::mlir::IntegerAttr	64-bit signless integer attribute whose value is non-negative

Sonuçlar:

Sonuç	Tanım
output	statically shaped tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements values

mhlo.is_finite (mhlo::IsFiniteOp)

IsFinite operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
x	ranked tensor of 4/6/8/16/32/64-bit float values

Sonuçlar:

Sonuç	Tanım
y	ranked tensor of bool values

mhlo.log (mhlo::LogOp)

Log operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

mhlo.log_plus_one (mhlo::Log1pOp)

Log1p operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

mhlo.logistic (mhlo::LogisticOp)

Logistic operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized 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

Örnek:

%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

Özellikler:

Bağlanmak	MLIR Type	Tanım
dimensions	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

İşlenen	Tanım
inputs	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.maximum (mhlo::MaxOp)

Max operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
lhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.minimum (mhlo::MinOp)

Min operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
lhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.minimum_broadcast_shapes (mhlo::MinimumBroadcastShapesOp)

Minimizes the rank of two or more shapes to be broadcasted

Sözdizimi:

operation ::= `mhlo.minimum_broadcast_shapes` $shapes attr-dict `:` type($shapes) `->` type($results)

Given two or more 1D tensors representing shapes, returns one 1D tensor for each operand, where operand i corresponds to output i .

The returned tensors have the property that they specify a shape which is a reshape of the corresponding input shape, and the broadcasted output shape (using shape::BroadcastOp) of the returned shapes is a reshape of the broadcasted output shape of the input shapes. Among all possibilities with this property, the one is chosen which minimizes the rank of each returned shape.

The general idea of this op is that it can be used for ops which have a broadcasting semantic to operate on shapes with a possibly smaller rank while preserving equivalence of the computed values. After computing the result of the op using reshaped operands, the result can be reshaped to the result that would have been originally computed.

Here is an example with two input shapes:

mhlo.minimum_broadcast_shapes [1, 2, 3, 1, 2, 1],
                                 [1, 1, 1, 2, 3] -> [6, 2, 1], [2, 3]

The broadcasted output shape of the operands is [1, 2, 3, 1, 2, 3], the broadcasted output shape of the outputs is [6, 2, 3]. These two shapes are reshapes of each other, and also each output is a reshape of the corresponding input.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
shapes	variadic of 1D tensor of index values

Sonuçlar:

Sonuç	Tanım
results	variadic of 1D tensor of index values

mhlo.multiply (mhlo::MulOp)

Mul operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
lhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.negate (mhlo::NegOp)

Neg operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.not (mhlo::NotOp)

Not operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of bool or 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of bool or 2/4/8/16/32/64-bit integer values

mhlo.optimization_barrier (mhlo::OptimizationBarrierOp)

OptimizationBarrier operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , HLO_PairwiseSameOperandAndResultType

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token

Sonuçlar:

Sonuç	Tanım
result	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token

mhlo.or (mhlo::OrOp)

Or operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
lhs	ranked tensor of bool or 2/4/8/16/32/64-bit integer values
rhs	ranked tensor of bool or 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

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

Interfaces: InferTypeOpInterface

Özellikler:

Bağlanmak	MLIR Type	Tanım
outfeed_config	::mlir::StringAttr	string attribute

Operands:

İşlenen	Tanım
inputs	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
token	belirteç

Sonuçlar:

Sonuç	Tanım
«unnamed»	belirteç

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

Örnek:

%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)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
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

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
padding_value	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.partition_id (mhlo::PartitionIdOp)

PartitionId operation

Sözdizimi:

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

Örnek:

%result = mhlo.partition_id : tensor<ui32>

Interfaces: InferTypeOpInterface

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 32-bit unsigned integer values

mhlo.popcnt (mhlo::PopulationCountOp)

PopulationCount operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 2/4/8/16/32/64-bit integer values

mhlo.power (mhlo::PowOp)

Pow operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
lhs	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.ragged_dot (mhlo::RaggedDotOp)

Ragged matrix multiplication over a single ragged dimension

This operation takes three tensor args---lhs, rhs, and group_sizes---and a "ragged_dot_dimension_numbers" attribute. Like dot_general, the lhs and rhs are allowed arbitrary batch and contracting dimensions. Additionally, the lhs is required to have one ragged dimension, and the rhs may have at most one group dimension. The op has three modes, depending on the kind of the lhs ragged dimension.

