'mhlo' Dialek

Operasi

mhlo.abs (mhlo::AbsOp)

Operasi perut

Sintaksis:

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

Melakukan operasi abs elemen demi elemen pada tensor operand dan menghasilkan tensor result .

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

Contoh:

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

Ciri: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Operan:

Operan

Keterangan

operand

tensor berperingkat bilangan bulat tanpa tanda 2/4/8/16/32/64-bit atau bilangan bulat float atau kompleks 4/6/8/16/32/64-bit dengan elemen float 32/64-bit atau bilangan bulat bertanda terkuantisasi seragam 2/4/8/16/32-bit atau bilangan bulat bertanda terkuantisasi seragam per sumbu 2/4/8/16/32-bit atau bilangan bulat tak bertanda terkuantisasi seragam 2/4/8/16/32-bit atau bilangan bulat tak bertanda terkuantisasi seragam per sumbu 2/4/8/16/32-bit

Hasil:

Hasil	Keterangan
result	tensor berperingkat bilangan bulat tanpa tanda 2/4/8/16/32/64-bit atau bilangan bulat bertanda terkuantisasi seragam 2/4/8/16/32-bit atau bilangan bulat bertanda terkuantisasi seragam per sumbu 2/4/8/16/32-bit atau bilangan bulat tak bertanda terkuantisasi seragam 2/4/8/16/32-bit atau bilangan bulat tak bertanda terkuantisasi seragam per sumbu 2/4/8/16/32-bit

mhlo.acos (mhlo::AcosOp)

Operasi Acos

Sintaksis:

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

Melakukan operasi acos per elemen pada tensor operand dan menghasilkan tensor result .

Contoh:

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

Ciri-ciri: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Antarmuka: InferShapedTypeOpInterface , InferTypeOpInterface

Operan:

Operan	Keterangan
operand	tensor tipe float atau kompleks 4/6/8/16/32/64-bit dengan nilai elemen float 32/64-bit

Hasil:

Hasil	Keterangan
result	tensor tipe float atau kompleks 4/6/8/16/32/64-bit dengan nilai elemen float 32/64-bit

mhlo.acosh (mhlo::AcoshOp)

Operasi Acosh

Sintaksis:

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

Melakukan operasi acosh per elemen pada tensor operand dan menghasilkan tensor result .

Contoh:

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

Ciri-ciri: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Antarmuka: InferShapedTypeOpInterface , InferTypeOpInterface

Operan:

Operan	Keterangan
operand	tensor tipe float atau kompleks 4/6/8/16/32/64-bit dengan nilai elemen float 32/64-bit

Hasil:

Hasil	Keterangan
result	tensor tipe float atau kompleks 4/6/8/16/32/64-bit dengan nilai elemen float 32/64-bit

mhlo.add (mhlo::AddOp)

Tambahkan operasi

Sintaksis:

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

Melakukan penambahan elemen demi elemen dari dua tensor lhs dan rhs dan menghasilkan tensor result .

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

Contoh:

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

Ciri: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu
rhs	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

Hasil:

Hasil	Keterangan
result	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

mhlo.add_dependency (mhlo::AddDependencyOp)

Operasi AddDependency

Sintaksis:

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

Operasi ini bersifat pribadi bagi kompiler XLA, jadi belum memiliki spesifikasi.

Secara informal, operasi ini memiliki dua operan: operan data dan token. Keluaran dari operasi ini adalah operan data. Ketika digunakan dengan AfterAll, operasi ini memungkinkan pengurutan operasi tanpa efek samping (operasi yang tidak menghasilkan nilai token).

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
operand	tensor berperingkat dari tipe float 4/6/8/16/32/64-bit atau bool atau tipe integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau tensor berperingkat dari nilai terkuantisasi integer per sumbu atau token atau token stablehlo
token	token atau token stablehlo

Hasil:

Hasil	Keterangan
output	tensor berperingkat dari tipe float 4/6/8/16/32/64-bit atau bool atau tipe integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau tensor berperingkat dari nilai terkuantisasi integer per sumbu atau token atau token stablehlo

mhlo.after_all (mhlo::AfterAllOp)

Operasi AfterAll

Sintaksis:

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

Memastikan bahwa operasi yang menghasilkan inputs dieksekusi sebelum operasi apa pun yang bergantung pada result .

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

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
inputs	variadic token

Hasil:

Hasil	Keterangan
result	token

mhlo.all_gather (mhlo::AllGatherOp)

Operasi AllGather

Dalam setiap grup proses di kisi proses, menggabungkan nilai tensor operan dari setiap proses di sepanjang all_gather_dim dan menghasilkan tensor hasil. computation diterapkan secara terpisah untuk setiap operan di operands , menghasilkan satu hasil per operan.

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

Contoh:

%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>

Sifat: SameOperandsAndResultElementType

Atribut:

Atribut	Tipe MLIR	Keterangan
all_gather_dim	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit yang nilainya bukan negatif
replica_groups	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
channel_handle	::mlir::mhlo::ChannelHandleAttr	dua bilangan bulat 64-bit 'handle' dan 'type'
use_global_device_ids	::mlir::UnitAttr	atribut unit

Operan:

Operan	Keterangan
operands	variadic dari tensor peringkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau nilai integer terkuantisasi per sumbu

Hasil:

Hasil	Keterangan
"tanpa nama"	variadic dari tensor peringkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau nilai integer terkuantisasi per sumbu

mhlo.all_reduce (mhlo::AllReduceOp)

Operasi AllReduce

Dalam setiap grup proses di kisi proses, terapkan computation fungsi reduksi ke nilai tensor operan dari setiap proses dan hasilkan tensor hasil. computation diterapkan secara terpisah untuk setiap operan dalam operands , menghasilkan satu hasil per operan.

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

Contoh:

%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>

Ciri-ciri: InferTensorType , SingleBlockImplicitTerminator<ReturnOp> , SingleBlock

Antarmuka: InferShapedTypeOpInterface , InferTypeOpInterface

Atribut:

Atribut	Tipe MLIR	Keterangan
replica_groups	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
channel_handle	::mlir::mhlo::ChannelHandleAttr	dua bilangan bulat 64-bit 'handle' dan 'type'
use_global_device_ids	::mlir::UnitAttr	atribut unit

Operan:

Operan	Keterangan
operands	variadic dari tensor peringkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau nilai integer terkuantisasi per sumbu

Hasil:

Hasil	Keterangan
"tanpa nama"	variadic dari tensor peringkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau nilai integer terkuantisasi per sumbu

mhlo.all_to_all (mhlo::AllToAllOp)

Operasi AllToAll

Dalam setiap grup proses di kisi proses, membagi nilai tensor operand sepanjang split_dimension menjadi beberapa bagian, menyebarkan bagian yang terbagi di antara proses, menggabungkan bagian yang tersebar sepanjang concat_dimension dan menghasilkan tensor result .

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

Contoh:

%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>

Ciri: AlwaysSpeculatableImplTrait , InferTensorType , SameOperandsElementType , SameOperandsShape , SameVariadicOperandSize

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
split_dimension	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit yang nilainya bukan negatif
concat_dimension	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit yang nilainya bukan negatif
split_count	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit yang nilainya positif
replica_groups	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
channel_handle	::mlir::mhlo::ChannelHandleAttr	dua bilangan bulat 64-bit 'handle' dan 'type'

Operan:

Operan	Keterangan
operand	variadic dari tensor peringkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau nilai integer terkuantisasi per sumbu

Hasil:

Hasil	Keterangan
"tanpa nama"	variadic dari tensor peringkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau nilai integer terkuantisasi per sumbu

mhlo.and (mhlo::AndOp)

Dan operasi

Sintaksis:

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

Melakukan AND elemen-bijaksana dari dua tensor lhs dan rhs dan menghasilkan tensor result

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

Contoh:

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

Ciri: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor peringkat bool atau nilai integer 2/4/8/16/32/64-bit
rhs	tensor peringkat bool atau nilai integer 2/4/8/16/32/64-bit

Hasil:

Hasil	Keterangan
result	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

mhlo.asin (mhlo::AsinOp)

Operasi Asin

Sintaksis:

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

Melakukan operasi asin elemen demi elemen pada tensor operand dan menghasilkan tensor result .

