Commit Graph

21 Commits

Author SHA1 Message Date
River Riddle
c9ad4621ce NFC: Move AffineApplyOp to the AffineOps dialect. This also moves the isValidDim/isValidSymbol methods from Value to the AffineOps dialect.
PiperOrigin-RevId: 232386632
2019-03-29 16:12:40 -07:00
River Riddle
b499277fb6 Remove remaining usages of OperationInst in lib/Transforms.
PiperOrigin-RevId: 232323671
2019-03-29 16:10:53 -07:00
Nicolas Vasilache
d4921f4a96 Address Performance issue in NestedMatcher
A performance issue was reported due to the usage of NestedMatcher in
ComposeAffineMaps. The main culprit was the ubiquitous copies that were
occuring when appending even a single element in `matchOne`.

This CL generally simplifies the implementation and removes one level of indirection by getting rid of
auxiliary storage as well as simplifying the API.
The users of the API are updated accordingly.

The implementation was tested on a heavily unrolled example with
ComposeAffineMaps and is now close in performance with an implementation based
on stateless InstWalker.

As a reminder, the whole ComposeAffineMaps pass is slated to disappear but the bug report was very useful as a stress test for NestedMatchers.

Lastly, the following cleanups reported by @aminim were addressed:
1. make NestedPatternContext scoped within runFunction rather than at the Pass level. This was caused by a previous misunderstanding of Pass lifetime;
2. use defensive assertions in the constructor of NestedPatternContext to make it clear a unique such locally scoped context is allowed to exist.

PiperOrigin-RevId: 231781279
2019-03-29 16:04:07 -07:00
Chris Lattner
b42bea215a Change AffineApplyOp to produce a single result, simplifying the code that
works with it, and updating the g3docs.

PiperOrigin-RevId: 231120927
2019-03-29 15:40:38 -07:00
Nicolas Vasilache
81c7f2e2f3 Cleanup resource management and rename recursive matchers
This CL follows up on a memory leak issue related to SmallVector growth that
escapes the BumpPtrAllocator.
The fix is to properly use ArrayRef and placement new to define away the
issue.

The following renaming is also applied:
1. MLFunctionMatcher -> NestedPattern
2. MLFunctionMatches -> NestedMatch

As a consequence all allocations are now guaranteed to live on the BumpPtrAllocator.

PiperOrigin-RevId: 231047766
2019-03-29 15:39:53 -07:00
River Riddle
75c21e1de0 Wrap cl::opt flags within passes in a category with the pass name. This improves the help output of tools like mlir-opt.
Example:

dma-generate options:

  -dma-fast-mem-capacity                 - Set fast memory space  ...
  -dma-fast-mem-space=<uint>             - Set fast memory space  ...

loop-fusion options:

  -fusion-compute-tolerance=<number>     - Fractional increase in  ...
  -fusion-maximal                        - Enables maximal loop fusion

loop-tile options:

  -tile-size=<uint>                      - Use this tile size for  ...

loop-unroll options:

  -unroll-factor=<uint>                  - Use this unroll factor  ...
  -unroll-full                           - Fully unroll loops
  -unroll-full-threshold=<uint>          - Unroll all loops with  ...
  -unroll-num-reps=<uint>                - Unroll innermost loops  ...

loop-unroll-jam options:

  -unroll-jam-factor=<uint>              - Use this unroll jam factor ...

PiperOrigin-RevId: 231019363
2019-03-29 15:39:38 -07:00
River Riddle
6859f33292 Migrate VectorOrTensorType/MemRefType shape api to use int64_t instead of int.
PiperOrigin-RevId: 230605756
2019-03-29 15:33:20 -07:00
Nicolas Vasilache
362557e11c Simplify compositions of AffineApply
This CL is the 6th and last on the path to simplifying AffineMap composition.
This removes `AffineValueMap::forwardSubstitutions` and replaces it by simple
calls to `fullyComposeAffineMapAndOperands`.

PiperOrigin-RevId: 228962580
2019-03-29 15:11:56 -07:00
Nicolas Vasilache
cfa5831960 Uniformize composition of AffineApplyOp by construction
This CL is the 5th on the path to simplifying AffineMap composition.
This removes the distinction between normalized single-result AffineMap and
more general composed multi-result map.

