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Add static pass registration and change mlir-opt to use it. Future work is needed to refactor the registration for PassManager usage.
Change build targets to alwayslink to enforce registration.
PiperOrigin-RevId: 220390178
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Introduce analysis to check memref accesses (in MLFunctions) for out of bound
ones. It works as follows:
$ mlir-opt -memref-bound-check test/Transforms/memref-bound-check.mlir
/tmp/single.mlir:10:12: error: 'load' op memref out of upper bound access along dimension tensorflow/mlir#1
%x = load %A[%idxtensorflow/mlir#0, %idxtensorflow/mlir#1] : memref<9 x 9 x i32>
^
/tmp/single.mlir:10:12: error: 'load' op memref out of lower bound access along dimension tensorflow/mlir#1
%x = load %A[%idxtensorflow/mlir#0, %idxtensorflow/mlir#1] : memref<9 x 9 x i32>
^
/tmp/single.mlir:10:12: error: 'load' op memref out of upper bound access along dimension tensorflow/mlir#2
%x = load %A[%idxtensorflow/mlir#0, %idxtensorflow/mlir#1] : memref<9 x 9 x i32>
^
/tmp/single.mlir:10:12: error: 'load' op memref out of lower bound access along dimension tensorflow/mlir#2
%x = load %A[%idxtensorflow/mlir#0, %idxtensorflow/mlir#1] : memref<9 x 9 x i32>
^
/tmp/single.mlir:12:12: error: 'load' op memref out of upper bound access along dimension tensorflow/mlir#1
%y = load %B[%idy] : memref<128 x i32>
^
/tmp/single.mlir:12:12: error: 'load' op memref out of lower bound access along dimension tensorflow/mlir#1
%y = load %B[%idy] : memref<128 x i32>
^
#map0 = (d0, d1) -> (d0, d1)
#map1 = (d0, d1) -> (d0 * 128 - d1)
mlfunc @test() {
%0 = alloc() : memref<9x9xi32>
%1 = alloc() : memref<128xi32>
for %i0 = -1 to 9 {
for %i1 = -1 to 9 {
%2 = affine_apply #map0(%i0, %i1)
%3 = load %0[%2tensorflow/mlir#0, %2tensorflow/mlir#1] : memref<9x9xi32>
%4 = affine_apply #map1(%i0, %i1)
%5 = load %1[%4] : memref<128xi32>
}
}
return
}
- Improves productivity while manually / semi-automatically developing MLIR for
testing / prototyping; also provides an indirect way to catch errors in
transformations.
- This pass is an easy way to test the underlying affine analysis
machinery including low level routines.
Some code (in getMemoryRegion()) borrowed from @andydavis cl/218263256.
While on this:
- create mlir/Analysis/Passes.h; move Pass.h up from mlir/Transforms/ to mlir/
- fix a bug in AffineAnalysis.cpp::toAffineExpr
TODO: extend to non-constant loop bounds (straightforward). Will transparently
work for all accesses once floordiv, mod, ceildiv are supported in the
AffineMap -> FlatAffineConstraints conversion.
PiperOrigin-RevId: 219397961
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distinction. FunctionPasses can now choose to get called on all functions, or
have the driver split CFG/ML Functions up for them. NFC.
PiperOrigin-RevId: 218775885
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make operations provide a list of canonicalizations that can be applied to
them. This allows canonicalization to be general to any IR definition.
As part of this, sink PatternMatch.h/cpp down to the IR library to fix a
layering problem.
PiperOrigin-RevId: 218773981
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helper function, in preparation for it being used by other passes.
There is still a lot of room for improvement in its design, this patch is
intended as an NFC refactoring, and the improvements will continue after this
lands.
PiperOrigin-RevId: 218737116
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is a straight-forward change, but required adding missing moveBefore() methods
on operations (requiring moving some traits around to make C++ happy). This
also fixes a constness issue with the getBlock/getFunction() methods on
Instruction, and adds a missing getFunction() method on MLFuncBuilder.
PiperOrigin-RevId: 218523905
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- Add a few canonicalization patterns to fold memref_cast into
load/store/dealloc.
- Canonicalize alloc(constant) into an alloc with a constant shape followed by
a cast.
- Add a new PatternRewriter::updatedRootInPlace API to make this more convenient.
SimplifyAllocConst and the testcase is heavily based on Uday's implementation work, just
in a different framework.
PiperOrigin-RevId: 218361237
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few TODOs,
and add some casting support to Operation.
PiperOrigin-RevId: 218219340
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the pattern matcher / canonicalizer, and rename existing eraseFromBlock methods
to align with it.
PiperOrigin-RevId: 218104455
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PatternMatcher clients up to date and provide a funnel point for newly added
operations. This is also progress towards the canonicalizer supporting
CFGFunctions.
This paves the way for more complex patterns, but by itself doesn't do much
useful, so no testcase.
PiperOrigin-RevId: 218101737
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Also rename Operation::is to Operation::isa
Introduce Operation::cast
All of these are for consistency with global dyn_cast/cast/isa operators.
PiperOrigin-RevId: 217878786
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Use the general function buil wrapper instead of the CFG/ML specific one.
PiperOrigin-RevId: 217335607
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pass, build up the worklist infra in anticipation of improving the pattern
matcher to match more than one node.
PiperOrigin-RevId: 217330579
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So we can use it as a library.
PiperOrigin-RevId: 217267049
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- Make it so OpPointer implicitly converts to SSAValue* when the underlying op
has a single value. This eliminates a lot more ->getResult() calls and makes
the behavior more LLVM-like
- Fill out PatternBenefit to be typed instead of just a typedef for int with
magic numbers.
- Simplify various code due to these changes.
PiperOrigin-RevId: 217020717
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out canonicalization pass to drive it, and a simple (x-x) === 0 pattern match
as a test case.
There is a tremendous number of improvements that need to land, and the
matcher/rewriter and patterns will be split out of this file, but this is a
starting point.
PiperOrigin-RevId: 216788604
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