TritonOps¶

`tt.call` (triton::CallOp)¶

Call operation

Syntax:

operation ::= `tt.call` $callee `(` $operands `)` attr-dict `:` functional-type($operands, results)

The tt.call operation represents a direct call to a function that is within the same symbol scope as the call. The operands and result types of the call must match the specified function type. The callee is encoded as a symbol reference attribute named “callee”.

Example:

%2 = tt.call @my_add(%0, %1) : (f32, f32) -> f32

Traits: TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: CallOpInterface, SymbolUserOpInterface

Attributes:¶

Attribute	MLIR Type	Description
`callee`	::mlir::FlatSymbolRefAttr	flat symbol reference attribute

Operands:¶

Operand	Description
`operands`	variadic of any type

Results:¶

Result	Description
«unnamed»	variadic of any type

`tt.func` (triton::FuncOp)¶

An operation with a name containing a single SSACFG region

Operations within the function cannot implicitly capture values defined outside of the function, i.e. Functions are IsolatedFromAbove. All external references must use function arguments or attributes that establish a symbolic connection (e.g. symbols referenced by name via a string attribute like SymbolRefAttr). An external function declaration (used when referring to a function declared in some other module) has no body. While the MLIR textual form provides a nice inline syntax for function arguments, they are internally represented as “block arguments” to the first block in the region.

Only dialect attribute names may be specified in the attribute dictionaries for function arguments, results, or the function itself.

Example:

// External function definitions.
tt.func @abort()
tt.func @scribble(i32, i64, memref<? x 128 x f32, #layout_map0>) -> f64

// A function that returns its argument twice:
tt.func @count(%x: i64) -> (i64, i64)
  attributes {fruit: "banana"} {
  return %x, %x: i64, i64
}

// A function with an argument attribute
tt.func @example_fn_arg(%x: i32 {swift.self = unit})

// A function with a result attribute
tt.func @example_fn_result() -> (f64 {dialectName.attrName = 0 : i64})

// A function with an attribute
tt.func @example_fn_attr() attributes {dialectName.attrName = false}

Traits: AffineScope, AutomaticAllocationScope, IsolatedFromAbove, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: CallableOpInterface, FunctionOpInterface, OpAsmOpInterface, Symbol

Attributes:¶

Attribute	MLIR Type	Description
`sym_name`	::mlir::StringAttr	string attribute
`function_type`	::mlir::TypeAttr	type attribute of function type
`sym_visibility`	::mlir::StringAttr	string attribute
`arg_attrs`	::mlir::ArrayAttr	Array of dictionary attributes
`res_attrs`	::mlir::ArrayAttr	Array of dictionary attributes

`tt.reinterpret_tensor_descriptor` (triton::ReinterpretTensorDescOp)¶

Reinterpret a pointer as a tensor descriptor

Syntax:

operation ::= `tt.reinterpret_tensor_descriptor` $rawDesc attr-dict `:` qualified(type($rawDesc))  `to` qualified(type($result))

This Op exists to help the transition from untyped raw TMA objects to typed Tensor descriptor objects. Ideally, we can remove this once the APIs are fully fleshed out.

Traits: AlwaysSpeculatableImplTrait, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`rawDesc`	ptr

Results:¶

Result	Description
`result`	Tensor descriptor type (`::mlir::triton::TensorDescType`) in Triton IR type system

`tt.return` (triton::ReturnOp)¶

Function return operation

Syntax:

operation ::= `tt.return` attr-dict ($srcs^ `:` type($srcs))?

The tt.return operation represents a return operation within a function. The operation takes variable number of operands and produces no results. The operand number and types must match the signature of the function that contains the operation.

Example:

tt.func @foo() : (i32, f8) {
  ...
  tt.return %0, %1 : i32, f8
}

Traits: AlwaysSpeculatableImplTrait, HasParent<FuncOp>, ReturnLike, TensorSizeTrait, Terminator, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), RegionBranchTerminatorOpInterface

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`srcs`	variadic of any type

`tt.addptr` (triton::AddPtrOp)¶

Syntax:

operation ::= `tt.addptr` $ptr `,` $offset attr-dict `:` type($result) `,` type($offset)

Traits: AlwaysSpeculatableImplTrait, Elementwise, SameOperandsAndResultEncoding, SameOperandsAndResultShape, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`ptr`	ptr or ranked tensor of ptr values
`offset`	integer or ranked tensor of integer values

Results:¶

Result	Description
`result`	ptr or ranked tensor of ptr values

`tt.advance` (triton::AdvanceOp)¶

Advance a tensor pointer by offsets

Syntax:

operation ::= `tt.advance` $ptr `,` `[` $offsets `]` attr-dict `:` type($result)

Traits: AlwaysSpeculatableImplTrait, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`ptr`	ptr
`offsets`	variadic of 32-bit signless integer

Results:¶

Result	Description
`result`	ptr

`tt.assert` (triton::AssertOp)¶

Device-side assert, as in CUDA for correctness checking

Syntax:

operation ::= `tt.assert` $condition `,` $message attr-dict `:` type($condition)

tt.assert takes a condition tensor and a message string. If the condition is false, the message is printed, and the program is aborted.