In mode 1, the shape-signature is [b,m,k], [g,b,k,n], [b,g] -> [b,m,n] . Here the ragged dimension is an lhs non-contracting dimension ( m ). The dimensions b and k represent batch and contracting dimensions respectively. The rhs is required to have a group dimension ( g ).

In mode 2, the shape-signature is [b,m,k], [b,k,n], [b,g] -> [g,b,m,n] . Here the ragged dimension is an lhs/rhs contracting dimension ( k ).

In mode 3, the shape-signature is [b,m,k], [b,k,n], [g] -> [b,m,n] . Here the ragged dimension is an lhs/rhs batch dimension ( b ).

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
ragged_dot_dimension_numbers	::mlir::mhlo::RaggedDotDimensionNumbersAttr	Attribute that models the dimension information for ragged dot.
precision_config	::mlir::ArrayAttr	Precision Config attribute

Operands:

İşlenen	Tanım
lhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
group_sizes	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values

mhlo.real (mhlo::RealOp)

Real operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float values

mhlo.real_dynamic_slice (mhlo::RealDynamicSliceOp)

RealDynamicSlice operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
start_indices	1D tensor of index or 2/4/8/16/32/64-bit integer values
limit_indices	1D tensor of index or 2/4/8/16/32/64-bit integer values
strides	1D tensor of index or 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%results:2 = "mhlo.recv"(%token) {
  // channel_id = 5 : i64,
  // channel_type = #stablehlo<channel_type DEVICE_TO_DEVICE>,
  channel_handle = #mhlo.channel_handle<handle = 5, type = 1>,
  is_host_transfer = false,
  source_target_pairs = dense<[[0, 1], [1, 2]]> : tensor<2x2xi64>
} : (!mhlo.token) -> (tensor<3x4xi32>, !mhlo.token)

Özellikler:

Bağlanmak	MLIR Type	Tanım
channel_handle	::mlir::mhlo::ChannelHandleAttr	two 64-bit integers 'handle' and 'type'
is_host_transfer	::mlir::BoolAttr	bool attribute
source_target_pairs	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

İşlenen	Tanım
token	belirteç

Sonuçlar:

Sonuç

Tanım

«unnamed»

variadic of statically shaped tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or statically shaped tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%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

Özellikler:

Bağlanmak	MLIR Type	Tanım
dimensions	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

İşlenen	Tanım
inputs	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
init_values	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.reduce_precision (mhlo::ReducePrecisionOp)

ReducePrecision operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
exponent_bits	::mlir::IntegerAttr	32-bit signless integer attribute whose value is positive
mantissa_bits	::mlir::IntegerAttr	32-bit signless integer attribute whose value is non-negative

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float values

Sonuçlar:

Sonuç	Tanım
output	ranked tensor of 4/6/8/16/32/64-bit float values

mhlo.reduce_scatter (mhlo::ReduceScatterOp)

ReduceScatter operation

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

Örnek:

%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>

Özellikler:

Bağlanmak	MLIR Type	Tanım
scatter_dimension	::mlir::IntegerAttr	64-bit signless integer attribute whose value is non-negative
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::UnitAttr	unit attribute

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%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

Özellikler:

Bağlanmak	MLIR Type	Tanım
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

Operands:

İşlenen	Tanım
inputs	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
init_values	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.remainder (mhlo::RemOp)

Rem operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
lhs	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.replica_id (mhlo::ReplicaIdOp)

ReplicaId operation

Sözdizimi:

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

Örnek:

%result = mhlo.replica_id : tensor<ui32>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 32-bit unsigned integer values

mhlo.reshape (mhlo::ReshapeOp)

Reshape operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , HLO_CompatibleOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	statically shaped or single bounded dimension tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values or token or nested tuple with any combination of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized 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)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
dimensions	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

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

Traits: InferTensorType

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Özellikler:

Bağlanmak	MLIR Type	Tanım
rng_distribution	::mlir::mhlo::RngDistributionAttr	XLA PRNG distribution to be used.

