This CL adds support for vectorization using more interesting 2-D and 3-D
patterns. Note in particular the fact that we match some pretty complex
imperfectly nested 2-D patterns with a quite minimal change to the
implementation: we just add a bit of recursion to traverse the matched
patterns and actually vectorize the loops.
For instance, vectorizing the following loop by 128:
```
for %i3 = 0 to %0 {
%7 = affine_apply (d0) -> (d0)(%i3)
%8 = load %arg0[%c0_0, %7] : memref<?x?xf32>
}
```
Currently generates:
```
#map0 = ()[s0] -> (s0 + 127)
#map1 = (d0) -> (d0)
for %i3 = 0 to #map0()[%0] step 128 {
%9 = affine_apply #map1(%i3)
%10 = alloc() : memref<1xvector<128xf32>>
%11 = "n_d_unaligned_load"(%arg0, %c0_0, %9, %10, %c0) :
(memref<?x?xf32>, index, index, memref<1xvector<128xf32>>, index) ->
(memref<?x?xf32>, index, index, memref<1xvector<128xf32>>, index)
%12 = load %10[%c0] : memref<1xvector<128xf32>>
}
```
The above is subject to evolution.
PiperOrigin-RevId: 219629745
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
This CL is a first in a series that implements early vectorization of
increasingly complex patterns. In particular, early vectorization will support
arbitrary loop nesting patterns (both perfectly and imperfectly nested), at
arbitrary depths in the loop tree.
This first CL builds the minimal support for applying 1-D patterns.
It relies on an unaligned load/store op abstraction that can be inplemented
differently on different HW.
Future CLs will support higher dimensional patterns, but 1-D patterns already
exhibit interesting properties.
In particular, we want to separate pattern matching (i.e. legality both
structural and dependency analysis based), from profitability analysis, from
application of the transformation.
As a consequence patterns may intersect and we need to verify that a pattern
can still apply by the time we get to applying it.
A non-greedy analysis on profitability that takes into account pattern
intersection is left for future work.
Additionally the CL makes the following cleanups:
1. the matches method now returns a value, not a reference;
2. added comments about the MLFunctionMatcher and MLFunctionMatches usage by
value;
3. added size and empty methods to matches;
4. added a negative vectorization test with a conditional, this exhibited a
but in the iterators. Iterators now return nullptr if the underlying storage
is nullpt.
PiperOrigin-RevId: 219299489
Unbounded identity maps do not affect the accesses through MemRefs in any way.
A previous CL dropped such maps only if they were alone in the composition. Go
further and drop such maps everywhere they appear in the composition.
Update the parser test to check for unique'd hoisted map to be present but
without assuming any particular order. Because some of the hoisted identity
maps still apear due to the nested "for" statements, we need to check for them.
However, they no longer appear above the non-identity maps because they are no
longer necessary for the extfunc memref declarations that are textually first
in the test file. This order may change further as map simplification is
improved, there is no reason to assume a particular order.
PiperOrigin-RevId: 219287280
- Added a mechanism for specifying pattern matching more concisely like LLVM.
- Added support for canonicalization of addi/muli over vector/tensor splat
- Added ValueType to Attribute class hierarchy
- Allowed creating constant splat
PiperOrigin-RevId: 219149621
As per MLIR spec, the absence of affine maps in MemRef type is interpreted as
an implicit identity affine map. Therefore, MemRef types declared with
explicit or implicit identity map should be considered equal at the MemRefType
level. During MemRefType construction, drop trivial identity affine map
compositions. A trivial identity composition consists of a single unbounded
identity map. It is unclear whether affine maps should be composed in-place to
a single map during MemRef type construction, so non-trivial compositions that
could have been simplified to an identity are NOT removed. We chose to drop
the trivial identity map rather than inject it in places that assume its
present implicitly because it makes the code simpler by reducing boilerplate;
identity mappings are obvious defaults.
Update tests that were checking for the presence of trivial identity map
compositions in the outputs.
