433 lines
21 KiB
C++
433 lines
21 KiB
C++
/*===- TableGen'erated file -------------------------------------*- C++ -*-===*\
|
|
|* *|
|
|
|* Interface Definitions *|
|
|
|* *|
|
|
|* Automatically generated file, do not edit! *|
|
|
|* *|
|
|
\*===----------------------------------------------------------------------===*/
|
|
|
|
|
|
/// Return `true` if the given Value may bufferize to a new buffer
|
|
/// allocation. If it is statically unknown that the given Value
|
|
/// bufferizes to a buffer allocation, `true` should be returned.
|
|
bool mlir::bufferization::BufferizableOpInterface::bufferizesToAllocation(::mlir::Value value) {
|
|
return getImpl()->bufferizesToAllocation(getImpl(), getOperation(), value);
|
|
}
|
|
|
|
/// Return `true` if the given OpOperand bufferizes to a memory read. This
|
|
/// method will never be called on OpOperands that do not have a tensor
|
|
/// type.
|
|
///
|
|
/// Note: It is always safe to consider an OpOperand as a memory read,
|
|
/// even if it does actually not read; however, this can introduce
|
|
/// unnecessary out-of-place bufferization decisions. One-Shot Analysis
|
|
/// considers OpOperands of unknown ops (that do not implement this
|
|
/// interface) as reading OpOperands.
|
|
bool mlir::bufferization::BufferizableOpInterface::bufferizesToMemoryRead(::mlir::OpOperand & opOperand, const ::mlir::bufferization::AnalysisState & state) {
|
|
return getImpl()->bufferizesToMemoryRead(getImpl(), getOperation(), opOperand, state);
|
|
}
|
|
|
|
/// Return `true` if the given OpOperand bufferizes to a memory write.
|
|
///
|
|
/// This method will never be called on OpOperands that do not have a
|
|
/// tensor type.
|
|
///
|
|
/// This method will never be called on OpOperands that do not have an
|
|
/// aliasing Value. Intuitively, it does not make sense for an OpOperand
|
|
/// to bufferize to a memory write without returning an aliasing tensor,
|
|
/// because the write would have no visible effect outside of the op.
|
|
///
|
|
/// Note: It is always safe to consider an OpOperand as a memory write,
|
|
/// even if it does actually not write; however, this can introduce
|
|
/// unnecessary out-of-place bufferization decisions. One-Shot Analysis
|
|
/// considers OpOperands of unknown ops (that do not implement this
|
|
/// interface) as writing OpOperands.
|
|
bool mlir::bufferization::BufferizableOpInterface::bufferizesToMemoryWrite(::mlir::OpOperand & opOperand, const ::mlir::bufferization::AnalysisState & state) {
|
|
return getImpl()->bufferizesToMemoryWrite(getImpl(), getOperation(), opOperand, state);
|
|
}
|
|
|
|
/// Return `true` if the operation bufferizes to IR that performs only
|
|
/// element-wise accesses on the specified tensor operands. (The operands
|
|
/// must have the same shape.) The `bufferize` method must be implemented
|
|
/// in such a way that it is free of loop-carried dependences. I.e., all
|
|
/// loads at a position appear before all stores at the same position.
|
|
///
|
|
/// Example: Consider a hypothetical op element-wise op, where the "ins"
|
|
/// bufferize to a memory read and the "outs" bufferize to a memory write.
|
|
/// ```
|
|
/// test.element_wise ins(%0), outs(%1) : tensor<3xf32>
|
|
/// ```
|
|
///
|
|
/// The following is a valid access pattern:
|
|
/// ```
|
|
/// load(%0[1])
|
|
/// store(%1[1])
|
|
/// load(%0[2])
|
|
/// store(%1[2])
|
|
/// load(%0[0])
|
|
/// store(%1[0])
|
|
/// ```
|
|
///
|
|
/// The following would be an invalid (not element-wise) access pattern:
|
|
/// ```
|
|
/// load(%0[1])
|
|
/// store(%0[1])
|
|
/// load(%0[1])
|
|
/// ...