Contoh:

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

Ciri-ciri: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Antarmuka: InferShapedTypeOpInterface , InferTypeOpInterface

Operan:

Operan	Keterangan
operand	tensor tipe float atau kompleks 4/6/8/16/32/64-bit dengan nilai elemen float 32/64-bit

Hasil:

Hasil	Keterangan
result	tensor tipe float atau kompleks 4/6/8/16/32/64-bit dengan nilai elemen float 32/64-bit

mhlo.async_done (mhlo::AsyncDoneOp)

Operasi AsyncDone

Operasi ini bersifat pribadi bagi kompiler XLA, jadi belum memiliki spesifikasi.

Secara informal, operasi ini akan memblokir hingga akhir komputasi asinkron. Operasi ini akan mengembalikan hasil akhir komputasi asinkron.

Lihat dokumentasi untuk AsyncStart untuk informasi lebih lanjut.

Antarmuka: InferTypeOpInterface

Operan:

Operan	Keterangan
bundle	async_bundle dengan kombinasi tensor peringkat apa pun dengan tipe float atau bool 4/6/8/16/32/64-bit atau tipe integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau nilai integer terkuantisasi per sumbu atau nilai token atau token stablehlo

Hasil:

Hasil

Keterangan

"tanpa nama"

Bahasa Indonesia: variadic dari tensor peringkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks bertipe 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau nilai integer terkuantisasi per sumbu atau token atau token stablehlo atau tupel bersarang dengan kombinasi apa pun dari tensor peringkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks bertipe 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau memref bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks bertipe 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau tensor peringkat integer per sumbu nilai terkuantisasi atau nilai token

mhlo.async_start (mhlo::AsyncStartOp)

Operasi AsyncStart

Operasi ini bersifat pribadi bagi kompiler XLA, jadi belum memiliki spesifikasi.

Secara informal, operasi ini memulai komputasi asinkron.

Ini digunakan ketika terdapat fungsi yang berisi penantian asinkron (seperti DMA) dan komputasi on-thread. Misalnya, suatu fungsi mungkin terdiri dari sebuah komputasi, sebuah DMA, komputasi lain, sebuah DMA kedua, dan sebuah komputasi akhir. Ini akan direpresentasikan sebagai async_start diikuti oleh async_update dan async_done. async_start akan melakukan komputasi pertama on-thread dan kemudian memulai DMA. async_update akan menunggu DMA selesai jika belum selesai, lalu mengeksekusi komputasi kedua dalam fungsi tersebut, dan memulai DMA kedua. Terakhir, async_done akan menunggu DMA terakhir ini, lalu menjalankan komputasi terakhir yang perlu dijalankan on-thread dan mengembalikan hasil komputasi akhir tersebut.

operands diteruskan langsung ke komputasi. Fungsi called_computation adalah fungsi yang akan dijalankan secara asinkron. execution_thread adalah nama utas tempat utas tersebut akan dijalankan. Utas utama disebut "main". Semua utas memiliki nama.

Ini mengembalikan semua status yang dibutuhkan di antara operasi asinkron. Setelah buffer ditetapkan, nilai yang dikembalikan mewakili ruang yang dibutuhkan untuk menyimpan input, hasil, dan scratchpad apa pun yang dibutuhkan atau diedit oleh operasi asinkron.

Atribut:

Atribut	Tipe MLIR	Keterangan
called_computation	::mlir::FlatSymbolRefAttr	atribut referensi simbol datar
execution_thread	::mlir::AtributString	atribut string

Operan:

Operan

Keterangan

inputs

Bahasa Indonesia: variadic dari tensor peringkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks bertipe 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau nilai integer terkuantisasi per sumbu atau token atau token stablehlo atau tupel bersarang dengan kombinasi apa pun dari tensor peringkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks bertipe 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau memref bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks bertipe 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau tensor peringkat integer per sumbu nilai terkuantisasi atau nilai token

Hasil:

Hasil	Keterangan
"tanpa nama"	async_bundle dengan kombinasi tensor peringkat apa pun dengan tipe float atau bool 4/6/8/16/32/64-bit atau tipe integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau nilai integer terkuantisasi per sumbu atau nilai token atau token stablehlo

mhlo.async_update (mhlo::AsyncUpdateOp)

Operasi AsyncUpdate

Operasi ini bersifat pribadi bagi kompiler XLA, jadi belum memiliki spesifikasi.

Secara informal, operasi ini memblokir komputasi asinkron hingga mencapai batas sinkronisasi. Ini mengembalikan bundle setelah beroperasi di atasnya.

Lihat dokumentasi untuk AsyncStart untuk informasi lebih lanjut.

Antarmuka: InferTypeOpInterface

Operan:

Operan	Keterangan
bundle	async_bundle dengan kombinasi tensor peringkat apa pun dengan tipe float atau bool 4/6/8/16/32/64-bit atau tipe integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau nilai integer terkuantisasi per sumbu atau nilai token atau token stablehlo

Hasil:

Hasil	Keterangan
"tanpa nama"	async_bundle dengan kombinasi tensor peringkat apa pun dengan tipe float atau bool 4/6/8/16/32/64-bit atau tipe integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai integer terkuantisasi per tensor atau nilai integer terkuantisasi per sumbu atau nilai token atau token stablehlo

mhlo.atan2 (mhlo::Atan2Op)

Operasi Atan2

Sintaksis:

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

Melakukan operasi atan2 elemen demi elemen pada tensor lhs dan rhs dan menghasilkan tensor result .

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

Contoh:

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

Ciri: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor berperingkat tipe float atau kompleks 4/6/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor
rhs	tensor berperingkat tipe float atau kompleks 4/6/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor

Hasil:

Hasil	Keterangan
result	tensor berperingkat tipe float atau kompleks 4/6/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor

mhlo.atanh (mhlo::AtanhOp)

Operasi Atanh

Sintaksis:

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

Melakukan operasi atanh elemen demi elemen pada tensor operand dan menghasilkan tensor result .

Contoh:

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

Ciri-ciri: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Antarmuka: InferShapedTypeOpInterface , InferTypeOpInterface

Operan:

Operan	Keterangan
operand	tensor tipe float atau kompleks 4/6/8/16/32/64-bit dengan nilai elemen float 32/64-bit

Hasil:

Hasil	Keterangan
result	tensor tipe float atau kompleks 4/6/8/16/32/64-bit dengan nilai elemen float 32/64-bit

mhlo.batch_norm_grad (mhlo::BatchNormGradOp)

Operasi BatchNormGrad

Menghitung gradien beberapa masukan BatchNormTrainingOp yang dipropagasi balik dari grad_output , dan menghasilkan tensor grad_operand , grad_scale , dan grad_offset .

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

Contoh:

%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>)

Ciri: AlwaysSpeculatableImplTrait , InferTensorType

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
epsilon	::mlir::FloatAttr	Atribut float 32-bit
feature_index	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit yang nilainya bukan negatif

Operan:

Operan	Keterangan
operand	tensor peringkat nilai float 4/6/8/16/32/64-bit
scale	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit
mean	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit
variance	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit
grad_output	tensor peringkat nilai float 4/6/8/16/32/64-bit

Hasil:

Hasil	Keterangan
grad_operand	tensor peringkat nilai float 4/6/8/16/32/64-bit
grad_scale	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit
grad_offset	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit

mhlo.batch_norm_inference (mhlo::BatchNormInferenceOp)

Operasi BatchNormInference

Menormalkan tensor operand di semua dimensi kecuali dimensi feature_index dan menghasilkan tensor result .

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

Contoh:

%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>

Ciri: AlwaysSpeculatableImplTrait , InferTensorType

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
epsilon	::mlir::FloatAttr	Atribut float 32-bit
feature_index	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit yang nilainya bukan negatif

Operan:

Operan	Keterangan
operand	tensor peringkat nilai float 4/6/8/16/32/64-bit
scale	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit
offset	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit
mean	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit
variance	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit

Hasil:

Hasil	Keterangan
result	tensor peringkat nilai float 4/6/8/16/32/64-bit

mhlo.batch_norm_training (mhlo::BatchNormTrainingOp)

Operasi BatchNormTraining

Menghitung rata-rata dan varians di seluruh dimensi batch dan spasial dan menormalkan tensor operand , untuk setiap fitur dalam dimensi feature_index dan menghasilkan output , tensor batch_mean dan batch_var .

