This commit adds support for bufferizing external functions that have no
body. Such functions were previously rejected by One-Shot Bufferize if
they returned a tensor value.
This commit is in preparation of removing the deprecated
`func-bufferize` pass. That pass can bufferize external functions.
Also update a few comments.
This commit changes the format of the materialization error message.
Previously: `failed to legalize unresolved materialization from ('f64')
to 'f32' that remained live after conversion`
Now: `failed to legalize unresolved materialization from ('f64') to
('f32') that remained live after conversion`
This commit is in preparation of merging the 1:1 and 1:N dialect
conversions. At that point, target materializations may create more than
one SSA value. I am sending this change as a separate PR to keep the
main PR smaller.
**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>
This commit makes source/target/argument materializations (via the
`TypeConverter` API) optional.
By default (`ConversionConfig::buildMaterializations = true`), the
dialect conversion infrastructure tries to legalize all unresolved
materializations right after the main transformation process has
succeeded. If at least one unresolved materialization fails to resolve,
the dialect conversion fails. (With an error message such as `failed to
legalize unresolved materialization ...`.) Automatic materializations
through the `TypeConverter` API can now be deactivated. In that case,
every unresolved materialization will show up as a
`builtin.unrealized_conversion_cast` op in the output IR.
There used to be a complex and error-prone analysis in the dialect
conversion that predicted the future uses of unresolved
materializations. Based on that logic, some casts (that were deemed to
unnecessary) were folded. This analysis was needed because folding
happened at a point of time when some IR changes (e.g., op replacements)
had not materialized yet.
This commit removes that analysis. Any folding of cast ops now happens
after all other IR changes have been materialized and the uses can
directly be queried from the IR. This simplifies the analysis
significantly. And certain helper data structures such as
`inverseMapping` are no longer needed for the analysis. The folding
itself is done by `reconcileUnrealizedCasts` (which also exists as a
standalone pass).
After casts have been folded, the remaining casts are materialized
through the `TypeConverter`, as usual. This last step can be deactivated
in the `ConversionConfig`.
`ConversionConfig::buildMaterializations = false` can be used to debug
error messages such as `failed to legalize unresolved materialization
...`. (It is also useful in case automatic materializations are not
needed.) The materializations that failed to resolve can then be seen as
`builtin.unrealized_conversion_cast` ops in the resulting IR. (This is
better than running with `-debug`, because `-debug` shows IR where some
IR changes have not been materialized yet.)
Note: This is a reupload of #104668, but with correct handling of cyclic
unrealized_conversion_casts that may be generated by the dialect
conversion.
Handle caller/callee type mismatch using `castOrReallocMemRefValue`
instead of just a `CastOp`. The method insert a reallocation + copy if
it cannot be statically guaranteed that a direct cast would be valid.
Fix#105916.
This commit makes source/target/argument materializations (via the
`TypeConverter` API) optional.
By default (`ConversionConfig::buildMaterializations = true`), the
dialect conversion infrastructure tries to legalize all unresolved
materializations right after the main transformation process has
succeeded. If at least one unresolved materialization fails to resolve,
the dialect conversion fails. (With an error message such as `failed to
legalize unresolved materialization ...`.) Automatic materializations
through the `TypeConverter` API can now be deactivated. In that case,
every unresolved materialization will show up as a
`builtin.unrealized_conversion_cast` op in the output IR.
There used to be a complex and error-prone analysis in the dialect
conversion that predicted the future uses of unresolved
materializations. Based on that logic, some casts (that were deemed to
unnecessary) were folded. This analysis was needed because folding
happened at a point of time when some IR changes (e.g., op replacements)
had not materialized yet.
This commit removes that analysis. Any folding of cast ops now happens
after all other IR changes have been materialized and the uses can
directly be queried from the IR. This simplifies the analysis
significantly. And certain helper data structures such as
`inverseMapping` are no longer needed for the analysis. The folding
itself is done by `reconcileUnrealizedCasts` (which also exists as a
standalone pass).
After casts have been folded, the remaining casts are materialized
through the `TypeConverter`, as usual. This last step can be deactivated
in the `ConversionConfig`.
`ConversionConfig::buildMaterializations = false` can be used to debug
error messages such as `failed to legalize unresolved materialization
...`. (It is also useful in case automatic materializations are not
needed.) The materializations that failed to resolve can then be seen as
`builtin.unrealized_conversion_cast` ops in the resulting IR. (This is
better than running with `-debug`, because `-debug` shows IR where some
IR changes have not been materialized yet.)