|
|
/// ```
|
|
///
|
|
/// Element-wise ops can sometimes bufferize more efficiently: a RaW
|
|
/// conflict between two operands of the same op can be avoided if it is
|
|
/// guaranteed that an original element value is no longer needed after
|
|
/// writing a computed element value at the same location. E.g., such an
|
|
/// optimization is possible in the above example if %0 and %1 are
|
|
/// equivalent tensors. (It is not possible, if %0 and %1 are merely
|
|
/// aliasing. It is not necessary if %0 and %1 are not aliasing at all,
|
|
/// because there would be no conflict anyway.)
|
|
///
|
|
/// Note: Tensor operands that are not included in `opOperands` can be
|
|
/// ignored. A conservative implementation of this interface method may
|
|
/// always return "false".
|
|
bool mlir::bufferization::BufferizableOpInterface::bufferizesToElementwiseAccess(const ::mlir::bufferization::AnalysisState & state, ::llvm::ArrayRef<::mlir::OpOperand *> opOperands) {
|
|
return getImpl()->bufferizesToElementwiseAccess(getImpl(), getOperation(), state, opOperands);
|
|
}
|
|
|
|
/// Return `true` if the given OpResult bufferizes to a memory write.
|
|
/// This is the same property as `bufferizesToMemoryWrite`, but from The
|
|
/// perspective of OpResults.
|
|
///
|
|
/// This method will never be called on OpResults that do not have a
|
|
/// tensor type.
|
|
///
|
|
/// This method has a default implementation. By default, it returns
|
|
/// `true` if any of the following three cases applies.
|
|
///
|
|
/// 1. There is no corresponding aliasing OpOperand.
|
|
///
|
|
/// Example: `tensor.generate ... : tensor<10xf32>`
|
|
/// The op fills a newly allocated buffer and bufferizes to a memory
|
|
/// write.
|
|
///
|
|
/// Counter-example: bufferization.alloc_tensor
|
|
/// The op just allocates and does not specify the data of the tensor,
|
|
/// so resultBufferizesToMemoryWrite is overridden to return false.
|
|
///
|
|
/// 2. At least one aliasing OpOperand bufferizes to a memory write.
|
|
///
|
|
/// Example: `tensor.insert %f into %t[...] : tensor<?xf32>`
|
|
/// The destination OpOperand bufferizes to a memory write, so the
|
|
/// result also bufferizes to a memory write.
|
|
///
|
|
/// 3. At least one aliasing OpOperand's value is defined inside the
|
|
/// defining op of the given OpResult and it is a memory write.
|
|
///
|
|
/// According to this rule, an aliasing OpOperand value that is defined
|
|
/// inside this op and is bufferizing to a memory write makes the given
|
|
/// OpResult bufferize to a memory write.
|
|
///
|
|
/// Example:
|
|
/// ```
|
|
/// %r = scf.if ... -> tensor<?xf32> {
|
|
/// %1 = tensor.insert %f into %t[...] : tensor<?xf32>
|
|
/// scf.yield %1 : tensor<?xf32>
|
|
/// } else { ... }
|
|
/// ```
|
|
/// The scf.if result bufferizes to a memory write because %1 (an
|
|
/// OpResult defined inside the scf.if op) bufferizes to a memory
|
|
/// write.
|
|
bool mlir::bufferization::BufferizableOpInterface::resultBufferizesToMemoryWrite(::mlir::OpResult opResult, const ::mlir::bufferization::AnalysisState & state) {
|
|
return getImpl()->resultBufferizesToMemoryWrite(getImpl(), getOperation(), opResult, state);
|
|
}
|
|
|
|
/// Return `true` if the given OpOperand must bufferize in-place. Alias
|
|
/// sets and inplace attributes will be set up accordingly before making
|
|
/// any other bufferization decisions. This method will never be called on
|
|
/// OpOperands that do not have a tensor type.
|
|
///
|
|
/// Note: Unranked tensor OpOperands always bufferize in-place. This could
|
|
/// be extended in the future. Unranked tensors are used with external
|
|
/// functions only.