Traits: TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: MemoryEffectOpInterface (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{MemoryEffects::Write on ::mlir::triton::GlobalMemory}

Attributes:¶

Attribute	MLIR Type	Description
`message`	::mlir::StringAttr	string attribute

Operands:¶

Operand	Description
`condition`	1-bit signless integer or tensor of 1-bit signless integer values

`tt.atomic_cas` (triton::AtomicCASOp)¶

Atomic cas

Syntax:

operation ::= `tt.atomic_cas` $sem `,` $scope `,` $ptr `,` $cmp `,` $val attr-dict `:`
              functional-type(operands, $result)

compare $cmp with data $old at location $ptr,

if $old == $cmp, store $val to $ptr,

else store $old to $ptr,

return $old

Traits: SameOperandsAndResultEncoding, SameOperandsAndResultShape, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: MemoryEffectOpInterface (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{MemoryEffects::Read on ::mlir::triton::GlobalMemory}, MemoryEffects::Effect{MemoryEffects::Write on ::mlir::triton::GlobalMemory}

Attributes:¶

Attribute	MLIR Type	Description
`sem`	::mlir::triton::MemSemanticAttr	allowed 32-bit signless integer cases: 1, 2, 3, 4 {{% markdown %}}Enum cases: * relaxed (`RELAXED`) * acquire (`ACQUIRE`) * release (`RELEASE`) * acq_rel (`ACQUIRE_RELEASE`){{% /markdown %}}
`scope`	::mlir::triton::MemSyncScopeAttr	allowed 32-bit signless integer cases: 1, 2, 3 {{% markdown %}}Enum cases: * gpu (`GPU`) * cta (`CTA`) * sys (`SYSTEM`){{% /markdown %}}

Operands:¶

Operand	Description
`ptr`	ptr or ranked tensor of ptr values
`cmp`	floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr
`val`	floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

Results:¶

Result	Description
`result`	floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

`tt.atomic_rmw` (triton::AtomicRMWOp)¶

Atomic rmw

Syntax:

operation ::= `tt.atomic_rmw` $atomic_rmw_op `,` $sem `,` $scope `,` $ptr `,` $val (`,` $mask^)?  attr-dict `:`
              functional-type(operands, $result)

load data at $ptr, do $rmw_op with $val, and store result to $ptr.

return old value at $ptr

Traits: SameOperandsAndResultEncoding, SameOperandsAndResultShape, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: MemoryEffectOpInterface (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{MemoryEffects::Read on ::mlir::triton::GlobalMemory}, MemoryEffects::Effect{MemoryEffects::Write on ::mlir::triton::GlobalMemory}

Attributes:¶

Attribute	MLIR Type	Description
`atomic_rmw_op`	::mlir::triton::RMWOpAttr	allowed 32-bit signless integer cases: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 {{% markdown %}}Enum cases: * and (`AND`) * or (`OR`) * xor (`XOR`) * add (`ADD`) * fadd (`FADD`) * max (`MAX`) * min (`MIN`) * umax (`UMAX`) * umin (`UMIN`) * exch (`XCHG`){{% /markdown %}}
`sem`	::mlir::triton::MemSemanticAttr	allowed 32-bit signless integer cases: 1, 2, 3, 4 {{% markdown %}}Enum cases: * relaxed (`RELAXED`) * acquire (`ACQUIRE`) * release (`RELEASE`) * acq_rel (`ACQUIRE_RELEASE`){{% /markdown %}}
`scope`	::mlir::triton::MemSyncScopeAttr	allowed 32-bit signless integer cases: 1, 2, 3 {{% markdown %}}Enum cases: * gpu (`GPU`) * cta (`CTA`) * sys (`SYSTEM`){{% /markdown %}}

Operands:¶

Operand	Description
`ptr`	ptr or ranked tensor of ptr values
`val`	floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr
`mask`	1-bit signless integer or ranked tensor of 1-bit signless integer values

Results:¶

Result	Description
`result`	floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

`tt.bitcast` (triton::BitcastOp)¶

Cast between types of the same bitwidth

Syntax:

operation ::= `tt.bitcast` $src attr-dict `:` type($src) `->` type($result)

Traits: AlwaysSpeculatableImplTrait, Elementwise, SameOperandsAndResultEncoding, SameOperandsAndResultShape, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`src`	floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

Results:¶

Result	Description
`result`	floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

`tt.broadcast` (triton::BroadcastOp)¶

Broadcast a tensor

Syntax:

operation ::= `tt.broadcast` $src attr-dict `:` type($src) `->` type($result)

For a given tensor, broadcast changes one or more dimensions with size 1 to a new size, e.g. tensor<1x32x1xf32> -> tensor<2x32x4xf32>. You cannot change the size of a non-1 dimension.

Traits: AlwaysSpeculatableImplTrait, SameOperandsAndResultElementType, SameOperandsAndResultEncoding, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`src`	ranked tensor of floating-point or integer or ptr values

Results:¶

Result	Description
`result`	ranked tensor of floating-point or integer or ptr values

`tt.cat` (triton::CatOp)¶

Concatenate 2 tensors

Syntax:

operation ::= `tt.cat` $lhs `,` $rhs attr-dict `:` type($lhs) `->` type($result)

Traits: SameOperandsAndResultElementType, SameTypeOperands, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`lhs`	ranked tensor of floating-point or integer or ptr values
`rhs`	ranked tensor of floating-point or integer or ptr values

Results:¶

Result	Description
`result`	ranked tensor of floating-point or integer or ptr values

`tt.clampf` (triton::ClampFOp)¶

Clamp operation for floating point types

Syntax:

operation ::= `tt.clampf` $x `,` $min `,` $max `,` `propagateNan` `=` $propagateNan attr-dict `:` type($result)

Clamp operation for floating point types.

The operation takes three arguments: x, min, and max. It returns a tensor of the same shape as x with its values clamped to the range [min, max].

Traits: AlwaysSpeculatableImplTrait, Elementwise, SameOperandsAndResultType, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`propagateNan`	::mlir::triton::PropagateNanAttr	allowed 32-bit signless integer cases: 0, 65535 {{% markdown %}}Enum cases: * none (`NONE`) * all (`ALL`){{% /markdown %}}

Operands:¶

Operand	Description
`x`	floating-point or ranked tensor of floating-point values
`min`	floating-point or ranked tensor of floating-point values
`max`	floating-point or ranked tensor of floating-point values

Results:¶

Result	Description
`result`	floating-point or ranked tensor of floating-point values

`tt.dot` (triton::DotOp)¶

Dot

Syntax:

operation ::= `tt.dot` $a`,` $b`,` $c (`,` `inputPrecision` `=` $inputPrecision^)? attr-dict `:`
              type($a) `*` type($b) `->` type($d)

$d = matrix_multiply($a, $b) + $c. $inputPrecision describes how to exercise the TC when the inputs are f32. It can be one of: tf32, tf32x3, ieee. tf32: use TC with tf32 ops. tf32x3: implement the 3xTF32 trick. For more info see the pass in F32DotTC.cpp ieee: don’t use TC, implement dot in software. If the GPU does not have Tensor cores or the inputs are not f32, this flag is ignored.

