This allows for users to provide operand_range and result_range in builder.create<> calls, instead of requiring an explicit copy into a separate data structure like SmallVector/std::vector.
PiperOrigin-RevId: 284360710
This class represents a generic abstraction over the different ways to represent a range of Values: ArrayRef<Value *>, operand_range, result_range. This class will allow for removing the many instances of explicit SmallVector<Value *, N> construction. It has the same memory cost as ArrayRef, and only suffers cost from indexing(if+elsing the different underlying representations).
This change only updates a few of the existing usages, with more to be changed in followups; e.g. 'build' API.
PiperOrigin-RevId: 284307996
Statistics are a way to keep track of what the compiler is doing and how effective various optimizations are. It is useful to see what optimizations are contributing to making a particular program run faster. Pass-instance specific statistics take this even further as you can see the effect of placing a particular pass at specific places within the pass pipeline, e.g. they could help answer questions like "what happens if I run CSE again here".
Statistics can be added to a pass by simply adding members of type 'Pass::Statistics'. This class takes as a constructor arguments: the parent pass pointer, a name, and a description. Statistics can be dumped by the pass manager in a similar manner to how pass timing information is dumped, i.e. via PassManager::enableStatistics programmatically; or -pass-statistics and -pass-statistics-display via the command line pass manager options.
Below is an example:
struct MyPass : public OperationPass<MyPass> {
Statistic testStat{this, "testStat", "A test statistic"};
void runOnOperation() {
...
++testStat;
...
}
};
$ mlir-opt -pass-pipeline='func(my-pass,my-pass)' foo.mlir -pass-statistics
Pipeline Display:
===-------------------------------------------------------------------------===
... Pass statistics report ...
===-------------------------------------------------------------------------===
'func' Pipeline
MyPass
(S) 15 testStat - A test statistic
MyPass
(S) 6 testStat - A test statistic
List Display:
===-------------------------------------------------------------------------===
... Pass statistics report ...
===-------------------------------------------------------------------------===
MyPass
(S) 21 testStat - A test statistic
PiperOrigin-RevId: 284022014
Now that we have unrolling as a declarative pattern, we can drop a full pass that has gone stale. In the future we may want to add specific unrolling patterns for VectorTransferReadOp.
PiperOrigin-RevId: 283806880
In the replaceAllUsesExcept utility function called from loop coalescing the
iteration over the use-chain is incorrect. The use list nodes (IROperands) have
next/prev links, and bluntly resetting the use would make the loop to continue
on uses of the value that was replaced instead of the original one. As a
result, it could miss the existing uses and update the wrong ones. Make sure we
increment the iterator before updating the use in the loop body.
Reported-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#291.
PiperOrigin-RevId: 283754195
This CL refactors some of the MLIR vector dependencies to allow decoupling VectorOps, vector analysis, vector transformations and vector conversions from each other.
This makes the system more modular and allows extracting VectorToVector into VectorTransforms that do not depend on vector conversions.
This refactoring exhibited a bunch of cyclic library dependencies that have been cleaned up.
PiperOrigin-RevId: 283660308
tensorflow/mlir#162 introduced a bug that
incorrectly allowed fusion of producer loops with multiple outgoing
edges. This commit fixes that problem. It also introduces a new flag to
disable sibling loop fusion so that we can test producer-consumer fusion
in isolation.
Closestensorflow/mlir#259
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/259 from dcaballe:dcaballe/fix_multi_out_edge_producer_fusion 578d5661705fd5c56c555832d5e0528df88c5282
PiperOrigin-RevId: 283531105
To simplify the lowering into SPIR-V, while still respecting the ABI
requirements of SPIR-V/Vulkan, split the process into two
1) While lowering a function to SPIR-V (when the function is an entry
point function), allow specifying attributes on arguments and
function itself that describe the ABI of the function.
2) Add a pass that materializes the ABI described in the function.
Two attributes are needed.
