This commit adds a new `matchAndRewrite` overload to `ConversionPattern`
to support 1:N replacements. This is the first of two main PRs that
merge the 1:1 and 1:N dialect conversion drivers.
The existing `matchAndRewrite` function supports only 1:1 replacements,
as can be seen from the `ArrayRef<Value>` parameter.
```c++
LogicalResult ConversionPattern::matchAndRewrite(
Operation *op, ArrayRef<Value> operands /*adaptor values*/,
ConversionPatternRewriter &rewriter) const;
```
This commit adds a `matchAndRewrite` overload that is called by the
dialect conversion driver. By default, this new overload dispatches to
the original 1:1 `matchAndRewrite` implementation. Existing
`ConversionPattern`s do not need to be changed as long as there are no
1:N type conversions or value replacements.
```c++
LogicalResult ConversionPattern::matchAndRewrite(
Operation *op, ArrayRef<ValueRange> operands /*adaptor values*/,
ConversionPatternRewriter &rewriter) const {
// Note: getOneToOneAdaptorOperands produces a fatal error if at least one
// ValueRange has 0 or more than 1 value.
return matchAndRewrite(op, getOneToOneAdaptorOperands(operands), rewriter);
}
```
The `ConversionValueMapping`, which keeps track of value replacements
and materializations, still does not support 1:N replacements. We still
rely on argument materializations to convert N replacement values back
into a single value. The `ConversionValueMapping` will be generalized to
1:N mappings in the second main PR.
Before handing the adaptor values to a `ConversionPattern`, all argument
materializations are "unpacked". The `ConversionPattern` receives N
replacement values and does not see any argument materializations. This
implementation strategy allows us to use the 1:N infrastructure/API in
`ConversionPattern`s even though some functionality is still missing in
the driver. This strategy was chosen to keep the sizes of the PRs
smaller and to make it easier for downstream users to adapt to API
changes.
This commit also updates the the "decompose call graphs" transformation
and the "sparse tensor codegen" transformation to use the new 1:N
`ConversionPattern` API.
Note for LLVM conversion: If you are using a type converter with 1:N
type conversion rules or if your patterns are performing 1:N
replacements (via `replaceOpWithMultiple` or
`applySignatureConversion`), conversion pattern applications will start
failing (fatal LLVM error) with this error message: `pattern 'name' does
not support 1:N conversion`. The name of the failing pattern is shown in
the error message. These patterns must be updated to the new 1:N
`matchAndRewrite` API.
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.
`getDescriptorFromTensorTuple` and `getMutDescriptorFromTensorTuple`
extract the tensor type from an `unrealized_conversion_cast` op that
serves as a workaround for missing 1:N dialect conversion support.
This commit changes these functions so that they explicitly receive the
tensor type as a function argument. This is in preparation of merging
the 1:1 and 1:N conversion drivers. The conversion patterns in this file
will soon start receiving multiple SSA values (`ValueRange`) from their
adaptors (instead of a single value that is the result of
`unrealized_conversion_cast`). It will no longer be possible to take the
tensor type from the `unrealized_conversion_cast` op. The
`unrealized_conversion_cast` workaround will disappear entirely.
This commit adds a new function
`ConversionPatternRewriter::replaceOpWithMultiple`. This function is
similar to `replaceOp`, but it accepts multiple `ValueRange`
replacements, one per op result.
Note: This function is not an overload of `replaceOp` because of
ambiguous overload resolution that would make the API difficult to use.
This commit aligns "block signature conversions" with "op replacements":
both support 1:N replacements now. Due to incomplete 1:N support in the
dialect conversion driver, an argument materialization is inserted when
an SSA value is replaced with multiple values; same as block signature
conversions already work around the problem. These argument
materializations are going to be removed in a subsequent commit that
adds full 1:N support. The purpose of this PR is to add missing features
gradually in small increments.
This commit also updates two MLIR transformations that have their custom
workarounds around missing 1:N support. These can already start using
`replaceOpWithMultiple`.
Co-authored-by: Markus Böck <markus.boeck02@gmail.com>
Currently, the lowering for vector.step lives
under a folder. This is not ideal if we want
to do transformation on it and defer the
materizaliztion of the constants much later.
This commits adds a rewrite pattern that
could be used by using
`transform.structured.vectorize_children_and_apply_patterns`
transform dialect operation.
Moreover, the rewriter of vector.step is also
now used in -convert-vector-to-llvm pass where
it handles scalable and non-scalable types as
LLVM expects it.
As a consequence of removing the vector.step
lowering as its folder, linalg vectorization
will keep vector.step intact.
This commit simplifies the result type of materialization functions.
Previously: `std::optional<Value>`
Now: `Value`
The previous implementation allowed 3 possible return values:
- Non-null value: The materialization function produced a valid
materialization.
- `std::nullopt`: The materialization function failed, but another
materialization can be attempted.
- `Value()`: The materialization failed and so should the dialect
conversion. (Previously: Dialect conversion can roll back.)
This commit removes the last variant. It is not particularly useful
because the dialect conversion will fail anyway if all other
materialization functions produced `std::nullopt`.
Furthermore, in contrast to type conversions, at least one
materialization callback is expected to succeed. In case of a failing
type conversion, the current dialect conversion can roll back and try a
different pattern. This also used to be the case for materializations,
but that functionality was removed with #107109: failed materializations
can no longer trigger a rollback. (They can just make the entire dialect
conversion fail without rollback.) With this in mind, it is even less
useful to have an additional error state for materialization functions.
This commit is in preparation of merging the 1:1 and 1:N type
converters. Target materializations will have to return multiple values
instead of a single one. With this commit, we can keep the API simple:
`SmallVector<Value>` instead of `std::optional<SmallVector<Value>>`.
Note for LLVM integration: All 1:1 materializations should return
`Value` instead of `std::optional<Value>`. Instead of `std::nullopt`
return `Value()`.
Sparse tensors are always ranked tensors. Encodings cannot be attached
to unranked tensors. Change the type constraint to `RankedTensorOf`, so
that we generate `TypedValue<RankedTensorType>` instead of
`TypedValue<TensorType>`. This removes the need for type casting in some
cases.
Also improve the verifiers (missing `return` statements) and switch a
few other `AnyTensor` to `AnyRankedTensor`.
This commit is in preparation of a dialect conversion commit that
required fixes in the sparse dialect.
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.
This PR fixes a bug in `SparseTensorDimOpRewriter` when `tensor.dim` has
an unranked tensor type. To prevent crashes, we now use
`tryGetSparseTensorType` instead of `getSparseTensorType`. Fixes
#107807.
As specified in the docs,
1) raw_string_ostream is always unbuffered and
2) the underlying buffer may be used directly
( 65b13610a5 for further reference )
* Don't call raw_string_ostream::flush(), which is essentially a no-op.
* Avoid unneeded calls to raw_string_ostream::str(), to avoid excess indirection.
When only all-dense "sparse" tensors occur in a function prototype, the
assembler would skip the method conversion purely based on input/output
counts. It should rewrite based on the presence of any annotation,
however.
There was some inconsistency with ConvertVectorToLLVM Pass builder,
files and option names.
This patch aims to move all occurences to ConvertVectorToLLVM.
When a type/attribute is defined in TableGen, a type constraint can be
used for parameters, but the type constraint verification was missing.
Example:
```
def TestTypeVerification : Test_Type<"TestTypeVerification"> {
let parameters = (ins AnyTypeOf<[I16, I32]>:$param);
// ...
}
```
No verification code was generated to ensure that `$param` is I16 or
I32.
When type constraints a present, a new method will generated for types
and attributes: `verifyInvariantsImpl`. (The naming is similar to op
verifiers.) The user-provided verifier is called `verify` (no change).
There is now a new entry point to type/attribute verification:
`verifyInvariants`. This function calls both `verifyInvariantsImpl` and
`verify`. If neither of those two verifications are present, the
`verifyInvariants` function is not generated.
When a type/attribute is not defined in TableGen, but a verifier is
needed, users can implement the `verifyInvariants` function. (This
function was previously called `verify`.)
Note for LLVM integration: If you have an attribute/type that is not
defined in TableGen (i.e., just C++), you have to rename the
verification function from `verify` to `verifyInvariants`. (Most
attributes/types have no verification, in which case there is nothing to
do.)
Depends on #102657.
`PassManager::run` loads the dependent dialects for each pass into the
current context prior to invoking the individual passes. If the
dependent dialect is already loaded into the context, this should be a
no-op. However, if there are extensions registered in the
`DialectRegistry`, the dependent dialects are unconditionally registered
into the context.
This poses a problem for dynamic pass pipelines, however, because they
will likely be executing while the context is in an immutable state
(because of the parent pass pipeline being run).
To solve this, we'll update the extension registration API on
`DialectRegistry` to require a type ID for each extension that is
registered. Then, instead of unconditionally registered dialects into a
context if extensions are present, we'll check against the extension
type IDs already present in the context's internal `DialectRegistry`.
The context will only be marked as dirty if there are net-new extension
types present in the `DialectRegistry` populated by
`PassManager::getDependentDialects`.
Note: this PR removes the `addExtension` overload that utilizes
`std::function` as the parameter. This is because `std::function` is
copyable and potentially allocates memory for the contained function so
we can't use the function pointer as the unique type ID for the
extension.
Downstream changes required:
- Existing `DialectExtension` subclasses will need a type ID to be
registered for each subclass. More details on how to register a type ID
can be found here:
8b68e06731/mlir/include/mlir/Support/TypeID.h (L30)
- Existing uses of the `std::function` overload of `addExtension` will
need to be refactored into dedicated `DialectExtension` classes with
associated type IDs. The attached `std::function` can either be inlined
into or called directly from `DialectExtension::apply`.
---------
Co-authored-by: Mehdi Amini <joker.eph@gmail.com>
This is described in (N2) https://pvs-studio.com/en/blog/posts/cpp/1126/
so caught by the PVS Studio analyzer.
Warning message -
V502 Perhaps the '?:' operator works in a different way than it was
expected. The '?:' operator has a lower priority than the '+' operator.
LoopEmitter.cpp 983
V502 Perhaps the '?:' operator works in a different way than it was
expected. The '?:' operator has a lower priority than the '+' operator.
LoopEmitter.cpp 1039
The assert should be
assert(bArgs.size() == reduc.size() + (needsUniv ? 1 : 0));
since + has higher precedence and ? has lower.
This further can be reduce to
assert(aArgs.size() == reduc.size() + needsUniv);
because needUniv is a bool value which is implicitly converted to 0 or
Some of the options only fed into the full sparse pipeline. However,
some backends prefer to use the sparse minipipeline. This change exposes
some important optimization flags to the pass as well. This prepares
some SIMDization of PyTorch sparsified code.
A `sparse_tensor.iterate` iterates over a sparse iteration space
extracted from `sparse_tensor.extract_iteration_space` operation
introduced in https://github.com/llvm/llvm-project/pull/88554.
This is a proof of concept recognition of the most basic forms of ReLu
operations, used to show-case sparsification of end-to-end PyTorch
models. In the long run, we must avoid lowering such constructs too
early (with this need for raising them back).
See discussion at
https://discourse.llvm.org/t/min-max-abs-relu-recognition-starter-project/78918
I'm planning to remove StringRef::equals in favor of
StringRef::operator==.
- StringRef::operator==/!= outnumber StringRef::equals by a factor of
10 under mlir/ in terms of their usage.
- The elimination of StringRef::equals brings StringRef closer to
std::string_view, which has operator== but not equals.
- S == "foo" is more readable than S.equals("foo"), especially for
!Long.Expression.equals("str") vs Long.Expression != "str".