Traits: AlwaysSpeculatableImplTrait, DotLike, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`inputPrecision`	::mlir::triton::InputPrecisionAttr	allowed 32-bit signless integer cases: 0, 1, 2 {{% markdown %}}Enum cases: * tf32 (`TF32`) * tf32x3 (`TF32x3`) * ieee (`IEEE`){{% /markdown %}}
`maxNumImpreciseAcc`	::mlir::IntegerAttr	32-bit signless integer attribute

Operands:¶

Operand	Description
`a`	ranked tensor of floating-point or integer values
`b`	ranked tensor of floating-point or integer values
`c`	ranked tensor of floating-point or integer values

Results:¶

Result	Description
`d`	ranked tensor of floating-point or integer values

`tt.dot_scaled` (triton::DotScaledOp)¶

Dot_scaled

Syntax:

operation ::= `tt.dot_scaled` $lhs (`scale` $lhs_scale^)? `,` $rhs (`scale` $rhs_scale^)? `,` $c `lhs` `=` $lhs_type `rhs` `=` $rhs_type attr-dict
              `:` type($lhs) (`,` type($lhs_scale)^)? `*` type($rhs) (`,` type($rhs_scale)^)? `->` type($d)

$d = matrix_multiply(scale($lhs, $lhs_scale), scale($rhs, $rhs_scale)) + $c. Where scale(x, s) is a function that applies the scale per block following microscaling spec.

Traits: AlwaysSpeculatableImplTrait, AttrSizedOperandSegments, DotLike, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`lhs_type`	::mlir::triton::ScaleDotElemTypeAttr	allowed 32-bit signless integer cases: 0, 1, 2, 3, 4, 5 {{% markdown %}}Enum cases: * e4m3 (`E4M3`) * e5m2 (`E5M2`) * e2m3 (`E2M3`) * e3m2 (`E3M2`) * e2m1 (`E2M1`) * bf16 (`BF16`){{% /markdown %}}
`rhs_type`	::mlir::triton::ScaleDotElemTypeAttr	allowed 32-bit signless integer cases: 0, 1, 2, 3, 4, 5 {{% markdown %}}Enum cases: * e4m3 (`E4M3`) * e5m2 (`E5M2`) * e2m3 (`E2M3`) * e3m2 (`E3M2`) * e2m1 (`E2M1`) * bf16 (`BF16`){{% /markdown %}}

Operands:¶

Operand	Description
`lhs`	ranked tensor of floating-point or 8-bit signless integer values
`rhs`	ranked tensor of floating-point or 8-bit signless integer values
`c`	ranked tensor of floating-point values
`lhs_scale`	ranked tensor of 8-bit signless integer values
`rhs_scale`	ranked tensor of 8-bit signless integer values

Results:¶

Result	Description
`d`	ranked tensor of floating-point values

`tt.elementwise_inline_asm` (triton::ElementwiseInlineAsmOp)¶

Inline assembly applying an elementwise operation to a group of packed elements.

Syntax:

operation ::= `tt.elementwise_inline_asm` $asm_string attr-dict ($args^ `:` type($args))? `->` type($result)

Runs an inline asm block to generate one or more tensors.

The asm block is given packed_element elements at a time. Exactly which elems it receives is unspecified.

Traits: Elementwise, SameOperandsAndResultEncoding, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: MemoryEffectOpInterface

Attributes:¶

Attribute	MLIR Type	Description
`asm_string`	::mlir::StringAttr	string attribute
`constraints`	::mlir::StringAttr	string attribute
`pure`	::mlir::BoolAttr	bool attribute
`packed_element`	::mlir::IntegerAttr	32-bit signless integer attribute

Operands:¶

Operand	Description
`args`	variadic of floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

Results:¶

Result	Description
`result`	variadic of floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

`tt.expand_dims` (triton::ExpandDimsOp)¶

Expand_dims

Syntax:

operation ::= `tt.expand_dims` $src attr-dict `:` type($src) `->` type($result)

Traits: AlwaysSpeculatableImplTrait, SameOperandsAndResultElementType, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	32-bit signless integer attribute

Operands:¶

Operand	Description
`src`	ranked tensor of floating-point or integer or ptr values

Results:¶

Result	Description
`result`	ranked tensor of floating-point or integer or ptr values

`tt.experimental_descriptor_load` (triton::ExperimentalDescriptorLoadOp)¶

Load from descriptor

Syntax:

operation ::= `tt.experimental_descriptor_load` $desc `[` $indices `]`
              oilist(
              `cacheModifier` `=` $cache |
              `evictionPolicy` `=` $evict
              )
              attr-dict `:` qualified(type($desc)) `->` type($result)

This operation will be lowered to Nvidia TMA load operation on targets supporting it. desc is a tensor descriptor object. The destination tensor type and shape must match the descriptor otherwise the result is undefined.

This is an escape hatch and is only there for testing/experimenting. This op will be removed in the future.