1) Attribute on arguments of the entry point function that describe
the descriptor_set, binding, storage class, etc, of the
spv.globalVariable this argument will be replaced by
2) Attribute on function that specifies workgroup size, etc. (for now
only workgroup size).
Add the pass -spirv-lower-abi-attrs to materialize the ABI described
by the attributes.
This change makes the SPIRVBasicTypeConverter class unnecessary and is
removed, further simplifying the SPIR-V lowering path.
PiperOrigin-RevId: 282387587
Change vector op names from VectorFooOp to Vector_FooOp and from
vector::VectorFooOp to vector::FooOp.
Closestensorflow/mlir#257
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/257 from Kayjukh:master dfc3a0e04114885aaec8740d5951d6984d6e1577
PiperOrigin-RevId: 281967461
This moves the different canonicalizations of regions into one place and invokes them in the fixed-point iteration of the canonicalizer.
PiperOrigin-RevId: 281617072
This is a simple multi-level DCE pass that operates pretty generically on
the IR. Its key feature compared to the existing peephole dead op folding
that happens during canonicalization is being able to delete recursively
dead cycles of the use-def graph, including block arguments.
PiperOrigin-RevId: 281568202
This CL uses the pattern rewrite infrastructure to implement a simple VectorOps -> VectorOps legalization strategy to unroll coarse-grained vector operations into finer grained ones.
The transformation is written using local pattern rewrites to allow composition with other rewrites. It proceeds by iteratively introducing fake cast ops and cleaning canonicalizing or lowering them away where appropriate.
This is an example of writing transformations as compositions of local pattern rewrites that should enable us to make them significantly more declarative.
PiperOrigin-RevId: 281555100
This method is needed for N->1 conversion patterns to retrieve remapped
Values used in the original N operations.
Closestensorflow/mlir#237
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/237 from dcaballe:dcaballe/getRemappedValue 1f64fadcf2b203f7b336ff0c5838b116ae3625db
PiperOrigin-RevId: 281321881
This CL utilizies the more robust fusion feasibility analysis being built out in LoopFusionUtils, which will eventually be used to replace the current affine loop fusion pass.
PiperOrigin-RevId: 281112340
This is step 1/n in refactoring infrastructure along the Vector dialect to make it ready for retargetability and composable progressive lowering.
PiperOrigin-RevId: 280529784
This CL moves VectorOps to Tablegen and cleans up the implementation.
This is almost NFC but 2 changes occur:
1. an interface change occurs in the padding value specification in vector_transfer_read:
the value becomes non-optional. As a shortcut we currently use %f0 for all paddings.
This should become an OpInterface for vectorization in the future.
2. the return type of vector.type_cast is trivial and simplified to `memref<vector<...>>`
Relevant roundtrip and invalid tests that used to sit in core are moved to the vector dialect.
The op documentation is moved to the .td file.
PiperOrigin-RevId: 280430869
This refactors the implementation of block signature(type) conversion to not insert fake cast operations to perform the type conversion, but to instead create a new block containing the proper signature. This has the benefit of enabling the use of pre-computed analyses that rely on mapping values. It also leads to a much cleaner implementation overall. The major user facing change is that applySignatureConversion will now replace the entry block of the region, meaning that blocks generally shouldn't be cached over calls to applySignatureConversion.
PiperOrigin-RevId: 280226936
This also previously triggered the warning:
warning: missing field 'isRecursivelyLegal' initializer [-Wmissing-field-initializers]
legalOperations[op] = {action};
^
PiperOrigin-RevId: 279399175
A pattern rewriter hook, mergeBlock, is added that allows for merging the operations of one block into the end of another. This is used to support a canonicalization pattern for branch operations that folds the branch when the successor has a single predecessor(the branch block).