|
|
bool mlir::bufferization::BufferizableOpInterface::mustBufferizeInPlace(::mlir::OpOperand & opOperand, const ::mlir::bufferization::AnalysisState & state) {
|
|
return getImpl()->mustBufferizeInPlace(getImpl(), getOperation(), opOperand, state);
|
|
}
|
|
|
|
/// Return the Values that may alias with a given OpOperand when
|
|
/// bufferized in-place. This method will never be called on OpOperands
|
|
/// that do not have a tensor type.
|
|
///
|
|
/// This method can return multiple Values, indicating that a given
|
|
/// OpOperand may at runtime alias with any (or multiple) of the returned
|
|
/// Values.
|
|
///
|
|
/// Each alias is specified with a degree of certainty:
|
|
///
|
|
/// * MAYBE (`isDefinite = false`): At runtime, buffer(opOperand) may
|
|
/// alias with the specified Value.
|
|
/// * DEFINITE (`isDefinite = true`, default): At runtime,
|
|
/// buffer(opOperand) is guaranteed to alias the buffer of the specified
|
|
/// Value. This is a stronger property than MAYBE and allows for more
|
|
/// precise analyses. DEFINITE properties should be used when possible.
|
|
///
|
|
/// Furthermore, each alias is specified with a buffer relation:
|
|
///
|
|
/// * `BufferRelation::Equivalent`: Both aliases are the exact same
|
|
/// buffer. I.e., same size, no offset, same strides.
|
|
/// * `BufferRelation::Unknown`: There is no further information apart
|
|
/// from the fact that both buffers alias.
|
|
///
|
|
/// False positives are allowed in the list of Values, but they can
|
|
/// adversely affect the accuracy of the anlysis. On the contrary,
|
|
/// omitting potential aliases is incorrect.
|
|
///
|
|
/// One possible (conservative) implementation of this interface method,
|
|
/// that is always safe, is to return all tensor Values with
|
|
/// BufferRelation::Unknown and MAYBE.
|
|
///
|
|
/// Examples:
|
|
///
|
|
/// ```
|
|
/// // aliasingValues(%t) = DEFINITE {Equivalent %r}
|
|
/// %r = tensor.insert_slice %f into %t : tensor<10xf32>
|
|
///
|
|
/// // aliasingValues(%t) = DEFINITE {Unknown %r}
|
|
/// // Note: "Buffer is subset of buffer" relationship are not yet
|
|
/// // supported, so "Unknown" is the best we can do for now.
|
|
/// %r = tensor.extract_slice %t[0]][5][1]
|
|
/// : tensor<10xf32> to tensor<5xf32>
|
|
///
|
|
/// // aliasingValues(%t1) = MAYBE {Equivalent %r}
|
|
/// // aliasingValues(%t2) = MAYBE {Equivalent %r}
|
|
/// %r = arith.select %c, %t1, %t2 : tensor<10xf32>
|
|
///
|
|
/// // A hypothetical op that bufferizes to rolling a dice and based on
|
|
/// // the result to either return buffer(%t) or a newly allocated copy
|
|
/// // thereof.
|
|
/// // aliasingValues(%t) = MAYBE {Equivalent %r}
|
|
/// %r = "dummy.alias_or_copy(%t) : (tensor<10xf32>) -> (tensor<10xf32>)"
|
|
/// ```
|
|
::mlir::bufferization::AliasingValueList mlir::bufferization::BufferizableOpInterface::getAliasingValues(::mlir::OpOperand & opOperand, const ::mlir::bufferization::AnalysisState & state) {
|
|
return getImpl()->getAliasingValues(getImpl(), getOperation(), opOperand, state);
|
|
}
|
|
|
|
/// Return the OpOperands that alias with a given Value when bufferized
|
|
/// in-place. This method will never be called on Values that do not
|
|
/// have a tensor type.
|
|
///
|
|
/// By default, this method is the inverse of `getAliasingValues`. Ops
|
|
/// with a region that yield values may want to override this method to
|
|
/// return the OpOperands that are yielded by the terminator.
|
|
///
|
|
/// This method can return multiple OpOperands, indicating that a given
|
|
/// Value may at runtime alias with any (or multiple) of the returned
|
|
/// OpOperands.
|
|
///
|
|
/// This property is specified with a degree of certainty:
|
|
///
|
|
/// * MAYBE (`isDefinite = false`): At runtime, buffer(value) may alias
|
|
/// with the specified OpOperand.