PiperOrigin-RevId: 218862454
This check was being performed in AllocOp::verify. However it is not specific
to AllocOp and should apply to all MemRef type declarations. At the same time,
the unique *Type factory functions in MLIRContext do not have access to
location information necessary to properly emit diagnostics. Emit the error in
Parser where the location information is available. Keep the error emission in
AllocOp for the cases of programmatically-constructed, e.g. through Builders,
IR with a note. Once we decided on the diagnostic infrastructure in type
construction system, the type-related checks should be removed from specific
Ops.
Correct several parser test cases that have been using affine maps of
mismatching dimensionality.
This CL prepares for an upcoming change that will drop trivial identity affine
map compositions during MemRefType construction. In that case, the
dimensionality mismatch error must be emitted before dropping the identity map,
i.e. during the type construction at the latest and before "verify" being
called.
PiperOrigin-RevId: 218844127
1) We incorrectly reassociated non-reassociative operations like subi, causing
miscompilations.
2) When constant folding, we didn't add users of the new constant back to the
worklist for reprocessing, causing us to miss some cases (pointed out by
Uday).
The code for tensorflow/mlir#2 is gross, but I'll add the new APIs in a followup patch.
PiperOrigin-RevId: 218803984
- Introduce Fourier-Motzkin variable elimination to eliminate a dimension from
a system of linear equalities/inequalities. Update isEmpty to use this.
Since FM is only exact on rational/real spaces, an emptiness check based on
this is guaranteed to be exact whenever it says the underlying set is empty;
if it says, it's not empty, there may still be no integer points in it.
Also, supports a version that computes "dark shadows".
- Test this by checking for "always false" conditionals in if statements.
- Unique IntegerSet's that are small (few constraints, few variables). This
basically means the canonical empty set and other small sets that are
likely commonly used get uniqued; allows checking for the canonical empty set
by pointer. IntegerSet::kUniquingThreshold gives the threshold constraint size
for uniqui'ing.
- rename simplify-affine-expr -> simplify-affine-structures
Other cleanup
- IntegerSet::numConstraints, AffineMap::numResults are no longer needed;
remove them.
- add copy assignment operators for AffineMap, IntegerSet.
- rename Invalid() -> Null() on AffineExpr, AffineMap, IntegerSet
- Misc cleanup for FlatAffineConstraints API
PiperOrigin-RevId: 218690456
- Adds FlatAffineConstraints::isEmpty method to test if there are no solutions to the system.
- Adds GCD test check if equality constraints have no solution.
- Adds unit test cases.
PiperOrigin-RevId: 218546319
"shape_cast" only applies to tensors, and there are other operations that
actually affect shape, for example "reshape". Rename "shape_cast" to
"tensor_cast" in both the code and the documentation.
PiperOrigin-RevId: 218528122
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
For some of the constant vector / tesor, if the compiler doesn't need to
interpret their elements content, they can be stored in this class to save the
serialize / deserialize cost.
syntax:
`opaque<` tensor-type `,` opaque-string `>`
opaque-string ::= `0x` [0-9a-fA-F]*
PiperOrigin-RevId: 218399426
- 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
This was left as a TODO in the code. Move the type verification from
MLFuncVerifier::verifyReturn to ReturnOp::verify. Since the return operation
can only appear as the last statement of an MLFunction, i.e. where the
surrounding block is the function itself, it is easy to access the function
descriptor (ReturnOp::verify already relies on this). From the function
descriptor, one can easily access the type information. Note that this
slightly modifies the error message due to the use of emitOpError instead of a
plain emitError.
Drop the obsolete TODO comment in MLFunction::verify about checking that
"return" only appears as the last operation of an MLFunction since
ReturnOp::verify explicitly checks for that.
PiperOrigin-RevId: 218347843
This was left as a TODO in the code. Note that the spec does not explicitly
prohibit the first basic block from having a predecessor, and may be worth
updating.
The error is reported at the location of the cfgfunc to which the basic block
belongs since the location information of the block label is not propagated
beyond the IR parser. Arguably, pointing to a function that starts with an
ill-formed block is better than pointing to the first operation in that block
as it makes easier to follow the code down until the first block label.
