Use the BufferizationState symbol table cache when resolving CallOp
callee types in getBufferType(), avoiding repeated SymbolTableCollection
creation. Add a const accessor (backed by a mutable cache) so const
state can reuse the same tables. Completes a marked TODO.
Fixes#128329, Fixes#128330, Fixes#173565, Fixes#114730
There is an assertion failure in `-one-shot-bufferize` when tensors that
have an element type that can't be a memref element type are
encountered.
f8d3f47e1f/mlir/include/mlir/IR/BuiltinTypes.h (L440)
We can't emit a to_tensor for ops that do implement
`BufferizableOpInterface`, and i don't think quantizing is the right
move either, so erroring seemed like the best fit.
After some trial and error, `defaultGetBufferType` seems like the most
functional and least invasive place to put this check.
Generally, to_tensor and to_buffer already perform sufficient
verification. However, there are some unnecessarily strict constraints:
* builtin tensor requires its buffer counterpart to always be memref
* to_buffer on ranked tensor requires to always return memref
These checks are assertions (i.e. preconditions), however, they actually
prevent an apparently useful bufferization where builtin tensors could
become custom buffers. Lift these assertions, maintaining the
verification procedure unchanged, to allow builtin -> custom
bufferizations at operation boundary level.
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.
Support custom types (2/N): allow value-owning operations (e.g.
allocation ops) to bufferize custom tensors into custom buffers. This
requires BufferizableOpInterface::getBufferType() to return
BufferLikeType instead of BaseMemRefType.
Affected implementors of the interface are updated accordingly.
Relates to ee070d08163ac09842d9bf0c1315f311df39faf1.
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)
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.
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.
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).
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.
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).
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.
When profiling one-shot-bufferization over large programs, I found that
the analysis would spend a large amount of time checking whether
two operations are "inside mutually exclusive regions". This change
adds a cache for that information, which can result in a noticeable
speedup depending on program structure.
Edit the `findValueInReverseUseDefChain` method to accept `OpOperand`
instead of the `Value` type, This change will make sure that the
populated `visitedOpOperands` argument is fully accurate and contains
the opOperand we have started the reverse chain from.
Note that PointerUnion::{is,get} have been soft deprecated in
PointerUnion.h:
// FIXME: Replace the uses of is(), get() and dyn_cast() with
// isa<T>, cast<T> and the llvm::dyn_cast<T>
I'm not touching PointerUnion::dyn_cast for now because it's a bit
complicated; we could blindly migrate it to dyn_cast_if_present, but
we should probably use dyn_cast when the operand is known to be
non-null.
In many cases the emptyTensorElimination can not transform or eliminate
the empty tensor which is being inserted into the
`SubsetInsertionOpInterface`.
Two major reasons for that:
1- Failing when trying to find a legal/suitable insertion point for the
`subsetExtract` which is about to replace the empty tensor. However, we
may try to handle this issue by moving the needed values which
responsible on building the `subsetExtract` nearby the empty tensor
(which is about to be eliminated). Thus increasing the probability to
find a legal insertion point.
2-The EmptyTensorElimination transform replaces the tensor.empty's uses
all at once in one apply, rather than replacing only the specific use
which was visited in the use-def chain (when traversing from the
tensor.insert_slice). This scenario of replacing all the uses of the
tensor.empty may lead into additional read effects after bufferization
of the specific subset extract/subview which should not be the case.
Both cases may result in many copies in the coming bufferization which
can not be canonicalized.
The first case can be noticed when having a `tensor.empty` followed by
`SubsetInsertionOpInterface` (or in simple words `tensor.insert_slice`),
which have been lowered from `tensor/tosa.concat`.
The second case can be noticed when having a `tensor.empty`, with many
uses and leading to applying the transformation only once, since the
whole uses have been replaced at once.
The first commit in the PR only adds the lit tests for the cases shown
above (NFC), to emphasize how the transform works, in the coming MRs
will upload a slight changes to handle these case.
The second commit in this PR, we want to replace only the specific use
which was visited in the `use-def` chain (when traversing from the
`tensor.insert_slice`'s source).
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>
```
**Description:**
`OneShotModuleBufferize` deals with the bufferization of `FuncOp`,
`CallOp` and `ReturnOp` but they are hard-coded. Any custom
function-like operations will not be handled. The PR replaces a part of
`FuncOp` and `CallOp` with `FunctionOpInterface` and `CallOpInterface`
in `OneShotModuleBufferize` so that custom function ops and call ops can
be bufferized.
**Related Discord Discussion:**
[Link](https://discord.com/channels/636084430946959380/642426447167881246/1280556809911799900)
---------
Co-authored-by: erick-xanadu <110487834+erick-xanadu@users.noreply.github.com>
Tensor copy insertion currently uses memory_space = 0 when creating a
tensor copy using alloc_tensor. This memory space should instead be the
default memory space provided in bufferization options.
This doesn't change functionality, but lets us avoid attaching all the
interfaces after 513cdb82223a106f183b49a40d9acb1f7efbbe7e turned casting
without loading into an error.
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
This commit renames 4 pattern rewriter API functions:
* `updateRootInPlace` -> `modifyOpInPlace`
* `startRootUpdate` -> `startOpModification`
* `finalizeRootUpdate` -> `finalizeOpModification`
* `cancelRootUpdate` -> `cancelOpModification`
The term "root" is a misnomer. The root is the op that a rewrite pattern
matches against
(https://mlir.llvm.org/docs/PatternRewriter/#root-operation-name-optional).