Example:
^bb0:
%c0_i32 = constant 0 : i32
br ^bb1(%c0_i32 : i32)
^bb1(%x : i32):
return %x : i32
becomes:
^bb0:
%c0_i32 = constant 0 : i32
return %c0_i32 : i32
PiperOrigin-RevId: 278677825
The current lowering of loops to GPU only supports lowering of loop
nests where the loops mapped to workgroups and workitems are perfectly
nested. Here a new lowering is added to handle lowering of imperfectly
nested loop body with the following properties
1) The loops partitioned to workgroups are perfectly nested.
2) The loop body of the inner most loop partitioned to workgroups can
contain one or more loop nests that are to be partitioned across
workitems. Each individual loops nests partitioned to workitems should
also be perfectly nested.
3) The number of workgroups and workitems are not deduced from the
loop bounds but are passed in by the caller of the lowering as values.
4) For statements within the perfectly nested loop nest partitioned
across workgroups that are not loops, it is valid to have all threads
execute that statement. This is NOT verified.
PiperOrigin-RevId: 277958868
Rewrite patterns may make modifications to the CFG, including dropping edges between blocks. This change adds a simple unreachable block elimination run at the end of each iteration to ensure that the CFG remains valid.
PiperOrigin-RevId: 277545805
When we removed a pattern, we removed it from worklist but not from
worklistMap. Then, when we tried to add a new pattern on the same Operation
again, the pattern wasn't added since it already existed in the
worklistMap (but not in the worklist).
Closestensorflow/mlir#211
PiperOrigin-RevId: 277319669
In some cases, it may be desirable to mark entire regions of operations as legal. This provides an additional granularity of context to the concept of "legal". The `ConversionTarget` supports marking operations, that were previously added as `Legal` or `Dynamic`, as `recursively` legal. Recursive legality means that if an operation instance is legal, either statically or dynamically, all of the operations nested within are also considered legal. An operation can be marked via `markOpRecursivelyLegal<>`:
```c++
ConversionTarget &target = ...;
/// The operation must first be marked as `Legal` or `Dynamic`.
target.addLegalOp<MyOp>(...);
target.addDynamicallyLegalOp<MySecondOp>(...);
/// Mark the operation as always recursively legal.
target.markOpRecursivelyLegal<MyOp>();
/// Mark optionally with a callback to allow selective marking.
target.markOpRecursivelyLegal<MyOp, MySecondOp>([](Operation *op) { ... });
/// Mark optionally with a callback to allow selective marking.
target.markOpRecursivelyLegal<MyOp>([](MyOp op) { ... });
```
PiperOrigin-RevId: 277086382
This allows for them to be used on other non-function, or even other function-like, operations. The algorithms are already generic, so this is simply changing the derived pass type. The majority of this change is just ensuring that the nesting of these passes remains the same, as the pass manager won't auto-nest them anymore.
PiperOrigin-RevId: 276573038
This allows mixing linalg operations with vector transfer operations (with additional modifications to affine ops) and is a step towards solving tensorflow/mlir#189.
PiperOrigin-RevId: 275543361
This Chapter now introduces and makes use of the Interface concept
in MLIR to demonstrate ShapeInference.
END_PUBLIC
Closestensorflow/mlir#191
PiperOrigin-RevId: 275085151
The current SignatureConversion framework (part of DialectConversion)
allows remapping input arguments to a function from 1->0, 1->1 or
1->many arguments during conversion. Another case is where the
argument itself is dropped, but it's use are remapped to another
Value*.
An example of this is: The Vulkan/SPIR-V spec requires entry functions
to be of type void(void). The GPU -> SPIR-V conversion implemented
this without having the DialectConversion framework track the
remapping that lead to some undefined behavior. The changes here
addresses that.
PiperOrigin-RevId: 275059656
b843cc5d5a introduced a new op LICM transformation and a LoopLike interface,
but missed the CMake aspects of it. This should fix the build.