|
|
/// * DEFINITE (`isDefinite = true`, default): At runtime,
|
|
/// buffer(value) is guaranteed to alias the buffer of the specified
|
|
/// OpOperand. This is a stronger property than MAYBE and allows for
|
|
/// more precise analyses. DEFINITE properties should be used when
|
|
/// possible.
|
|
///
|
|
/// For each alias, a BufferRelation can be specified:
|
|
///
|
|
/// * `BufferRelation::Equivalent`: Both aliases are the exact same
|
|
/// buffer. I.e., same size, no offset, same strides.
|
|
/// * `BufferRelation::Unknown`: There is no further information apart
|
|
/// from the fact that both buffers alias.
|
|
///
|
|
/// False positives are allowed in the list of OpOperands, but they can
|
|
/// adversely affect the accuracy of the anlysis. On the contrary,
|
|
/// omitting potential aliases is incorrect.
|
|
///
|
|
/// One possible (conservative) implementation of this interface method,
|
|
/// that is always safe, is to return all tensor OpOperands with
|
|
/// BufferRelation::Unknown and MAYBE.
|
|
///
|
|
/// Note: If the returned list of OpOperands is empty, this op definitely
|
|
/// bufferizes to a new allocation. In that case `bufferizesToAllocation`
|
|
/// must return `true`.
|
|
///
|
|
/// Examples:
|
|
///
|
|
/// ```
|
|
/// // aliasingOpOperands(%r) = DEFINITE {Equivalent %t}
|
|
/// %r = tensor.insert_slice %f into %t : tensor<10xf32>
|
|
///
|
|
/// // aliasingOpOperands(%r) = DEFINITE {Unknown %t}
|
|
/// %r = tensor.extract_slice %t[0]][5][1]
|
|
/// : tensor<10xf32> to tensor<5xf32>
|
|
///
|
|
/// // aliasingOpOperands(%r) = DEFINITE {Equivalent %t1, Equivalent %t2}
|
|
/// %r = arith.select %c, %t1, %t2 : tensor<10xf32>
|
|
///
|
|
/// // aliasingOpOperands(%r) = MAYBE {}
|
|
/// %r = tensor.empty() : tensor<10xf32>
|
|
/// ```
|
|
::mlir::bufferization::AliasingOpOperandList mlir::bufferization::BufferizableOpInterface::getAliasingOpOperands(::mlir::Value value, const ::mlir::bufferization::AnalysisState & state) {
|
|
return getImpl()->getAliasingOpOperands(getImpl(), getOperation(), value, state);
|
|
}
|
|
|
|
/// Resolve all inplacability conflicts by inserting explicit
|
|
/// `bufferization.alloc_tensor` ops. Examples of inplacability conflicts
|
|
/// are read-after-write conflicts or writes into non-writable buffers.
|
|
///
|
|
/// This method should rewrite the IR in such a way that for each tensor
|
|
/// OpOperand t, buffer(t) can be directly used when during bufferization.
|
|
/// The bufferization does no longer have to care about inplacability
|
|
/// conflicts.
|
|
///
|
|
/// This method can query analysis information from the given analysis
|
|
/// state.
|
|
::llvm::LogicalResult mlir::bufferization::BufferizableOpInterface::resolveConflicts(::mlir::RewriterBase & rewriter, const ::mlir::bufferization::AnalysisState & analysisState, const ::mlir::bufferization::BufferizationState & bufferizationState) {
|
|
return getImpl()->resolveConflicts(getImpl(), getOperation(), rewriter, analysisState, bufferizationState);
|
|
}
|
|
|
|
/// Bufferize this op, i.e., rewrite it into a memref-based equivalent.
|
|
/// Buffers of tensor SSA values can be retrieved via `getBuffer`.
|
|
/// Uses of tensor results of the existing tensor op can be replaced with
|
|
/// `replaceOpWithBufferizedValues` or `replaceOpWithNewBufferizedOp`.
|
|
/// These two functions automatically handle the tensor-to-memref type
|
|
/// conversion.