Operands:

İşlenen	Tanım
a	0D tensor of bool or 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float values
b	0D tensor of bool or 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float values
shape	1D tensor of index or 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of bool or 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float 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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
rng_algorithm	::mlir::mhlo::RngAlgorithmAttr	XLA PRNG algorithm to be used.

Operands:

İşlenen	Tanım
initial_state	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float values

Sonuçlar:

Sonuç	Tanım
output_state	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float values
output	statically shaped tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float values

mhlo.round_nearest_afz (mhlo::RoundOp)

Round operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float values

mhlo.round_nearest_even (mhlo::RoundNearestEvenOp)

RoundNearestEven operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float values

mhlo.rsqrt (mhlo::RsqrtOp)

Rsqrt operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

mhlo.scan (mhlo::ScanOp)

Scan operation

Applies a reduction function body to inputs and inits along the dimension and produces results (comprising outputs and carries ).

If is_reverse is true, the scan is performed in reverse order. is_associative indicates whether the reduction function is associative.

See: https://www.tensorflow.org/xla/operation_semantics#scan

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

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Özellikler:

Bağlanmak	MLIR Type	Tanım
dimension	::mlir::IntegerAttr	64-bit signless integer attribute whose value is non-negative
is_reverse	::mlir::BoolAttr	bool attribute
is_associative	::mlir::mhlo::AssociativityAttr	Associativity of the scan operation.

Operands:

İşlenen	Tanım
inputs	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
inits	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
results	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%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 = [3, 4],
    inserted_window_dims = [1],
    input_batching_dims = [0],
    scatter_indices_batching_dims = [1],
    scatter_dims_to_operand_dims = [2, 1],
    index_vector_dim = 3>,
  indices_are_sorted = false,
  unique_indices = false
} : (tensor<2x3x4x2xi64>, tensor<2x2x3x2xi64>, tensor<2x2x3x2x2xi64>) -> tensor<2x3x4x2xi64>

Traits: RecursiveMemoryEffects , SameVariadicOperandSize

Interfaces: InferTypeOpInterface

Özellikler:

Bağlanmak	MLIR Type	Tanım
scatter_dimension_numbers	::mlir::mhlo::ScatterDimensionNumbersAttr	Attribute that models the dimension information for scatter
indices_are_sorted	::mlir::BoolAttr	bool attribute
unique_indices	::mlir::BoolAttr	bool attribute

Operands:

İşlenen	Tanım
inputs	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
scatter_indices	ranked tensor of integer or index values
updates	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.select (mhlo::SelectOp)

İşlem seçin

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , HLO_BroadcastingElementwise , InferTensorType

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
pred	ranked tensor of bool values
on_true	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
on_false	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%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

Özellikler:

Bağlanmak	MLIR Type	Tanım
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

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
source	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
init_value	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%result = "mhlo.send"(%operand, %token) {
  // channel_id = 5 : i64,
  // channel_type = #stablehlo<channel_type DEVICE_TO_DEVICE>,
  channel_handle = #mhlo.channel_handle<handle = 5, type = 1>,
  is_host_transfer = false,
  source_target_pairs = dense<[[0, 1], [1, 2]]> : tensor<2x2xi64>
} : (tensor<3x4xi32>, !mhlo.token) -> !mhlo.token

Interfaces: InferTypeOpInterface

Özellikler:

Bağlanmak	MLIR Type	Tanım
channel_handle	::mlir::mhlo::ChannelHandleAttr	two 64-bit integers 'handle' and 'type'
is_host_transfer	::mlir::BoolAttr	bool attribute
source_target_pairs	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

İşlenen	Tanım
inputs	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
token	belirteç

Sonuçlar:

Sonuç	Tanım
«unnamed»	belirteç

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , InferTensorType

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
dimension	::mlir::IntegerAttr	64-bit signless integer attribute whose value is non-negative

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
size	tensor of 32-bit signless integer values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.shift_left (mhlo::ShiftLeftOp)

