649 lines
29 KiB
TableGen
649 lines
29 KiB
TableGen
//===-- BufferizableOpInterface.td - Bufferizable Ops ------*- tablegen -*-===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#ifndef BUFFERIZABLE_OP_INTERFACE
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#define BUFFERIZABLE_OP_INTERFACE
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include "mlir/IR/OpBase.td"
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def BufferizableOpInterface : OpInterface<"BufferizableOpInterface"> {
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let description = [{
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An op interface for One-Shot Bufferize. Ops that implement this interface
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interface can be analyzed and bufferized using One-Shot Bufferize.
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Note: All "bufferizesTo*" and "getAliasing*" interface methods must be
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implemented conservatively. If it is not statically known whether an
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OpOperand/Value bufferizes in a certain way (e.g., to a memory write),
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the worst case must be assumed (e.g., that it does). Similarly,
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"getAliasing*" interface methods may always return additional OpOperands or
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Values, but must not miss an OpOperand or Value that could potentially
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alias at runtime.
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}];
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let cppNamespace = "::mlir::bufferization";
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let methods = [
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InterfaceMethod<
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/*desc=*/[{
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Return `true` if the given Value may bufferize to a new buffer
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allocation. If it is statically unknown that the given Value
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bufferizes to a buffer allocation, `true` should be returned.
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}],
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/*retType=*/"bool",
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/*methodName=*/"bufferizesToAllocation",
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/*args=*/(ins "::mlir::Value":$value),
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/*methodBody=*/"",
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/*defaultImplementation=*/"return false;"
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>,
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InterfaceMethod<
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/*desc=*/[{
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Return `true` if the given OpOperand bufferizes to a memory read. This
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method will never be called on OpOperands that do not have a tensor
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type.
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Note: It is always safe to consider an OpOperand as a memory read,
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even if it does actually not read; however, this can introduce
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unnecessary out-of-place bufferization decisions. One-Shot Analysis
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considers OpOperands of unknown ops (that do not implement this
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interface) as reading OpOperands.
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}],
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/*retType=*/"bool",
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/*methodName=*/"bufferizesToMemoryRead",
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/*args=*/(ins "::mlir::OpOperand &":$opOperand,
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"const ::mlir::bufferization::AnalysisState &":$state),
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/*methodBody=*/"",
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/*defaultImplementation=*/[{
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// Does not have to be implemented for ops without tensor OpOperands.
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llvm_unreachable("bufferizesToMemoryRead not implemented");
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}]
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>,
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InterfaceMethod<
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/*desc=*/[{
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Return `true` if the given OpOperand bufferizes to a memory write.
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This method will never be called on OpOperands that do not have a
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tensor type.
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This method will never be called on OpOperands that do not have an
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aliasing Value. Intuitively, it does not make sense for an OpOperand
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to bufferize to a memory write without returning an aliasing tensor,
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because the write would have no visible effect outside of the op.
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Note: It is always safe to consider an OpOperand as a memory write,
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even if it does actually not write; however, this can introduce
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unnecessary out-of-place bufferization decisions. One-Shot Analysis
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considers OpOperands of unknown ops (that do not implement this
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interface) as writing OpOperands.
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}],
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/*retType=*/"bool",
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/*methodName=*/"bufferizesToMemoryWrite",
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/*args=*/(ins "::mlir::OpOperand &":$opOperand,
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"const ::mlir::bufferization::AnalysisState &":$state),
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/*methodBody=*/"",
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/*defaultImplementation=*/[{
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// Does not have to be implemented for ops without tensor OpOperands.
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// Does not have to be implemented for OpOperands that do not have an
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// aliasing Value.
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llvm_unreachable("bufferizesToMemoryWrite not implemented");
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}]
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>,
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InterfaceMethod<
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/*desc=*/[{
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Return `true` if the operation bufferizes to IR that performs only
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element-wise accesses on the specified tensor operands. (The operands
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must have the same shape.) The `bufferize` method must be implemented
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in such a way that it is free of loop-carried dependences. I.e., all
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loads at a position appear before all stores at the same position.
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Example: Consider a hypothetical op element-wise op, where the "ins"
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bufferize to a memory read and the "outs" bufferize to a memory write.