One nice byproduct of making the implementation driven by single-result is
that the multi-result extension is a trivial change: the implementation is
still single-result and we just use:

```
unsigned idx = getIndexOf(...);
map.getResult(idx);
```

This CL also fixes an AffineNormalizer implementation issue related to symbols.
Namely it stops performing substitutions on symbols in AffineNormalizer and
instead concatenates them all to be consistent with the call to
`AffineMap::compose(AffineMap)`. This latter call to `compose` cannot perform
simplifications of symbols coming from different maps based on positions only:
i.e. dims are applied and renumbered but symbols must be concatenated.

The only way to determine whether symbols from different AffineApply are the
same is to look at the concrete values. The canonicalizeMapAndOperands is thus
extended with behavior to support replacing operands that appear multiple
times.

Lastly, this CL demonstrates that the implementation is correct by rewriting
ComposeAffineMaps using only `makeComposedAffineApply`. The implementation
uses a matcher because AffineApplyOp are introduced as composed operations on
the fly instead of iteratively forwardSubstituting. For this purpose, a walker
would revisit freshly introduced AffineApplyOp. Regardless, ComposeAffineMaps
is scheduled to disappear, this CL replaces the implementation based on
iterative `forwardSubstitute` by a composed-by-construction
`makeComposedAffineApply`.
Remaining calls to `forwardSubstitute` will be removed in the next CL.

PiperOrigin-RevId: 228830443
2019-03-29 15:08:40 -07:00
Nicolas Vasilache
c6f798a976 Introduce AffineMap::compose(AffineMap)
This CL is the 2nd on the path to simplifying AffineMap composition.
This CL uses the now accepted `AffineExpr::compose(AffineMap)` to
implement `AffineMap::compose(AffineMap)`.

Implications of keeping the simplification function in
Analysis are documented where relevant.

PiperOrigin-RevId: 228276646
2019-03-29 15:04:20 -07:00
Nicolas Vasilache
618c6a74c6 [MLIR] Introduce normalized single-result unbounded AffineApplyOp
Supervectorization does not plan on handling multi-result AffineMaps and
non-canonical chains of > 1 AffineApplyOp.
This CL introduces a simpler abstraction and composition of single-result
unbounded AffineApplyOp by using the existing unbound AffineMap composition.

This CL adds a simple API call and relevant tests:

```c++
OpPointer<AffineApplyOp> makeNormalizedAffineApply(
  FuncBuilder *b, Location loc, AffineMap map, ArrayRef<Value*> operands);
```

which creates a single-result unbounded AffineApplyOp.
The operands of AffineApplyOp are not themselves results of AffineApplyOp by
consrtuction.

This represent the simplest possible interface to complement the composition
of (mathematical) AffineMap, for the cases when we are interested in applying
it to Value*.

In this CL the composed AffineMap is not compressed (i.e. there exist operands
that are not part of the result). A followup commit will compress to normal
form.

The single-result unbounded AffineApplyOp abstraction will be used in a
followup CL to support the MaterializeVectors pass.

PiperOrigin-RevId: 227879021
2019-03-29 14:56:37 -07:00
Nicolas Vasilache
5b87a5ef4b [MLIR] Drop strict super-vector requirement in MaterializeVector
The strict requirement (i.e. at least 2 HW vectors in a super-vector) was a
premature optimization to avoid interfering with other vector code potentially
introduced via other means.

This CL avoids this premature optimization and the spurious errors it causes
when super-vector size == HW vector size (which is a possible corner case).

This may be revisited in the future.

PiperOrigin-RevId: 227763966
2019-03-29 14:54:49 -07:00
River Riddle
54948a4380 Split the standard types from builtin types and move them into separate source files(StandardTypes.cpp/h). After this cl only FunctionType and IndexType are builtin types, but IndexType will likely become a standard type when the ml/cfgfunc merger is done. Mechanical NFC.
PiperOrigin-RevId: 227750918
2019-03-29 14:54:07 -07:00
Chris Lattner
7974889f54 Update and generalize various passes to work on both CFG and ML functions,
simplifying them in minor ways.  The only significant cleanup here
is the constant folding pass.  All the other changes are simple and easy,
but this is still enough to shrink the compiler by 45LOC.