ShiftLeft operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
lhs	ranked tensor of 2/4/8/16/32/64-bit integer values
rhs	ranked tensor of 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 2/4/8/16/32/64-bit integer values

mhlo.shift_right_arithmetic (mhlo::ShiftRightArithmeticOp)

ShiftRightArithmetic operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
lhs	ranked tensor of 2/4/8/16/32/64-bit integer values
rhs	ranked tensor of 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 2/4/8/16/32/64-bit integer values

mhlo.shift_right_logical (mhlo::ShiftRightLogicalOp)

ShiftRightLogical operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
lhs	ranked tensor of 2/4/8/16/32/64-bit integer values
rhs	ranked tensor of 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 2/4/8/16/32/64-bit integer values

mhlo.sign (mhlo::SignOp)

Sign operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 2/4/8/16/32/64-bit signless integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 2/4/8/16/32/64-bit signless integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

mhlo.sine (mhlo::SineOp)

Sine operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

mhlo.sinh (mhlo::SinhOp)

Sinh operation

Sözdizimi:

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

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

Örnek:

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

Traits: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Operands:

İşlenen	Tanım
operand	tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements values

Sonuçlar:

Sonuç	Tanım
result	tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements values

mhlo.slice (mhlo::SliceOp)

Slice operation

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

Örnek:

%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)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
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

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%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

Özellikler:

Bağlanmak	MLIR Type	Tanım
dimension	::mlir::IntegerAttr	64-bit signless integer attribute
is_stable	::mlir::BoolAttr	bool attribute

Operands:

İşlenen	Tanım
inputs	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.sqrt (mhlo::SqrtOp)

Sqrt operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized 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)

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float values
random	ranked tensor of 2/4/8/16/32/64-bit unsigned integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.subtract (mhlo::SubtractOp)

Subtract operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
lhs	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
rhs	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 2/4/8/16/32/64-bit integer or 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.tan (mhlo::TanOp)

Tan operation

Sözdizimi:

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.

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

mhlo.tanh (mhlo::TanhOp)

Tanh operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements or per-tensor integer quantized values

mhlo.topk (mhlo::TopKOp)

TopK operation

Sözdizimi:

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

Örnek:

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

Traits: InferTensorType , RecursiveMemoryEffects

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Özellikler:

Bağlanmak	MLIR Type	Tanım
k	::mlir::IntegerAttr	64-bit signless integer attribute
largest	::mlir::BoolAttr	bool attribute

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
values	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
indices	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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.

Örnek:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
dim	::mlir::IntegerAttr	64-bit signless integer attribute
batch_dims	::mlir::IntegerAttr	64-bit signless integer attribute

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values
index	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

mhlo.trace (mhlo::TraceOp)

Trace operation

Sözdizimi:

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.

Örnek:

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

Özellikler:

Bağlanmak	MLIR Type	Tanım
tag	::mlir::StringAttr	string attribute

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , HLO_CompatibleOperandsAndResultElementType

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

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
permutation	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%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)

Effects: MemoryEffects::Effect{}

Özellikler:

Bağlanmak	MLIR Type	Tanım
left_side	::mlir::BoolAttr	bool attribute
lower	::mlir::BoolAttr	bool attribute
unit_diagonal	::mlir::BoolAttr	bool attribute
transpose_a	::mlir::mhlo::TransposeAttr	Transpose options

Operands:

İşlenen	Tanım
a	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements values
b	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements values

Sonuçlar:

Sonuç	Tanım
«unnamed»	ranked tensor of 4/6/8/16/32/64-bit float or complex type with 32/64-bit float elements values

mhlo.tuple (mhlo::TupleOp)

Tuple operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

İşlenen

Tanım

val

variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values or token or nested tuple with any combination of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç

Tanım

result

nested tuple with any combination of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token values

mhlo.uniform_dequantize (mhlo::UniformDequantizeOp)

UniformDequantize operation

Sözdizimi:

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

Örnek:

%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)

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of per-tensor integer quantized or per-axis integer quantized values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float values

mhlo.uniform_quantize (mhlo::UniformQuantizeOp)

UniformQuantize operation

Sözdizimi:

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

Örnek:

%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)

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
operand	ranked tensor of 4/6/8/16/32/64-bit float or 2/4/8/16/32-bit uniform quantized signed integer or 2/4/8/16/32-bit uniform quantized per axis signed integer or 2/4/8/16/32-bit uniform quantized unsigned integer or 2/4/8/16/32-bit uniform quantized per axis unsigned integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of per-tensor integer quantized or per-axis integer quantized 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

Örnek:

%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

Operands:

İşlenen

Tanım

operand

variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values

Sonuçlar:

Sonuç

Tanım

«unnamed»

variadic of ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values or ranked tensor of per-axis integer quantized values or token or memref of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized values

mhlo.xla.rng_get_and_update_state (mhlo::XlaRngGetAndUpdateStateOp)

XlaRngGetAndUpdateState operation

Sözdizimi:

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

Özellikler:

Bağlanmak	MLIR Type	Tanım
delta	::mlir::IntegerAttr	64-bit signless integer attribute

Sonuçlar:

Sonuç	Tanım
«unnamed»	statically shaped tensor of 64-bit unsigned integer values

mhlo.xor (mhlo::XorOp)

Xor operation

Sözdizimi:

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

Örnek:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

İşlenen	Tanım
lhs	ranked tensor of bool or 2/4/8/16/32/64-bit integer values
rhs	ranked tensor of bool or 2/4/8/16/32/64-bit integer values

Sonuçlar:

Sonuç	Tanım
result	ranked tensor of 4/6/8/16/32/64-bit float or bool or 2/4/8/16/32/64-bit integer or complex type with 32/64-bit float elements or per-tensor integer quantized or per-axis integer quantized values

Özellikler

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 ...
}

Parametreler:

Parametre	C++ type	Tanım
argTupleIndices	::llvm::ArrayRef<int64_t>	Boyut
resultIndex	int64_t
resultTupleIndices	::llvm::ArrayRef<int64_t>	Boyut
isMustAlias	bool

AssociativityAttr

Associativity of the scan operation.

Sözdizimi:

#mhlo.associativity<
  ::mlir::mhlo::Associativity   # value
>

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::Associativity	an enum of type Associativity

ChannelHandleAttr

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

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
halletmek	int64_t
tip	int64_t

ComparisonDirectionAttr

Which comparison operation to perform.

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::ComparisonDirection	an enum of type ComparisonDirection

ComparisonTypeAttr

Which comparison type to use.

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::ComparisonType	an enum of type ComparisonType

ConvDimensionNumbersAttr

Structure of dimension information for conv op

Parametreler:

Parametre	C++ type	Tanım
inputBatchDimension	int64_t
inputFeatureDimension	int64_t
inputSpatialDimensions	::llvm::ArrayRef<int64_t>	Boyut
kernelInputFeatureDimension	int64_t
kernelOutputFeatureDimension	int64_t
kernelSpatialDimensions	::llvm::ArrayRef<int64_t>	Boyut
outputBatchDimension	int64_t
outputFeatureDimension	int64_t
outputSpatialDimensions	::llvm::ArrayRef<int64_t>	Boyut

CrossProgramPrefetchAttr

Argument that is prefetched from another program

Sözdizimi:

#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.

Örneğin,

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.

Parametreler:

Parametre	C++ type	Tanım
parametre	int64_t
endeksler	::llvm::ArrayRef<int64_t>	Boyut
telafi etmek	std::optional<int64_t>

CustomCallScheduleAttr

Specifies the desired schedule for the custom-call.

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::CustomCallSchedule	an enum of type CustomCallSchedule

DequantizeModeAttr

_Dequantization mode. Only MIN COMBINED is supported.

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::DequantizeMode	an enum of type DequantizeMode

DomainKindAttr

Kind of domain metatdata attached to an HLO domain.

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::DomainKind	an enum of type DomainKind

DotAlgorithmAttr

Attribute that models the algorithm constraints to use for computing dot.

