'bufferization' Dialect

Bufferization in MLIR is the process of converting the tensor type to the memref type. The bufferization dialect is intended to collect operations/interfaces specific to the bufferization passes.

Overview of the bufferization infrastructure and important conceptual details related to using the MLIR dialect conversion infrastructure can be found in bufferization and buffer deallocation.

Operation definition ¶

bufferization.alloc_tensor (::mlir::bufferization::AllocTensorOp) ¶

buffer allocation in tensor land

bufferization.alloc_tensor materializes an uninitialized tensor with a given shape (dynamic or static). It always bufferizes to a new buffer allocation of the given shape. The optional copy operand specifies the contents of the tensors. If no copy operand is specified, reading from the result of an alloc_tensor op yields an undefined value.

If copy is specified, no dynamic sizes should be passed, since they are the same as the dynamic sizes of the copy operand.

alloc_tensor is a helper op for bufferization. The operation is provided as an anchor that marks the beginning of a new tensor SSA use-def chain. It can be used to control in-place bufferization decisions during One-Shot Bufferize: The bufferized result of a bufferization.alloc_tensor does not alias with any other buffer, so it can be used to resolve read-after-write conflicts that would have been introduced by the in-place bufferization of another op.

The optional memory_space attribute specifies the memory space when bufferizing this op. The memory space is inferred from copy if specified. If neither copy nor memory_space is specified, the default memory space is used during bufferization.

Both dense and sparse tensor types are supported. The result of a bufferization.alloc_tensor is a tensor value that can be used like any other tensor value. In practice, it is often used as the “out” operand of another op. Sparse tensor allocations should always be used in a local construction operation and never escape the function boundary directly.

Example:

%c = bufferization.alloc_tensor(%d1, %d2) : tensor<?x?xf32, #SparseMatrix>
%0 = linalg.matmul
  ins(%a, %b: tensor<?x?xf32, #SparseMatrix>, tensor<?x?xf32, #SparseMatrix>)
  outs(%c: tensor<?x?xf32, #SparseMatrix>) -> tensor<?x?xf32, #SparseMatrix>
return %0 : tensor<?x?xf32, #SparseMatrix>

Traits: AttrSizedOperandSegments

Interfaces: BufferizableOpInterface, ReifyRankedShapedTypeOpInterface

Attributes: ¶

Operands: ¶

Results: ¶

bufferization.clone (::mlir::bufferization::CloneOp) ¶

Syntax:

operation ::= `bufferization.clone` $input attr-dict `:` type($input) `to` type($output)

Clones the data in the input view into an implicitly defined output view.

Usage:

%arg1 = bufferization.clone %arg0 : memref<?xf32> to memref<?xf32>

Valid implementations of this operation may alias the input and output views or create an actual copy. Mutating the source or result of the clone operation after the clone operation thus leads to undefined behavior.

Interfaces: AllocationOpInterface, CopyOpInterface, MemoryEffectOpInterface

Operands: ¶

Results: ¶

bufferization.dealloc_tensor (::mlir::bufferization::DeallocTensorOp) ¶

Releases underlying sparse storage format of given tensor

Syntax:

operation ::= `bufferization.dealloc_tensor` $tensor attr-dict `:` type($tensor)

bufferization.dealloc_tensor is a buffer deallocation in tensor land. This op can be used for manual buffer deallocation. Some bufferizations (such as One-Shot Bufferize) take care of buffer deallocation, in which case this op is usually not needed. Details can be found in the documentation of the respective bufferization passes.

In case of a dense tensor, this op lowers to a memref.dealloc op during bufferization.

In case of a sparse tensor, this op releases the underlying sparse storage format for a tensor that materialized earlier through a new operation, a convert operation with annotated destination tensor type (unless the convert is folded away), or a bufferization.alloc_tensor operation. The release operation should only be called once for any materialized tensor. After this operation, any subsequent memref querying operation on the tensor returns undefined results.

Example:

bufferization.dealloc_tensor %tensor : tensor<1024x1024xf64, #CSR>

Interfaces: BufferizableOpInterface

Operands: ¶

bufferization.to_memref (::mlir::bufferization::ToMemrefOp) ¶

tensor to memref cast operation

Syntax:

operation ::= `bufferization.to_memref` $tensor attr-dict `:` type($memref)

Casts a tensor to a memref.

// Result type is memref<4x?xf32, #layout, 42>
%12 = bufferization.to_memref %10 : memref<4x?xf32, #layout, 42>

Note, that mutating the result of the to_memref operation leads to undefined behavior.

This operation is a specialized variant of the built-in unrealized_conversion_cast and is intended for use in the context of gradual bufferization.

Traits: SameOperandsAndResultElementType, SameOperandsAndResultShape

Interfaces: BufferizableOpInterface, NoSideEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands: ¶

Results: ¶

bufferization.to_tensor (::mlir::bufferization::ToTensorOp) ¶

memref to tensor operation

Syntax:

operation ::= `bufferization.to_tensor` $memref attr-dict `:` type($memref)

Create a tensor from a memref, making an independent copy of the element data. The result value is a tensor whose shape and element type match the memref operand.

The opposite of this op is to_memref. Together, these two ops are useful for source/target materializations when doing type conversions involving tensors and memrefs.

Example:

// Produces a value of tensor<4x?xf32> type.
%12 = bufferization.to_tensor %10 : memref<4x?xf32, #layout, memspace0>

If tensor load is used in the bufferization steps, mutating the source buffer after loading leads to undefined behavior.

Traits: SameOperandsAndResultElementType, SameOperandsAndResultShape

Interfaces: BufferizableOpInterface

Operands: ¶

Results: ¶