PiperOrigin-RevId: 218343654
The SparseElementsAttr uses (COO) Coordinate List encoding to represents a
sparse tensor / vector. Specifically, the coordinates and values are stored as
two dense elements attributes. The first dense elements attribute is a 2-D
attribute with shape [N, ndims], which contains the indices of the elements
with nonzero values in the constant vector/tensor. The second elements
attribute is a 1-D attribute list with shape [N], which supplies the values for
each element in the first elements attribute. ndims is the rank of the
vector/tensor and N is the total nonzero elements.
The syntax is:
`sparse<` (tensor-type | vector-type)`, ` indices-attribute-list, values-attribute-list `>`
Example: a sparse tensor
sparse<vector<3x4xi32>, [[0, 0], [1, 2]], [1, 2]> represents the dense tensor
[[1, 0, 0, 0]
[0, 0, 2, 0]
[0, 0, 0, 0]]
PiperOrigin-RevId: 217764319
The syntax of dense vecor/tensor attribute value is
`dense<` (tensor-type | vector-type)`,` attribute-list`>`
and
attribute-list ::= `[` attribute-list (`, ` attribute-list)* `]`.
The construction of the dense vector/tensor attribute takes a vector/tensor
type and a character array as arguments. The size of the input array should be
larger than the size specified by the type argument. It also assumes the
elements of the vector or tensor have been trunked to the data type sizes in
the input character array, so it extends the trunked data to 64 bits when it is
retrieved.
PiperOrigin-RevId: 217762811
multiple TODOs.
- replace the fake test pass (that worked on just the first loop in the
MLFunction) to perform DMA pipelining on all suitable loops.
- nested DMAs work now (DMAs in an outer loop, more DMAs in nested inner loops)
- fix bugs / assumptions: correctly copy memory space and elemental type of source
memref for double buffering.
- correctly identify matching start/finish statements, handle multiple DMAs per
loop.
- introduce dominates/properlyDominates utitilies for MLFunction statements.
- move checkDominancePreservationOnShifts to LoopAnalysis.h; rename it
getShiftValidity
- refactor getContainingStmtPos -> findAncestorStmtInBlock - move into
Analysis/Utils.h; has two users.
- other improvements / cleanup for related API/utilities
- add size argument to dma_wait - for nested DMAs or in general, it makes it
easy to obtain the size to use when lowering the dma_wait since we wouldn't
want to identify the matching dma_start, and more importantly, in general/in the
future, there may not always be a dma_start dominating the dma_wait.
- add debug information in the pass
PiperOrigin-RevId: 217734892
This CL implements a very simple loop vectorization **test** and the basic
infrastructure to support it.
The test simply consists in:
1. matching the loops in the MLFunction and all the Load/Store operations
nested under the loop;
2. testing whether all the Load/Store are contiguous along the innermost
memory dimension along that particular loop. If any reference is
non-contiguous (i.e. the ForStmt SSAValue appears in the expression), then
the loop is not-vectorizable.
The simple test above can gradually be extended with more interesting
behaviors to account for the fact that a layout permutation may exist that
enables contiguity etc. All these will come in due time but it is worthwhile
noting that the test already supports detection of outer-vetorizable loops.
In implementing this test, I also added a recursive MLFunctionMatcher and some
sugar that can capture patterns
such as `auto gemmLike = Doall(Doall(Red(LoadStore())))` and allows iterating
on the matched IR structures. For now it just uses in order traversal but
post-order DFS will be useful in the future once IR rewrites start occuring.
One may note that the memory management design decision follows a different
pattern from MLIR. After evaluating different designs and how they quickly
increase cognitive overhead, I decided to opt for the simplest solution in my
view: a class-wide (threadsafe) RAII context.
This way, a pass that needs MLFunctionMatcher can just have its own locally
scoped BumpPtrAllocator and everything is cleaned up when the pass is destroyed.
If passes are expected to have a longer lifetime, then the contexts can easily
be scoped inside the runOnMLFunction call and storage lifetime reduced.
Lastly, whatever the scope of threading (module, function, pass), this is
expected to also be future-proof wrt concurrency (but this is a detail atm).
PiperOrigin-RevId: 217622889
Updates ComposeAffineMaps test pass to use this method.
