This change makes block/region walkers consistent with operation
walkers. An operation walk enumerates the current operation. Similarly,
block/region walks should enumerate the current block/region.
Example:
```
// Current behavior:
op1->walk([](Operation *op2) { /* op1 is enumerated */ });
block1->walk([](Block *block2) { /* block1 is NOT enumerated */ });
region1->walk([](Block *block) { /* blocks of region1 are NOT enumerated */ });
region1->walk([](Region *region2) { /* region1 is NOT enumerated });
// New behavior:
op1->walk([](Operation *op2) { /* op1 is enumerated */ });
block1->walk([](Block *block2) { /* block1 IS enumerated */ });
region1->walk([](Block *block) { /* blocks of region1 ARE enumerated */ });
region1->walk([](Region *region2) { /* region1 IS enumerated });
```
Fixes a bug in `SplitDeallocWhenNotAliasingAnyOther`. This pattern used
to generate invalid IR (op dominance error). We never noticed this bug
in existing test cases because other patterns and/or foldings were
applied afterwards and those rewrites "fixed up" the IR again. (The bug
is visible when running `mlir-opt -debug`.) Also add additional comments
to the implementation and simplify the code a bit.
Apart from the fixed dominance error, this change is NFC. Without this
change, buffer deallocation tests will fail when running with #74270.
There is currently an op interface for subset insertion ops
(`SubsetInsertionOpInterface`), but not for subset extraction ops. This
commit adds `SubsetExtractionOpInterface` to `mlir/Interfaces`, as well
as a common dependent op interface: `SubsetOpInterface`.
- `SubsetOpInterface` is for ops that operate on tensor subsets. It
provides interface methods to check if two subset ops operate on
equivalent or disjoint subsets. Ops that implement this interface must
implement either `SubsetExtractionOpInterface` or
`SubsetInsertionOpInterface`.
- `SubsetExtractionOpInterface` is for ops that extract from a tensor at
a subset. E.g., `tensor.extract_slice`, `tensor.gather`,
`vector.transfer_read`. Current implemented only on
`tensor.extract_slice`.
- `SubsetInsertionOpInterface` is for ops that insert into a destination
tensor at a subset. E.g., `tensor.insert_slice`,
`tensor.parallel_insert_slice`, `tensor.scatter`,
`vector.transfer_write`. Currently only implemented on
`tensor.insert_slice`, `tensor.parallel_insert_slice`.
Other changes:
- Rename `SubsetInsertionOpInterface.td` to `SubsetOpInterface.td`.
- Add helper functions to `ValueBoundsOpInterface.cpp` for checking
whether two slices are disjoint.
The new interfaces will be utilized by a new "loop-invariant subset
hoisting"
transformation. (This new transform is roughly
what `Linalg/Transforms/SubsetHoisting.cpp` is doing, but in a generic
and interface-driven way.)
`SubsetInsertionOpInterface` is an interface for ops that insert into a
destination tensor at a subset. It is currently used by the
bufferization framework to support efficient
`tensor.extract_slice/insert_slice` bufferization and to drive "empty
tensor elimination".
This commit moves the interface to `mlir/Interfaces`. This is in
preparation of adding a new "loop-invariant subset hoisting"
transformation to
`mlir/Transforms/Utils/LoopInvariantCodeMotionUtils.cpp`, which will
utilize `SubsetInsertionOpInterface`. (This new transform is roughly
what `Linalg/Transforms/SubsetHoisting.cpp` is doing, but in a generic
and interface-driven way.)
Add a new attribute `bufferization.manual_deallocation` that can be
attached to allocation and deallocation ops. Buffers that are allocated
with this attribute are assigned an ownership of "false". Such buffers
can be deallocated manually (e.g., with `memref.dealloc`) if the
deallocation op also has the attribute set. Previously, the
ownership-based buffer deallocation pass used to reject IR with existing
deallocation ops. This is no longer the case if such ops have this new
attribute.
This change is useful for the sparse compiler, which currently
deallocates the sparse tensor buffers by itself.
Cyclic function call graphs are generally not supported by One-Shot
Bufferize. However, they can be allowed when a function does not have
tensor arguments or results. This is because it is then no longer
necessary that the callee will be bufferized before the caller.
The empty tensor elimination pass semantics have changed recently: when
applied to a module, the One-Shot Module Analysis is run. Otherwise, the
regular One-Shot Analysis is run. The latter one is slightly different
because it ignores function boundaries and treats function block
arguments as "read-only".
This commit updates the transform dialect op to behave in the same way.
* Fixes#67977, a crash in `empty-tensor-elimination`.
* Also improves `linalg.copy` canonicalization.
* Also improves indentation indentation in `mlir-linalg-ods-yaml-gen.cpp`.
Values that are the result of buffer allocation ops are guaranteed to
*not* be the same allocation as block arguments of containing blocks.
This fact can be used to allow for more aggressive simplification of
`bufferization.dealloc` ops.
Inserting clones requires a lot of assumptions to hold on the input IR, e.g., all writes to a buffer need to dominate all reads. This is not guaranteed by one-shot bufferization and isn't easy to verify, thus it could quickly lead to incorrect results that are hard to debug. This commit changes the mechanism of how an ownership indicator is materialized when there is not already a unique ownership present. Additionally, we don't create copies of returned memrefs anymore when we don't have ownership. Instead, we insert assert operations to make sure we have ownership at runtime, or otherwise report to the user that correctness could not be guaranteed.
The buffer deallocation pipeline now works on modules and functions.
Also add extra test cases that run the buffer deallocation pipeline on
modules and functions. (Test cases that insert a helper function.)