Traits: TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: MemoryEffectOpInterface (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{MemoryEffects::Read on ::mlir::triton::GlobalMemory}

Attributes:¶

Attribute	MLIR Type	Description
`cache`	::mlir::triton::CacheModifierAttr	allowed 32-bit signless integer cases: 1, 2, 3, 4, 5, 6, 7 {{% markdown %}}Enum cases: * none (`NONE`) * ca (`CA`) * cg (`CG`) * wb (`WB`) * cs (`CS`) * wt (`WT`) * cv (`CV`){{% /markdown %}}
`evict`	::mlir::triton::EvictionPolicyAttr	allowed 32-bit signless integer cases: 1, 2, 3 {{% markdown %}}Enum cases: * evict_normal (`NORMAL`) * evict_first (`EVICT_FIRST`) * evict_last (`EVICT_LAST`){{% /markdown %}}

Operands:¶

Operand	Description
`desc`	Tensor descriptor type (`::mlir::triton::TensorDescType`) in Triton IR type system
`indices`	variadic of 32-bit signless integer

Results:¶

Result	Description
`result`	ranked tensor of floating-point or integer or ptr values

`tt.experimental_descriptor_store` (triton::ExperimentalDescriptorStoreOp)¶

Store value based on descriptor

Syntax:

operation ::= `tt.experimental_descriptor_store` $desc `[` $indices `]` `,` $src
              attr-dict `:` qualified(type($desc)) `,` type($src)

This operation will be lowered to Nvidia TMA store operation on targets supporting it. desc is a tensor descriptor object. The shape and types of src must match the descriptor otherwise the result is undefined.

This is an escape hatch and is only there for testing/experimenting. This op will be removed in the future.

Traits: TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: MemoryEffectOpInterface (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{MemoryEffects::Read on ::mlir::triton::GlobalMemory, MemoryEffects::Write on ::mlir::triton::GlobalMemory}

Operands:¶

Operand	Description
`desc`	Tensor descriptor type (`::mlir::triton::TensorDescType`) in Triton IR type system
`src`	ranked tensor of floating-point or integer or ptr values
`indices`	variadic of 32-bit signless integer

`tt.experimental_tensormap_create` (triton::ExperimentalTensormapCreateOp)¶

Create a new TMA descriptor on device

Syntax:

operation ::= `tt.experimental_tensormap_create` $desc_ptr `,` $global_address `,`
              `[` $box_dim `]` `,`
              `[` $global_dim `]` `,`
              `[` $global_stride `]` `,`
              `[` $element_stride `]`
              attr-dict `:` functional-type(operands, results)

Traits: AttrSizedOperandSegments, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: MemoryEffectOpInterface (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{MemoryEffects::Read on ::mlir::triton::GlobalMemory, MemoryEffects::Write on ::mlir::triton::GlobalMemory}

Attributes:¶

Attribute	MLIR Type	Description
`elem_type`	::mlir::IntegerAttr	32-bit signless integer attribute whose value is non-negative whose maximum value is 12
`interleave_layout`	::mlir::IntegerAttr	32-bit signless integer attribute whose value is non-negative whose maximum value is 2
`swizzle_mode`	::mlir::IntegerAttr	32-bit signless integer attribute whose value is non-negative whose maximum value is 3
`fill_mode`	::mlir::IntegerAttr	32-bit signless integer attribute whose value is non-negative whose maximum value is 1

Operands:¶

Operand	Description
`desc_ptr`	Pointer type (`::mlir::triton::PointerType`) in Triton IR type system
`global_address`	Pointer type (`::mlir::triton::PointerType`) in Triton IR type system
`box_dim`	variadic of 32-bit signless integer
`global_dim`	variadic of 32-bit signless integer
`global_stride`	variadic of 64-bit signless integer
`element_stride`	variadic of 32-bit signless integer

`tt.experimental_tensormap_fenceproxy_acquire` (triton::ExperimentalTensormapFenceproxyAcquireOp)¶

Acquire fence on a tensormap object

Syntax:

operation ::= `tt.experimental_tensormap_fenceproxy_acquire` $desc_ptr attr-dict `:` qualified(type($desc_ptr))

Traits: TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: MemoryEffectOpInterface (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{MemoryEffects::Write on ::mlir::triton::GlobalMemory}

Operands:¶

Operand	Description
`desc_ptr`	Pointer type (`::mlir::triton::PointerType`) in Triton IR type system

`tt.extern_elementwise` (triton::ExternElementwiseOp)¶

Syntax:

operation ::= `tt.extern_elementwise` operands attr-dict `:` functional-type(operands, $result)

call an external function $symbol implemented in $libpath/$libname with $args return $libpath/$libname:$symbol($args…)

Traits: Elementwise, SameOperandsAndResultEncoding, SameVariadicOperandSize, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, MemoryEffectOpInterface

Attributes:¶

Attribute	MLIR Type	Description
`libname`	::mlir::StringAttr	string attribute
`libpath`	::mlir::StringAttr	string attribute
`symbol`	::mlir::StringAttr	string attribute
`pure`	::mlir::BoolAttr	bool attribute

Operands:¶

Operand	Description
`srcs`	variadic of floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

Results:¶

Result	Description
`result`	floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

`tt.fp_to_fp` (triton::FpToFpOp)¶

Floating point casting for custom types

Syntax:

operation ::= `tt.fp_to_fp` $src attr-dict  (`,` `rounding` `=` $rounding^)? `:` type($src) `->` type($result)

Floating point casting for custom types (F8), and non-default rounding modes.

F8 <-> FP16, BF16, FP32, FP64

Traits: AlwaysSpeculatableImplTrait, Elementwise, SameOperandsAndResultEncoding, SameOperandsAndResultShape, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`rounding`	::mlir::triton::RoundingModeAttr	allowed 32-bit signless integer cases: 0, 1 {{% markdown %}}Enum cases: * rtz (`RTZ`) * rtne (`RTNE`){{% /markdown %}}

Operands:¶

Operand	Description
`src`	floating-point or ranked tensor of floating-point values

Results:¶

Result	Description
`result`	floating-point or ranked tensor of floating-point values

`tt.gather` (triton::GatherOp)¶

Local gather operation

Syntax:

operation ::= `tt.gather` $src `[` $indices `]` attr-dict `:`
              functional-type(operands, results)

Gather elements from the input tensor using the indices tensor along a single specified axis. The output tensor has the same shape as the indices tensor. The input and indices tensors must have the same number of dimension, and each dimension of the indices tensor that is not the gather dimension cannot be greater than the corresponding dimension in the input tensor.