Updates affine map composition test cases to handle the new pass, which can be reused when this method is used in a future instruction combine pass.
PiperOrigin-RevId: 217163351
Associate BasicBlocks with the function being parsed to avoid leaks in the case of parse failures. Associating with the function means that we can no longer determine if defined/fwd declared simply by considering if a BasicBlock has an associated function, so track forward declared block references explicitly (this should also allow flagging multiple undeclared fwd references). Split out getting the named block from defining it, in the case of definition move the block to the end of the function.
Also destroy all forward reference placeholders in FunctionParser.
Return parse failure in parseAttributeDict if there is no left brace instead of
asserting.
PiperOrigin-RevId: 217049507
- add util to create a private / exclusive / single use affine
computation slice for an op stmt (see method doc comment); a single
multi-result affine_apply op is prepended to the op stmt to provide all
results needed for its operands as a function of loop iterators and symbols.
- use it for DMA pipelining (to create private slices for DMA start stmt's);
resolve TODOs/feature request (b/117159533)
- move createComposedAffineApplyOp to Transforms/Utils; free it from taking a
memref as input / generalize it.
PiperOrigin-RevId: 216926818
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
This attribute represents a reference to a splat vector or tensor, where all
the elements have the same value. The syntax of the attribute is:
`splat<` (tensor-type | vector-type)`,` attribute-value `>`
PiperOrigin-RevId: 216537997
Add target independent standard DMA ops: dma.start, dma.wait. Update pipeline
data transfer to use these to detect DMA ops.
While on this
- return failure from mlir-opt::performActions if a pass generates invalid output
- improve error message for verify 'n' operand traits
PiperOrigin-RevId: 216429885
1) affineint (as it is named) is not a type suitable for general computation (e.g. the multiply/adds in an integer matmul). It has undefined width and is undefined on overflow. They are used as the indices for forstmt because they are intended to be used as indexes inside the loop.
2) It can be used in both cfg and ml functions, and in cfg functions. As you mention, “symbols” are not affine, and we use affineint values for symbols.
3) Integers aren’t affine, the algorithms applied to them can be. :)
4) The only suitable use for affineint in MLIR is for indexes and dimension sizes (i.e. the bounds of those indexes).
PiperOrigin-RevId: 216057974
- Fold the lower/upper bound of a loop to a constant whenever the result of the
application of the bound's affine map on the operand list yields a constant.
- Update/complete 'for' stmt's API to set lower/upper bounds with operands.
Resolve TODOs for ForStmt::set{Lower,Upper}Bound.
- Moved AffineExprConstantFolder into AffineMap.cpp and added
AffineMap::constantFold to be used by both AffineApplyOp and
ForStmt::constantFoldBound.
PiperOrigin-RevId: 215997346
with a new one (of a potentially different rank/shape) with an optional index
remapping.
- introduce Utils::replaceAllMemRefUsesWith
- use this for DMA double buffering
(This CL also adds a few temporary utilities / code that will be done away with
once:
1) abstract DMA op's are added
2) memref deferencing side-effect / trait is available on op's
3) b/117159533 is resolved (memref index computation slices).
PiperOrigin-RevId: 215831373
- introduce mlir::{floorDiv, ceilDiv, mod} for constant inputs in
mlir/Support/MathExtras.h
- consistently use these everywhere in IR, Analysis, and Transforms.
PiperOrigin-RevId: 215580677
mode. We even diagnose mistakes nicely (aside from the a/an vowel confusion
which isn't worth worrying about):
test/IR/invalid.mlir split at line tensorflow/mlir#399:8:34: error: 'note' diagnostic emitted when expecting a 'error'
%x = "bar"() : () -> i32 // expected-error {{operand defined here}}
^
PiperOrigin-RevId: 214773208
This CL retricts shorthand notation printing to only the bounds that can
be roundtripped unambiguously; i.e.:
1. ()[]->(%some_cst) ()[]
2. ()[s0]->(s0) ()[%some_symbol]
Upon inspection it turns out that the constant case was lossy so this CL also
updates it.
Note however that fixing this issue exhibits a potential issues in unroll.mlir.