Sözdizimi:

#mhlo.dot_algorithm<
  Type,   # lhsPrecisionType
  Type,   # rhsPrecisionType
  Type,   # accumulationType
  int64_t,   # lhsComponentCount
  int64_t,   # rhsComponentCount
  int64_t,   # numPrimitiveOperations
  bool   # allowImpreciseAccumulation
>

Parametreler:

Parametre	C++ type	Tanım
lhsPrecisionType	Type
rhsPrecisionType	Type
accumulationType	Type
lhsComponentCount	int64_t
rhsComponentCount	int64_t
numPrimitiveOperations	int64_t
allowImpreciseAccumulation	bool

DotDimensionNumbersAttr

Attribute that models the dimension information for dot.

Parametreler:

Parametre	C++ type	Tanım
lhsBatchingDimensions	::llvm::ArrayRef<int64_t>	Boyut
rhsBatchingDimensions	::llvm::ArrayRef<int64_t>	Boyut
lhsContractingDimensions	::llvm::ArrayRef<int64_t>	Boyut
rhsContractingDimensions	::llvm::ArrayRef<int64_t>	Boyut

FftTypeAttr

XLA fast fourier transform type.

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::FftType	an enum of type FftType

FusionKindAttr

Fusion kind

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::FusionKind	an enum of type FusionKind

GatherDimensionNumbersAttr

Attribute that models the dimension information for gather

Parametreler:

Parametre	C++ type	Tanım
offsetDims	::llvm::ArrayRef<int64_t>	Boyut
collapsedSliceDims	::llvm::ArrayRef<int64_t>	Boyut
operandBatchingDims	::llvm::ArrayRef<int64_t>	Boyut
startIndicesBatchingDims	::llvm::ArrayRef<int64_t>	Boyut
startIndexMap	::llvm::ArrayRef<int64_t>	Boyut
indexVectorDim	int64_t

OutputOperandAliasAttr

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

Sözdizimi:

#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>.

Parametreler:

Parametre	C++ type	Tanım
outputTupleIndices	::llvm::ArrayRef<int64_t>	Boyut
operandIndex	int64_t
operandTupleIndices	::llvm::ArrayRef<int64_t>	Boyut

PrecisionAttr

XLA precision for an operand. Has backend specific meaning.

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::Precision	an enum of type Precision

RaggedDotDimensionNumbersAttr

Attribute that models the dimension information for ragged dot.

Parametreler:

Parametre	C++ type	Tanım
dotDimensionNumbers	::mlir::mhlo::DotDimensionNumbersAttr	Attribute that models the dimension information for dot.
lhsRaggedDimensions	::llvm::ArrayRef<int64_t>	Boyut
rhsGroupDimensions	::llvm::ArrayRef<int64_t>	Boyut

ResultAccuracyAttr

The requested accuracy for unary ops.

Parametreler:

Parametre	C++ type	Tanım
atol	APFloat
rtol	APFloat
ulps	int64_t
mod	::mlir::mhlo::ResultAccuracyModeAttr	XLA result accuracy mode.

ResultAccuracyModeAttr

XLA result accuracy mode.

Sözdizimi:

#mhlo.result_accuracy_mode<
  ::mlir::mhlo::ResultAccuracyMode   # value
>

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::ResultAccuracyMode	an enum of type ResultAccuracyMode

RngAlgorithmAttr

XLA PRNG algorithm to be used.

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::RngAlgorithm	an enum of type RngAlgorithm

RngDistributionAttr

XLA PRNG distribution to be used.

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::RngDistribution	an enum of type RngDistribution

ScatterDimensionNumbersAttr

Attribute that models the dimension information for scatter

Parametreler:

Parametre	C++ type	Tanım
updateWindowDims	::llvm::ArrayRef<int64_t>	Boyut
insertedWindowDims	::llvm::ArrayRef<int64_t>	Boyut
inputBatchingDims	::llvm::ArrayRef<int64_t>	Boyut
scatterIndicesBatchingDims	::llvm::ArrayRef<int64_t>	Boyut
scatterDimsToOperandDims	::llvm::ArrayRef<int64_t>	Boyut
indexVectorDim	int64_t

TransposeAttr

Transpose options

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
değer	::mlir::mhlo::Transpose	an enum of type Transpose

TypeExtensionsAttr

Attribute that extends tensor type with MHLO type properties.