When inserting an argument/source/target materialization, the dialect
conversion framework first inserts a "dummy"
`unrealized_conversion_cast` op (during the rewrite process) and then
(in the "finialize" phase) replaces these cast ops with the IR generated
by the type converter callback.
This is the case for all materializations, except when ops are being
replaced with values that have a different type. In that case, the
dialect conversion currently directly emits a source materialization.
This commit changes the implementation, such that a temporary
`unrealized_conversion_cast` is also inserted in that case.
This commit simplifies the code base: all materializations now happen in
`legalizeUnresolvedMaterialization`. This commit makes it possible to
decouple source/target/argument materializations from the dialect
conversion (to reduce the complexity of the code base). Such
materializations can then also be optional. This will be implemented in
a follow-up commit.
Depends on #101476.
---------
Co-authored-by: Jakub Kuderski <jakub@nod-labs.com>
Adding a pass that is expected to run after the deallocation pipeline
and will move buffer deallocations right after their last user or
dependency, thus optimizing the allocation liveness.
With this PR I am trying to address:
https://github.com/llvm/llvm-project/issues/63230.
What changed:
- While merging identical blocks, don't add a block argument if it is
"identical" to another block argument. I.e., if the two block arguments
refer to the same `Value`. The operations operands in the block will
point to the argument we already inserted. This needs to happen to all
the arguments we pass to the different successors of the parent block
- After merged the blocks, get rid of "unnecessary" arguments. I.e., if
all the predecessors pass the same block argument, there is no need to
pass it as an argument.
- This last simplification clashed with
`BufferDeallocationSimplification`. The reason, I think, is that the two
simplifications are clashing. I.e., `BufferDeallocationSimplification`
contains an analysis based on the block structure. If we simplify the
block structure (by merging and/or dropping block arguments) the
analysis is invalid . The solution I found is to do a more prudent
simplification when running that pass.
**Note-1**: I ran all the integration tests
(`-DMLIR_INCLUDE_INTEGRATION_TESTS=ON`) and they passed.
**Note-2**: I fixed a bug found by @Dinistro in #97697 . The issue was
that, when looking for redundant arguments, I was not considering that
the block might have already some arguments. So the index (in the block
args list) of the i-th `newArgument` is `i+numOfOldArguments`.
With this PR I am trying to address:
https://github.com/llvm/llvm-project/issues/63230.
What changed:
- While merging identical blocks, don't add a block argument if it is
"identical" to another block argument. I.e., if the two block arguments
refer to the same `Value`. The operations operands in the block will
point to the argument we already inserted. This needs to happen to all
the arguments we pass to the different successors of the parent block
- After merged the blocks, get rid of "unnecessary" arguments. I.e., if
all the predecessors pass the same block argument, there is no need to
pass it as an argument.
- This last simplification clashed with
`BufferDeallocationSimplification`. The reason, I think, is that the two
simplifications are clashing. I.e., `BufferDeallocationSimplification`
contains an analysis based on the block structure. If we simplify the
block structure (by merging and/or dropping block arguments) the
analysis is invalid . The solution I found is to do a more prudent
simplification when running that pass.
**Note**: this a rework of #96871 . I ran all the integration tests
(`-DMLIR_INCLUDE_INTEGRATION_TESTS=ON`) and they passed.
In nested symbols, the dealloc_helper function generated by lower
deallocations pass was incorrectly positioned, causing calls fail. This
patch fixes this issue.
With this PR I am trying to address:
https://github.com/llvm/llvm-project/issues/63230.
What changed:
- While merging identical blocks, don't add a block argument if it is
"identical" to another block argument. I.e., if the two block arguments
refer to the same `Value`. The operations operands in the block will
point to the argument we already inserted
- After merged the blocks, get rid of "unnecessary" arguments. I.e., if
all the predecessors pass the same block argument, there is no need to
pass it as an argument.
- This last simplification clashed with
`BufferDeallocationSimplification`. The reason, I think, is that the two
simplifications are clashing. I.e., `BufferDeallocationSimplification`
contains an analysis based on the block structure. If we simplify the
block structure (by merging and/or dropping block arguments) the
analysis is invalid . The solution I found is to do a more prudent
simplification when running that pass.
**Note**: many tests are still not passing. But I wanted to submit the
code before changing all the tests (and probably adding a couple), so
that we can agree in principle on the algorithm/design.