L488 exhibits a map ()[s0] -> (1)()[%arg0] which could be simplified down to
()[]->(1)()[].
This does not seem like a bug but maybe an undesired complexity in the maps
generated by unrolling.
bondhugula@, care to take a look?
PiperOrigin-RevId: 214531410
This CL adds support for `mulf` which is necessary to write/emit a simple scalar
matmul in MLIR. This CL does not consider automation of generation of ops but
mulf is important and useful enough to be added on its own atm.
PiperOrigin-RevId: 214496098
The AsmPrinter wrongly assumes that all single ssa-id AffineMap
are the identity map for the purpose of printing.
This CL adds the missing level of indirection as well as a test.
This bug was originally shaken off by the experimental TC->MLIR path.
Before this CL, the test would print:
```
mlfunc @mlfuncsimplemap(%arg0 : affineint, %arg1 : affineint, %arg2 : affineint) {
for %i0 = 0 to %arg0 {
for %i1 = 0 to %i0 {
~~~ should be %arg1
%c42_i32 = constant 42 : i32
}
}
return
}
```
PiperOrigin-RevId: 214120817
verifier. We get most of this infrastructure directly from LLVM, we just
need to adapt it to our CFG abstraction.
This has a few unrelated changes engangled in it:
- getFunction() in various classes was const incorrect, fix it.
- This moves Verifier.cpp to the analysis library, since Verifier depends on
dominance and these are both really analyses.
- IndexedAccessorIterator::reference was defined wrong, leading to really
exciting template errors that were fun to diagnose.
- This flips the boolean sense of the foldOperation() function in constant
folding pass in response to previous patch feedback.
PiperOrigin-RevId: 214046593
optimization pass:
- Give the ability for operations to implement a constantFold hook (a simple
one for single-result ops as well as general support for multi-result ops).
- Implement folding support for constant and addf.
- Implement support in AbstractOperation and Operation to make this usable by
clients.
- Implement a very simple constant folding pass that does top down folding on
CFG and ML functions, with a testcase that exercises all the above stuff.
Random cleanups:
- Improve the build APIs for ConstantOp.
- Stop passing "-o -" to mlir-opt in the testsuite, since that is the default.
PiperOrigin-RevId: 213749809
- extend loop unroll-jam similar to loop unroll for affine bounds
- extend both loop unroll/unroll-jam to deal with cleanup loop for non multiple
of unroll factor.
- extend promotion of single iteration loops to work with affine bounds
- fix typo bugs in loop unroll
- refactor common code b/w loop unroll and loop unroll-jam
- move prototypes of non-pass transforms to LoopUtils.h
- add additional builder methods.
- introduce loopUnrollUpTo(factor) to unroll by either factor or trip count,
whichever is less.
- remove Statement::isInnermost (not used for now - will come back at the right
place/in right form later)
PiperOrigin-RevId: 213471227
mlir-translate is a tool to translate from/to MLIR. The translations are registered at link time and intended for use in tests. An identity transformation (mlir-to-mlir) is registered by default as example and used in the parser test where simply parsing & printing required.
The TranslateFunctions take filenames (instead of MemoryBuffer) to allow translations special write behavior (e.g., writing to uncommon filesystems).
PiperOrigin-RevId: 213370448
unroll/unroll-and-jam more powerful; add additional affine expr builder methods
- use previously added analysis/simplification to infer multiple of unroll
factor trip counts, making loop unroll/unroll-and-jam more general.
- for loop unroll, support bounds that are single result affine map's with the
same set of operands. For unknown loop bounds, loop unroll will now work as
long as trip count can be determined to be a multiple of unroll factor.
- extend getConstantTripCount to deal with single result affine map's with the
same operands. move it to mlir/Analysis/LoopAnalysis.cpp
- add additional builder utility methods for affine expr arithmetic
(difference, mod/floordiv/ceildiv w.r.t postitive constant). simplify code to
use the utility methods.
- move affine analysis routines to AffineAnalysis.cpp/.h from
AffineStructures.cpp/.h.
- Rename LoopUnrollJam to LoopUnrollAndJam to match class name.