The efficient_layout attribute is set when the compiler has determined an optimized layout for the operation, indicating that it should not be changed.

Traits: AlwaysSpeculatableImplTrait, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	32-bit signless integer attribute
`efficient_layout`	::mlir::UnitAttr	unit attribute

Operands:¶

Operand	Description
`src`	ranked tensor of floating-point or integer or ptr values
`indices`	ranked tensor of integer values

Results:¶

Result	Description
`result`	ranked tensor of floating-point or integer or ptr values

`tt.get_num_programs` (triton::GetNumProgramsOp)¶

Syntax:

operation ::= `tt.get_num_programs` $axis attr-dict `:` type($result)

Traits: AlwaysSpeculatableImplTrait, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`axis`	::mlir::triton::ProgramIDDimAttr	allowed 32-bit signless integer cases: 0, 1, 2 {{% markdown %}}Enum cases: * x (`X`) * y (`Y`) * z (`Z`){{% /markdown %}}

Results:¶

Result	Description
`result`	32-bit signless integer

`tt.get_program_id` (triton::GetProgramIdOp)¶

Syntax:

operation ::= `tt.get_program_id` $axis attr-dict `:` type($result)

Traits: AlwaysSpeculatableImplTrait, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`axis`	::mlir::triton::ProgramIDDimAttr	allowed 32-bit signless integer cases: 0, 1, 2 {{% markdown %}}Enum cases: * x (`X`) * y (`Y`) * z (`Z`){{% /markdown %}}

Results:¶

Result	Description
`result`	32-bit signless integer

`tt.histogram` (triton::HistogramOp)¶

Return a histgram of the inputs.

Syntax:

operation ::= `tt.histogram` $src attr-dict `:` type($src) `->` type($result)

Return the histogram of the input tensor. The number of bins is equal to the dimension of the output tensor. Each bins has a width of 1 and bins start at 0.

Traits: AlwaysSpeculatableImplTrait, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`src`	ranked tensor of integer values

Results:¶

Result	Description
`result`	ranked tensor of integer values

`tt.int_to_ptr` (triton::IntToPtrOp)¶

Cast int64 to pointer

Syntax:

operation ::= `tt.int_to_ptr` $src attr-dict `:` type($src) `->` type($result)

Traits: AlwaysSpeculatableImplTrait, Elementwise, SameOperandsAndResultEncoding, SameOperandsAndResultShape, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`src`	64-bit signless integer or tensor of 64-bit signless integer values

Results:¶

Result	Description
`result`	ptr or ranked tensor of ptr values

`tt.join` (triton::JoinOp)¶

Join two tensors along a new, minor dimension

Syntax:

operation ::= `tt.join` $lhs `,` $rhs attr-dict `:` type($lhs) `->` type($result)

For example, if the two input tensors are 4x8xf32, returns a tensor of shape 4x8x2xf32.

Because Triton tensors always have a power-of-two number of elements, the two input tensors must have the same shape.

Traits: SameTypeOperands, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`lhs`	ranked tensor of floating-point or integer or ptr values
`rhs`	ranked tensor of floating-point or integer or ptr values

Results:¶

Result	Description
`result`	ranked tensor of floating-point or integer or ptr values

`tt.load` (triton::LoadOp)¶

Load from a tensor of pointers or from a tensor pointer

Syntax:

operation ::= `tt.load` $ptr (`,` $mask^)? (`,` $other^)?
              oilist(
              `cacheModifier` `=` $cache |
              `evictionPolicy` `=` $evict
              )
              attr-dict `:` type($ptr)

Traits: AttrSizedOperandSegments, SameLoadStoreOperandsAndResultEncoding, SameLoadStoreOperandsAndResultShape, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: InferTypeOpInterface, MemoryEffectOpInterface

Attributes:¶

Attribute	MLIR Type	Description
`boundaryCheck`	::mlir::DenseI32ArrayAttr	i32 dense array attribute
`padding`	::mlir::triton::PaddingOptionAttr	allowed 32-bit signless integer cases: 1, 2 {{% markdown %}}Enum cases: * zero (`PAD_ZERO`) * nan (`PAD_NAN`){{% /markdown %}}
`cache`	::mlir::triton::CacheModifierAttr	allowed 32-bit signless integer cases: 1, 2, 3, 4, 5, 6, 7 {{% markdown %}}Enum cases: * none (`NONE`) * ca (`CA`) * cg (`CG`) * wb (`WB`) * cs (`CS`) * wt (`WT`) * cv (`CV`){{% /markdown %}}
`evict`	::mlir::triton::EvictionPolicyAttr	allowed 32-bit signless integer cases: 1, 2, 3 {{% markdown %}}Enum cases: * evict_normal (`NORMAL`) * evict_first (`EVICT_FIRST`) * evict_last (`EVICT_LAST`){{% /markdown %}}
`isVolatile`	::mlir::BoolAttr	bool attribute

Operands:¶

Operand	Description
`ptr`	ptr or ranked tensor of ptr values or ptr
`mask`	1-bit signless integer or ranked tensor of 1-bit signless integer values
`other`	floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

Results:¶

Result	Description
`result`	floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

`tt.make_range` (triton::MakeRangeOp)¶

Make range

Syntax:

operation ::= `tt.make_range` attr-dict `:` type($result)

Returns an 1D int32 tensor.

