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Support custom types (3/N): allow custom tensor and buffer types in
function signatures and at call-sites. This is one of the major building
blocks to move in the direction of module-level one-shot-bufferization
support.
To achieve this, `BufferizationOptions::FunctionArgTypeConverterFn`
callback is converted to work with tensor-like and buffer-like types,
instead of the builtin counterparts. The default behavior for builtins
remains unchanged, while custom types by default go through
`TensorLikeType::getBufferType()` which is a general conversion
interface.
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See https://github.com/llvm/llvm-project/pull/147168 for more info.
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These are identified by misc-include-cleaner. I've filtered out those
that break builds. Also, I'm staying away from llvm-config.h,
config.h, and Compiler.h, which likely cause platform- or
compiler-specific build failures.
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Following the addition of TensorLike and BufferLike type interfaces (see
00eaff3e9c897c263a879416d0f151d7ca7eeaff), introduce minimal changes
required to bufferize a custom tensor operation into a custom buffer
operation.
To achieve this, new interface methods are added to TensorLike type
interface that abstract away the differences between existing (tensor ->
memref) and custom conversions.
The scope of the changes is intentionally limited (for example,
BufferizableOpInterface is untouched) in order to first understand the
basics and reach consensus design-wise.
---
Notable changes:
* mlir::bufferization::getBufferType() returns BufferLikeType (instead
of BaseMemRefType)
* ToTensorOp / ToBufferOp operate on TensorLikeType / BufferLikeType.
Operation argument "memref" renamed to "buffer"
* ToTensorOp's tensor type inferring builder is dropped (users now need
to provide the tensor type explicitly)
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Generally, bufferization should be able to create a memref from a tensor
without needing to know more than just a mlir::Type. Thus, change
BufferizationOptions::UnknownTypeConverterFn to accept just a type
(mlir::TensorType for now) instead of mlir::Value. Additionally, apply
the same rationale to getMemRefType() helper function.
Both changes are prerequisites to enable custom types support in
one-shot bufferization.
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interface methods (#141466)
The PR continues the work started in #141019 by adding the `BufferizationState` class also to the `getBufferType` and `resolveConflicts` interface methods, together with the additional support functions that are used throughout the bufferization infrastructure.
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Follow-up on #138143, which was reverted due to a missing update a method signature (more specifically, the bufferization interface for `tensor::ConcatOp`) that was not catched before merging. The old PR description is reported in the next lines.
This PR is a follow-up on https://github.com/llvm/llvm-project/pull/138125, and adds a bufferization state class providing information about the IR. The information currently consists of a cached list of symbol tables, which aims to solve the quadratic scaling of the bufferization task with respect to the number of symbols. The PR breaks API compatibility: the bufferize method of the BufferizableOpInterface has been enriched with a reference to a BufferizationState object.
The bufferization state must be kept in a valid state by the interface implementations. For example, if an operation with the Symbol trait is inserted or replaced, its parent SymbolTable must be updated accordingly (see, for example, the bufferization of arith::ConstantOp, where the symbol table of the module gets the new global symbol inserted). Similarly, the invalidation of a symbol table must be performed if an operation with the SymbolTable trait is removed (this can be performed using the invalidateSymbolTable method, introduced in https://github.com/llvm/llvm-project/pull/138014).
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(#141012)
Reverts llvm/llvm-project#138143
The PR for the BufferizationState is temporarily reverted due to API incompatibilities that have been initially missed during the update and were not catched by PR checks.
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This PR is a follow-up on #138125, and adds a bufferization state class providing information about the IR. The information currently consists of a cached list of symbol tables, which aims to solve the quadratic scaling of the bufferization task with respect to the number of symbols. The PR breaks API compatibility: the `bufferize` method of the `BufferizableOpInterface` has been enriched with a reference to a `BufferizationState` object.
The bufferization state must be kept in a valid state by the interface implementations. For example, if an operation with the `Symbol` trait is inserted or replaced, its parent `SymbolTable` must be updated accordingly (see, for example, the bufferization of `arith::ConstantOp`, where the symbol table of the module gets the new global symbol inserted). Similarly, the invalidation of a symbol table must be performed if an operation with the `SymbolTable` trait is removed (this can be performed using the `invalidateSymbolTable` method, introduced in #138014).