- add an additional simplification for simplifyFloorDiv, simplifyCeilDiv
- Rename AffineMap::getNumOperands() getNumInputs: an affine map by itself does
not have operands. Operands are passed to it through affine_apply, from loop
bounds/if condition's, etc., operands are stored in the latter.
This should be sufficiently powerful for now as far as unroll/unroll-and-jam go for TPU
code generation, and can move to other analyses/transformations.
Loop nests like these are now unrolled without any cleanup loop being generated.
for %i = 1 to 100 {
// unroll factor 4: no cleanup loop will be generated.
for %j = (d0) -> (d0) (%i) to (d0) -> (5*d0 + 3) (%i) {
%x = "foo"(%j) : (affineint) -> i32
}
}
for %i = 1 to 100 {
// unroll factor 4: no cleanup loop will be generated.
for %j = (d0) -> (d0) (%i) to (d0) -> (d0 - d mod 4 - 1) (%i) {
%y = "foo"(%j) : (affineint) -> i32
}
}
for %i = 1 to 100 {
for %j = (d0) -> (d0) (%i) to (d0) -> (d0 + 128) (%i) {
%x = "foo"() : () -> i32
}
}
TODO(bondhugula): extend this to LoopUnrollAndJam as well in the next CL (with minor
changes).
PiperOrigin-RevId: 212661212
Previously the error could mislead into thinking it was a parser bug instead of the input being erroneous. Update to make it clearer.
PiperOrigin-RevId: 212271145
Ensure delimiters are absent where not expected. This is only checked in the case where operand count is known. This allows for the currently accepted case where there is a operand list with no delimiter and variable number of operands (which could be empty), followed by a delimited operand list.
PiperOrigin-RevId: 212202064
loop counts. Improve / refactor loop unroll / loop unroll and jam.
- add utility to remove single iteration loops.
- use this utility to promote single iteration loops after unroll/unroll-and-jam
- use loopUnrollByFactor for loopUnrollFull and remove most of the latter.
- add methods for getting constant loop trip count
PiperOrigin-RevId: 212039569
- Compress the identifier/kind of a Function into a single word.
- Eliminate otherFailure from verifier now that we always have a location
- Eliminate the error string from the verifier now that we always have
locations.
- Simplify the parser's handling of fn forward references, using the location
tracked by the function.
PiperOrigin-RevId: 211985101
terminators. Improve mlir-opt to print better location info in the split-files
case.
Before:
error: unexpected error: branch has 2 operands, but target block has 1
br bb1(%0tensorflow/mlir#1, %0tensorflow/mlir#0 : i17, i1)
^
after:
invalid.mlir split at line tensorflow/mlir#305:6:3: error: unexpected error: branch has 2 operands, but target block has 1
br bb1(%0tensorflow/mlir#1, %0tensorflow/mlir#0 : i17, i1)
^
It still isn't optimal (it would be better to have just the original file and
line number but is a step forward, and doing the optimal thing would be a lot
more complicated.
PiperOrigin-RevId: 211917067
- handle floordiv/ceildiv in AffineExprFlattener; update the simplification to
work even if mod/floordiv/ceildiv expressions appearing in the tree can't be eliminated.
- refactor the flattening / analysis to move it out of lib/Transforms/
- fix MutableAffineMap::isMultipleOf
- add AffineBinaryOpExpr:getAdd/getMul/... utility methods
PiperOrigin-RevId: 211540536
- Add a new -verify mode to the mlir-opt tool that allows writing test cases
for optimization and other passes that produce diagnostics.
- Refactor existing the -check-parser-errors flag to mlir-opt into a new
-split-input-file option which is orthogonal to -verify.
- Eliminate the special error hook the parser maintained and use the standard
MLIRContext's one instead.
- Enhance the default MLIRContext error reporter to print file/line/col of
errors when it is available.
- Add new createChecked() methods to the builder that create ops and invoke
the verify hook on them, use this to detected unhandled code in the
RaiseControlFlow pass.