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

Contoh:

%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>)

Ciri: AlwaysSpeculatableImplTrait , InferTensorType

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
epsilon	::mlir::FloatAttr	Atribut float 32-bit
feature_index	::mlir::IntegerAttr	Atribut integer tanpa tanda 64-bit yang nilainya bukan negatif

Operan:

Operan	Keterangan
operand	tensor peringkat nilai float 4/6/8/16/32/64-bit
scale	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit
offset	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit

Hasil:

Hasil	Keterangan
output	tensor peringkat nilai float 4/6/8/16/32/64-bit
batch_mean	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit
batch_var	Tensor 1D dengan nilai float 4/6/8/16/32/64-bit

mhlo.bitcast (mhlo::BitcastOp)

Operasi Bitcast

Sintaksis:

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

Operasi ini bersifat pribadi bagi kompiler XLA, jadi belum memiliki spesifikasi.

Secara informal, operasi ini mengubah bentuk masukan dengan cara tidak mengubah susunan fisik elemen.

Operasi ini memerlukan informasi tata letak untuk memahami "penataan fisik elemen", dan dukungan tata letak di MHLO saat ini masih dalam tahap pengerjaan.

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
operand	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

Hasil:

Hasil	Keterangan
"tanpa nama"	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

mhlo.bitcast_convert (mhlo::BitcastConvertOp)

Operasi BitcastConvert

Sintaksis:

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

Melakukan operasi bitcast pada tensor operand dan menghasilkan tensor result di mana bit dari seluruh tensor operand ditafsirkan ulang menggunakan tipe tensor result .

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

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
operand	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

Hasil:

Hasil	Keterangan
"tanpa nama"	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

mhlo.broadcast (mhlo::BroadcastOp)

Operasi siaran

Operasi ini sedang dalam perjalanan keluar dari StableHLO, jadi tidak termasuk dalam spesifikasi: https://github.com/openxla/stablehlo/issues/3

Secara informal, operasi ini melakukan hal yang sama seperti Siaran XLA: https://www.tensorflow.org/xla/operation_semantics#broadcast

Contoh:

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

Ciri: AlwaysSpeculatableImplTrait , InferTensorType , SameOperandsAndResultElementType

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
broadcast_sizes	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit

Operan:

Operan	Keterangan
operand	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

Hasil:

Hasil	Keterangan
"tanpa nama"	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

mhlo.broadcast_in_dim (mhlo::BroadcastInDimOp)

Operasi BroadcastInDim

Memperluas dimensi dan/atau peringkat tensor input dengan menduplikasi data dalam tensor operand dan menghasilkan tensor result .

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

Contoh:

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

Ciri: AlwaysSpeculatableImplTrait , HLO_CompatibleOperandsAndResultElementType

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
broadcast_dimensions	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit

Operan:

Operan	Keterangan
operand	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

Hasil:

Hasil	Keterangan
"tanpa nama"	tensor berdimensi statis atau berbatas tunggal dengan tipe float atau bool 4/6/8/16/32/64-bit atau tipe integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

mhlo.case (mhlo::CaseOp)

Operasi kasus

Menghasilkan keluaran dari pelaksanaan tepat satu function dari branches tergantung pada nilai index .

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

Contoh:

%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>)

Ciri-ciri: RecursiveMemoryEffects , SingleBlockImplicitTerminator<ReturnOp> , SingleBlock

Antarmuka: InferTypeOpInterface

Operan:

Operan	Keterangan
index	tensor nilai integer tanpa tanda 32-bit

Hasil:

Hasil	Keterangan
"tanpa nama"	variadic dari tensor peringkat bertipe float 4/6/8/16/32/64-bit atau bool atau integer 2/4/8/16/32/64-bit atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau tensor peringkat bertipe integer terkuantisasi per-sumbu atau token

mhlo.cbrt (mhlo::CbrtOp)

Operasi Cbrt

Sintaksis:

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

Melakukan operasi akar kubik per elemen pada tensor operand dan menghasilkan tensor result .

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

Contoh:

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

Ciri: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	Keakuratan yang diminta untuk operasi unary.

Operan:

Operan	Keterangan
operand	tensor berperingkat tipe float atau kompleks 4/6/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor

Hasil:

Hasil	Keterangan
result	tensor berperingkat tipe float atau kompleks 4/6/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor

mhlo.ceil (mhlo::CeilOp)

Operasi langit-langit

Sintaksis:

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

Melakukan pemotongan elemen demi elemen pada tensor operand dan menghasilkan tensor result .

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

Contoh:

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

Ciri: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
operand	tensor peringkat dengan nilai float 4/6/8/16/32/64-bit atau nilai terkuantisasi integer per-tensor

Hasil:

Hasil	Keterangan
result	tensor peringkat dengan nilai float 4/6/8/16/32/64-bit atau nilai terkuantisasi integer per-tensor

mhlo.cholesky (mhlo::CholeskyOp)

Operasi koleski

Menghitung dekomposisi Cholesky pada sekumpulan matriks.

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

Contoh:

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

Ciri: AlwaysSpeculatableImplTrait , InferTensorType , SameOperandsAndResultElementType

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
lower	::mlir::BoolAttr	atribut bool

Operan:

Operan	Keterangan
a	tensor peringkat tipe float atau kompleks 4/6/8/16/32/64-bit dengan nilai elemen float 32/64-bit

Hasil:

Hasil	Keterangan
"tanpa nama"	tensor peringkat tipe float atau kompleks 4/6/8/16/32/64-bit dengan nilai elemen float 32/64-bit

mhlo.clamp (mhlo::ClampOp)

Operasi penjepit

Sintaksis:

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

Menjepit setiap elemen tensor operand antara nilai minimum dan maksimum dan menghasilkan tensor result .

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

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait , HLO_BroadcastingElementwise , InferTensorType , SameOperandsAndResultElementType

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
min	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu
operand	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu
max	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

Hasil:

Hasil	Keterangan
result	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

mhlo.collective_broadcast (mhlo::CollectiveBroadcastOp)

Operasi Siaran Kolektif

Dalam setiap grup proses di kisi proses, kirimkan nilai tensor operand dari proses sumber ke proses target dan hasilkan tensor result .

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

Contoh:

%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>

Ciri-ciri: CompatibleOperandsAndResultType

Antarmuka: InferShapedTypeOpInterface , InferTypeOpInterface

Atribut:

Atribut	Tipe MLIR	Keterangan
replica_groups	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
channel_handle	::mlir::mhlo::ChannelHandleAttr	dua bilangan bulat 64-bit 'handle' dan 'type'

Operan:

Operan	Keterangan
operand	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

Hasil:

Hasil	Keterangan
"tanpa nama"	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

mhlo.collective_permute (mhlo::CollectivePermuteOp)

Operasi CollectivePermute

Dalam setiap grup proses di kisi proses, mengirimkan nilai tensor operand dari proses sumber ke proses target dan menghasilkan tensor result .

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

Contoh:

%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>

Ciri: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
source_target_pairs	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
channel_handle	::mlir::mhlo::ChannelHandleAttr	dua bilangan bulat 64-bit 'handle' dan 'type'

Operan:

Operan	Keterangan
operand	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

Hasil:

Hasil	Keterangan
"tanpa nama"	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

mhlo.compare (mhlo::BandingkanOp)

Bandingkan operasi

Sintaksis:

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

Melakukan perbandingan elemen demi elemen dari tensor lhs dan rhs menurut comparison_direction dan compare_type , dan menghasilkan tensor result .

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

Contoh:

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

Ciri: AlwaysSpeculatableImplTrait , Elementwise , InferTensorType , SameOperandsAndResultShape , SameOperandsElementType

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
comparison_direction	::mlir::mhlo::ComparisonDirectionAttr	Operasi perbandingan mana yang harus dilakukan.
compare_type	::mlir::mhlo::ComparisonTypeAttr	Jenis perbandingan mana yang akan digunakan.