* Properly handle the case where an op is deleted and thus no other
interfaces should be processed anymore.
* Don't add ownership indicator arguments and results to function
declarations
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 is necessary to support deallocation of IR with gpu.launch
operations because it does not implement the RegionBranchOpInterface.
Implementing the interface would require it to support regions with
unstructured control flow and produced arguments/results.
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.
In line with #66515, change `MutableArrayRange::begin`/`end` to
enumerate `OpOperand &` instead of `Value`. Also remove
`ForOp::getIterOpOperands`/`setIterArg`, which are now redundant.
Note: `MutableOperandRange` cannot be made a derived class of
`indexed_accessor_range_base` (like `OperandRange`), because
`MutableOperandRange::assign` can change the number of operands in the
range.
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.
Since ownership based buffer deallocation requires a few passes to be run in a somewhat fixed sequence, it makes sense to have a pipeline for convenience (and to reduce the number of transform ops to represent default deallocation).
Add a method to the BufferDeallocationOpInterface that allows operations to implement the interface and provide custom logic to compute the ownership indicators of values it defines. As a demonstrating example, this new method is implemented by the `arith.select` operation.
This new interface allows operations to implement custom handling of ownership values and insertion of dealloc operations which is useful when an op cannot implement the interfaces supported by default by the buffer deallocation pass (e.g., because they are not exactly compatible or because there are some additional semantics to it that would render the default implementations in buffer deallocation invalid, or because no interfaces exist for this
kind of behavior and it's not worth introducing one plus a default implementation in buffer deallocation). Additionally, it can also be used to provide more efficient handling for a specific op than the interface based default
implementations can.
Add a new Buffer Deallocation pass with the intend to replace the old
one. For now it is added as a separate pass alongside in order to allow
downstream users to migrate over gradually. This new pass has the goal
of inserting fewer clone operations and supporting additional use-cases.
Please refer to the Buffer Deallocation section in the updated
Bufferization.md file for more information on how this new pass works.
This commit generalizes empty tensor elimination to operate on subset
ops. No new test cases are added because all current subset ops were
already supported previously. From this perspective, this change is NFC.
A new interface method (and a helper method) are added to
`SubsetInsertionOpInterface` to build the subset of the destination
tensor.
This commit generalizes the special
tensor.extract_slice/tensor.insert_slice bufferization rules to tensor
subset ops.
Ops that insert a tensor into a tensor at a specified subset (e.g.,
tensor.insert_slice, tensor.scatter) can implement the
`SubsetInsertionOpInterface`.
Apart from adding a new op interface (extending the API), this change is
NFC. The only ops that currently implement the new interface are
tensor.insert_slice and tensor.parallel_insert_slice, and those ops were
are supported by One-Shot Bufferize.
Since buffer deallocation requires a few passes to be run in a somewhat fixed
sequence, it makes sense to have a pipeline for convenience (and to reduce the
number of transform ops to represent default deallocation).
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D159432
Add a method to the BufferDeallocationOpInterface that allows operations to
implement the interface and provide custom logic to compute the ownership
indicators of values it defines. As a demonstrating example, this new method is
implemented by the `arith.select` operation.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D158828
This new interface allows operations to implement custom handling of ownership
values and insertion of dealloc operations which is useful when an op cannot
implement the interfaces supported by default by the buffer deallocation pass
(e.g., because they are not exactly compatible or because there are some
additional semantics to it that would render the default implementations in
buffer deallocation invalid, or because no interfaces exist for this kind of
behavior and it's not worth introducing one plus a default implementation in
buffer deallocation). Additionally, it can also be used to provide more
efficient handling for a specific op than the interface based default
implementations can.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D158756
Add a new Buffer Deallocation pass replacing the old one with the goal of
inserting fewer clone operations and supporting additional use-cases.
Please refer to the Buffer Deallocation section in the updated
Bufferization.md file for more information on how this new pass works.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D158421
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
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.
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
Functions are always callable operations and thus every operation
implementing the `FunctionOpInterface` also implements the
`CallableOpInterface`. The only exception was the FuncOp in the toy
example. To make implementation of the `FunctionOpInterface` easier,
this commit lets `FunctionOpInterface` inherit from
`CallableOpInterface` and merges some of their methods. More precisely,
the `CallableOpInterface` has methods to get the argument and result
attributes and a method to get the result types of the callable region.
These methods are always implemented the same way as their analogues in
`FunctionOpInterface` and thus this commit moves all the argument and
result attribute handling methods to the callable interface as well as
the methods to get the argument and result types. The
`FuntionOpInterface` then does not have to declare them as well, but
just inherits them from the `CallableOpInterface`.
Adding the inheritance relation also required to move the
`FunctionOpInterface` from the IR directory to the Interfaces directory
since IR should not depend on Interfaces.
Reviewed By: jpienaar, springerm
Differential Revision: https://reviews.llvm.org/D157988
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
Moves the lowering of `bufferization.dealloc` to memref into a separate pass,
but still registers the pattern in the conversion pass. This is helpful when
some tensor values (and thus `to_memref` or `to_tensor` operations) still
remain, e.g., when the function boundaries are not converted, or when constant
tensors are converted to memref.get_global at a later point.
However, it is still recommended to perform all bufferization before
deallocation to avoid memory leaks as all memref allocations inserted after the
deallocation pass was applied, have to be handled manually.
Note: The buffer deallocation pass assumes that memref values defined by
`bufferization.to_memref` don't return ownership and don't have to be
deallocated. `bufferization.to_tensor` operations are handled similarly to
`bufferization.clone` operations with the exception that the result value is
not handled because it's a tensor (not a memref).
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D159180