|
|
///
|
|
/// The implementation of this method must be consistent with the
|
|
/// remaining methods, in particular `getAliasingOpOperands`. I.e., a
|
|
/// tensor result `r` may only be replaced with:
|
|
///
|
|
/// a) One of the buffers in getAliasingOpOperands(r).
|
|
/// b) Or: A newly allocated buffer (only if `bufferizesToAllocation`).
|
|
///
|
|
/// This method will never be called on ops that do not have at least one
|
|
/// tensor operand/result.
|
|
///
|
|
/// The return value of this method indicates whether there was an error
|
|
/// while bufferizing this op (such as failing to create a new buffer
|
|
/// allocation op). The bufferization driver immediately stops bufferizing
|
|
/// the input IR and returns `failure` in that case. If this op is
|
|
/// expected to survive bufferization, `success` should be returned
|
|
/// (together with `allow-unknown-ops` enabled).
|
|
///
|
|
/// If the bufferized operation has symbol semantics (i.e., it defines a
|
|
/// symbol or a symbol table), the implementation is expected to update
|
|
/// the cached symbol tables -- within the bufferization state -- to keep
|
|
/// them in a consistent state.
|
|
///
|
|
/// Note: If this op supports unstructured control flow in its regions,
|
|
/// then this function should also bufferize all block signatures that
|
|
/// belong to this op. Branch ops (that branch to a block) are typically
|
|
/// bufferized together with the block signature (this is just a
|
|
/// suggestion to make sure IR is valid at every point in time and could
|
|
/// be done differently).
|
|
::llvm::LogicalResult mlir::bufferization::BufferizableOpInterface::bufferize(::mlir::RewriterBase & rewriter, const ::mlir::bufferization::BufferizationOptions & options, ::mlir::bufferization::BufferizationState & state) {
|
|
return getImpl()->bufferize(getImpl(), getOperation(), rewriter, options, state);
|
|
}
|
|
|
|
/// Return `true` if the given Value can be written to in-place. Value is
|
|
/// either an OpResult of this operation or a BlockArgument of a block of
|
|
/// this operation.
|
|
///
|
|
/// Most OpResult buffers can be written to, but some ops such as
|
|
/// ConstantOp may bufferize to non-writable (read-only) memory locations.
|
|
/// Therefore, by default, this method returns `true` for OpResults. This
|
|
/// method will never be called on OpResults that do not have a tensor
|
|
/// type.
|
|
///
|
|
/// Whether a BlockArgument can be written to or not depends on the
|
|
/// operation. This method conservatively returns `false`. This method
|
|
/// will never be called on BlockArguments that do not have a tensor type.
|
|
bool mlir::bufferization::BufferizableOpInterface::isWritable(::mlir::Value value, const ::mlir::bufferization::AnalysisState & state) {
|
|
return getImpl()->isWritable(getImpl(), getOperation(), value, state);
|
|
}
|
|
|
|
/// Return `true` if the `uRead` and `uWrite` do not constitute a RaW
|
|
/// conflict. If they are conflicting or if it is unknown whether they are
|
|
/// conflicting, return `false`. This method will never be called with
|
|
/// OpOperands that do not have a tensor type. At least one of the two
|
|
/// given OpOperands belongs to this operation.
|
|
///
|
|
/// This method can be implemented to specify custom RaW analysis rules.
|
|
/// If this method returns `true` the given OpOperands are not considered
|
|
/// to be conflicting and do not force out-of-place bufferization. (There
|
|
/// may still be other conflicts that do.)
|
|
bool mlir::bufferization::BufferizableOpInterface::isNotConflicting(::mlir::OpOperand * uRead, ::mlir::OpOperand * uWrite, const ::mlir::bufferization::AnalysisState & state) {
|
|
return getImpl()->isNotConflicting(getImpl(), getOperation(), uRead, uWrite, state);
|
|
}
|
|
|
|
/// Return `failure` if this op does not pass the analysis. This method
|
|
/// is run during One-Shot Bufferize (after all post-analysis steps). If
|
|
/// the op does not pass the analysis, bufferization is aborted.
|
|
///
|
|
/// This method can be used to check expected invariants and limitations
|
|
/// of the current bufferization implementation.