Values span from $start to $end (exclusive), with step = 1

Traits: AlwaysSpeculatableImplTrait, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`start`	::mlir::IntegerAttr	32-bit signless integer attribute
`end`	::mlir::IntegerAttr	32-bit signless integer attribute

Results:¶

Result	Description
`result`	ranked tensor of integer values

`tt.make_tensor_descriptor` (triton::MakeTensorDescOp)¶

Make a tensor descriptor type with meta information of the parent tensor and block size

Syntax:

operation ::= `tt.make_tensor_descriptor` $base `,` `[` $shape `]` `,` `[` $strides `]` attr-dict `:` type($base) `,` type($result)

tt.make_tensor_descriptor takes both meta information of the parent tensor and the block size, and returns a descriptor object which can be used to load/store from the tensor in global memory.

Traits: AlwaysSpeculatableImplTrait, SameVariadicOperandSize, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`base`	ptr
`shape`	variadic of 32-bit signless integer
`strides`	variadic of 64-bit signless integer

Results:¶

Result	Description
`result`	Tensor descriptor type (`::mlir::triton::TensorDescType`) in Triton IR type system

`tt.make_tensor_ptr` (triton::MakeTensorPtrOp)¶

Make a tensor pointer type with meta information of the parent tensor and the block specified

Syntax:

operation ::= `tt.make_tensor_ptr` $base `,` `[` $shape `]` `,` `[` $strides `]` `,` `[` $offsets `]` attr-dict `:` type($result)

tt.make_tensor_ptr takes both meta information of the parent tensor and the block tensor, then it returns a pointer to the block tensor, e.g. returns a type of tt.ptr<tensor<8x8xf16>>.

Traits: AlwaysSpeculatableImplTrait, SameVariadicOperandSize, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`order`	::mlir::DenseI32ArrayAttr	i32 dense array attribute

Operands:¶

Operand	Description
`base`	ptr
`shape`	variadic of 64-bit signless integer
`strides`	variadic of 64-bit signless integer
`offsets`	variadic of 32-bit signless integer

Results:¶

Result	Description
`result`	ptr

`tt.mulhiui` (triton::MulhiUIOp)¶

Most significant N bits of the 2N-bit product of two integers

Syntax:

operation ::= `tt.mulhiui` $x `,` $y attr-dict `:` type($x)

Most significant N bits of the 2N-bit product of two integers.

Traits: AlwaysSpeculatableImplTrait, Elementwise, SameOperandsAndResultType, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`x`	integer or ranked tensor of integer values
`y`	integer or ranked tensor of integer values

Results:¶

Result	Description
`result`	integer or ranked tensor of integer values

`tt.precise_divf` (triton::PreciseDivFOp)¶

Precise div for floating point types

Syntax:

operation ::= `tt.precise_divf` $x `,` $y attr-dict `:` type($x)

Precise div for floating point types.

Traits: AlwaysSpeculatableImplTrait, Elementwise, SameOperandsAndResultType, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`x`	floating-point or ranked tensor of floating-point values
`y`	floating-point or ranked tensor of floating-point values

Results:¶

Result	Description
`result`	floating-point or ranked tensor of floating-point values

`tt.precise_sqrt` (triton::PreciseSqrtOp)¶

Precise sqrt for floating point types

Syntax:

operation ::= `tt.precise_sqrt` $x attr-dict `:` type($x)

Precise sqrt for floating point types.

Traits: AlwaysSpeculatableImplTrait, Elementwise, SameOperandsAndResultType, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`x`	floating-point or ranked tensor of floating-point values

Results:¶

Result	Description
`result`	floating-point or ranked tensor of floating-point values

`tt.print` (triton::PrintOp)¶

Device-side print, as in CUDA for debugging

Syntax:

operation ::= `tt.print` $prefix attr-dict (`:` $args^ `:` type($args))?

tt.print takes a literal string prefix and an arbitrary number of scalar or tensor arguments that should be printed. format are generated automatically from the arguments.

Traits: SameVariadicOperandSize, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: MemoryEffectOpInterface (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{MemoryEffects::Write on ::mlir::triton::GlobalMemory}

Attributes:¶

Attribute	MLIR Type	Description
`prefix`	::mlir::StringAttr	string attribute
`hex`	::mlir::BoolAttr	bool attribute
`isSigned`	::mlir::DenseI32ArrayAttr	i32 dense array attribute

Operands:¶

Operand	Description
`args`	variadic of floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

`tt.ptr_to_int` (triton::PtrToIntOp)¶

Cast pointer to int64

Syntax:

operation ::= `tt.ptr_to_int` $src attr-dict `:` type($src) `->` type($result)

Traits: AlwaysSpeculatableImplTrait, Elementwise, SameOperandsAndResultEncoding, SameOperandsAndResultShape, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`src`	ptr or ranked tensor of ptr values

Results:¶

Result	Description
`result`	64-bit signless integer or tensor of 64-bit signless integer values

`tt.reduce` (triton::ReduceOp)¶

Reduction using generic combination algorithm

Traits: AlwaysSpeculatableImplTrait, SameOperandsEncoding, SameOperandsShape, SingleBlock, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	32-bit signless integer attribute

Operands:¶

Operand	Description
`srcs`	variadic of ranked tensor of floating-point or integer or ptr values

Results:¶

Result	Description
`result`	variadic of floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

`tt.reduce.return` (triton::ReduceReturnOp)¶

Terminator for reduce operator

Syntax:

operation ::= `tt.reduce.return` $result attr-dict `:` type($result)

Traits: AlwaysSpeculatableImplTrait, HasParent<ReduceOp>, ReturnLike, TensorSizeTrait, Terminator, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), RegionBranchTerminatorOpInterface

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`result`	variadic of any type

`tt.reshape` (triton::ReshapeOp)¶

Reinterpret a tensor to a different shape. It may change elements order if the attribute is set.

Syntax:

operation ::= `tt.reshape` $src (`allow_reorder` $allow_reorder^)? (`efficient_layout` $efficient_layout^)? attr-dict `:` type($src) `->` type($result)

reinterpret a tensor to a different shape.