The one pass left to merge is the CSE pass, which will be move involved, so I'm
splitting it out to its own patch (which I'll tackle right after this).

This is step 28/n towards merging instructions and statements.

PiperOrigin-RevId: 227328115
2019-03-29 14:49:52 -07:00
Chris Lattner
456ad6a8e0 Standardize naming of statements -> instructions, revisting the code base to be
consistent and moving the using declarations over.  Hopefully this is the last
truly massive patch in this refactoring.

This is step 21/n towards merging instructions and statements, NFC.

PiperOrigin-RevId: 227178245
2019-03-29 14:44:30 -07:00
Chris Lattner
69d9e990fa Eliminate the using decls for MLFunction and CFGFunction standardizing on
Function.

This is step 18/n towards merging instructions and statements, NFC.

PiperOrigin-RevId: 227139399
2019-03-29 14:43:13 -07:00
Chris Lattner
5187cfcf03 Merge Operation into OperationInst and standardize nomenclature around
OperationInst.  This is a big mechanical patch.

This is step 16/n towards merging instructions and statements, NFC.

PiperOrigin-RevId: 227093712
2019-03-29 14:42:23 -07:00
Nicolas Vasilache
4adc169bd0 [MLIR] Add AffineMap composition and use it in Materialization
This CL adds the following free functions:
```
/// Returns the AffineExpr e o m.
AffineExpr compose(AffineExpr e, AffineMap m);
/// Returns the AffineExpr f o g.
AffineMap compose(AffineMap f, AffineMap g);
```

This addresses the issue that AffineMap composition is only available at a
distance via AffineValueMap and is thus unusable on Attributes.
This CL thus implements AffineMap composition in a more modular and composable
way.

This CL does not claim that it can be a good replacement for the
implementation in AffineValueMap, in particular it does not support bounded
maps atm.

Standalone tests are added that replicate some of the logic of the AffineMap
composition pass.

Lastly, affine map composition is used properly inside MaterializeVectors and
a standalone test is added that requires permutation_map composition with a
projection map.

PiperOrigin-RevId: 224376870
2019-03-29 14:20:22 -07:00
Nicolas Vasilache
258dae5d73 [MLIR][Slicing] Apply cleanups
This CL applies a few last cleanups from a previous CL that have been
missed during the previous submit.

PiperOrigin-RevId: 222454774
2019-03-29 14:08:17 -07:00
Nicolas Vasilache
5c16564bca [MLIR][Slicing] Add utils for computing slices.
This CL adds tooling for computing slices as an independent CL.
The first consumer of this analysis will be super-vector materialization in a
followup CL.

In particular, this adds:
1. a getForwardStaticSlice function with documentation, example and a
standalone unit test;
2. a getBackwardStaticSlice function with documentation, example and a
standalone unit test;
3. a getStaticSlice function with documentation, example and a standalone unit
test;
4. a topologicalSort function that is exercised through the getStaticSlice
unit test.

The getXXXStaticSlice functions take an additional root (resp. terminators)
parameter which acts as a boundary that the transitive propagation algorithm
is not allowed to cross.

PiperOrigin-RevId: 222446208
2019-03-29 14:08:02 -07:00
Nicolas Vasilache
89d9913a20 [MLIR][VectorAnalysis] Add a VectorAnalysis and standalone tests
This CL adds some vector support in prevision of the upcoming vector
materialization pass. In particular this CL adds 2 functions to:
1. compute the multiplicity of a subvector shape in a supervector shape;
2. help match operations on strict super-vectors. This is defined for a given
subvector shape as an operation that manipulates a vector type that is an
integral multiple of the subtype, with multiplicity at least 2.

This CL also adds a TestUtil pass where we can dump arbitrary testing of
functions and analysis that operate at a much smaller granularity than a pass
(e.g. an analysis for which it is convenient to write a bit of artificial MLIR
and write some custom test). This is in order to keep using Filecheck for
things that essentially look and feel like C++ unit tests.

PiperOrigin-RevId: 222250910
2019-03-29 14:02:17 -07:00