In the origin implementation, the empty tensor elimination will add a
`tensor.cast` and eliminate the tensor even if they have different
element type(f32, bf16). Here add a check for element type and skip the
elimination if they are different.
This commit fixes a crash in the ownership-based buffer deallocation
pass when indirectly calling a function via SSA value. Such functions
must be conservatively assumed to be public.
Fixes#94780.
This change corrects an invalid behavior in pass
`--buffer-loop-hoisting`. The pass is in charge of extracting buffer
allocations (e.g., `memref.alloca`) from loop regions (e.g., `scf.for`)
when possible. This works OK for looks with sequential execution
semantics. However, a buffer allocated in the body of a parallel loop
may be concurrently accessed by multiple thread to store its local data.
Extracting such buffer from the loop causes all threads to wrongly share
the same memory region.
In the following example, dimension 1 of the input tensor is reversed.
Dimension 0 is traversed with a parallel loop.
```
func.func @f(%input: memref<2x3xf32>) -> memref<2x3xf32> {
%c0 = index.constant 0
%c1 = index.constant 1
%c2 = index.constant 2
%c3 = index.constant 3
%output = memref.alloc() : memref<2x3xf32>
scf.parallel (%index) = (%c0) to (%c2) step (%c1) {
// Create subviews for working input and output slices
%input_slice = memref.subview %input[%index, 2][1, 3][1, -1] : memref<2x3xf32> to memref<1x3xf32, strided<[3, -1], offset: ?>>
%output_slice = memref.subview %output[%index, 0][1, 3][1, 1] : memref<2x3xf32> to memref<1x3xf32, strided<[3, 1], offset: ?>>
// Copy the input slice into this temporary buffer. This intermediate
// copy is unnecessary, but is used for illustration purposes.
%temp = memref.alloc() : memref<1x3xf32>
memref.copy %input_slice, %temp : memref<1x3xf32, strided<[3, -1], offset: ?>> to memref<1x3xf32>
// Copy temporary buffer into output slice
memref.copy %temp, %output_slice : memref<1x3xf32> to memref<1x3xf32, strided<[3, 1], offset: ?>>
scf.reduce
}
return %output : memref<2x3xf32>
}
```
The patch submitted here prevents `%temp = memref.alloc() :
memref<1x3xf32>` from being hoisted when the containing op is
`scf.parallel` or `scf.forall`. A new op trait called
`HasParallelRegion` is introduced and assigned to these two ops to
indicate that their regions have parallel execution semantics.
@joker-eph @ftynse @nicolasvasilache @sabauma
This patch generalizes tensor.expand_shape and memref.expand_shape to
consume the output shape as a list of SSA values. This enables us to
implement generic reshape operations with dynamic shapes using
collapse_shape/expand_shape pairs.
The output_shape input to expand_shape follows the static/dynamic
representation that's also used in `tensor.extract_slice`.
Differential Revision: https://reviews.llvm.org/D140821
---------
Signed-off-by: Gaurav Shukla<gaurav.shukla@amd.com>
Signed-off-by: Gaurav Shukla <gaurav.shukla@amd.com>
Co-authored-by: Ramiro Leal-Cavazos <ramiroleal050@gmail.com>
This patch generalizes tensor.expand_shape and memref.expand_shape to
consume the output shape as a list of SSA values. This enables us to
implement generic reshape operations with dynamic shapes using
collapse_shape/expand_shape pairs.
The output_shape input to expand_shape follows the static/dynamic
representation that's also used in `tensor.extract_slice`.
Differential Revision: https://reviews.llvm.org/D140821
Co-authored-by: Ramiro Leal-Cavazos <ramiroleal050@gmail.com>
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.
```
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 commit adds the `BufferOriginAnalysis`, which can be queried to
check if two buffer SSA values originate from the same allocation. This
new analysis is used in the buffer deallocation pass to fold away or
simplify `bufferization.dealloc` ops more aggressively.
The `BufferOriginAnalysis` is based on the `BufferViewFlowAnalysis`,
which collects buffer SSA value "same buffer" dependencies. E.g., given
IR such as:
```
%0 = memref.alloc()
%1 = memref.subview %0
%2 = memref.subview %1
```
The `BufferViewFlowAnalysis` will report the following "reverse"
dependencies (`resolveReverse`) for `%2`: {`%2`, `%1`, `%0`}. I.e., all
buffer SSA values in the reverse use-def chain that originate from the
same allocation as `%2`. The `BufferOriginAnalysis` is built on top of
that. It handles only simple cases at the moment and may conservatively
return "unknown" around certain IR with branches, memref globals and
function arguments.
