This fixes an issue in One-Shot Bufferize that could lead to missing buffer copies in the future. This bug can currently not be triggered because of the order in which ops are analyzed (always bottom-to-top). However, if we consider different traversal orders for the analysis in the future, this bug can cause subtle issues that are difficult to debug.
Example:
```
%0 = ...
%1 = tensor.insert ... into %0
%2 = tensor.extract_slice %0
tensor.extract %2[...]
```
In case of a top-to-bottom analysis of the above IR, the `tensor.insert` is analyzed before the `tensor.extract_slice`. In that case, the `tensor.insert` will bufferize in-place because %2 is not yet known to become an alias of %0 (and therefore causing a conflict).
With this change, the `tensor.insert` will bufferize out-of-place, regardless of the traversal order.
Differential Revision: https://reviews.llvm.org/D135049
This fixes a bug where a required buffer copy was not inserted.
Not only written aliases, but also read aliases should be taken into account when computing common enclosing repetitive regions. Furthermore, for writing ops, it does not matter where the destination tensor is defined, but where the op itself is located.
Differential Revision: https://reviews.llvm.org/D135420
This method allows to declare regions as "repetitive" even if the parent op does not implement the RegionBranchOpInterface.
This is needed to support loop-like ops that have parallel semantics but do not branch between regions.
Differential Revision: https://reviews.llvm.org/D133113
The patch introduces the required changes to update the pass declarations and definitions to use the new autogenerated files and allow dropping the old infrastructure.
Reviewed By: mehdi_amini, rriddle
Differential Review: https://reviews.llvm.org/D132838
The patch introduces the required changes to update the pass declarations and definitions to use the new autogenerated files and allow dropping the old infrastructure.
Reviewed By: mehdi_amini, rriddle
Differential Review: https://reviews.llvm.org/D132838
It's only used from there, and this lets us remove the dependency from Analysis
to the Arith dialect.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D132928
tensor.pad is lowered to tensor.generate + tensor.insert_slice during bufferization. For best performance with constant padding values, users should vectorize the IR before bufferizing it.
This change also relaxes tje restriction that no new ops that bufferize to a memory write should be added during bufferization. Since bufferization has been split into two steps a while ago (tensor copy insertion + bufferization), it is reasonable to allow this now.
Differential Revision: https://reviews.llvm.org/D132355
bufferization.to_memref ops are not supported in One-Shot Analysis. They often trigger a failed assertion that can be confusing. Instead, scan for to_memref ops before running the analysis and immediately abort with a proper error message.
Differential Revision: https://reviews.llvm.org/D132027
AllocTensorElimination does currently not support chains where the type is
changing. AllocTensorElimination used to generate invalid IR for such
inputs. With this commit, AllocTensorElimination does no longer apply to
such inputs. (It can be extended to support such IR if needed.)
Differential Revision: https://reviews.llvm.org/D131880
Using a loop init_arg inside of the loop is not supported. This change adds a pre-processing pass that resolves such IR with copies.
Differential Revision: https://reviews.llvm.org/D131689
bufferization.writable is used in most cases instead. All remaining test cases are updated. Some code that is no longer needed is deleted.
Differential Revision: https://reviews.llvm.org/D129739
The `unknownTypeConversion` bufferization option (enum) is now a type converter function option. Some logic of `getMemRefType` is now handled by that function.
This change makes type conversion more controllable. Previously, there were only two options when generating memref types for non-bufferizable ops: Static identity layout or fully dynamic layout. With this change, users of One-Shot Bufferize can provide a function with custom logic.
Differential Revision: https://reviews.llvm.org/D129273
The result shape of a rank-reducing subview cannot be inferred in the general case. Just the result rank is not enough. The only thing that we can infer is the layout map.
This change also improves the bufferization patterns of tensor.extract_slice and tensor.insert_slice to fully support rank-reducing operations.
Differential Revision: https://reviews.llvm.org/D129144
This change updates all remaining bufferization patterns (except for scf.while) and the remaining bufferization infrastructure to infer the memory space whenever possible instead of falling back to "0". (If a default memory space is set in the bufferization options, we still fall back to that value if the memory space could not be inferred.)
Differential Revision: https://reviews.llvm.org/D128423
This is useful because the result type of an op can sometimes be inferred from its body (e.g., `scf.if`). This will be utilized in subsequent changes.
Also introduces a new `getBufferType` interface method on BufferizableOpInterface. This method is useful for computing a bufferized block argument type with respect to OpOperand types of the parent op.
Differential Revision: https://reviews.llvm.org/D128420
This attribute is currently supported on AllocTensorOp only. Future changes will add support to other ops. Furthermore, the memory space is not propagated properly in all bufferization patterns and some of the core bufferization infrastructure. This will be addressed in a subsequent change.
Differential Revision: https://reviews.llvm.org/D128274
All bufferizable ops that bufferize to an allocation receive a `bufferization.escape` attribute during TensorCopyInsertion.
Differential Revision: https://reviews.llvm.org/D128137
With the recent refactorings, this class is no longer needed. We can use BufferizationOptions in all places were BufferizationState was used.
Differential Revision: https://reviews.llvm.org/D127653
This change changes the bufferization so that it utilizes the new TensorCopyInsertion pass. One-Shot Bufferize no longer calls the One-Shot Analysis. Instead, it relies on the TensorCopyInsertion pass to make the entire IR fully inplacable. The `bufferize` implementations of all ops are simplified; they no longer have to account for out-of-place bufferization decisions. These were already materialized in the IR in the form of `bufferization.alloc_tensor` ops during the TensorCopyInsertion pass.
Differential Revision: https://reviews.llvm.org/D127652
If `create-deallocs=0`, mark all bufferization.alloc_tensor ops as escaping. (Unless they already have an `escape` attribute.) In the absence of analysis information, check SSA use-def chains to see if the value may be yielded.
Differential Revision: https://reviews.llvm.org/D127302
Bufferization of the func dialect must go through `OneShotModuleBufferize`. With this change, the analysis interface methods of the BufferizableOpInterface of func dialect ops can be used together with the normal `OneShotBufferize`. (In the absence of analysis information, they will return conservative results.)
Differential Revision: https://reviews.llvm.org/D127299
The TensorCopyInsertion pass resolves out-of-place bufferization decisions by inserting explicit `bufferization.alloc_tensor` ops. This change moves that functionality into a new BufferizableOpInterface method, so that it can be overridden by op implementations. Some op bufferizations must insert additional `alloc_tensor` ops to make sure that certain aliasing invariants are not violated (e.g., scf::ForOp). This will be addressed in a subsequent change.
Differential Revision: https://reviews.llvm.org/D126817
This pass runs the One-Shot Analysis to find out which tensor OpOperands must bufferize out-of-place. It then rewrites those tensor OpOperands to explicit allocations with a copy in the form of `bufferization.alloc_tensor`. The resulting IR can then be bufferized without having to care about read-after-write conflicts.
This change makes it possible to connect One-Shot Analysis to other bufferizations such as the sparse compiler.
Differential Revision: https://reviews.llvm.org/D126573
