The methods in `SideEffectUtils.h` (and their implementations in
`SideEffectUtils.cpp`) seem to have similar intent to methods already
existing in `SideEffectInterfaces.h`. Move the decleration (and
implementation) from `SideEffectUtils.h` (and `SideEffectUtils.cpp`)
into `SideEffectInterfaces.h` (and `SideEffectInterface.cpp`).
Also drop the `SideEffectInterface::hasNoEffect` method in favor of
`mlir::isMemoryEffectFree` which actually recurses into the operation
instead of just relying on the `hasRecursiveMemoryEffectTrait`
exclusively.
Differential Revision: https://reviews.llvm.org/D137857
Expose `function-boundary-type-conversion` in `OneShotBufferizeOp`. To
reuse options between passes and transform operations, create a
`BufferizationEnums.td`.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D137833
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
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
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
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
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
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
This simplifies the bufferization itself and is in preparation of connecting with the sparse compiler.
Differential Revision: https://reviews.llvm.org/D126814
Users should explicitly run `-buffer-results-to-out-params` instead.
The purpose of this change is to remove `finalizeBuffers`, which made it difficult to extend the bufferization to custom buffer types.
Differential Revision: https://reviews.llvm.org/D126253
The buffer deallocation pass must now be run explicitly when `allow-return-alloc` is set.
This results in a few extra buffer copies in unoptimized test cases. The proper way to avoid such copies is to relax the OpOperand/OpResult aliasing contract on ops such as scf.for. Some of these copies can also be avoided by improving the buffer deallocation pass.
Differential Revision: https://reviews.llvm.org/D126252
Now that analysis and bufferization are better separated, post-analysis steps are no longer needed. Users can directly interleave analysis and bufferization as needed.
Differential Revision: https://reviews.llvm.org/D126571
Also fixes integration of the pass into One-Shot Bufferize and adds additional test cases.
BufferResultsToOutParams can be used with "identity-layout-map" and "fully-dynamic-layout-map". "infer-layout-map" is not supported.
Differential Revision: https://reviews.llvm.org/D125636
This change adds a new op `alloc_tensor` to the bufferization dialect. During bufferization, this op is always lowered to a buffer allocation (unless it is "eliminated" by a pre-processing pass). It is useful to have such an op in tensor land, because it allows users to model tensor SSA use-def chains (which drive bufferization decisions) and because tensor SSA use-def chains can be analyzed by One-Shot Bufferize, while memref values cannot.
This change also replaces all uses of linalg.init_tensor in bufferization-related code with bufferization.alloc_tensor.
linalg.init_tensor and bufferization.alloc_tensor are similar, but the purpose of the former one is just to carry a shape. It does not indicate a memory allocation.
linalg.init_tensor is not suitable for modelling SSA use-def chains for bufferization purposes, because linalg.init_tensor is marked as not having side effects (in contrast to alloc_tensor). As such, it is legal to move linalg.init_tensor ops around/CSE them/etc. This is not desirable for alloc_tensor; it represents an explicit buffer allocation while still in tensor land and such allocations should not suddenly disappear or get moved around when running the canonicalizer/CSE/etc.
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Differential Revision: https://reviews.llvm.org/D126003
This changes replaces the `fully-dynamic-layout-maps` options (which was badly named) with two new options:
* `unknown-type-conversion` controls the layout maps on buffer types for which no layout map can be inferred.
* `function-boundary-type-conversion` controls the layout maps on buffer types inside of function signatures.
Differential Revision: https://reviews.llvm.org/D125615
This change integrates the BufferResultsToOutParamsPass into One-Shot Module Bufferization. This improves memory management (deallocation) when buffers are returned from a function.
Note: This currently only works with statically-sized tensors. The generated code is not very efficient yet and there are opportunities for improvment (fewer copies). By default, this new functionality is deactivated.
Differential Revision: https://reviews.llvm.org/D125376
Ops that are created during the bufferization were not analyzed (when run with One-Shot Bufferize), and users should instead create memref ops directly.
Futhermore, this fixes an issue where an op was erased (and put on the `erasedOps` list), but subsequently a new tensor op was created at the same memory location. This op was then not bufferized. Disallowing the creation of new tensor ops simplifies the bufferization and fixes such issues.
Differential Revision: https://reviews.llvm.org/D125017
Now that dialect constructors are generated in the .cpp file, we can
drop all of the dependent dialect includes from the .h file.
Differential Revision: https://reviews.llvm.org/D124298
This makes the API easier to use. Also allows us to check for incorrect API usage for easier debugging.
Differential Revision: https://reviews.llvm.org/D124265
* Move Module Bufferization to the bufferization dialect. The implementation is split into `OneShotModuleBufferize.cpp` and `FuncBufferizableOpInterfaceImpl.cpp`, so that the external model implementation can be easily moved to the func dialect in the future.
* Split and clean up test cases. A few test cases are still remaining in Linalg and will be updated separately.
* `linalg.inplaceable` is renamed to `bufferization.writable` to accurately reflect its current usage.
* Attributes and their verifiers are moved from the Linalg dialect to the Bufferization dialect.
* Expand documentation.
* Add a new flag to One-Shot Bufferize to allow for function boundary bufferization.
Differential Revision: https://reviews.llvm.org/D122229
FuncOps are now less special. They must still be analyzed + bufferized in a certain order, but they are now bufferized same as other ops that have a region: Bufferize the op first (`bufferize` interface method), then bufferize the region body with other bufferization patterns. In the case of FuncOps, the function signature is bufferized together with ReturnOps. Similar to how, e.g., scf.for ops are bufferized together with scf.yield ops.
This change is essentially a reimplementation of the FuncOp bufferization, but mostly NFC from a user's perspective (apart from error messages). This change is in preparation of moving the code to the bufferization dialect.
Differential Revision: https://reviews.llvm.org/D123214
The bufferization driver was previously using a GreedyPatternRewriter. This was problematic because bufferization must traverse ops top-to-bottom. The GreedyPatternRewriter was previously configured via `useTopDownTraversal`, but this was a hack; this API was just meant for performance improvements and should not affect the result of the rewrite.
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Differential Revision: https://reviews.llvm.org/D123618
This fixes a bufferization issue with ops that are not supported by the buffer deallocation pass when `allow-return-allocs=0`.
Differential Revision: https://reviews.llvm.org/D122304
New buffer allocations can now be returned/yielded from blocks with `allow-return-allocs`. One-Shot Bufferize deallocates all buffers at the end of the block. If this is not possible (because the buffer escapes the block), this is now done by the existing BufferDeallocation pass.
Differential Revision: https://reviews.llvm.org/D121527
Such IR is rejected by default, but can be allowed with `allow-return-memref`. In preparation of future refactorings, do not deallocate such buffers.
One-Shot Analysis now gathers information about yielded tensors, so that we know during the actual bufferization whether a newly allocated buffer should be deallocated again. (Otherwise, it will leak. This will be addressed in a subsequent commit that also makes `allow-return-memref` a non-experimental flag.)
As a cleanup, `allow-return-memref` is now part of OneShotBufferizationOptions. (It was previously ignored by AlwaysCopyBufferizationState.) Moreover, AlwaysCopyBufferizationState now asserts that `create-deallocs` is deactivated to prevent surprising behavior.
Differential Revision: https://reviews.llvm.org/D121521