Sözdizimi:

#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 .

Parametreler:

Parametre	C++ type	Tanım
sınırlar	::llvm::ArrayRef<int64_t>

Türler

AsyncBundleType

Opaque collection of other types

Sözdizimi:

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

Parametreler:

Parametre	C++ type	Tanım
türleri	::llvm::ArrayRef<Type>

Numaralandırmalar

İlişkisellik

Associativity of the scan operation.

Vakalar:

Sembol	Değer	Sicim
BELKİ	0	BELKİ
DOĞRU	1	DOĞRU
YANLIŞ	2	YANLIŞ

ComparisonDirection

Which comparison operation to perform.

Vakalar:

Sembol	Değer	Sicim
EQ	0	EQ
NE	1	NE
GE	2	GE
GT	3	GT
LE	4	LE
LT	5	LT

ComparisonType

Which comparison type to use.

Vakalar:

Sembol	Değer	Sicim
NOT TİPİ	0	NOT TİPİ
BATMADAN YÜZMEK	1	BATMADAN YÜZMEK
TOTALORDER	2	TOTALORDER
İMZALANDI	3	İMZALANDI
İMZALANMAMIŞ	4	İMZALANMAMIŞ

CustomCallApiVersion

Custom call API version

Vakalar:

Sembol	Değer	Sicim
API_VERSION_UNSPECIFIED	0	API_VERSION_UNSPECIFIED
API_VERSION_ORIGINAL	1	API_VERSION_ORIGINAL
API_VERSION_STATUS_RETURNING	2	API_VERSION_STATUS_RETURNING
API_VERSION_STATUS_RETURNING_UNIFIED	3	API_VERSION_STATUS_RETURNING_UNIFIED
API_VERSION_TYPED_FFI	4	API_VERSION_TYPED_FFI

CustomCallSchedule

Specifies the desired schedule for the custom-call.

Vakalar:

Sembol	Değer	Sicim
HİÇBİRİ	0	HİÇBİRİ
EN SONUNCU	1	EN SONUNCU
EN ERKEN	2	EN ERKEN

DequantizeMode

_Dequantization mode. Only MIN COMBINED is supported.

Vakalar:

Sembol	Değer	Sicim
MIN_COMBINED	0	MIN_COMBINED

DomainKind

Kind of domain metatdata attached to an HLO domain.

Vakalar:

Sembol	Değer	Sicim
sharding	0	sharding

FftType

XLA fast fourier transform type.

Vakalar:

Sembol	Değer	Sicim
FFT	0	FFT
IFFT	1	IFFT
RFFT	2	RFFT
IRFFT	3	IRFFT

FusionKind

Fusion kind

Vakalar:

Sembol	Değer	Sicim
kLoop	0	kLoop
kInput	1	kInput
kOutput	2	kOutput
kCustom	3	kCustom

Kesinlik

XLA precision for an operand. Has backend specific meaning.

Vakalar:

Sembol	Değer	Sicim
VARSAYILAN	0	VARSAYILAN
YÜKSEK	1	YÜKSEK
EN YÜKSEK	2	EN YÜKSEK

ResultAccuracyMode

XLA result accuracy mode.

Vakalar:

Sembol	Değer	Sicim
VARSAYILAN	0	VARSAYILAN
EN YÜKSEK	1	EN YÜKSEK
TOLERANS	2	TOLERANS

RngAlgorithm

XLA PRNG algorithm to be used.

Vakalar:

Sembol	Değer	Sicim
VARSAYILAN	0	VARSAYILAN
THREE_FRY	1	THREE_FRY
PHILOX	2	PHILOX

RngDistribution

XLA PRNG distribution to be used.

Vakalar:

Sembol	Değer	Sicim
ÜNİFORMA	1	ÜNİFORMA
NORMAL	2	NORMAL

Transpoze

Transpose options

Vakalar:

Sembol	Değer	Sicim
TRANSPOSE_INVALID	0	TRANSPOSE_INVALID
NO_TRANSPOSE	1	NO_TRANSPOSE
TRANSPOZ	2	TRANSPOZ
ADJOINT	3	ADJOINT