Operan:

Operan	Keterangan
lhs	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu
rhs	tensor berperingkat bertipe float atau bool 4/6/8/16/32/64-bit atau integer atau kompleks 2/4/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi integer per tensor atau nilai terkuantisasi integer per sumbu

Hasil:

Hasil	Keterangan
"tanpa nama"	tensor peringkat nilai bool

mhlo.complex (mhlo::ComplexOp)

Operasi yang kompleks

Sintaksis:

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

Melakukan konversi elemen demi elemen ke nilai kompleks dari pasangan nilai riil dan imajiner, lhs dan rhs , dan menghasilkan tensor result .

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

Contoh:

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

Ciri: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape , SameOperandsElementType

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor peringkat nilai float 32/64-bit
rhs	tensor peringkat nilai float 32/64-bit

Hasil:

Hasil	Keterangan
result	tensor peringkat tipe kompleks dengan nilai elemen float 32/64-bit

mhlo.composite (mhlo::CompositeOp)

Operasi komposit

Sintaksis:

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

Mengenkapsulasi operasi yang terdiri (terdiri) dari operasi StableHLO lainnya, mengambil inputs dan composite_attributes serta menghasilkan results . Semantik operasi diimplementasikan oleh atribut decomposition . Operasi composite dapat diganti dengan dekomposisinya tanpa mengubah semantik program. Jika memasukkan dekomposisi tidak memberikan semantik operasi yang sama, lebih baik menggunakan custom_call .

Bidang version (defaultnya adalah 0 ) digunakan untuk menunjukkan kapan semantik gabungan berubah.

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

Contoh:

%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>

Antarmuka: SymbolUserOpInterface

Atribut:

Atribut	Tipe MLIR	Keterangan
name	::mlir::StringAttr	atribut string
composite_attributes	::mlir::DictionaryAttr	kamus nilai atribut bernama
decomposition	::mlir::FlatSymbolRefAttr	atribut referensi simbol datar
version	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit

Operan:

Operan

Keterangan

inputs

variadik tensor peringkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau nilai terkuantisasi integer per-sumbu atau token atau tupel bersarang dengan kombinasi tensor peringkat 4/6/8/16/32/64-bit float atau bool atau bilangan bulat 2/4/8/16/32/64-bit atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau memref 4/6/8/16/32/64-bit float atau bool atau bilangan bulat 2/4/8/16/32/64-bit atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau tensor peringkat nilai terkuantisasi bilangan bulat per sumbu atau nilai token

Hasil:

Hasil

Keterangan

«tidak disebutkan namanya»

variadik tensor peringkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau nilai terkuantisasi integer per-sumbu atau token atau tupel bersarang dengan kombinasi tensor peringkat 4/6/8/16/32/64-bit float atau bool atau bilangan bulat 2/4/8/16/32/64-bit atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau memref 4/6/8/16/32/64-bit float atau bool atau bilangan bulat 2/4/8/16/32/64-bit atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau tensor peringkat nilai terkuantisasi bilangan bulat per sumbu atau nilai token

mhlo.concatenate (mhlo::ConcatenateOp)

Operasi penggabungan

Menggabungkan sejumlah variadik tensor dalam inputs sepanjang dimensi dimension dalam urutan yang sama dengan argumen yang diberikan dan menghasilkan tensor result .

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

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait , SameOperandsAndResultElementType

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
dimension	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 64-bit yang nilainya non-negatif

Operan:

Operan	Keterangan
val	variadik tensor peringkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

Hasil:

Hasil	Keterangan
«tidak disebutkan namanya»	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.constant (mhlo::ConstantOp)

Operasi konstan

Menghasilkan tensor output dari value konstan.

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

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait , ConstantLike

Antarmuka: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
value	::mlir::ElementsAttr	atribut vektor/tensor konstan

Hasil:

Hasil	Keterangan
output	tensor berbentuk statis dengan 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau nilai terkuantisasi bilangan bulat per sumbu

mhlo.convert (mhlo::ConvertOp)

Konversi operasi

Sintaksis:

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

Melakukan konversi berdasarkan elemen dari satu jenis elemen ke jenis elemen lainnya pada tensor operand dan menghasilkan tensor result .

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

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

Antarmuka: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
operand	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

Hasil:

Hasil	Keterangan
result	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.convolution (mhlo::ConvolutionOp)

Operasi konvolusi

Sintaksis:

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)

Menghitung perkalian titik antara jendela lhs dan irisan rhs dan menghasilkan result .

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

Contoh:

%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>

Ciri-ciri: Sifat AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
window_strides	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
padding	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
lhs_dilation	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
rhs_dilation	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
window_reversal	::mlir::DenseElementsAttr	atribut vektor/tensor boolean konstan
dimension_numbers	::mlir::mhlo::ConvDimensionNumbersAttr	Struktur informasi dimensi untuk konv op
feature_group_count	::mlir::IntegerAttr	Atribut integer tak bertanda 64-bit yang nilainya positif
batch_group_count	::mlir::IntegerAttr	Atribut integer tak bertanda 64-bit yang nilainya positif
precision_config	::mlir::ArrayAttr	Atribut Konfigurasi Presisi

Operan:

Operan	Keterangan
lhs	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
rhs	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

Hasil:

Hasil	Keterangan
«tidak disebutkan namanya»	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.copy (mhlo::CopyOp)

Operasi penyalinan

Sintaksis:

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

Operasi ini bersifat pribadi untuk kompiler XLA, sehingga belum memiliki spesifikasi.

Secara informal, operasi ini merupakan salinan operand . Bergantung pada metadata yang dilampirkan pada operasi, perilakunya bisa sangat berbeda dari larangan operasi.

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
cross_program_prefetch_index	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 32-bit

Operan:

Operan

Keterangan

operand

tensor peringkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu atau token atau tupel bersarang dengan kombinasi tensor peringkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau memref 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau tensor peringkat nilai terkuantisasi bilangan bulat per sumbu atau nilai token

Hasil:

Hasil

Keterangan

result

tensor peringkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu atau token atau tupel bersarang dengan kombinasi tensor peringkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau memref 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau tensor peringkat nilai terkuantisasi bilangan bulat per sumbu atau nilai token

mhlo.cosh (mhlo::CoshOp)

Operasi cosh

Sintaksis:

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

Melakukan operasi cosh berdasarkan elemen pada tensor operand dan menghasilkan tensor result .

Contoh:

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

Sifat: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Antarmuka: InferShapedTypeOpInterface , InferTypeOpInterface

Operan:

Operan	Keterangan
operand	tensor float 4/6/8/16/32/64-bit atau tipe kompleks dengan nilai elemen float 32/64-bit

Hasil:

Hasil	Keterangan
result	tensor float 4/6/8/16/32/64-bit atau tipe kompleks dengan nilai elemen float 32/64-bit

mhlo.cosine (mhlo::CosineOp)

Operasi kosinus

Sintaksis:

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

Melakukan operasi kosinus berdasarkan elemen pada tensor operand dan menghasilkan tensor result .

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

Contoh:

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

Sifat: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	Akurasi yang diminta untuk operasi unary.

Operan:

Operan	Keterangan
operand	tensor peringkat float 4/6/8/16/32/64-bit atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor

Hasil:

Hasil	Keterangan
result	tensor peringkat float 4/6/8/16/32/64-bit atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor

mhlo.count_leading_zeros (mhlo::ClzOp)

Operasi Klz

Sintaksis:

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

Melakukan penghitungan berdasarkan elemen jumlah bit nol di depan dalam tensor operand dan menghasilkan tensor result .

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

Contoh:

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

Sifat: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
operand	tensor peringkat nilai integer 2/4/8/16/32/64-bit

Hasil:

Hasil	Keterangan
result	tensor peringkat nilai integer 2/4/8/16/32/64-bit

mhlo.create_token (mhlo::CreateTokenOp)

Operasi BuatToken

Sintaksis:

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

Operasi ini sedang dalam proses keluar dari StableHLO, sehingga tidak disertakan dalam spesifikasi: https://github.com/openxla/stablehlo/issues/3

Secara informal, operasi ini melakukan hal yang sama seperti AfterAllOp dengan 0 input: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#after_all

Contoh:

%output = mhlo.create_token : !mhlo.token

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Hasil:

Hasil	Keterangan
output	token

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

Operasi CrossReplicaSum

Operasi ini sedang dalam proses keluar dari StableHLO, sehingga tidak disertakan dalam spesifikasi: https://github.com/openxla/stablehlo/issues/3

Secara informal, operasi ini melakukan hal yang sama seperti AllReduceOp dengan channel_id = 0 , use_global_device_ids = false dan penambahan penerapan computation : https://github.com/openxla/stablehlo/blob/main/docs/spec.md#all_reduce

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
replica_groups	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit

Operan:

Operan	Keterangan
operand	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

Hasil:

Hasil	Keterangan
«tidak disebutkan namanya»	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.custom_call (mhlo::CustomCallOp)

Operasi Panggilan Khusus

Sintaksis:

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

Meringkas operasi yang ditentukan implementasi call_target_name yang mengambil inputs dan called_computations serta menghasilkan results .