A rewriter must be notified of all in-place op modifications, not just
in-place modifications of the root
(https://mlir.llvm.org/docs/PatternRewriter/#pattern-rewriter). The old
function names were confusing and have contributed to various broken
rewrite patterns.
Note: The new function names use the term "modify" instead of "update"
for consistency with the `RewriterBase::Listener` terminology
(`notifyOperationModified`).
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.
One-Shot Bufferize no longer deallocates buffers, so `deallocationFn`
can be removed.
Note: There is a `bufferization.dealloc_tensor` op that now always
bufferizes to `memref.dealloc`. This op will be phased out soon.
Remove the yielded tensor analysis. This analysis was used to detect
cases where One-Shot Bufferize cannot deallocate buffers. Deallocation
has recently been removed from One-Shot Bufferize. Buffers are now
deallocated by the buffer deallocation pass. This analysis is no longer
needed.
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.
This reverts commit 6a91dfedeb956dfa092a6a3f411e8b02f0d5d289.
This caused problems in downstream projects. We are reverting to give
them more time for integration.
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
One-Shot Bufferize correctly handles RaW conflicts around repetitive regions (loops). Specical handling is needed for parallel regions. These are a special kind of repetitive regions that can have additional RaW conflicts that would not be present if the regions would be executed sequentially.
Example:
```
%0 = bufferization.alloc_tensor()
scf.forall ... {
%1 = linalg.fill ins(...) outs(%0)
...
scf.forall.in_parallel {
tensor.parallel_insert_slice %1 into ...
}
}
```
A separate (private) buffer must be allocated for each iteration of the `scf.forall` loop.
This change adds a new interface method to `BufferizableOpInterface` to detect parallel regions. By default, regions are assumed to be sequential.
A buffer is privatized if an OpOperand bufferizes to a memory read inside a parallel region that is different from the parallel region where operand's value is defined.
Differential Revision: https://reviews.llvm.org/D159286
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
`getBufferType` computes the bufferized type of an SSA value without bufferizing any IR. This is useful for predicting the bufferized type of iter_args of a loop.
To avoid endless recursion (e.g., in the case of "scf.for", the type of the iter_arg depends on the type of init_arg and the type of the yielded value; the type of the yielded value depends on the type of the iter_arg again), `fixedTypes` was used to fall back to "fixed" type. A simpler way is to maintain an "invocation stack". `getBufferType` implementations can then inspect the invocation stack to detect repetitive computations (typically when computing the bufferized type of a block argument).
Also improve error messages in case of inconsistent memory spaces inside of a loop.
Differential Revision: https://reviews.llvm.org/D158060
This revision is needed to support bufferization of `cf.br`/`cf.cond_br`. It will also be useful for better analysis of loop ops.
This revision generalizes `getAliasingOpResults` to `getAliasingValues`. An OpOperand can now not only alias with OpResults but also with BlockArguments. In the case of `cf.br` (will be added in a later revision): a `cf.br` operand will alias with the corresponding argument of the destination block.
If an op does not implement the `BufferizableOpInterface`, the analysis in conservative. It previously assumed that an OpOperand may alias with each OpResult. It now assumes that an OpOperand may alias with each OpResult and each BlockArgument of the entry block.
Differential Revision: https://reviews.llvm.org/D157957
This implication was already done de-facto and there were plenty of users and wrapper functions specifically used to handle the "return-like or RegionBranchTerminatorOpInterface" case. These simply existed due to up until recently missing features in ODS.
With the new capabilities of traits, we can make `ReturnLike` imply `RegionBranchTerminatorOpInterface` and auto generate proper definitions for its methods.
Various occurrences and wrapper methods used for `isa<RegionBranchTerminatorOpInterface>() || hasTrait<ReturnLike>()` have all been removed.
Differential Revision: https://reviews.llvm.org/D157402
EmptyTensorElimination is a pre-bufferization transformation that replaces "tensor.empty" ops with "tensor.extract_slice" ops. This revision adds support for cases where the input IR contains "tensor.cast" ops.
Differential Revision: https://reviews.llvm.org/D156167
The `getEnclosingRepetitiveRegion` functions walk the ancestor regions everytime which can be expensive especially when there are multiple regions inbetween. This commit adds a cache to the bufferization analysis to remember the result of the walk.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D154710
Instead of passing traversal options as a long list of arguments, store them in a TraversalConfig object and pass that object.
Differential Revision: https://reviews.llvm.org/D143927
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.
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 follows a previous patch that updated calls
`op.cast<T>()-> cast<T>(op)`. However some cases could not handle an
unprefixed `cast` call due to occurrences of variables named cast, or
occurring inside of class definitions which would resolve to the method.
All C++ files that did not work automatically with `cast<T>()` are
updated here to `llvm::cast` and similar with the intention that they
can be easily updated after the methods are removed through a
find-replace.
See https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check
for the clang-tidy check that is used and then update printed
occurrences of the function to include `llvm::` before.
One can then run the following:
```
ninja -C $BUILD_DIR clang-tidy
run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\
-export-fixes /tmp/cast/casts.yaml mlir/*\
-header-filter=mlir/ -fix
rm -rf $BUILD_DIR/tools/mlir/**/*.inc
```
Differential Revision: https://reviews.llvm.org/D150348