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```
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test.element_wise ins(%0), outs(%1) : tensor<3xf32>
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```
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The following is a valid access pattern:
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```
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load(%0[1])
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store(%1[1])
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load(%0[2])
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store(%1[2])
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load(%0[0])
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store(%1[0])
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```
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The following would be an invalid (not element-wise) access pattern:
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```
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load(%0[1])
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store(%0[1])
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load(%0[1])
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...
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```
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Element-wise ops can sometimes bufferize more efficiently: a RaW
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conflict between two operands of the same op can be avoided if it is
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guaranteed that an original element value is no longer needed after
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writing a computed element value at the same location. E.g., such an
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optimization is possible in the above example if %0 and %1 are
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equivalent tensors. (It is not possible, if %0 and %1 are merely
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aliasing. It is not necessary if %0 and %1 are not aliasing at all,
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because there would be no conflict anyway.)
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Note: Tensor operands that are not included in `opOperands` can be
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ignored. A conservative implementation of this interface method may
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always return "false".
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}],
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/*retType=*/"bool",
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/*methodName=*/"bufferizesToElementwiseAccess",
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/*args=*/(ins "const ::mlir::bufferization::AnalysisState &":$state,
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"::llvm::ArrayRef<::mlir::OpOperand *>":$opOperands),
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/*methodBody=*/"",
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/*defaultImplementation=*/[{
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// It is always safe to assume that the op is not element-wise.
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return false;
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}]
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>,
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InterfaceMethod<
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/*desc=*/[{
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Return `true` if the given OpResult bufferizes to a memory write.
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This is the same property as `bufferizesToMemoryWrite`, but from The
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perspective of OpResults.
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This method will never be called on OpResults that do not have a
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tensor type.
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This method has a default implementation. By default, it returns
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`true` if any of the following three cases applies.
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1. There is no corresponding aliasing OpOperand.
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Example: `tensor.generate ... : tensor<10xf32>`
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The op fills a newly allocated buffer and bufferizes to a memory
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write.
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Counter-example: bufferization.alloc_tensor
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The op just allocates and does not specify the data of the tensor,
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so resultBufferizesToMemoryWrite is overridden to return false.
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2. At least one aliasing OpOperand bufferizes to a memory write.
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Example: `tensor.insert %f into %t[...] : tensor<?xf32>`
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The destination OpOperand bufferizes to a memory write, so the
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result also bufferizes to a memory write.
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3. At least one aliasing OpOperand's value is defined inside the
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defining op of the given OpResult and it is a memory write.
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According to this rule, an aliasing OpOperand value that is defined
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inside this op and is bufferizing to a memory write makes the given
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OpResult bufferize to a memory write.
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Example:
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```
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%r = scf.if ... -> tensor<?xf32> {
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%1 = tensor.insert %f into %t[...] : tensor<?xf32>
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scf.yield %1 : tensor<?xf32>
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} else { ... }
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```
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The scf.if result bufferizes to a memory write because %1 (an
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OpResult defined inside the scf.if op) bufferizes to a memory
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write.
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}],
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/*retType=*/"bool",
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/*methodName=*/"resultBufferizesToMemoryWrite",
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/*args=*/(ins "::mlir::OpResult":$opResult,
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"const ::mlir::bufferization::AnalysisState &":$state),
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/*methodBody=*/"",
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/*defaultImplementation=*/[{
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assert(opResult.getDefiningOp() == $_op.getOperation() &&
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"invalid OpResult");
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return ::mlir::bufferization::detail::defaultResultBufferizesToMemoryWrite(
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opResult, state);
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}]
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>,
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InterfaceMethod<
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/*desc=*/[{
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Return `true` if the given OpOperand must bufferize in-place. Alias
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sets and inplace attributes will be set up accordingly before making
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any other bufferization decisions. This method will never be called on
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OpOperands that do not have a tensor type.
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Note: Unranked tensor OpOperands always bufferize in-place. This could
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be extended in the future. Unranked tensors are used with external
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functions only.
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}],
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/*retType=*/"bool",
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/*methodName=*/"mustBufferizeInPlace",
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/*args=*/(ins "::mlir::OpOperand &":$opOperand,
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"const ::mlir::bufferization::AnalysisState &":$state),
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/*methodBody=*/"",
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/*defaultImplementation=*/[{
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return ::llvm::isa<::mlir::UnrankedTensorType>(opOperand.get().getType());
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}]
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>,
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InterfaceMethod<
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/*desc=*/[{
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Return the Values that may alias with a given OpOperand when
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bufferized in-place. This method will never be called on OpOperands
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that do not have a tensor type.
