Add new `typeDescription` (description was already used by base constraint class) field to type to allow writing longer descriptions about a type being defined. This allows for providing additional information/rationale for a defined type. This currently uses `description` as the heading/name for the type in the generated documentation.
PiperOrigin-RevId: 273299332
See RFC: https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/xE2IzfhE3Wg.
Opaque location stores two pointers, one of them points to some data structure that is external to MLIR, and the other one is unique for each type and represents type id of that data structure. OpaqueLoc also stores an optional location that can be used if the first one is not suitable.
OpaqueLoc is managed similar to FileLineColLoc. It is passed around by MLIR transformations and can be used in compound locations like CallSiteLoc.
PiperOrigin-RevId: 273266510
This allows confirming that a scalar argument has the same element type as a shaped one. It's easy to validate a type is shaped on its own if that's desirable, so this shouldn't make that use case harder. This matches the behavior of other traits that operate on element type (e.g. AllElementTypesMatch). Also this makes the code simpler because now we just use getElementTypeOrSelf.
Verified that all uses in core already check the type is shaped in another way.
PiperOrigin-RevId: 273068507
1. Rename a few ops to make it clear they operate on *element* types.
2. Remove unused and generic operand and result ODS names (e.g. $res, $arg, $input). These are just clutter and don't make the op definitions any clearer.
3. Give test cases with duplicate names clearer names.
4. Add missing test case for no operands in SameOperandAndResultElementType.
PiperOrigin-RevId: 273067933
Use `getParentOfType<FunctionOp>()` instead of `cast<FuncOp>(getParentOp())`
to avoid crash when return ops are used inside spv.selection/spv.loop.
PiperOrigin-RevId: 273006041
This is fixing a build failure, usually non-deterministic because of
parallelism in the build, but could be reliably reproduced:
ninja projects/mlir/test/lib/TestDialect/CMakeFiles/MLIRTestDialect.dir/TestPatterns.cpp.o
PiperOrigin-RevId: 272998436
Adding support for OpUndef instruction. Updating the dialect
generation script to fix a few bugs in the instruction spec
generation.
PiperOrigin-RevId: 272975685
Add builder functions for spv._address_of, spv.EntryPoint,
spv.ExecutionMode and spv.Load to make it easier to create these
operations.
Fix a minor bug in printing of spv.EntryPoint
Add a utility function to get the attribute name associated with a
decoration.
PiperOrigin-RevId: 272952846
Certain lowering patterns were reported as [missing](https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/dkdmHa77sSQ).
This CL adds them and allows Linalg/roundtrip.mlir and Linalg/loops.mlir to lower to LLVM directly. Those 2 tests are updated to additionally check that the direct lowering to LLVM does not crash.
The following points, left as TODOs still need to be addressed for correct end-to-end execution:
1. the lowering for ConvOp needs to pass attributes such as strides and dilations; the external library call needs to support it.
2. the lowering for GenericOp needs to support lowering to loops as a DialectConversion pattern. This is blocked on the DialectConversion infrastructure accepting an OperationFolder.
PiperOrigin-RevId: 272878131
The GPUIndexIntrinsicOpLowering template is currently used by the code in both the GPUToNVVM and GPUToROCDL dirs.
Moving it to a common location to remove code duplication.
Closestensorflow/mlir#163
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/163 from deven-amd:deven-refactor-gpu-index-ops-lowering b8dc2a5f5353df196039b6ff2ad42106028693ed
PiperOrigin-RevId: 272863297
Some dialects have implicit conversions inherent in their modeling, meaning that a call may have a different type that the type that the callable expects. To support this, a hook is added to the dialect interface that allows for materializing conversion operations during inlining when there is a mismatch. A hook is also added to the callable interface to allow for introspecting the expected result types.
PiperOrigin-RevId: 272814379
This allows for the inliner to work on arbitrary call operations. The updated inliner will also work bottom-up through the callgraph enabling support for multiple levels of inlining.
PiperOrigin-RevId: 272813876
The first dim length of the axisStats attribute should equals to the slice size
of the input argument when splitted by the axis dimension.
PiperOrigin-RevId: 272798042
This CL implements the last remaining bit of the [strided memref proposal](https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
The syntax is a bit more explicit than what was originally proposed and resembles:
`memref<?x?xf32, offset: 0 strides: [?, 1]>`
Nonnegative strides and offsets are currently supported. Future extensions will include negative strides.
This also gives a concrete example of syntactic sugar for the ([RFC] Proposed Changes to MemRef and Tensor MLIR Types)[https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/-wKHANzDNTg].
The underlying implementation still uses AffineMap layout.
PiperOrigin-RevId: 272717437
Module names are optional so it makes more sense to take and return an optional
any time the name is involved. Also update the language reference to reflect
the module names.
PiperOrigin-RevId: 272684698
Modules are now Ops and, as such, can be nested. They do not produce an SSA
value so there is no possibility to refer to them in the IR. Introduce support
for symbol names attached to the module Op so that it can be referred to using
SymbolRefAttrs. The name is optional, for example the implicit top-level module
does not have a name.
PiperOrigin-RevId: 272671600
This makes the name of the conversion pass more consistent with the naming
scheme, since it actually converts from the Loop dialect to the Standard
dialect rather than working with arbitrary control flow operations.
PiperOrigin-RevId: 272612112
As specified in the MLIR language reference and rationale documents, `memref`
types should not be allowed to have `index` as element types. As observed in
https://groups.google.com/a/tensorflow.org/forum/#!msg/mlir/P49hVWqTMNc/nW89a4i_AgAJ
this restriction was lifted when canonicalization unit tests for affine
operations were introduced, without sufficient motivation to lift the
restriction itself. The test in question can be trivially rewritten (return
the value from a function instead of storing it to prevent DCE from removing
the producer operation) and the restriction put back in place.
If `memref<...x index>` is relevant for some use cases, the relaxation of the
type system can be implemented separately with appropriate modifications to the
documentation.
PiperOrigin-RevId: 272607043
This also adds coverage with a missing test, which uncovered a bug in the conditional for testing whether an offset is dynamic or not.
PiperOrigin-RevId: 272505798
Similar to spv.loop, spv.selection is another op for modelling
SPIR-V structured control flow. It covers both OpBranchConditional
and OpSwitch with OpSelectionMerge.
Instead of having a `spv.SelectionMerge` op to directly model
selection merge instruction for indicating the merge target,
we use regions to delimit the boundary of the selection: the
merge target is the next op following the `spv.selection` op.
This way it's easier to discover all blocks belonging to
the selection and it plays nicer with the MLIR system.
PiperOrigin-RevId: 272475006
The concept-based polymorphism structure was missing an inheritance link
between the concept and the model. The interface class did not re-export the
base class constructor, which made it unusable with llvm::isa calls. Fix these
and reformat the code around.
PiperOrigin-RevId: 272452062
This is a follow-up to the PRtensorflow/mlir#146 which introduced the ROCDL Dialect. This PR introduces a pass to lower GPU Dialect to the ROCDL Dialect. As with the previous PR, this one builds on the work done by @whchung, and addresses most of the review comments in the original PR.
Closestensorflow/mlir#154
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/154 from deven-amd:deven-lower-gpu-to-rocdl 809893e08236da5ab6a38e3459692fa04247773d
PiperOrigin-RevId: 272390729
In SPIR-V we can have multiple symbols corresponding to the same
enum value. This is because when an extension is introduced into
the core spec, its suffix is typically removed, e.g., 'VulkanKHR'
memory model becomes 'Vulkan' memory model in SPIR-V 1.5.
Previously we just keep the first symbol for an enum value. That
symbol is not necessarily a better one. This CL changes to sort
symbols, grouped by enum values, alphabetically and then keep
the first one, which is typically shorter and without the extension
suffix. We also fix up certain ones like HlslSemanticGOOGLE.
PiperOrigin-RevId: 272290363
This exposes hooks for accessing internal dominance nodes, and updating the internal DFS numbers.
Closestensorflow/mlir#151
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/151 from schweitzpgi:dominance_hooks 69d14214a423b816cbd59feffcacdd02f3b5f921
PiperOrigin-RevId: 272287352
A recent ABI compatibility change affected the conversion from standard
CallOp/CallIndirectOp to LLVM::CallOp by changing its signature. In order to
analyze the signature, the code was looking up the callee symbol in the module.
This is incorrect since, during the conversion, the module may contain both the
original and the converted function op that have the same symbol name. There is
no strict guarantee on which of the two symbols will be found by the lookup.
The conversion was not failing because the type legalizer converts the LLVM
types to themselves making the original and the converted function signatures
ultimately produce the same type.
Instead of looking up the function signature to get the list of result types,
use the types of the CallOp/CallIndirectOp results which must match those of
the function in valid IR. These types are guaranteed to be the original,
unconverted types when converting the operation. Furthermore, this avoids the
need to perform a lookup of a symbol name in the module which may be expensive.
Finally, propagate attributes as-is from the original op to the converted op
since they share the attribute name for the callee of direct calls and the rest
of attributes are not affected by the conversion. This removes the need for
additional contorsions between direct and indirect calls to extract the name of
the optional callee attribute only to insert it back. This also prevents the
conversion from unintentionally dropping the other attributes of the op.
PiperOrigin-RevId: 272218871
This CL finishes the implementation of the Linalg + Affine type unification of the [strided memref RFC](https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
As a consequence, the !linalg.view type, linalg::DimOp, linalg::LoadOp and linalg::StoreOp can now disappear and Linalg can use standard types everywhere.
PiperOrigin-RevId: 272187165
Perform second reduce only with first warp. This requires an additional __sync_threads(), but doesn't need special handling when the last warp is small. This simplifies support for block sizes that are not multiple of 32.
Supporting partial warp reduce will be done in a separate CL.
PiperOrigin-RevId: 272168917
For the cases where there are multiple levels of nested pass managers, the parent thread ID is not enough to distinguish the parent of a given pass pipeline. Passing in the parent pass gives an exact anchor point.
PiperOrigin-RevId: 272105461
According to the SPIR-V spec:
"Length is the number of elements in the array. It must be at least 1."
Closestensorflow/mlir#160
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/160 from denis0x0D:sandbox/array_len 0840dc0986ad0088a3aa7d5d8d3e97d489377ed9
PiperOrigin-RevId: 272094669
Add DeclareOpInterfaceFunctions to enable specifying whether OpInterfaceMethods
for an OpInterface should be generated automatically. This avoids needing to
declare the extra methods, while also allowing adding function declaration by way of trait/inheritance.
Most of this change is mechanical/extracting classes to be reusable.
PiperOrigin-RevId: 272042739
The current implementation always uses string comparison, but if the operation is registered the AbstractOperation instance can be used to provide faster pointer comparison.
PiperOrigin-RevId: 272041333
This CL finishes the implementation of the lowering part of the [strided memref RFC](https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
Strided memrefs correspond conceptually to the following templated C++ struct:
```
template <typename Elem, size_t Rank>
struct {
Elem *ptr;
int64_t offset;
int64_t sizes[Rank];
int64_t strides[Rank];
};
```
The linearization procedure for address calculation for strided memrefs is the same as for linalg views:
`base_offset + SUM_i index_i * stride_i`.
The following CL will unify Linalg and Standard by removing !linalg.view in favor of strided memrefs.
PiperOrigin-RevId: 272033399
Jacques Pienaar