tt-mlir documentation

tt.cpu_module (tt::CPUModuleOp)

Module-wrapper operation for CPU ops

Syntax:

operation ::= `tt.cpu_module` attr-dict-with-keyword regions

Custom module operation that can a single ModuleOp, which should contain all funcs which should be run on CPU.

Example:

tt.cpu_module {
  module {
    func.func foo() { ... }
  }
}

Traits: IsolatedFromAbove, NoRegionArguments, NoTerminator, SingleBlock, SymbolTable

tt.device_module (tt::DeviceModuleOp)

Module-wrapper operation for device ops

Syntax:

operation ::= `tt.device_module` attr-dict-with-keyword $bodyRegion

Custom module operation that can a single ModuleOp, which should contain all funcs which should be run on device.

Example:

tt.device_module {
  module {
    func.func foo() { ... }
  }
}

Traits: IsolatedFromAbove, NoRegionArguments, NoTerminator, SingleBlock, SymbolTable

tt.get_tuple_element (tt::GetTupleElementOp)

GetTupleElement operation

Syntax:

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

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

Example:

%result = tt.get_tuple_element %operand[0] : (tuple<tensor<32x32xbf16>, tensor<1x32xf32>>) -> tensor<32x32xbf16>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

tt.tuple (tt::TupleOp)

Tuple operation

Syntax:

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

Produces a result tuple from operands operands.

Example:

%result = tt.tuple %operand0, %operand1 : tuple<tensor<32xbf16, tensor<1x32xf32>>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, InferTypeOpInterface, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results: