mlir.dialects._bufferization_ops_gen ==================================== .. py:module:: mlir.dialects._bufferization_ops_gen Attributes ---------- .. autoapisummary:: mlir.dialects._bufferization_ops_gen._ods_ir Classes ------- .. autoapisummary:: mlir.dialects._bufferization_ops_gen._Dialect mlir.dialects._bufferization_ops_gen.AllocTensorOp mlir.dialects._bufferization_ops_gen.CloneOp mlir.dialects._bufferization_ops_gen.DeallocOp mlir.dialects._bufferization_ops_gen.DeallocTensorOp mlir.dialects._bufferization_ops_gen.MaterializeInDestinationOp mlir.dialects._bufferization_ops_gen.ToBufferOp mlir.dialects._bufferization_ops_gen.ToTensorOp Functions --------- .. autoapisummary:: mlir.dialects._bufferization_ops_gen.alloc_tensor mlir.dialects._bufferization_ops_gen.clone mlir.dialects._bufferization_ops_gen.dealloc mlir.dialects._bufferization_ops_gen.dealloc_tensor mlir.dialects._bufferization_ops_gen.materialize_in_destination mlir.dialects._bufferization_ops_gen.to_buffer mlir.dialects._bufferization_ops_gen.to_tensor Module Contents --------------- .. py:data:: _ods_ir .. py:class:: _Dialect(descriptor: object) Bases: :py:obj:`_ods_ir` .. py:attribute:: DIALECT_NAMESPACE :value: 'bufferization' .. py:class:: AllocTensorOp(result, dynamic_sizes, *, copy=None, size_hint=None, memory_space=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` ``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. The optional ``size_hint`` operand specifies the number of non-zero elements for sparse tensors. The value of ``size_hint`` should be not less than 1 and not larger than the linear size of the corresponding dense tensor type. If this requirement is not met, the behavior of the operator is undefined. 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: .. code:: mlir %c = bufferization.alloc_tensor(%d1, %d2) : tensor %0 = linalg.matmul ins(%a, %b: tensor, tensor) outs(%c: tensor) -> tensor return %0 : tensor .. code:: mlir %c = bufferization.alloc_tensor(%d1, %d2) size_hint = %noe : tensor Note: An ``alloc_tensor`` with a ``copy`` should also be expressed as an ``alloc_tensor`` without ``copy``, followed by a ``copy_tensor``. .. py:attribute:: OPERATION_NAME :value: 'bufferization.alloc_tensor' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: dynamic_sizes() -> _ods_ir .. py:method:: copy() -> Optional[_ods_ir] .. py:method:: size_hint() -> Optional[_ods_ir] .. py:method:: memory_space() -> Optional[_ods_ir] .. py:method:: result() -> _ods_ir Shortcut to get an op result if it has only one (throws an error otherwise). .. py:function:: alloc_tensor(result, dynamic_sizes, *, copy=None, size_hint=None, memory_space=None, loc=None, ip=None) -> _ods_ir .. py:class:: CloneOp(output, input, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` Clones the data in the input view into an implicitly defined output view. Usage: .. code:: mlir %arg1 = bufferization.clone %arg0 : memref to memref 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. .. py:attribute:: OPERATION_NAME :value: 'bufferization.clone' .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: input() -> _ods_ir .. py:method:: output() -> _ods_ir .. py:function:: clone(output, input, *, loc=None, ip=None) -> _ods_ir .. py:class:: DeallocOp(memrefs, conditions, retained, *, results=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` This operation deallocates each of the given memrefs if there is no alias to that memref in the list of retained memrefs and the corresponding condition value is set. This condition can be used to indicate and pass on ownership of memref values (or in other words, the responsibility of deallocating that memref). If two memrefs alias each other, only one will be deallocated to avoid double free situations. The number of variadic ``memref`` operands (the memrefs to be deallocated) must equal the number of variadic ``condition`` operands and correspond to each other element-wise. The ``memref`` operands must be the originally allocated memrefs, however, the ``retained`` memref operands may be arbitrary memrefs. This operation returns a variadic number of ``updatedConditions`` operands, one updated condition per retained memref. An updated condition indicates the ownership of the respective retained memref. It is computed as the disjunction of all ``conditions`` operands where the corresponding to ``memrefs`` operand aliases with the retained memref. If the retained memref has no aliases among ``memrefs``, the resulting updated condition is 'false'. This is because all memrefs that need to be deallocated within one basic block should be added to the same ``bufferization.dealloc`` operation at