If allow_reorder is set the compiler is free to change the order of elements to generate more efficient code.

If efficient_layout is set, this is a hint that the destination layout should be kept for performance reason. The compiler is still free to change it for better performance.

Traits: AlwaysSpeculatableImplTrait, SameOperandsAndResultElementType, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`allow_reorder`	::mlir::UnitAttr	unit attribute
`efficient_layout`	::mlir::UnitAttr	unit attribute

Operands:¶

Operand	Description
`src`	ranked tensor of floating-point or integer or ptr values

Results:¶

Result	Description
`result`	ranked tensor of floating-point or integer or ptr values

`tt.scan` (triton::ScanOp)¶

Associative scan using generic combination algorithm

Traits: AlwaysSpeculatableImplTrait, SameOperandsAndResultEncoding, SameOperandsAndResultShape, SingleBlock, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`axis`	::mlir::IntegerAttr	32-bit signless integer attribute
`reverse`	::mlir::BoolAttr	bool attribute

Operands:¶

Operand	Description
`srcs`	variadic of ranked tensor of floating-point or integer or ptr values

Results:¶

Result	Description
`result`	variadic of ranked tensor of floating-point or integer or ptr values

`tt.scan.return` (triton::ScanReturnOp)¶

Terminator for scan operator

Syntax:

operation ::= `tt.scan.return` $result attr-dict `:` type($result)

Traits: AlwaysSpeculatableImplTrait, HasParent<ScanOp>, ReturnLike, TensorSizeTrait, Terminator, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), RegionBranchTerminatorOpInterface

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`result`	variadic of any type

`tt.splat` (triton::SplatOp)¶

Splat

Syntax:

operation ::= `tt.splat` $src attr-dict `:` type($src) `->` type($result)

Traits: AlwaysSpeculatableImplTrait, SameOperandsAndResultElementType, SameOperandsAndResultEncoding, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`src`	floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr

Results:¶

Result	Description
`result`	ranked tensor of floating-point or integer or ptr values

`tt.split` (triton::SplitOp)¶

Splits a tensor into two, along its last dimension

Syntax:

operation ::= `tt.split` $src attr-dict `:` type($src) `->` type($outLHS)

The input must be a tensor whose last dimension has size 2. Returns two tensors, src[…, 0] and src[…, 1].

For example, if the input shape is 4x8x2xf32, returns two tensors of shape 4x8xf32.

Traits: TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:¶

Operand	Description
`src`	ranked tensor of floating-point or integer or ptr values

Results:¶

Result	Description
`outLHS`	ranked tensor of floating-point or integer or ptr values
`outRHS`	ranked tensor of floating-point or integer or ptr values

`tt.store` (triton::StoreOp)¶

Store by a tensor of pointers or by a tensor pointer

Syntax:

operation ::= `tt.store` $ptr `,` $value (`,` $mask^)?
              oilist(`cacheModifier` `=` $cache | `evictionPolicy` `=` $evict)
              attr-dict `:` type($ptr)

Traits: SameLoadStoreOperandsEncoding, SameLoadStoreOperandsShape, TensorSizeTrait, VerifyTensorLayoutsTrait

Interfaces: MemoryEffectOpInterface (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{MemoryEffects::Write on ::mlir::triton::GlobalMemory}

Attributes:¶

Attribute	MLIR Type	Description
`boundaryCheck`	::mlir::DenseI32ArrayAttr	i32 dense array attribute
`cache`	::mlir::triton::CacheModifierAttr	allowed 32-bit signless integer cases: 1, 2, 3, 4, 5, 6, 7 {{% markdown %}}Enum cases: * none (`NONE`) * ca (`CA`) * cg (`CG`) * wb (`WB`) * cs (`CS`) * wt (`WT`) * cv (`CV`){{% /markdown %}}
`evict`	::mlir::triton::EvictionPolicyAttr	allowed 32-bit signless integer cases: 1, 2, 3 {{% markdown %}}Enum cases: * evict_normal (`NORMAL`) * evict_first (`EVICT_FIRST`) * evict_last (`EVICT_LAST`){{% /markdown %}}

Operands:¶

Operand	Description
`ptr`	ptr or ranked tensor of ptr values or ptr
`value`	floating-point or ranked tensor of floating-point values or integer or ranked tensor of integer values or ptr or ranked tensor of ptr values or ptr
`mask`	1-bit signless integer or ranked tensor of 1-bit signless integer values

`tt.trans` (triton::TransOp)¶

Rearrange the dimensions of a tensor

Syntax:

operation ::= `tt.trans` $src attr-dict `:` type($src) `->` type($result)

For example, given a tensor x with shape [1,2,4], transpose(x) with order=[2,0,1] rearranges the tensor to have shape [4,1,2].

Although this op is called “trans”, it implements both tl.trans() and tl.permute(). (“permute” might be a better name, but it’s called “trans” because originally it only supported 2D tensors.)

Implementation note on encodings:¶

In the TritonGPU dialect (and probably others), an encoding is chosen for this op’s output so it’s a nop from the perspective of code generation.

For example, suppose tensor x has an encoding such that GPU thread [i,j,k] has a register containing element [i,j,k] of the tensor. Now we transpose x with order [2,1,0], i.e. we reverse the order of its dimensions. In TritonGPU, we will choose a layout for the output of the transpose so that GPU thread [i,j,k] has element [k,j,i] of transpose(x). But this is the same element it had before! All we’ve done is “rename” the element that thread [i,j,k] has.

The “real” transpose – i.e. moving data between GPU threads – occurs in convertLayout ops that appear before and/or after the operation.

We do this so that you can chain multiple data-movement ops (e.g. transpose+reshape+concat) without going to shared memory after each one.