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

Contoh:

%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.

Antarmuka: MemoryEffectOpInterface

Atribut:

Atribut	Tipe MLIR	Keterangan
call_target_name	::mlir::StringAttr	atribut string
has_side_effect	::mlir::BoolAttr	atribut bool
backend_config	::mlir::Atribut	atribut string atau kamus nilai atribut bernama
api_version	::mlir::mhlo::CustomCallApiVersionAttr	Versi API panggilan khusus
called_computations	::mlir::ArrayAttr	atribut array ref simbol datar
custom_call_schedule	::mlir::mhlo::CustomCallScheduleAttr	Menentukan jadwal yang diinginkan untuk panggilan khusus.
operand_layouts	::mlir::ArrayAttr	Array atribut tata letak (tensor 1D tipe indeks).
result_layouts	::mlir::ArrayAttr	Array atribut tata letak (tensor 1D tipe indeks).
output_operand_aliases	::mlir::ArrayAttr	Aliasing atribut untuk output dan operan CustomCall

Operan:

Operan

Keterangan

inputs

variadik tensor 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu atau memref 4/6/8/16/32/64-bit float atau bool atau Tipe bilangan bulat 2/4/8/16/32/64-bit atau kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau token atau tupel bersarang dengan kombinasi tensor 4/6/8/16/32/64-bit float atau bool atau bilangan bulat 2/4/8/16/32/64-bit atau tipe kompleks dengan elemen float 32/64-bit atau bilangan bulat per-tensor nilai terkuantisasi bilangan bulat atau bilangan bulat per sumbu atau memref 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai token

Hasil:

Hasil

Keterangan

«tidak disebutkan namanya»

variadik tensor 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu atau memref 4/6/8/16/32/64-bit float atau bool atau Tipe bilangan bulat 2/4/8/16/32/64-bit atau kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau token atau tupel bersarang dengan kombinasi tensor 4/6/8/16/32/64-bit float atau bool atau bilangan bulat 2/4/8/16/32/64-bit atau tipe kompleks dengan elemen float 32/64-bit atau bilangan bulat per-tensor nilai terkuantisasi bilangan bulat atau bilangan bulat per sumbu atau memref 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai token

mhlo.divide (mhlo::DivOp)

Operasi div

Sintaksis:

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

Melakukan pembagian berdasarkan elemen dari dividen lhs dan pembagi rhs tensor dan menghasilkan tensor result .

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

Contoh:

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

Sifat: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
lhs	tensor peringkat bilangan bulat 2/4/8/16/32/64-bit atau tipe float atau kompleks 4/6/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau nilai terkuantisasi bilangan bulat per sumbu
rhs	tensor peringkat bilangan bulat 2/4/8/16/32/64-bit atau tipe float atau kompleks 4/6/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau nilai terkuantisasi bilangan bulat per sumbu

Hasil:

Hasil	Keterangan
result	tensor peringkat bilangan bulat 2/4/8/16/32/64-bit atau tipe float atau kompleks 4/6/8/16/32/64-bit dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau nilai terkuantisasi bilangan bulat per sumbu

mhlo.domain (mhlo::DomainOp)

Operasi domain

Operasi ini bersifat pribadi untuk kompiler XLA, sehingga belum memiliki spesifikasi.

Secara informal, operasi ini digunakan untuk mengelompokkan instruksi dengan properti DomainMetadata yang sama. ShardingMetadata adalah kasus penggunaan utama saat ini untuk mengelompokkan instruksi pada perangkat yang sama. Instruksi domain memberikan dua manfaat utama:

• Cegah instruksi pengoptimalan di seluruh domain secara tidak sengaja.
• Secara otomatis menetapkan metadata dari instruksi yang dibuat di domain. Tanpa instruksi domain, setiap jalur pengoptimalan HLO harus memeriksa dan menyebarkan metadata, yang akan mudah terlewatkan dan juga menambah kerumitan pada kompiler. Karena instruksi domain menghubungkan dua domain berbeda, setiap instruksi domain dikaitkan dengan dua DomainMetadata -- satu di sisi operan dan satu lagi di sisi pengguna domain.

Ciri-ciri: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
kind	::mlir::mhlo::DomainKindAttr	Jenis metadata domain yang dilampirkan ke domain HLO.
entry_metadata	::mlir::StringAttr	atribut string
exit_metadata	::mlir::StringAttr	atribut string

Operan:

Operan	Keterangan
operand	tensor peringkat dari 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau tensor peringkat dari nilai atau token terkuantisasi bilangan bulat per-sumbu

Hasil:

Hasil	Keterangan
result	tensor peringkat dari 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi bilangan bulat per-tensor atau tensor peringkat dari nilai atau token terkuantisasi bilangan bulat per-sumbu

mhlo.dot (mhlo::DotOp)

Operasi titik

Operasi ini sedang dalam proses keluar dari StableHLO, sehingga tidak disertakan dalam spesifikasi: https://github.com/openxla/stablehlo/issues/3

Secara informal, operasi ini melakukan hal yang sama seperti Dot XLA: https://www.tensorflow.org/xla/operation_semantics#dot

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
precision_config	::mlir::ArrayAttr	Atribut Konfigurasi Presisi

Operan:

Operan	Keterangan
lhs	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
rhs	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

Hasil:

Hasil	Keterangan
«tidak disebutkan namanya»	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.dot_general (mhlo::DotGeneralOp)

Operasi DotGeneral

Menghitung perkalian titik antara irisan lhs dan irisan rhs dan menghasilkan tensor result .

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

Contoh:

%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>

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
dot_dimension_numbers	::mlir::mhlo::DotDimensionNumbersAttr	Atribut yang memodelkan informasi dimensi untuk titik.
precision_config	::mlir::ArrayAttr	Atribut Konfigurasi Presisi
algorithm	::mlir::mhlo::DotAlgorithmAttr	Atribut yang memodelkan batasan algoritme yang akan digunakan untuk menghitung titik.

Operan:

Operan	Keterangan
lhs	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
rhs	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

Hasil:

Hasil	Keterangan
«tidak disebutkan namanya»	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.dynamic_broadcast_in_dim (mhlo::DynamicBroadcastInDimOp)

Operasi DynamicBroadcastInDim

Operasi ini secara fungsional identik dengan operasi Broadcast_in_dim , tetapi bentuk hasil ditentukan secara dinamis melalui output_dimensions .

Ia juga menerima atribut opsional untuk mengekspresikan pengetahuan statis tentang perilaku perluasan dimensi. Jika tidak ditentukan, semua dimensi diasumsikan kemungkinan mengembang. Himpunan dimensi yang diketahui mengembang dan himpunan dimensi yang diketahui tidak mengembang harus saling lepas dan harus merupakan bagian dari dimensi operan.