PiperOrigin-RevId: 275038533
When dealing with regions, or other patterns that need to generate temporary operations, it is useful to be able to replace other operations than the root op being matched. Before this PR, these operations would still be considered for legalization meaning that the conversion would either fail, erroneously need to mark these ops as legal, or add unnecessary patterns.
PiperOrigin-RevId: 274598513
This will allow for inlining newly devirtualized calls, as well as give a more accurate cost model(when we have one). Currently canonicalization will only run for nodes that have no child edges, as the child nodes may be erased during canonicalization. We can support this in the future, but it requires more intricate deletion tracking.
PiperOrigin-RevId: 274011386
When an operation with regions gets replaced, we currently require that all of the remaining nested operations are still converted even though they are going to be replaced when the rewrite is finished. This cl adds a tracking for a minimal set of operations that are known to be "dead". This allows for ignoring the legalization of operations that are won't survive after conversion.
PiperOrigin-RevId: 274009003
This PR is a stepping stone towards supporting generic multi-store
source loop nests in affine loop fusion. It extends the algorithm to
support fusion of multi-store loop nests that:
1. have only one store that writes to a function-local live out, and
2. the remaining stores are involved in loop nest self dependences
or no dependences within the function.
Closestensorflow/mlir#162
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/162 from dcaballe:dcaballe/multi-output-fusion 7fb7dec6fe8b45f5ce176f018bfe37b256420c45
PiperOrigin-RevId: 273773907
This is similar to the `inlineRegionBefore` hook, except the original blocks are unchanged. The region to be cloned *must* not have been modified during the conversion process at the point of cloning, i.e. it must belong an operation that has yet to be converted, or the operation that is currently being converted.
PiperOrigin-RevId: 273622533
- bodies would earlier appear in the order (i, i+3, i+2, i+1) instead of
(i, i+1, i+2, i+3) for example for factor 4.
- clean up hardcoded test cases
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#170
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/170 from bondhugula:ujam b66b405b2b1894a03b376952e32a9d0292042665
PiperOrigin-RevId: 273613131
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 generated build methods have result type before the arguments (operands and attributes, which are also now adjacent in the explicit create method). This also results in changing the create method's ordering to match most build method's ordering.
PiperOrigin-RevId: 271755054
- also remove stale terminology/references in docs
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#148
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/148 from bondhugula:cleanup e846b641a3c2936e874138aff480a23cdbf66591
PiperOrigin-RevId: 271618279
The strided MemRef RFC discusses a normalized descriptor and interaction with library calls (https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
Lowering of nested LLVM structs as value types does not play nicely with externally compiled C/C++ functions due to ABI issues.
Solving the ABI problem generally is a very complex problem and most likely involves taking
a dependence on clang that we do not want atm.
A simple workaround is to pass pointers to memref descriptors at function boundaries, which this CL implement.
PiperOrigin-RevId: 271591708
- fix store to load forwarding for a certain set of cases (where
forwarding shouldn't have happened); use AffineValueMap difference
based MemRefAccess equality checking; utility logic is also greatly
simplified
- add missing equality/inequality operators for AffineExpr ==/!= ints
- add == != operators on MemRefAccess
Closestensorflow/mlir#136
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/136 from bondhugula:store-load-forwarding d79fd1add8bcfbd9fa71d841a6a9905340dcd792
PiperOrigin-RevId: 270457011
computeDepth calls itself recursively, which may insert into minPatternDepth. minPatternDepth is a DenseMap, which invalidates iterators on insertion, so this may lead to asan failures.
PiperOrigin-RevId: 270374203
- allow symbols in index remapping provided for memref replacement
- fix memref normalize crash on cases with layout maps with symbols
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Reported by: Alex Zinenko
Closestensorflow/mlir#139
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/139 from bondhugula:memref-rep-symbols 2f48c1fdb5d4c58915bbddbd9f07b18541819233
PiperOrigin-RevId: 269851182
- add canonicalization pattern to compose maps into affine loads/stores;
templatize the pattern and reuse it for affine.apply as well
- rename getIndices -> getMapOperands() (getIndices is confusing since
these are no longer the indices themselves but operands to the map
whose results are the indices). This also makes the accessor uniform
across affine.apply/load/store. Change arg names on the affine
load/store builder to avoid confusion. Drop an unused confusing build
method on AffineStoreOp.