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As part of the work on transitioning bufferization dialect, ops, and
associated logic to operate on newly added type interfaces (see
00eaff3e9c897c263a879416d0f151d7ca7eeaff), rename the
bufferization.to_memref to highlight the generic nature of the op.
Bufferization process produces buffers while memref is a builtin type
rather than a generic term.
Preserve the current API (to_buffer still produces a memref), however,
as the new type interfaces are not used yet.
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Current one-shot bufferization infrastructure operates on top of
TensorType and BaseMemRefType. These are non-extensible base classes of
the respective builtins: tensor and memref. Thus, the infrastructure is
bound to work only with builtin tensor/memref types. At the same time,
there are customization points that allow one to provide custom logic to
control the bufferization behavior.
This patch introduces new type interfaces: tensor-like and buffer-like
that aim to supersede TensorType/BaseMemRefType within the bufferization
dialect and allow custom tensors / memrefs to be used. Additionally,
these new type interfaces are attached to the respective builtin types
so that the switch is seamless.
Note that this patch does very minimal initial work, it does NOT
refactor bufferization infrastructure.
See https://discourse.llvm.org/t/rfc-changing-base-types-for-tensors-and-memrefs-from-c-base-classes-to-type-interfaces/85509
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This PR changes the type of the command-line arguments representing
`LayoutMapOption` from `std::string` to the enum with the same name.
This allows for checking the values of programmable usages of the
corresponding options at compile time.
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OneShotBufferizePass's opFilter definition in runOnOperation() fails to
allow operations for all dialect when the dialectFilter has an empty
array value (as opposed to no value). This happens when constructing
OneShotBufferizePass from a OneShotBufferizePassOptions parameter with
an empty dialectFilter. This commit only does filtering if filterDialect
option has a value and it is not an empty array.
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`let constructor` is legacy (do not use in tree!) since the table gen
backend emits most of the glue logic to build a pass.
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It was related to the old bufferization mechanism, which has since been
retired.
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This change will keep the memory space information for the tensor if
there is any.
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(#114155)
This commit removes the last remaining components of the dialect
conversion-based bufferization passes.
Note for LLVM integration: If you depend on these components, migrate to
One-Shot Bufferize or copy them to your codebase.
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used (#91524)
As described in issue llvm/llvm-project#91518, a previous PR
llvm/llvm-project#78484 introduced the `defaultMemorySpaceFn` into
bufferization options, allowing one to inform OneShotBufferize that it
should use a specified function to derive the memory space attribute
from the encoding attribute attached to tensor types.
However, introducing this feature exposed unhandled edge cases,
examples of which are introduced by this change in the new test under
`test/Dialect/Bufferization/Transforms/one-shot-bufferize-encodings.mlir`.
Fixing the inconsistencies introduced by `defaultMemorySpaceFn` is
pretty simple. This change:
- Updates the `bufferization.to_memref` and `bufferization.to_tensor`
operations to explicitly include operand and destination types,
whereas previously they relied on type inference to deduce the
tensor types. Since the type inference cannot recover the correct
tensor encoding/memory space, the operand and result types must be
explicitly included. This is a small assembly format change, but it
touches a large number of test files.
- Makes minor updates to other bufferization functions to handle the
changes in building the above ops.
- Updates bufferization of `tensor.from_elements` to handle memory
space.
Integration/upgrade guide:
In downstream projects, if you have tests or MLIR files that explicitly
use
`bufferization.to_tensor` or `bufferization.to_memref`, then update
them to the new assembly format as follows:
```
%1 = bufferization.to_memref %0 : memref<10xf32>
%2 = bufferization.to_tensor %1 : memref<10xf32>
```
becomes
```
%1 = bufferization.to_memref %0 : tensor<10xf32> to memref<10xf32>
%2 = bufferization.to_tensor %0 : memref<10xf32> to tensor<10xf32>
```
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The dialect conversion-based bufferization passes have been migrated to
One-Shot Bufferize about two years ago. To clean up the code base, this
commit removes the `finalizing-bufferize` pass, one of the few remaining
parts of the old infrastructure. Most bufferization passes have already
been removed.