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This method can return multiple Values, indicating that a given
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OpOperand may at runtime alias with any (or multiple) of the returned
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Values.
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Each alias is specified with a degree of certainty:
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* MAYBE (`isDefinite = false`): At runtime, buffer(opOperand) may
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alias with the specified Value.
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* DEFINITE (`isDefinite = true`, default): At runtime,
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buffer(opOperand) is guaranteed to alias the buffer of the specified
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Value. This is a stronger property than MAYBE and allows for more
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precise analyses. DEFINITE properties should be used when possible.
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Furthermore, each alias is specified with a buffer relation:
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* `BufferRelation::Equivalent`: Both aliases are the exact same
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buffer. I.e., same size, no offset, same strides.
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* `BufferRelation::Unknown`: There is no further information apart
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from the fact that both buffers alias.
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False positives are allowed in the list of Values, but they can
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adversely affect the accuracy of the anlysis. On the contrary,
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omitting potential aliases is incorrect.
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One possible (conservative) implementation of this interface method,
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that is always safe, is to return all tensor Values with
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BufferRelation::Unknown and MAYBE.
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Examples:
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```
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// aliasingValues(%t) = DEFINITE {Equivalent %r}
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%r = tensor.insert_slice %f into %t : tensor<10xf32>
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// aliasingValues(%t) = DEFINITE {Unknown %r}
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// Note: "Buffer is subset of buffer" relationship are not yet
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// supported, so "Unknown" is the best we can do for now.
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%r = tensor.extract_slice %t[0]][5][1]
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: tensor<10xf32> to tensor<5xf32>
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// aliasingValues(%t1) = MAYBE {Equivalent %r}
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// aliasingValues(%t2) = MAYBE {Equivalent %r}
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%r = arith.select %c, %t1, %t2 : tensor<10xf32>
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// A hypothetical op that bufferizes to rolling a dice and based on
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// the result to either return buffer(%t) or a newly allocated copy
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// thereof.
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// aliasingValues(%t) = MAYBE {Equivalent %r}
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%r = "dummy.alias_or_copy(%t) : (tensor<10xf32>) -> (tensor<10xf32>)"
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```
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}],
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/*retType=*/"::mlir::bufferization::AliasingValueList",
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/*methodName=*/"getAliasingValues",
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/*args=*/(ins "::mlir::OpOperand &":$opOperand,
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"const ::mlir::bufferization::AnalysisState &":$state),
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/*methodBody=*/"",
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/*defaultImplementation=*/[{
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// Does not have to be implemented for ops without tensor OpOperands.
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assert(::llvm::isa<::mlir::bufferization::TensorLikeType>(opOperand.get().getType()) &&
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"expected OpOperand with tensor like type");
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llvm_unreachable("getAliasingValues not implemented");
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}]
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>,
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InterfaceMethod<
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/*desc=*/[{
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Return the OpOperands that alias with a given Value when bufferized
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in-place. This method will never be called on Values that do not
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have a tensor type.
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By default, this method is the inverse of `getAliasingValues`. Ops
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with a region that yield values may want to override this method to
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return the OpOperands that are yielded by the terminator.
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This method can return multiple OpOperands, indicating that a given
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Value may at runtime alias with any (or multiple) of the returned
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OpOperands.
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This property is specified with a degree of certainty:
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* MAYBE (`isDefinite = false`): At runtime, buffer(value) may alias
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with the specified OpOperand.
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* DEFINITE (`isDefinite = true`, default): At runtime,
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buffer(value) is guaranteed to alias the buffer of the specified
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OpOperand. This is a stronger property than MAYBE and allows for
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more precise analyses. DEFINITE properties should be used when
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possible.
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For each alias, a BufferRelation can be specified:
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* `BufferRelation::Equivalent`: Both aliases are the exact same
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buffer. I.e., same size, no offset, same strides.
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* `BufferRelation::Unknown`: There is no further information apart
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from the fact that both buffers alias.