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

Contoh:

%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>

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
broadcast_dimensions	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
known_expanding_dimensions	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
known_nonexpanding_dimensions	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit

Operan:

Operan	Keterangan
operand	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
output_dimensions	Tensor indeks 1D atau nilai integer 2/4/8/16/32/64-bit

Hasil:

Hasil	Keterangan
«tidak disebutkan namanya»	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.dynamic_conv (mhlo::DynamicConvOp)

Operasi DynamicConv

Operasi ini masih dalam proses, sehingga belum disertakan dalam spesifikasi: https://github.com/openxla/stablehlo/issues/8

Secara informal, operasi ini melakukan hal yang sama seperti ConvolutionOp kecuali padding ditentukan secara dinamis melalui d_padding : https://github.com/openxla/stablehlo/blob/main/docs/spec.md#convolution

Contoh:

%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>

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
window_strides	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
padding	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
lhs_dilation	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
rhs_dilation	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit
window_reversal	::mlir::DenseElementsAttr	atribut vektor/tensor boolean konstan
dimension_numbers	::mlir::mhlo::ConvDimensionNumbersAttr	Struktur informasi dimensi untuk konv op
feature_group_count	::mlir::IntegerAttr	Atribut integer tak bertanda 64-bit yang nilainya positif
batch_group_count	::mlir::IntegerAttr	Atribut integer tak bertanda 64-bit yang nilainya positif
precision_config	::mlir::ArrayAttr	Atribut Konfigurasi Presisi

Operan:

Operan	Keterangan
lhs	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
rhs	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
d_padding	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

Hasil:

Hasil	Keterangan
«tidak disebutkan namanya»	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.dynamic_gather (mhlo::DynamicGatherOp)

Operasi DynamicGather

Operasi ini masih dalam proses, sehingga belum disertakan dalam spesifikasi: https://github.com/openxla/stablehlo/issues/8

Secara informal, operasi ini melakukan hal yang sama seperti GatherOp kecuali bahwa slice_sizes ditentukan secara dinamis: https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather

Contoh:

%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>

Sifat: AlwaysSpeculatableImplTrait , InferTensorType

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
dimension_numbers	::mlir::mhlo::GatherDimensionNumbersAttr	Atribut yang memodelkan informasi dimensi untuk dikumpulkan
indices_are_sorted	::mlir::BoolAttr	atribut bool

Operan:

Operan	Keterangan
operand	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
start_indices	tensor peringkat nilai integer 2/4/8/16/32/64-bit
slice_sizes	tensor bilangan bulat 1 dimensi berbentuk statis dengan nilai bilangan bulat 2/4/8/16/32/64-bit

Hasil:

Hasil	Keterangan
«tidak disebutkan namanya»	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.dynamic_iota (mhlo::DynamicIotaOp)

Operasi DynamicIota

Operasi ini secara fungsional identik dengan operasi iota , tetapi bentuk hasil ditentukan secara dinamis melalui output_shape .

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

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
iota_dimension	::mlir::IntegerAttr	Atribut bilangan bulat tanpa tanda 64-bit yang nilainya non-negatif

Operan:

Operan	Keterangan
output_shape	Tensor indeks 1D atau nilai integer 2/4/8/16/32/64-bit

Hasil:

Hasil	Keterangan
result	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.dynamic_pad (mhlo::DynamicPadOp)

Operasi DynamicPad

Sintaksis:

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

Mengisi operand secara dinamis, dengan jumlah padding yang ditambahkan pada low-end/high-end/interior dilewatkan melalui tensor input.

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
operand	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
padding_value	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
edge_padding_low	Tensor indeks 1D atau nilai integer 2/4/8/16/32/64-bit
edge_padding_high	Tensor indeks 1D atau nilai integer 2/4/8/16/32/64-bit
interior_padding	Tensor indeks 1D atau nilai integer 2/4/8/16/32/64-bit

Hasil:

Hasil	Keterangan
result	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.dynamic_reshape (mhlo::DynamicReshapeOp)

Operasi DynamicReshape

Sintaksis:

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

Operasi ini secara fungsional identik dengan operasi pembentukan ulang , tetapi bentuk hasil ditentukan secara dinamis melalui output_shape .

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

Contoh:

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

Ciri-ciri: Sifat AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , InferShapedTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
operand	tensor 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
output_shape	Tensor indeks 1D atau nilai integer 2/4/8/16/32/64-bit

Hasil:

Hasil	Keterangan
result	tensor 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.dynamic_slice (mhlo::DynamicSliceOp)

Operasi DynamicSlice

Mengekstrak sepotong operand menggunakan indeks awal yang dihitung secara dinamis dan menghasilkan tensor result .

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

Contoh:

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

Sifat: AlwaysSpeculatableImplTrait , InferTensorType

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

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
slice_sizes	::mlir::DenseIntElementsAttr	Atribut elemen integer tanpa tanda 64-bit

Operan:

Operan	Keterangan
operand	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
start_indices	variadik tensor 0D dengan nilai integer 2/4/8/16/32/64-bit

Hasil:

Hasil	Keterangan
result	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.dynamic_update_slice (mhlo::DynamicUpdateSliceOp)

Operasi DynamicUpdateSlice

Sintaksis:

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

Menghasilkan tensor result yang sama dengan tensor operand kecuali potongan yang dimulai dari start_indices diperbarui dengan nilai dalam update .

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

Contoh:

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

Sifat: AlwaysSpeculatableImplTrait , InferTensorType

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
operand	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
update	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
start_indices	variadik tensor 0D dengan nilai integer 2/4/8/16/32/64-bit

Hasil:

Hasil	Keterangan
result	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.einsum (mhlo::EinsumOp)

Operasi Einsum

Operasi ini sedang dalam proses keluar dari StableHLO, sehingga tidak disertakan dalam spesifikasi: https://github.com/openxla/stablehlo/issues/3

Secara informal, operasi ini melakukan hal yang sama seperti einsum TF: https://www.tensorflow.org/api_docs/python/tf/einsum

Contoh:

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

Ciri-ciri: AlwaysSpeculatableImplTrait

Antarmuka: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Efek: MemoryEffects::Effect{}

Atribut:

Atribut	Tipe MLIR	Keterangan
einsum_config	::mlir::StringAttr	atribut string

Operan:

Operan	Keterangan
lhs	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu
rhs	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

Hasil:

Hasil	Keterangan
«tidak disebutkan namanya»	tensor berperingkat 4/6/8/16/32/64-bit float atau bool atau 2/4/8/16/32/64-bit integer atau tipe kompleks dengan elemen float 32/64-bit atau nilai terkuantisasi integer per-tensor atau nilai terkuantisasi integer per-sumbu

mhlo.erf (mhlo::ErfOp)

Operasi Erf

Sintaksis:

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

Melakukan operasi erf berdasarkan elemen pada tensor operand dan menghasilkan tensor result .

Contoh:

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

Sifat: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Efek: MemoryEffects::Effect{}

Operan:

Operan	Keterangan
operand	tensor peringkat nilai float 4/6/8/16/32/64-bit

Hasil:

Hasil	Keterangan
result	tensor peringkat nilai float 4/6/8/16/32/64-bit

mhlo.exponential (mhlo::ExpOp)

Operasi exp

Sintaksis:

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

Melakukan operasi eksponensial berdasarkan elemen pada tensor operand dan menghasilkan tensor result .

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

Contoh:

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

Sifat: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Contoh:

%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{}

Atribut:

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

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

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

Hasil:

Hasil	Keterangan
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.

Atribut:

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

Operands:

Operan	Keterangan
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

Hasil:

Hasil

Keterangan

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

Contoh:

%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{}

Atribut:

Atribut	MLIR Type	Keterangan
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:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , InferTensorType

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

Effects: MemoryEffects::Effect{}

Atribut:

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

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«unnamed»	tensor of 32-bit signless integer values

mhlo.get_tuple_element (mhlo::GetTupleElementOp)

GetTupleElement operation

Sintaksis:

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

Contoh:

%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{}

Atribut:

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

Operands:

Operan

Keterangan

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

Hasil:

Hasil

Keterangan

«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:

Operan	Keterangan
pred	ranked tensor of bool values

Hasil:

Hasil	Keterangan
«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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

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

Hasil:

Hasil	Keterangan
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

Contoh:

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

Atribut:

Atribut	MLIR Type	Keterangan
infeed_config	::mlir::StringAttr	string attribute
layout	::mlir::ArrayAttr	array attribute

Operands:

Operan	Keterangan
token	token

Hasil:

Hasil

Keterangan

«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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Atribut:

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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
x	ranked tensor of 4/6/8/16/32/64-bit float values

Hasil:

Hasil	Keterangan
y	ranked tensor of bool values

mhlo.log (mhlo::LogOp)

Log operation

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Contoh:

%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

Atribut:

Atribut	MLIR Type	Keterangan
dimensions	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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:

Operan	Keterangan
shapes	variadic of 1D tensor of index values

Hasil:

Hasil	Keterangan
results	variadic of 1D tensor of index values

mhlo.multiply (mhlo::MulOp)

Mul operation

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

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

Hasil:

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

mhlo.optimization_barrier (mhlo::OptimizationBarrierOp)

OptimizationBarrier operation

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , HLO_PairwiseSameOperandAndResultType

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Contoh:

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

Interfaces: InferTypeOpInterface

Atribut:

Atribut	MLIR Type	Keterangan
outfeed_config	::mlir::StringAttr	string attribute

Operands:

Operan	Keterangan
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	token

Hasil:

Hasil	Keterangan
«unnamed»	token

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

Contoh:

%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{}

Atribut:

Atribut	MLIR Type	Keterangan
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:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Sintaksis:

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

Contoh:

%result = mhlo.partition_id : tensor<ui32>

Interfaces: InferTypeOpInterface

Hasil:

Hasil	Keterangan
«unnamed»	ranked tensor of 32-bit unsigned integer values

mhlo.popcnt (mhlo::PopulationCountOp)

PopulationCount operation

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
operand	ranked tensor of 2/4/8/16/32/64-bit integer values

Hasil:

Hasil	Keterangan
result	ranked tensor of 2/4/8/16/32/64-bit integer values

mhlo.power (mhlo::PowOp)

Pow operation

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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{}

Atribut:

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

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

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

Hasil:

Hasil	Keterangan
result	ranked tensor of 4/6/8/16/32/64-bit float values

mhlo.real_dynamic_slice (mhlo::RealDynamicSliceOp)

RealDynamicSlice operation

Sintaksis:

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

Contoh:

%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:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Contoh:

%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)

Atribut:

Atribut	MLIR Type	Keterangan
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:

Operan	Keterangan
token	token

Hasil:

Hasil

Keterangan

«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

Contoh:

%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

Atribut:

Atribut	MLIR Type	Keterangan
dimensions	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
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:

Operan	Keterangan
operand	ranked tensor of 4/6/8/16/32/64-bit float values

Hasil:

Hasil	Keterangan
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

Contoh:

%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>

Atribut:

Atribut	MLIR Type	Keterangan
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:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Contoh:

%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

Atribut:

Atribut	MLIR Type	Keterangan
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:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

%result = mhlo.replica_id : tensor<ui32>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Hasil:

Hasil	Keterangan
«unnamed»	ranked tensor of 32-bit unsigned integer values

mhlo.reshape (mhlo::ReshapeOp)

Reshape operation

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , HLO_CompatibleOperandsAndResultElementType

Interfaces: ConditionallySpeculatable , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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{}

Atribut:

Atribut	MLIR Type	Keterangan
dimensions	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Contoh:

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

Traits: InferTensorType

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Atribut:

Atribut	MLIR Type	Keterangan
rng_distribution	::mlir::mhlo::RngDistributionAttr	XLA PRNG distribution to be used.

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Contoh:

%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{}

Atribut:

Atribut	MLIR Type	Keterangan
rng_algorithm	::mlir::mhlo::RngAlgorithmAttr	XLA PRNG algorithm to be used.

Operands:

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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
operand	ranked tensor of 4/6/8/16/32/64-bit float values

Hasil:

Hasil	Keterangan
result	ranked tensor of 4/6/8/16/32/64-bit float values

mhlo.round_nearest_even (mhlo::RoundNearestEvenOp)

RoundNearestEven operation

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
operand	ranked tensor of 4/6/8/16/32/64-bit float values

Hasil:

Hasil	Keterangan
result	ranked tensor of 4/6/8/16/32/64-bit float values

mhlo.rsqrt (mhlo::RsqrtOp)

Rsqrt operation

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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.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

Contoh:

%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

Atribut:

Atribut	MLIR Type	Keterangan
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:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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)

Select operation

Sintaksis:

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

Contoh:

%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:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Contoh:

%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

Atribut:

Atribut	MLIR Type	Keterangan
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:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Contoh:

%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

Atribut:

Atribut	MLIR Type	Keterangan
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:

Operan	Keterangan
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	token

Hasil:

Hasil	Keterangan
«unnamed»	token

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

Contoh:

%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{}

Atribut:

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

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
result	ranked tensor of 2/4/8/16/32/64-bit integer values

mhlo.shift_right_arithmetic (mhlo::ShiftRightArithmeticOp)

ShiftRightArithmetic operation

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
result	ranked tensor of 2/4/8/16/32/64-bit integer values

mhlo.shift_right_logical (mhlo::ShiftRightLogicalOp)

ShiftRightLogical operation

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
result	ranked tensor of 2/4/8/16/32/64-bit integer values

mhlo.sign (mhlo::SignOp)

Sign operation

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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.

Contoh:

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

Traits: CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Operands:

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

Hasil:

Hasil	Keterangan
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

Contoh:

%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{}

Atribut:

Atribut	MLIR Type	Keterangan
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:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Contoh:

%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

Atribut:

Atribut	MLIR Type	Keterangan
dimension	::mlir::IntegerAttr	64-bit signless integer attribute
is_stable	::mlir::BoolAttr	bool attribute

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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.

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Atribut:

Atribut	MLIR Type	Keterangan
result_accuracy	::mlir::mhlo::ResultAccuracyAttr	The requested accuracy for unary ops.

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Sintaksis:

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

Contoh:

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

Traits: InferTensorType , RecursiveMemoryEffects

Interfaces: InferShapedTypeOpInterface , InferTypeOpInterface

Atribut:

Atribut	MLIR Type	Keterangan
k	::mlir::IntegerAttr	64-bit signless integer attribute
largest	::mlir::BoolAttr	bool attribute

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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.

Contoh:

%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{}

Atribut:

Atribut	MLIR Type	Keterangan
dim	::mlir::IntegerAttr	64-bit signless integer attribute
batch_dims	::mlir::IntegerAttr	64-bit signless integer attribute

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Sintaksis:

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.

Contoh:

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

Atribut:

Atribut	MLIR Type	Keterangan
tag	::mlir::StringAttr	string attribute

Operands:

Operan	Keterangan
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

Contoh:

%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{}

Atribut:

Atribut	MLIR Type	Keterangan
permutation	::mlir::DenseIntElementsAttr	64-bit signless integer elements attribute

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Contoh:

%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{}

Atribut:

Atribut	MLIR Type	Keterangan
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:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
«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

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable , InferTypeOpInterface , NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Operan

Keterangan

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

Hasil:

Hasil

Keterangan

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

Sintaksis:

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

Contoh:

%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:

Operan	Keterangan
operand	ranked tensor of per-tensor integer quantized or per-axis integer quantized values

Hasil:

Hasil	Keterangan
result	ranked tensor of 4/6/8/16/32/64-bit float values

mhlo.uniform_quantize (mhlo::UniformQuantizeOp)

UniformQuantize operation

Sintaksis:

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

Contoh:

%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:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Contoh:

%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:

Operan

Keterangan

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

Hasil:

Hasil

Keterangan

«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

Sintaksis:

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

Atribut:

Atribut	MLIR Type	Keterangan
delta	::mlir::IntegerAttr	64-bit signless integer attribute

Hasil:

Hasil	Keterangan
«unnamed»	statically shaped tensor of 64-bit unsigned integer values

mhlo.xor (mhlo::XorOp)

Xor operation

Sintaksis:

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

Contoh:

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

Traits: AlwaysSpeculatableImplTrait , Commutative , CompatibleOperandsAndResultType , Elementwise , SameOperandsAndResultShape

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

Effects: MemoryEffects::Effect{}

Operands:

Operan	Keterangan
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

Hasil:

Hasil	Keterangan
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

Atribut

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 ...
}

Parameternya:

Parameter	C++ type	Keterangan
argTupleIndices	::llvm::ArrayRef<int64_t>	Dimensi
resultIndex	int64_t
resultTupleIndices	::llvm::ArrayRef<int64_t>	Dimensi
isMustAlias	bool

ChannelHandleAttr

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

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
menangani	int64_t
jenis	int64_t

ComparisonDirectionAttr

Which comparison operation to perform.

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
nilai	::mlir::mhlo::ComparisonDirection	an enum of type ComparisonDirection

ComparisonTypeAttr

Which comparison type to use.