- update incomplete doc comment for canonicalizeMapAndOperands (this was
missed from a previous update).
Addresses issue tensorflow/mlir#121
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#122
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/122 from bondhugula:compose-load-store e71de1771e56a85c4282c10cb43f30cef0701c4f
PiperOrigin-RevId: 269619540
When performing A->B->C conversion, an operation may still refer to an operand of A. This makes it necessary to unmap through multiple levels of replacement for a specific value.
PiperOrigin-RevId: 269367859
- turn copy/dma generation method into a utility in LoopUtils, allowing
it to be reused elsewhere.
- no functional/logic change to the pass/utility
- trim down header includes in files affected
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#124
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/124 from bondhugula:datacopy 9f346e62e5bd9dd1986720a30a35f302eb4d3252
PiperOrigin-RevId: 269106088
- take care of symbolic operands with alloc
- add missing check for compose map failure and a test case
- add test cases on strides
- drop incorrect check for one-to-one'ness
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#132
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/132 from bondhugula:normalize-memrefs 8aebf285fb0d7c19269d85255aed644657e327b7
PiperOrigin-RevId: 269105947
* Add GraphTraits that treat a block as a graph, Operation* as node and use-relationship for edges;
- Just basic graph output;
* Add use iterator to iterate over all uses of an Operation;
* Add testing pass to generate op graph;
This does not support arbitrary operations other than function nor nested regions yet.
PiperOrigin-RevId: 268121782
This will allow clients to implement a different collection strategy on these
values, including collecting each uses within the region for example.
PiperOrigin-RevId: 267803978
This defines a set of initial utilities for inlining a region(or a FuncOp), and defines a simple inliner pass for testing purposes.
A new dialect interface is defined, DialectInlinerInterface, that allows for dialects to override hooks controlling inlining legality. The interface currently provides the following hooks, but these are just premilinary and should be changed/added to/modified as necessary:
* isLegalToInline
- Determine if a region can be inlined into one of this dialect, *or* if an operation of this dialect can be inlined into a given region.
* shouldAnalyzeRecursively
- Determine if an operation with regions should be analyzed recursively for legality. This allows for child operations to be closed off from the legality checks for operations like lambdas.
* handleTerminator
- Process a terminator that has been inlined.
This cl adds support for inlining StandardOps, but other dialects will be added in followups as necessary.
PiperOrigin-RevId: 267426759
- address remaining comments from PR tensorflow/mlir#87 for better test coverage for
pipeline-data-transfer/replaceAllMemRefUsesWith
- remove dead tag allocs the same way they are removed for the replaced buffers
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#106
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/106 from bondhugula:followup 9e868666d047e8d43e5f82f43e4093b838c710fa
PiperOrigin-RevId: 267144774
- introduce utility to convert memrefs with non-identity layout maps to
ones with identity layout maps: convert the type and rewrite/remap all
its uses
- add this utility to -simplify-affine-structures pass for testing
purposes
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#104
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/104 from bondhugula:memref-normalize f2c914aa1890e8860326c9e33f9aa160b3d65e6d
PiperOrigin-RevId: 266985317
- the [begin, end) range identified for copying could end in between the
block, which makes hoisting invalid in some cases. Change the range
identification to always end with end of block.
- add test case to exercise these (with fast mem capacity set to minimal so
that single element memref buffers are generated at the innermost loop)
- the location of begin/end of the block range for data copying was
being confused with the insert points for copy in and copy out code.
In cases, where we choose to hoist transfers, these are separate.
- when copy loops are single iteration ones, promote their bodies at
the end of the pass.