the end of the block; if no aliasing memref is present, then it does not have to be deallocated and thus we don't need to claim ownership. If the memrefs to be deallocated are split over multiple dealloc operations (e.g., to avoid aliasing checks at runtime between the ``memref`` operands), then the results have to be manually combined using an ``arith.ori`` operation and all of them still require the same list of ``retained`` memref operands unless the (potentially empty) set of aliasing memrefs can be determined statically. In that case, the ``updatedCondition`` operand can be replaced accordingly (e.g., by a canonicalizer). Example: .. code:: mlir %0:3 = bufferization.dealloc (%a0, %a1 : memref<2xf32>, memref<4xi32>) if (%cond0, %cond1) retain (%r0, %r1, %r2 : memref, memref, memref<2xi32>) Deallocation will be called on ``%a0`` if ``%cond0`` is 'true' and neither ``%r0``, ``%r1``, or ``%r2`` are aliases of ``%a0``. ``%a1`` will be deallocated when ``%cond1`` is set to 'true' and none of ``%r0``, ``%r1``, ``%r2``, and ``%a0`` are aliases. Note that this can be an expensive operation if there are many operands that cannot be optimized away. The runtime cost of this operation (assuming that nothing is optimized away) is ``O(|memrefs|^2+|memrefs|*|retained|)``. The cost in terms of memory space is ``O(|memrefs|+|retained|)``. As a result, it is recommended to place it carefully in the IR such that most operands can be optimized away by running the ``buffer-deallocation-simplification`` pass. .. py:attribute:: OPERATION_NAME :value: 'bufferization.dealloc' .. py:attribute:: _ODS_OPERAND_SEGMENTS .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: memrefs() -> _ods_ir .. py:method:: conditions() -> _ods_ir .. py:method:: retained() -> _ods_ir .. py:method:: updatedConditions() -> _ods_ir .. py:function:: dealloc(memrefs, conditions, retained, *, results=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, DeallocOp] .. py:class:: DeallocTensorOp(tensor, *, loc=None, ip=None) Bases: :py:obj:`_ods_ir` ``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: .. code:: mlir bufferization.dealloc_tensor %tensor : tensor<1024x1024xf64, #CSR> .. py:attribute:: OPERATION_NAME :value: 'bufferization.dealloc_tensor' .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: tensor() -> _ods_ir .. py:function:: dealloc_tensor(tensor, *, loc=None, ip=None) -> DeallocTensorOp .. py:class:: MaterializeInDestinationOp(result, source, dest, *, restrict=None, writable=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` This op indicates that the data of the ``source`` tensor is guaranteed to materialize in ``dest``, which can be a tensor or a memref. In case of a tensor, ``source`` materializes in the future buffer of ``dest`` and a the updated destination tensor is returned. If this is not possible, e.g., because the destination tensor is read-only or because its original contents are still read later, the input IR fails to bufferize. In case of a memref, ``source`` materializes in ``dest``, which is already a buffer. The op has no results in that case. ``source``, ``dest`` and ``result`` (if present) must have the same runtime shape and element type. If the op has a result, the types of ``result`` and ``dest`` must match exactly (e.g., including any tensor encodings). By default, this op bufferizes to a memcpy from the future buffer of the ``source`` tensor to the future buffer of the ``dest`` tensor or to the ``dest`` buffer. However, transformations such as "empty tensor elimination" may rewrite IR such that a computation is performed directly in ``dest`` and no memcpy is needed. If ``dest`` is a buffer, the ``writable`` attribute must be specified and the ``restrict`` keyword can be specified. These attributes have the same meaning as the respective attributes of ``bufferization.to_tensor``. ``writable`` indicates that the ``dest`` buffer is considered writable. It does not make sense to materialize a computation in a read-only buffer, so ``writable`` is required. ``restrict`` indicates that there is no ``bufferization.to_tensor`` op and no other ``bufferization.materialize_in_destination`` op with ``dest`` (or an alias thereof) and "restrict". Only ops with this attribute are considered for "empty tensor elimination". As part of empty tensor elimination, a new ``to_tensor`` op with ``dest`` may be inserted and the ``restrict`` attribute is transferred from this op to the new ``to_tensor`` op. Having "restrict" on this op guarantees that performing empty tensor elimination would not create invalid IR (i.e., having multiple ``to_tensor restrict`` with aliasing buffers). Note: ``writable`` could be removed from this op because it must always be set for memref destinations. This op has that attribute to make clear the requirements on the ``dest`` operand in the op assembly format. Note: If ``dest`` is a tensor, ``tensor.insert_slice`` could be used for the same purpose, but since tensor dialect ops only indicate *what* should be computed but not *where*, it could fold away, causing the computation to materialize in a different buffer. .. py:attribute:: OPERATION_NAME :value: 'bufferization.materialize_in_destination' .