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
nilai	::mlir::mhlo::ComparisonType	an enum of type ComparisonType

ConvDimensionNumbersAttr

Structure of dimension information for conv op

Parameternya:

Parameter	C++ type	Keterangan
inputBatchDimension	int64_t
inputFeatureDimension	int64_t
inputSpatialDimensions	::llvm::ArrayRef<int64_t>	Dimensi
kernelInputFeatureDimension	int64_t
kernelOutputFeatureDimension	int64_t
kernelSpatialDimensions	::llvm::ArrayRef<int64_t>	Dimensi
outputBatchDimension	int64_t
outputFeatureDimension	int64_t
outputSpatialDimensions	::llvm::ArrayRef<int64_t>	Dimensi

CrossProgramPrefetchAttr

Argument that is prefetched from another program

Sintaksis:

#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.

Misalnya,

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.

Parameternya:

Parameter	C++ type	Keterangan
parameter	int64_t
indeks	::llvm::ArrayRef<int64_t>	Dimensi
mengimbangi	std::optional<int64_t>

CustomCallScheduleAttr

Specifies the desired schedule for the custom-call.

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
nilai	::mlir::mhlo::CustomCallSchedule	an enum of type CustomCallSchedule

DequantizeModeAttr

_Dequantization mode. Only MIN COMBINED is supported.

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
nilai	::mlir::mhlo::DequantizeMode	an enum of type DequantizeMode

DomainKindAttr

Kind of domain metatdata attached to an HLO domain.

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
nilai	::mlir::mhlo::DomainKind	an enum of type DomainKind

DotAlgorithmAttr

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

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
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.

Parameternya:

Parameter	C++ type	Keterangan
lhsBatchingDimensions	::llvm::ArrayRef<int64_t>	Dimensi
rhsBatchingDimensions	::llvm::ArrayRef<int64_t>	Dimensi
lhsContractingDimensions	::llvm::ArrayRef<int64_t>	Dimensi
rhsContractingDimensions	::llvm::ArrayRef<int64_t>	Dimensi

FftTypeAttr

XLA fast fourier transform type.

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
nilai	::mlir::mhlo::FftType	an enum of type FftType

FusionKindAttr

Fusion kind

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
nilai	::mlir::mhlo::FusionKind	an enum of type FusionKind

GatherDimensionNumbersAttr

Attribute that models the dimension information for gather

Parameternya:

Parameter	C++ type	Keterangan
offsetDims	::llvm::ArrayRef<int64_t>	Dimensi
collapsedSliceDims	::llvm::ArrayRef<int64_t>	Dimensi
operandBatchingDims	::llvm::ArrayRef<int64_t>	Dimensi
startIndicesBatchingDims	::llvm::ArrayRef<int64_t>	Dimensi
startIndexMap	::llvm::ArrayRef<int64_t>	Dimensi
indexVectorDim	int64_t

OutputOperandAliasAttr

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

Sintaksis:

#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>.

Parameternya:

Parameter	C++ type	Keterangan
outputTupleIndices	::llvm::ArrayRef<int64_t>	Dimensi
operandIndex	int64_t
operandTupleIndices	::llvm::ArrayRef<int64_t>	Dimensi

PrecisionAttr

XLA precision for an operand. Has backend specific meaning.

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
nilai	::mlir::mhlo::Precision	an enum of type Precision

RaggedDotDimensionNumbersAttr

Attribute that models the dimension information for ragged dot.

Parameternya:

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

ResultAccuracyAttr

The requested accuracy for unary ops.

Parameternya:

Parameter	C++ type	Keterangan
atol	APFloat
rtol	APFloat
ulps	int64_t
mode	::mlir::mhlo::ResultAccuracyModeAttr	XLA result accuracy mode.

ResultAccuracyModeAttr

XLA result accuracy mode.

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
nilai	::mlir::mhlo::ResultAccuracyMode	an enum of type ResultAccuracyMode

RngAlgorithmAttr

XLA PRNG algorithm to be used.

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
nilai	::mlir::mhlo::RngAlgorithm	an enum of type RngAlgorithm

RngDistributionAttr

XLA PRNG distribution to be used.

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
nilai	::mlir::mhlo::RngDistribution	an enum of type RngDistribution

ScatterDimensionNumbersAttr

Attribute that models the dimension information for scatter

Parameternya:

Parameter	C++ type	Keterangan
updateWindowDims	::llvm::ArrayRef<int64_t>	Dimensi
insertedWindowDims	::llvm::ArrayRef<int64_t>	Dimensi
inputBatchingDims	::llvm::ArrayRef<int64_t>	Dimensi
scatterIndicesBatchingDims	::llvm::ArrayRef<int64_t>	Dimensi
scatterDimsToOperandDims	::llvm::ArrayRef<int64_t>	Dimensi
indexVectorDim	int64_t

TransposeAttr

Transpose options

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
nilai	::mlir::mhlo::Transpose	an enum of type Transpose

TypeExtensionsAttr

Attribute that extends tensor type with MHLO type properties.

Sintaksis:

#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 .

Parameternya:

Parameter	C++ type	Keterangan
batas	::llvm::ArrayRef<int64_t>

Jenis

AsyncBundleType

Opaque collection of other types

Sintaksis:

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

Parameternya:

Parameter	C++ type	Keterangan
jenis	::llvm::ArrayRef<Type>

Enum

ComparisonDirection

Which comparison operation to perform.

Kasus:

Simbol	Nilai	Rangkaian
Keseimbangan	0	Keseimbangan
Timur Laut	1	Timur Laut
GE	2	GE
GT	3	GT
LE	4	LE
LEBIH BANYAK	5	LEBIH BANYAK

ComparisonType

Which comparison type to use.

Kasus:

Simbol	Nilai	Rangkaian
NOTYPE	0	NOTYPE
MENGAMBANG	1	MENGAMBANG
TOTALORDER	2	TOTALORDER
DITANDATANGANI	3	DITANDATANGANI
UNSIGNED	4	UNSIGNED

CustomCallApiVersion

Custom call API version

Kasus:

Simbol	Nilai	Rangkaian
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.

Kasus:

Simbol	Nilai	Rangkaian
TIDAK ADA	0	TIDAK ADA
TERBARU	1	TERBARU
PALING AWAL	2	PALING AWAL

DequantizeMode

_Dequantization mode. Only MIN COMBINED is supported.

Kasus:

Simbol	Nilai	Rangkaian
MIN_COMBINED	0	MIN_COMBINED

DomainKind

Kind of domain metatdata attached to an HLO domain.

Kasus:

Simbol	Nilai	Rangkaian
sharding	0	sharding

FftType

XLA fast fourier transform type.

Kasus:

Simbol	Nilai	Rangkaian
FFT	0	FFT
IFFT	1	IFFT
RFFT	2	RFFT
IRFFT	3	IRFFT

FusionKind

Fusion kind

Kasus:

Simbol	Nilai	Rangkaian
kLoop	0	kLoop
kInput	1	kInput
kOutput	2	kOutput
kCustom	3	kCustom

Presisi

XLA precision for an operand. Has backend specific meaning.

Kasus:

Simbol	Nilai	Rangkaian
BAWAAN	0	BAWAAN
TINGGI	1	TINGGI
PALING TINGGI	2	PALING TINGGI

ResultAccuracyMode

XLA result accuracy mode.

Kasus:

Simbol	Nilai	Rangkaian
BAWAAN	0	BAWAAN
PALING TINGGI	1	PALING TINGGI
TOLERANSI	2	TOLERANSI

RngAlgorithm

XLA PRNG algorithm to be used.

Kasus:

Simbol	Nilai	Rangkaian
BAWAAN	0	BAWAAN
THREE_FRY	1	THREE_FRY
PHILOX	2	PHILOX

RngDistribution

XLA PRNG distribution to be used.

Kasus:

Simbol	Nilai	Rangkaian
SERAGAM	1	SERAGAM
NORMAL	2	NORMAL

Mengubah urutan

Transpose options

Kasus:

Simbol	Nilai	Rangkaian
TRANSPOSE_INVALID	0	TRANSPOSE_INVALID
NO_TRANSPOSE	1	NO_TRANSPOSE
MENGUBAH URUTAN	2	MENGUBAH URUTAN
ADJOINT	3	ADJOINT