Note for LLVM integration: If you depend on this pass, migrate to
One-Shot Bufferize or copy the pass to your codebase.
Depends on #114152.
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one-shot-bufferize (#112505)
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This commit marks the type converter in `populate...` functions as
`const`. This is useful for debugging.
Patterns already take a `const` type converter. However, some
`populate...` functions do not only add new patterns, but also add
additional type conversion rules. That makes it difficult to find the
place where a type conversion was added in the code base. With this
change, all `populate...` functions that only populate pattern now have
a `const` type converter. Programmers can then conclude from the
function signature that these functions do not register any new type
conversion rules.
Also some minor cleanups around the 1:N dialect conversion
infrastructure, which did not always pass the type converter as a
`const` object internally.
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Handling parallel region RaW conflicts should usually be the
responsibility of the source program, rather than bufferization
analysis. However, to preserve current functionality, checks on parallel
regions is put behind a bufferization in this PR, which is on by
default. Default functionality will not change, but this PR enables the
option to leave parallelism checks out of the bufferization analysis.
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These passes have been depreciated for a long time and replaced by
one-shot bufferization. These passes are also unsafe because they do not
check for read-after-write conflicts.
Relands https://github.com/llvm/llvm-project/pull/93488 which failed on
buildbot. Fixes the failure by updating integration tests to use
one-shot-bufferize instead.
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Reverts llvm/llvm-project#93488
Buildbot failure:
https://lab.llvm.org/buildbot/#/builders/220/builds/39911
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These passes have been depreciated for a long time and replaced by
one-shot bufferization. These passes are also unsafe because they do not
check for read-after-write conflicts.
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(#89175)
This allows to configure both the op used for allocation and copy of
memrefs.
It also changes the default behavior because the default allocation in
`BufferizationOptions` creates `memref.alloc` with `alignment = 64`
where we used to create `memref.alloca` without any alignment before.
Fixes
```
// TODO: Use alloc/memcpy callback from BufferizationOptions if called via
// BufferizableOpInterface impl of ToMemrefOp.
```
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(#83964)
One-Shot Bufferize currently does not support loops where a yielded
value bufferizes to a buffer that is different from the buffer of the
region iter_arg. In such a case, the bufferization fails with an error
such as:
```
Yield operand #0 is not equivalent to the corresponding iter bbArg
scf.yield %0 : tensor<5xf32>
```
One common reason for non-equivalent buffers is that an op on the path
from the region iter_arg to the terminator bufferizes out-of-place. Ops
that are analyzed earlier are more likely to bufferize in-place.
This commit adds a new heuristic that gives preference to ops that are
reachable on the reverse SSA use-def chain from a region terminator and
are within the parent region of the terminator. This is expected to work
better than the existing heuristics for loops where an iter_arg is
written to multiple times within a loop, but only one write is fed into
the terminator.
Current users of One-Shot Bufferize are not affected by this change.
"Bottom-up" is still the default heuristic. Users can switch to the new
heuristic manually.
This commit also turns the "fuzzer" pass option into a heuristic,
cleaning up the code a bit.
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BufferizableOpInterface (#85690)
This doesn't change functionality, but lets us avoid attaching all the
interfaces after 513cdb82223a106f183b49a40d9acb1f7efbbe7e turned casting
without loading into an error.
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Rename listener callback names:
* `notifyOperationRemoved` -> `notifyOperationErased`
* `notifyBlockRemoved` -> `notifyBlockErased`
The current callback names are misnomers. The callbacks are triggered
when an operation/block is erased, not when it is removed (unlinked).
E.g.:
```c++
/// Notify the listener that the specified operation is about to be erased.
/// At this point, the operation has zero uses.
///
/// Note: This notification is not triggered when unlinking an operation.
virtual void notifyOperationErased(Operation *op) {}
```
This change is in preparation of adding listener support to the dialect
conversion. The dialect conversion internally unlinks IR before erasing
it at a later point of time. There is an important difference between
"remove" and "erase". Lister callback names should be accurate to avoid
confusion.