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: source() -> _ods_ir .. py:method:: dest() -> _ods_ir .. py:method:: restrict() -> bool .. py:method:: writable() -> bool .. py:method:: result() -> Optional[_ods_ir] Shortcut to get an op result if it has only one (throws an error otherwise). .. py:function:: materialize_in_destination(result, source, dest, *, restrict=None, writable=None, loc=None, ip=None) -> Union[_ods_ir, _ods_ir, MaterializeInDestinationOp] .. py:class:: ToBufferOp(buffer, tensor, *, read_only=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` An operation that returns the future buffer of a ``tensor``. .. code:: mlir // Result type is memref<4x?xf32, #layout, 0> %m = bufferization.to_buffer %t : tensor<4x?xf32> to memref<4x?xf32, #layout, 0> This operation is a specialized variant of the built-in ``unrealized_conversion_cast`` and is used to make sure that the IR stays valid at any point during the bufferization. The ``read_only`` attribute can optionally be set, indicating to the bufferization that the buffer returned by this op (or an alias created from the returned buffer) will not be written to. .. py:attribute:: OPERATION_NAME :value: 'bufferization.to_buffer' .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: tensor() -> _ods_ir .. py:method:: read_only() -> bool .. py:method:: buffer() -> _ods_ir .. py:function:: to_buffer(buffer, tensor, *, read_only=None, loc=None, ip=None) -> _ods_ir .. py:class:: ToTensorOp(result, buffer, *, restrict=None, writable=None, loc=None, ip=None) Bases: :py:obj:`_ods_ir` An operation that creates a tensor from a buffer. The result value is a tensor-like type that must match the corresponding buffer-like operand as per TensorLikeType::verifyCompatibleBufferType(). For builtins (TensorType and BaseMemRefType), this means that shapes and element types match between the tensor and the buffer. The opposite of this op is ``to_buffer``. Together, these two ops are useful for source/target materializations when doing type conversions involving tensors and buffers. Example: .. code:: mlir // Produces a value of tensor<4x?xf32> type. %t = bufferization.to_tensor %m : memref<4x?xf32, #layout, 0> to tensor<4x?xf32> If the ``writable`` unit attribute is set, the produced tensor is considered "writable" during bufferization. Otherwise, every OpOperand that bufferizes to a write to the future buffer of the resulting tensor (or an alias thereof) will bufferize out-of-place to prevent emitting any writes to ``memref`` during bufferization. The ``restrict`` unit attribute (similar to the C ``restrict`` keyword) indicates that the produced tensor result is the only way for the tensor IR to gain access to the ``memref`` operand (or an alias thereof). E.g., there must be no other ``to_tensor`` op with the same or with an aliasing ``memref`` operand. Note: Only ``to_tensor`` ops with the ``restrict`` unit attribute are supported by One-Shot Bufferize. Other IR is rejected. (To support ``to_tensor`` without ``restrict``, One-Shot Bufferize would have to analyze memref IR.) Ops that have incorrect usage of ``restrict`` may bufferize incorrectly. Example: .. code:: %t = bufferization.to_tensor %m restrict writable : memref<4xf32> to tensor<4xf32> // %t is writable, so the tensor.insert may bufferize in-place in the // absence of other conflicts. %r = tensor.insert %f into %t[%idx] : tensor<4xf32> ``to_tensor`` ops are not bufferized. They are expected to fold away after bufferization. If there are non-bufferizable ops in the IR and ``allowUnknownOps`` is set, they may be part of the resulting IR and not fold away. However, such IR is no longer bufferizable with One-Shot Bufferize. .. py:attribute:: OPERATION_NAME :value: 'bufferization.to_tensor' .. py:attribute:: _ODS_REGIONS :value: (0, True) .. py:method:: buffer() -> _ods_ir .. py:method:: restrict() -> bool .. py:method:: writable() -> bool .. py:method:: result() -> _ods_ir Shortcut to get an op result if it has only one (throws an error otherwise). .. py:function:: to_tensor(result, buffer, *, restrict=None, writable=None, loc=None, ip=None) -> _ods_ir