- change default fast mem space to 1 (setting it to zero made it
generate DMA op's that won't verify in the default case - since the
DMA ops have a check for src/dest memref spaces being different).
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Co-Authored-By: Mehdi Amini <joker.eph@gmail.com>
Closestensorflow/mlir#88
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/88 from bondhugula:datacopy 88697267c45e850c3ced87671e16e4a930c02a42
PiperOrigin-RevId: 266980911
This interface will allow for providing hooks to interrop with operation folding. The first hook, 'shouldMaterializeInto', will allow for controlling which region to insert materialized constants into. The folder will generally materialize constants into the top-level isolated region, this allows for materializing into a lower level ancestor region if it is more profitable/correct.
PiperOrigin-RevId: 266702972
This is done by providing a walk callback that returns a WalkResult. This result is either `advance` or `interrupt`. `advance` means that the walk should continue, whereas `interrupt` signals that the walk should stop immediately. An example is shown below:
auto result = op->walk([](Operation *op) {
if (some_invariant)
return WalkResult::interrupt();
return WalkResult::advance();
});
if (result.wasInterrupted())
...;
PiperOrigin-RevId: 266436700
This change refactors and cleans up the implementation of the operation walk methods. After this refactoring is that the explicit template parameter for the operation type is no longer needed for the explicit op walks. For example:
op->walk<AffineForOp>([](AffineForOp op) { ... });
is now accomplished via:
op->walk([](AffineForOp op) { ... });
PiperOrigin-RevId: 266209552
Refactor replaceAllMemRefUsesWith to split it into two methods: the new
method does the replacement on a single op, and is used by the existing
one.
- make the methods return LogicalResult instead of bool
- Earlier, when replacement failed (due to non-deferencing uses of the
memref), the set of ops that had already been processed would have
been replaced leaving the IR in an inconsistent state. Now, a
pass is made over all ops to first check for non-deferencing
uses, and then replacement is performed. No test cases were affected
because all clients of this method were first checking for
non-deferencing uses before calling this method (for other reasons).
This isn't true for a use case in another upcoming PR (scalar
replacement); clients can now bail out with consistent IR on failure
of replaceAllMemRefUsesWith. Add test case.
- multiple deferencing uses of the same memref in a single op is
possible (we have no such use cases/scenarios), and this has always
remained unsupported. Add an assertion for this.
- minor fix to another test pipeline-data-transfer case.
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#87
PiperOrigin-RevId: 265808183
Switch to C++14 standard method as llvm::make_unique has been removed (
https://reviews.llvm.org/D66259). Also mark some targets as c++14 to ease next
integrates.
PiperOrigin-RevId: 263953918
Often we want to ensure that block arguments are converted before operations that use them. This refactors the current implementation to be cleaner/less frequent by triggering conversion when a set of blocks are moved/inlined; or when legalization is successful.
PiperOrigin-RevId: 263795005
Since raw pointers are always passed around for IR construct without
implying any ownership transfer, it can be error prone to have implicit
ownership transferred the same way.
For example this code can seem harmless:
Pass *pass = ....
pm.addPass(pass);
pm.addPass(pass);
pm.run(module);
PiperOrigin-RevId: 263053082
There are currently several different terms used to refer to a parent IR unit in 'get' methods: getParent/getEnclosing/getContaining. This cl standardizes all of these methods to use 'getParent*'.
PiperOrigin-RevId: 262680287
This will allow for reusing the same pattern list, which may be costly to continually reconstruct, on multiple invocations.
PiperOrigin-RevId: 262664599
This CL is step 2/n towards building a simple, programmable and portable vector abstraction in MLIR that can go all the way down to generating assembly vector code via LLVM's opt and llc tools.
This CL adds the vector.extractelement operation to the MLIR vector dialect as well as the appropriate roundtrip test. Lowering to LLVM will occur in the following CL.
PiperOrigin-RevId: 262545089
River Riddle