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Collection of changes with the goal of being able to convert `encoding`
to `memorySpace` during bufferization
- new API for encoder to allow implementation to select destination
memory space
- update existing bufferization implementations to support the new
interface
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There is currently no lowering out of `ml_program` in the LLVM
repository. This change adds a lowering to `memref` so that it can be
lowered all the way to LLVM. This lowering was taken from the [reference
backend in
torch-mlir](https://github.com/llvm/torch-mlir/commit/f41695360019bde71d52ca7548944d5488779e12
).
I had tried implementing the `BufferizableOpInterface` for `ml_program`
instead of adding a new pass but that did not work because
`OneShotBufferize` does not visit module-level ops like
`ml_program.global`.
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The pattern rewriter documentation states that "*all* IR mutations [...]
are required to be performed via the `PatternRewriter`." This commit
adds two functions that were missing from the rewriter API:
`moveOpBefore` and `moveOpAfter`.
After an operation was moved, the `notifyOperationInserted` callback is
triggered. This allows listeners such as the greedy pattern rewrite
driver to react to IR changes.
This commit narrows the discrepancy between the kind of IR modification
that can be performed and the kind of IR modifications that can be
listened to.
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Add a new interface method to `BufferizableOpInterface`:
`hasTensorSemantics`. This method returns "true" if the op has tensor
semantics and should be bufferized.
Until now, we assumed that an op has tensor semantics if it has tensor
operands and/or tensor op results. However, there are ops like
`ml_program.global` that do not have any results/operands but must still
be bufferized (#75103). The new interface method can return "true" for
such ops.
This change also decouples `bufferization::bufferizeOp` a bit from the
func dialect.
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Remove the `opFilter` and `copyBeforeWrite` function arguments. These
options can already be configured in the `options` object.
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AllocationOpInterface to memref dialect directory (#66637)
Follow-up on #65578
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When cloning an op, the `notifyOperationInserted` callback is triggered
for all nested ops. Similarly, the `notifyOperationRemoved` callback
should be triggered for all nested ops when removing an op.
Listeners may inspect the IR during a `notifyOperationRemoved` callback.
Therefore, when multiple ops are removed in a single
`RewriterBase::eraseOp` call, the notifications must be triggered in an
order in which the ops could have been removed one-by-one:
* Op removals must be interleaved with `notifyOperationRemoved`
callbacks. A callback is triggered right before the respective op is
removed.
* Ops are removed post-order and in reverse order. Other traversal
orders could delete an op that still has uses. (This is not avoidable in
graph regions and with cyclic block graphs.)
Differential Revision: Imported from https://reviews.llvm.org/D144193.
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options, remove bufferization.escape attribute (#66619)
This commit removes the deallocation capabilities of
one-shot-bufferization. One-shot-bufferization should never deallocate
any memrefs as this should be entirely handled by the
ownership-based-buffer-deallocation pass going forward. This means the
`allow-return-allocs` pass option will default to true now,
`create-deallocs` defaults to false and they, as well as the escape
attribute indicating whether a memref escapes the current region, will
be removed. A new `allow-return-allocs-from-loops` option is added as a
temporary workaround for some bufferization limitations.
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pass options, remove bufferization.escape attribute"
This reverts commit 6a91dfedeb956dfa092a6a3f411e8b02f0d5d289.
This caused problems in downstream projects. We are reverting to give
them more time for integration.
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options, remove bufferization.escape attribute
This is the first commit in a series with the goal to rework the
BufferDeallocation pass. Currently, this pass heavily relies on copies
to perform correct deallocations, which leads to very slow code and
potentially high memory usage. Additionally, there are unsupported cases
such as returning memrefs which this series of commits aims to add
support for as well.
This first commit removes the deallocation capabilities of
one-shot-bufferization.One-shot-bufferization should never deallocate any
memrefs as this should be entirely handled by the buffer-deallocation pass
going forward. This means the allow-return-allocs pass option will
default to true now, create-deallocs defaults to false and they, as well
as the escape attribute indicating whether a memref escapes the current region,
will be removed.
The documentation should w.r.t. these pass option changes should also be
updated in this commit.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D156662
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The new Buffer Deallocation pass introduced in D158421 will not need the
AllocationOpInterface anymore, thus it is better to move those default
implementations to a place where they will still be used.