This analysis enables additional simplifications during
`-buffer-deallocation-simplification`. In particular, "regular" scf.for
loop nests, that yield buffers (or reallocations thereof) in the same
order as they appear in the iter_args, are now handled much more
efficiently. Such IR patterns are generated by the sparse compiler.
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.
Even when `private-function-dynamic-ownership` is set, ownership should
never be passed to the callee. This can lead to double deallocs (#77096)
or use-after-free in the caller because ownership is currently passed
regardless of whether there are any further uses of the buffer in the
caller or not.
Note: This is consistent with the fact that ownership is never passed to
nested regions.
This commit fixes#77096.
Add write memory effect for the print operation. The exact memory
behavior is implemented in other print-like operations such as
`transform::PrintOp` or `gpu::printf`.
Providing memory behavior allows using the operation in passes like
buffer deallocation instead of emitting an error.
The buffer deallocation pass checks the IR ("operation preconditions")
to make sure that there is no IR that is unsupported. In such a case,
the pass signals a failure.
The pass now rejects all ops with unknown memory effects. We do not know
whether such an op allocates memory or not. Therefore, the buffer
deallocation pass does not know whether a deallocation op should be
inserted or not.
Memory effects are queried from the `MemoryEffectOpInterface` interface.
Ops that do not implement this interface but have the
`RecursiveMemoryEffects` trait do not have any side effects (apart from
the ones that their nested ops may have).
Unregistered ops are now rejected by the pass because they do not
implement the `MemoryEffectOpInterface` and neither do we know if they
have `RecursiveMemoryEffects` or not. All test cases that currently have
unregistered ops are updated to use registered ops.
This commit simplifies a helper function in the ownership-based buffer
deallocation pass. Fixes a potential double-free (depending on the
scheduling of patterns).
This patch adds the target_cpu attribute to llvm.func MLIR operations
and updates the translation to/from LLVM IR to match "target-cpu"
function attributes.
This revision adds support to
`transform.structured.bufferize_to_allocation` to bufferize
`bufferization.alloc_tensor()` ops.
This is useful as a means path to control the bufferization of
`tensor.empty` ops that have bene previously
`bufferization.empty_tensor_to_alloc_tensor`'ed.
Update most test passes to use the transform-interpreter pass instead of
the test-transform-dialect-interpreter-pass. The new "main" interpreter
pass has a named entry point instead of looking up the top-level op with
`PossibleTopLevelOpTrait`, which is arguably a more understandable
interface. The change is mechanical, rewriting an unnamed sequence into
a named one and wrapping the transform IR in to a module when necessary.
Add an option to the transform-interpreter pass to target a tagged
payload op instead of the root anchor op, which is also useful for repro
generation.
Only the test in the transform dialect proper and the examples have not
been updated yet. These will be updated separately after a more careful
consideration of testing coverage of the transform interpreter logic.
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.
Empty tensor elimination is looking for
`bufferization.materialize_in_destination` ops with a `tensor.empty`
source. It replaces the `tensor.empty` with a `bufferization.to_tensor
restrict` of the memref destination. As part of this rewrite, the
`restrict` keyword should be removed, so that no second `to_tensor
restrict` op will be inserted. Such IR would be invalid.
`bufferization.materialize_in_destination` with memref destination and
without the `restrict` attribute are ignored by empty tensor
elimination.
Also relax the verifier of `materialize_in_destination`. The `restrict`
keyword is not generally needed because the op does not expose the
buffer as a tensor.
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.
Extend `bufferization.materialize_in_destination` to support memref
destinations. This op can now be used to indicate that a tensor
computation should materialize in a given buffer (that may have been
allocated by another component/runtime). The op still participates in
"empty tensor elimination".
Example:
```mlir
func.func @test(%out: memref<10xf32>) {
%t = tensor.empty() : tensor<10xf32>
%c = linalg.generic ... outs(%t: tensor<10xf32>) -> tensor<10xf32>
bufferization.materialize_in_destination %c in restrict writable %out : (tensor<10xf32>, memref<10xf32>) -> ()
return
}
```
After "empty tensor elimination", the above IR can bufferize without an
allocation:
```mlir
func.func @test(%out: memref<10xf32>) {
linalg.generic ... outs(%out: memref<10xf32>)
return
}
```
This change also clarifies the meaning of the `restrict` unit attribute
on `bufferization.to_tensor` ops.
* 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.)