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False positives are allowed in the list of OpOperands, but they can
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adversely affect the accuracy of the anlysis. On the contrary,
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omitting potential aliases is incorrect.
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One possible (conservative) implementation of this interface method,
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that is always safe, is to return all tensor OpOperands with
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BufferRelation::Unknown and MAYBE.
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Note: If the returned list of OpOperands is empty, this op definitely
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bufferizes to a new allocation. In that case `bufferizesToAllocation`
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must return `true`.
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Examples:
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```
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// aliasingOpOperands(%r) = DEFINITE {Equivalent %t}
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%r = tensor.insert_slice %f into %t : tensor<10xf32>
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// aliasingOpOperands(%r) = DEFINITE {Unknown %t}
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%r = tensor.extract_slice %t[0]][5][1]
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: tensor<10xf32> to tensor<5xf32>
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// aliasingOpOperands(%r) = DEFINITE {Equivalent %t1, Equivalent %t2}
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%r = arith.select %c, %t1, %t2 : tensor<10xf32>
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// aliasingOpOperands(%r) = MAYBE {}
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%r = tensor.empty() : tensor<10xf32>
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```
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}],
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/*retType=*/"::mlir::bufferization::AliasingOpOperandList",
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/*methodName=*/"getAliasingOpOperands",
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/*args=*/(ins "::mlir::Value":$value,
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"const ::mlir::bufferization::AnalysisState &":$state),
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/*methodBody=*/"",
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/*defaultImplementation=*/[{
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assert(isa<::mlir::bufferization::TensorLikeType>(value.getType()) &&
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"expected tensor like type");
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return ::mlir::bufferization::detail::defaultGetAliasingOpOperands(
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value, state);
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}]
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>,
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InterfaceMethod<
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/*desc=*/[{
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Resolve all inplacability conflicts by inserting explicit
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`bufferization.alloc_tensor` ops. Examples of inplacability conflicts
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are read-after-write conflicts or writes into non-writable buffers.
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This method should rewrite the IR in such a way that for each tensor
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OpOperand t, buffer(t) can be directly used when during bufferization.
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The bufferization does no longer have to care about inplacability
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conflicts.
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This method can query analysis information from the given analysis
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state.
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}],
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/*retType=*/"::llvm::LogicalResult",
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/*methodName=*/"resolveConflicts",
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/*args=*/(ins "::mlir::RewriterBase &":$rewriter,
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"const ::mlir::bufferization::AnalysisState &":$analysisState,
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"const ::mlir::bufferization::BufferizationState &":$bufferizationState),
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/*methodBody=*/"",
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/*defaultImplementation=*/[{
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auto bufferizableOp =
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::llvm::cast<BufferizableOpInterface>($_op.getOperation());
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return bufferizableOp.resolveTensorOpOperandConflicts(
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rewriter, analysisState, bufferizationState);
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}]
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>,
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InterfaceMethod<
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/*desc=*/[{
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Bufferize this op, i.e., rewrite it into a memref-based equivalent.
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Buffers of tensor SSA values can be retrieved via `getBuffer`.
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Uses of tensor results of the existing tensor op can be replaced with
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`replaceOpWithBufferizedValues` or `replaceOpWithNewBufferizedOp`.
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These two functions automatically handle the tensor-to-memref type
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conversion.
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The implementation of this method must be consistent with the
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remaining methods, in particular `getAliasingOpOperands`. I.e., a
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tensor result `r` may only be replaced with:
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a) One of the buffers in getAliasingOpOperands(r).
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b) Or: A newly allocated buffer (only if `bufferizesToAllocation`).
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This method will never be called on ops that do not have at least one
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tensor operand/result.
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The return value of this method indicates whether there was an error
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while bufferizing this op (such as failing to create a new buffer
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allocation op). The bufferization driver immediately stops bufferizing
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the input IR and returns `failure` in that case. If this op is
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expected to survive bufferization, `success` should be returned
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(together with `allow-unknown-ops` enabled).
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If the bufferized operation has symbol semantics (i.e., it defines a
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symbol or a symbol table), the implementation is expected to update
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the cached symbol tables -- within the bufferization state -- to keep
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them in a consistent state.
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Note: If this op supports unstructured control flow in its regions,
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then this function should also bufferize all block signatures that
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belong to this op. Branch ops (that branch to a block) are typically
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bufferized together with the block signature (this is just a
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suggestion to make sure IR is valid at every point in time and could
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be done differently).