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This revision adds support for unstructured control flow to the bufferization infrastructure. In particular: regions with multiple blocks, `cf.br`, `cf.cond_br`.
Two helper templates are added to `BufferizableOpInterface.h`, which can be implemented by ops that supported unstructured control flow in their regions (e.g., `func.func`) and ops that branch to another block (e.g., `cf.br`).
A block signature is always bufferized together with the op that owns the block.
Differential Revision: https://reviews.llvm.org/D158094
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Check for invalid pass flags when running `-one-shot-bufferize` instead of crashing with a failed assertion.
This fixes #64267.
Differential Revision: https://reviews.llvm.org/D158162
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separate function
When bufferizing "func.func", the entry block signature is bufferized. (Only functions with a single block are supported at the moment.) This functionality is moved into a separate function, so that it can be used for bufferizing unstructured control flow in the future.
Differential Revision: https://reviews.llvm.org/D158154
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The bufferization implementation of `func.func` and `func.call` can be simplified. It still contained code that was necessary when One-Shot Bufferize removed return values. This functionality has been extracted into a separate pass a while ago.
Differential Revision: https://reviews.llvm.org/D157893
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control flow
Report an error when trying to bufferize an op that contains unstructured control flow but for ops for which the bufferization implementation does not support unstructured control flow. At the moment, there are no ops for which unstructured control flow is supported.
Differential Revision: https://reviews.llvm.org/D157893
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To keep the pass simple, users should apply cleanup passes manually when necessary. In particular, `-cse -canonicalize` are often desireable to fold away self-copies that are created by the bufferization.
This addresses a comment in D120191.
Differential Revision: https://reviews.llvm.org/D155923
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This is useful for debugging.
Differential Revision: https://reviews.llvm.org/D143314
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The MLIR classes Type/Attribute/Operation/Op/Value support
cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast
functionality in addition to defining methods with the same name.
This change begins the migration of uses of the method to the
corresponding function call as has been decided as more consistent.
Note that there still exist classes that only define methods directly,
such as AffineExpr, and this does not include work currently to support
a functional cast/isa call.
Caveats include:
- This clang-tidy script probably has more problems.
- This only touches C++ code, so nothing that is being generated.
Context:
- https://mlir.llvm.org/deprecation/ at "Use the free function variants
for dyn_cast/cast/isa/…"
- Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443
Implementation:
This first patch was created with the following steps. The intention is
to only do automated changes at first, so I waste less time if it's
reverted, and so the first mass change is more clear as an example to
other teams that will need to follow similar steps.
Steps are described per line, as comments are removed by git:
0. Retrieve the change from the following to build clang-tidy with an
additional check:
https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check
1. Build clang-tidy
2. Run clang-tidy over your entire codebase while disabling all checks
and enabling the one relevant one. Run on all header files also.
3. Delete .inc files that were also modified, so the next build rebuilds
them to a pure state.
4. Some changes have been deleted for the following reasons:
- Some files had a variable also named cast
- Some files had not included a header file that defines the cast
functions
- Some files are definitions of the classes that have the casting
methods, so the code still refers to the method instead of the
function without adding a prefix or removing the method declaration
at the same time.
```
ninja -C $BUILD_DIR clang-tidy
run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\
-header-filter=mlir/ mlir/* -fix
rm -rf $BUILD_DIR/tools/mlir/**/*.inc
git restore mlir/lib/IR mlir/lib/Dialect/DLTI/DLTI.cpp\
mlir/lib/Dialect/Complex/IR/ComplexDialect.cpp\
mlir/lib/**/IR/\
mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp\
mlir/lib/Dialect/Vector/Transforms/LowerVectorMultiReduction.cpp\
mlir/test/lib/Dialect/Test/TestTypes.cpp\
mlir/test/lib/Dialect/Transform/TestTransformDialectExtension.cpp\
mlir/test/lib/Dialect/Test/TestAttributes.cpp\
mlir/unittests/TableGen/EnumsGenTest.cpp\
mlir/test/python/lib/PythonTestCAPI.cpp\
mlir/include/mlir/IR/
```
Differential Revision: https://reviews.llvm.org/D150123
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Having to choose from only static or dynamic layout for all function is limiting.
Differential Revision: https://reviews.llvm.org/D148074
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