|
|
::llvm::LogicalResult mlir::bufferization::BufferizableOpInterface::verifyAnalysis(const ::mlir::bufferization::AnalysisState & state) {
|
|
return getImpl()->verifyAnalysis(getImpl(), getOperation(), state);
|
|
}
|
|
|
|
/// Return the bufferized type of the given tensor value (without
|
|
/// bufferizing the IR). The value is either a BlockArgument of a block
|
|
/// that belongs to this op or an OpResult of the given op.
|
|
///
|
|
/// This method is useful when the bufferized type of value must be
|
|
/// predicted before modifying any IR.
|
|
///
|
|
/// Implementations may call `bufferization::getBufferType` to compute the
|
|
/// bufferized type of another SSA value. The same (unmodified)
|
|
/// `invocationStack` must be passed to that function. The stack contains
|
|
/// all SSA values for which a buffer type computation is currently in
|
|
/// progress. Implementations may inspect the stack to detect repetitive
|
|
/// computations for the same SSA value. (E.g., when bufferized types of a
|
|
/// loop.)
|
|
///
|
|
/// Note: This interface method should never be called directly from user
|
|
/// code. Always use `bufferization::getBufferType`.
|
|
::mlir::FailureOr<::mlir::bufferization::BufferLikeType> mlir::bufferization::BufferizableOpInterface::getBufferType(::mlir::Value value, const ::mlir::bufferization::BufferizationOptions & options, const ::mlir::bufferization::BufferizationState & state, ::llvm::SmallVector<::mlir::Value> & invocationStack) {
|
|
return getImpl()->getBufferType(getImpl(), getOperation(), value, options, state, invocationStack);
|
|
}
|
|
|
|
/// Return `true` if the given region of this op is repetitive. By default
|
|
/// this information is queried from the `RegionBranchOpInterface`. Ops
|
|
/// that do not implement this inferface can override this method to
|
|
/// declare regions as repetitive.
|
|
///
|
|
/// The RaW conflict detection of One-Shot Analysis is more strict inside
|
|
/// repetitive regions: Op dominance cannot always be used to rule out
|
|
/// certain potential conflicts (e.g., a conflicting write happening after
|
|
/// a read), because there may not be a meaningful ordering of certain ops
|
|
/// that are executed multiple times. This is described in more detail in
|
|
/// documentation of One-Shot Analysis.
|
|
bool mlir::bufferization::BufferizableOpInterface::isRepetitiveRegion(unsigned index) {
|
|
return getImpl()->isRepetitiveRegion(getImpl(), getOperation(), index);
|
|
}
|
|
|
|
/// Return `true` if the given region of this op is parallel, i.e.,
|
|
/// multiple instances of the region may be executing at the same time.
|
|
/// If a region is parallel, it must also be marked as "repetitive".
|
|
///
|
|
/// The RaW conflict detection of One-Shot Analysis is more strict inside
|
|
/// parallel regions: Buffer may have to be privatized.
|
|
///
|
|
/// By default, regions are assumed to be sequential.
|
|
bool mlir::bufferization::BufferizableOpInterface::isParallelRegion(unsigned index) {
|
|
return getImpl()->isParallelRegion(getImpl(), getOperation(), index);
|
|
}
|
|
|
|
/// Return "true" if the this op has tensor semantics and should be
|
|
/// bufferized. By default, ops with tensor operands, tensor op results
|
|
/// and/or tensor block arguments have tensor semantics.
|
|
///
|
|
/// This interface methods can be implemented by ops that should be
|
|
/// bufferized but do not have tensor semantics according to the above
|
|
/// definition. E.g., this function can return "true" for symbols.
|
|
bool mlir::bufferization::BufferizableOpInterface::hasTensorSemantics() {
|
|
return getImpl()->hasTensorSemantics(getImpl(), getOperation());
|
|
}
|
|
|
|
/// Return `true` if the op and this interface implementation supports
|
|
/// unstructured control flow. I.e., regions with multiple blocks. This is
|
|
/// not supported in most ops, so the default answer is `false`.
|
|
bool mlir::bufferization::BufferizableOpInterface::supportsUnstructuredControlFlow() {
|
|
return getImpl()->supportsUnstructuredControlFlow();
|
|
}
|