Traits: AlwaysSpeculatableImplTrait, SameOperandsAndResultElementType, TensorSizeTrait, VerifyTensorLayoutsTrait

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

Effects: MemoryEffects::Effect{}

Attributes:¶

Attribute	MLIR Type	Description
`order`	::mlir::DenseI32ArrayAttr	i32 dense array attribute

Operands:¶

Operand	Description
`src`	ranked tensor of floating-point or integer or ptr values

Results:¶

Result	Description
`result`	ranked tensor of floating-point or integer or ptr values

TritonOps¶

tt.call (triton::CallOp)¶

Attributes:¶

Operands:¶

Results:¶

tt.func (triton::FuncOp)¶

Attributes:¶

tt.reinterpret_tensor_descriptor (triton::ReinterpretTensorDescOp)¶

Operands:¶

Results:¶

tt.return (triton::ReturnOp)¶

Operands:¶

tt.addptr (triton::AddPtrOp)¶

Operands:¶

Results:¶

tt.advance (triton::AdvanceOp)¶

Operands:¶

Results:¶

tt.assert (triton::AssertOp)¶

Attributes:¶

Operands:¶

tt.atomic_cas (triton::AtomicCASOp)¶

Attributes:¶

Operands:¶

Results:¶

tt.atomic_rmw (triton::AtomicRMWOp)¶

Attributes:¶

Operands:¶

Results:¶

tt.bitcast (triton::BitcastOp)¶

Operands:¶

Results:¶

tt.broadcast (triton::BroadcastOp)¶

Operands:¶

Results:¶

tt.cat (triton::CatOp)¶

Operands:¶

Results:¶

tt.clampf (triton::ClampFOp)¶

Attributes:¶

Operands:¶

Results:¶

tt.dot (triton::DotOp)¶

Attributes:¶

Operands:¶

Results:¶

tt.dot_scaled (triton::DotScaledOp)¶

Attributes:¶

Operands:¶

Results:¶

tt.elementwise_inline_asm (triton::ElementwiseInlineAsmOp)¶

Attributes:¶

Operands:¶

Results:¶

tt.expand_dims (triton::ExpandDimsOp)¶

Attributes:¶

Operands:¶

Results:¶

tt.experimental_descriptor_load (triton::ExperimentalDescriptorLoadOp)¶

Attributes:¶

Operands:¶

Results:¶

tt.experimental_descriptor_store (triton::ExperimentalDescriptorStoreOp)¶

Operands:¶

tt.experimental_tensormap_create (triton::ExperimentalTensormapCreateOp)¶

Attributes:¶

Operands:¶

tt.experimental_tensormap_fenceproxy_acquire (triton::ExperimentalTensormapFenceproxyAcquireOp)¶

Operands:¶

tt.extern_elementwise (triton::ExternElementwiseOp)¶

Attributes:¶

Operands:¶

Results:¶

tt.fp_to_fp (triton::FpToFpOp)¶

Attributes:¶

Operands:¶

Results:¶

tt.gather (triton::GatherOp)¶

Attributes:¶

Operands:¶

`tt.call` (triton::CallOp)¶

`tt.func` (triton::FuncOp)¶

`tt.reinterpret_tensor_descriptor` (triton::ReinterpretTensorDescOp)¶

`tt.return` (triton::ReturnOp)¶

`tt.addptr` (triton::AddPtrOp)¶

`tt.advance` (triton::AdvanceOp)¶

`tt.assert` (triton::AssertOp)¶

`tt.atomic_cas` (triton::AtomicCASOp)¶

`tt.atomic_rmw` (triton::AtomicRMWOp)¶

`tt.bitcast` (triton::BitcastOp)¶

`tt.broadcast` (triton::BroadcastOp)¶

`tt.cat` (triton::CatOp)¶

`tt.clampf` (triton::ClampFOp)¶

`tt.dot` (triton::DotOp)¶

`tt.dot_scaled` (triton::DotScaledOp)¶

`tt.elementwise_inline_asm` (triton::ElementwiseInlineAsmOp)¶

`tt.expand_dims` (triton::ExpandDimsOp)¶

`tt.experimental_descriptor_load` (triton::ExperimentalDescriptorLoadOp)¶

`tt.experimental_descriptor_store` (triton::ExperimentalDescriptorStoreOp)¶

`tt.experimental_tensormap_create` (triton::ExperimentalTensormapCreateOp)¶

`tt.experimental_tensormap_fenceproxy_acquire` (triton::ExperimentalTensormapFenceproxyAcquireOp)¶

`tt.extern_elementwise` (triton::ExternElementwiseOp)¶

`tt.fp_to_fp` (triton::FpToFpOp)¶

`tt.gather` (triton::GatherOp)¶

`tt.get_num_programs` (triton::GetNumProgramsOp)¶

`tt.get_program_id` (triton::GetProgramIdOp)¶

`tt.histogram` (triton::HistogramOp)¶

`tt.int_to_ptr` (triton::IntToPtrOp)¶

`tt.join` (triton::JoinOp)¶

`tt.load` (triton::LoadOp)¶

`tt.make_range` (triton::MakeRangeOp)¶

`tt.make_tensor_descriptor` (triton::MakeTensorDescOp)¶

`tt.make_tensor_ptr` (triton::MakeTensorPtrOp)¶

`tt.mulhiui` (triton::MulhiUIOp)¶

`tt.precise_divf` (triton::PreciseDivFOp)¶

`tt.precise_sqrt` (triton::PreciseSqrtOp)¶

`tt.print` (triton::PrintOp)¶

`tt.ptr_to_int` (triton::PtrToIntOp)¶

`tt.reduce` (triton::ReduceOp)¶

`tt.reduce.return` (triton::ReduceReturnOp)¶

`tt.reshape` (triton::ReshapeOp)¶

`tt.scan` (triton::ScanOp)¶

`tt.scan.return` (triton::ScanReturnOp)¶

`tt.splat` (triton::SplatOp)¶

`tt.split` (triton::SplitOp)¶

`tt.store` (triton::StoreOp)¶