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}],
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/*retType=*/"::llvm::LogicalResult",
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/*methodName=*/"bufferize",
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/*args=*/(ins "::mlir::RewriterBase &":$rewriter,
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"const ::mlir::bufferization::BufferizationOptions &":$options,
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"::mlir::bufferization::BufferizationState &":$state),
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/*methodBody=*/"",
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/*defaultImplementation=*/[{
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llvm_unreachable("bufferize not implemented");
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return ::mlir::failure();
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}]
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>,
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InterfaceMethod<
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/*desc=*/[{
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Return `true` if the given Value can be written to in-place. Value is
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either an OpResult of this operation or a BlockArgument of a block of
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this operation.
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Most OpResult buffers can be written to, but some ops such as
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ConstantOp may bufferize to non-writable (read-only) memory locations.
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Therefore, by default, this method returns `true` for OpResults. This
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method will never be called on OpResults that do not have a tensor
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type.
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Whether a BlockArgument can be written to or not depends on the
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operation. This method conservatively returns `false`. This method
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will never be called on BlockArguments that do not have a tensor type.
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}],
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/*retType=*/"bool",
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/*methodName=*/"isWritable",
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/*args=*/(ins "::mlir::Value":$value,
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"const ::mlir::bufferization::AnalysisState &":$state),
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/*methodBody=*/"",
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/*defaultImplementation=*/[{
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return ::llvm::isa<::mlir::OpResult>(value);
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}]
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>,
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InterfaceMethod<
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/*desc=*/[{
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Return `true` if the `uRead` and `uWrite` do not constitute a RaW
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conflict. If they are conflicting or if it is unknown whether they are
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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.)
|
|
}],
|
|
/*retType=*/"bool",
|
|
/*methodName=*/"isNotConflicting",
|
|
/*args=*/(ins "::mlir::OpOperand *":$uRead,
|
|
"::mlir::OpOperand *":$uWrite,
|
|
"const ::mlir::bufferization::AnalysisState &":$state),
|
|
/*methodBody=*/"",
|
|
/*defaultImplementation=*/[{
|
|
return false;
|
|
}]
|
|
>,
|
|
InterfaceMethod<
|
|
/*desc=*/[{
|
|
Return `failure` if this op does not pass the analysis. This method
|
|
is run during One-Shot Bufferize (after all post-analysis steps). If
|
|
the op does not pass the analysis, bufferization is aborted.
|
|
|
|
This method can be used to check expected invariants and limitations
|
|
of the current bufferization implementation.
|
|
}],
|
|
/*retType=*/"::llvm::LogicalResult",
|
|
/*methodName=*/"verifyAnalysis",
|
|
/*args=*/(ins "const ::mlir::bufferization::AnalysisState &":$state),
|
|
/*methodBody=*/"",
|
|
/*defaultImplementation=*/[{
|
|
return ::mlir::success();
|
|
}]
|
|
>,
|
|
InterfaceMethod<
|
|
/*desc=*/[{
|
|
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`.
|
|
}],
|
|
/*retType=*/"::mlir::FailureOr<::mlir::bufferization::BufferLikeType>",
|
|
/*methodName=*/"getBufferType",
|
|
/*args=*/(ins "::mlir::Value":$value,
|
|
"const ::mlir::bufferization::BufferizationOptions &":$options,
|
|
"const ::mlir::bufferization::BufferizationState &":$state,
|
|
"::llvm::SmallVector<::mlir::Value> &":$invocationStack),
|
|
/*methodBody=*/"",
|
|
/*defaultImplementation=*/[{
|
|
assert(getOwnerOfValue(value) == $_op.getOperation() &&
|
|
"expected that value belongs to this op");
|
|
assert(invocationStack.back() == value &&
|
|
"inconsistant invocation stack");
|
|
return ::mlir::bufferization::detail::defaultGetBufferType(
|
|
value, options, state, invocationStack);
|
|
}]
|
|
>,
|
|
InterfaceMethod<
|
|
/*desc=*/[{
|
|
Return `true` if the given region of this op is repetitive. By default
|
|
this information is queried from the `RegionBranchOpInterface`. Ops
|
|
that do not implement this inferface can override this method to
|
|
declare regions as repetitive.
|
|
|
|
The RaW conflict detection of One-Shot Analysis is more strict inside
|
|
repetitive regions: Op dominance cannot always be used to rule out
|
|
certain potential conflicts (e.g., a conflicting write happening after
|
|
a read), because there may not be a meaningful ordering of certain ops
|
|
that are executed multiple times. This is described in more detail in
|
|
documentation of One-Shot Analysis.
|
|
}],
|
|
/*retType=*/"bool",
|
|
/*methodName=*/"isRepetitiveRegion",
|
|
/*args=*/(ins "unsigned":$index),
|
|
/*methodBody=*/"",
|
|
/*defaultImplementation=*/[{
|
|
return ::mlir::bufferization::detail::defaultIsRepetitiveRegion(
|
|
::llvm::cast<BufferizableOpInterface>($_op.getOperation()), index);
|
|
}]
|
|
>,
|
|
InterfaceMethod<
|
|
/*desc=*/[{
|
|
Return `true` if the given region of this op is parallel, i.e.,
|
|
multiple instances of the region may be executing at the same time.
|
|
If a region is parallel, it must also be marked as "repetitive".
|
|
|
|
The RaW conflict detection of One-Shot Analysis is more strict inside
|
|
parallel regions: Buffer may have to be privatized.
|
|
|
|
By default, regions are assumed to be sequential.
|
|
}],
|
|
/*retType=*/"bool",
|
|
/*methodName=*/"isParallelRegion",
|
|
/*args=*/(ins "unsigned":$index),
|
|
/*methodBody=*/"",
|
|
/*defaultImplementation=*/[{
|
|
return false;
|
|
}]
|
|
>,
|
|
InterfaceMethod<
|
|
/*desc=*/[{
|
|
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.
|
|
}],
|
|
/*retType=*/"bool",
|
|
/*methodName=*/"hasTensorSemantics",
|
|
/*args=*/(ins),
|
|
/*methodBody=*/"",
|
|
/*defaultImplementation=*/[{
|
|
return ::mlir::bufferization::detail
|
|
::defaultHasTensorSemantics($_op.getOperation());
|
|
}]
|
|
>,
|
|
StaticInterfaceMethod<
|
|
/*desc=*/[{
|
|
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`.
|
|
}],
|
|
/*retType=*/"bool",
|
|
/*methodName=*/"supportsUnstructuredControlFlow",
|
|
/*args=*/(ins),
|
|
/*methodBody=*/"",
|
|
/*defaultImplementation=*/[{
|
|
return false;
|
|
}]
|
|
>,
|
|
];
|
|
|
|
let extraClassDeclaration = [{
|
|
/// Resolve out-of-place tensor OpOperands with explicit allocations in the
|
|
/// form of `bufferization.alloc_tensor` ops.
|
|
::llvm::LogicalResult resolveTensorOpOperandConflicts(
|
|
::mlir::RewriterBase &rewriter,
|
|
const ::mlir::bufferization::AnalysisState &analysisState,
|
|
const ::mlir::bufferization::BufferizationState &bufferizationState);
|
|
|
|
/// Return `true` if the given OpOperand creates an alias but does neither
|
|
/// read nor write. This implies that `bufferizesToMemoryRead` and
|
|
/// `bufferizesToMemoryWrite` must return `false`. This method will never
|
|
/// be called on OpOperands that do not have a tensor type.
|
|
///
|
|
/// Examples of such ops are `tensor.extract_slice` and `tensor.cast`.
|
|
bool bufferizesToAliasOnly(
|
|
::mlir::OpOperand &opOperand,
|
|
const ::mlir::bufferization::AnalysisState &state) {
|
|
auto bufferizableOp =
|
|
::llvm::cast<::mlir::bufferization::BufferizableOpInterface>(getOperation());
|
|
return !bufferizableOp.bufferizesToMemoryRead(opOperand, state)
|
|
&& !bufferizableOp.bufferizesToMemoryWrite(opOperand, state)
|
|
&& bufferizableOp.getAliasingValues(opOperand, state)
|
|
.getNumAliases() != 0;
|
|
}
|
|
}];
|
|
}
|
|
|
|
#endif // BUFFERIZABLE_OP_INTERFACE
|