- Teach mlir-opt about expected-error @+, it previously only worked with @-
PiperOrigin-RevId: 211305770
Outside of IR/
- simplify a MutableAffineMap by flattening the affine expressions
- add a simplify affine expression pass that uses this analysis
- update the FlatAffineConstraints API (to be used in the next CL)
In IR:
- add isMultipleOf and getKnownGCD for AffineExpr, and make the in-IR
simplication of simplifyMod simpler and more powerful.
- rename the AffineExpr visitor methods to distinguish b/w visiting and
walking, and to simplify API names based on context.
The next CL will use some of these for the loop unrolling/unroll-jam to make
the detection for the need of cleanup loop powerful/non-trivial.
A future CL will finally move this simplification to FlatAffineConstraints to
make it more powerful. For eg., currently, even if a mod expr appearing in a
part of the expression tree can't be simplified, the whole thing won't be
simplified.
PiperOrigin-RevId: 211012256
- for test purposes, the unroll-jam pass unroll jams the first outermost loop.
While on this:
- fix StmtVisitor to allow overriding of function to iterate walk over children
of a stmt.
PiperOrigin-RevId: 210644813
This CL also includes two other minor changes:
- change the implemented syntax from 'if (cond)' to 'if cond', as specified by MLIR spec.
- a minor fix to the implementation of the ForStmt.
PiperOrigin-RevId: 210618122
This commit creates a static constexpr limit for the IntegerType
bitwidth and uses it. The check had to be moved because Token is
not aware of IR/Type and it was a sign the abstraction leaked:
bitwidth limit is not a property of the Token but of the IntegerType.
Added a positive and a negative test at the limit.
PiperOrigin-RevId: 210388192
This commit adds 2 tests:
1. a negative test in which the simplification of expression does not seem satisfactory.
This test should be updated once expression simplification works reasonably.
2. a positive test in which floordiv and ceildiv return the same result, properly enforced with CHECK-NOT
PiperOrigin-RevId: 210286267
This commit replaces // CHECK-EMPTY because it is an extremely confusing way of
allowing (but not checking for) empty lines. The problem is that // CHECK-EMPTY
is **only a comment** and does not do anything.
I originally tried to use // CHECK-EMPTY: but errors occured due to missing
newlines.
The intended behavior of the test is to enforce nothing (not even a newline)
is printed and the proper way to check for this is to use CHECK-NOT.
Thanks to @rxwei for helping me figure out to use CHECK-NOT properly.
PiperOrigin-RevId: 210286262
This revamps implementation of the loop bounds in the ForStmt, using general representation that supports operands. The frequent case of constant bounds is supported
via special access methods.
This also includes:
- Operand iterators for the Statement class.
- OpPointer::is() method to query the class of the Operation.
- Support for the bound shorthand notation parsing and printing.
- Validity checks for the bound operands used as dim ids and symbols
I didn't mean this CL to be so large. It just happened this way, as one thing led to another.
PiperOrigin-RevId: 210204858
new VectorOrTensorType class that provides a common interface between vector
and tensor since a number of operations will be uniform across them (including
extract_element). Improve the LoadOp verifier.
I also updated the MLIR spec doc as well.
PiperOrigin-RevId: 209953189
FlatAffineConstraints, and MutableAffineMap.
All four classes introduced reside in lib/Analysis and are not meant to be
used in the IR (from lib/IR or lib/Parser/). They are all mutable, alloc'ed,
dealloc'ed - although with their fields pointing to immutable affine
expressions (AffineExpr *).
While on this, update simplifyMod to fold mod to a zero when possible.
PiperOrigin-RevId: 209618437
- Have the parser rewrite forward references to their resolved values at the
end of parsing.
- Implement verifier support for detecting malformed function attrs.
- Add efficient query for (in general, recursive) attributes to tell if they
contain a function.
As part of this, improve other general infrastructure:
- Implement support for verifying OperationStmt's in ml functions, refactoring
and generalizing support for operations in the verifier.
- Refactor location handling code in mlir-opt to have the non-error expecting
form of mlir-opt invocations to report error locations precisely.
- Fix parser to detect verifier failures and report them through errorReporter
instead of printing the error and crashing.
This regresses the location info for verifier errors in the parser that were
previously ascribed to the function. This will get resolved in future patches
by adding support for function attributes, which we can use to manage location
information.
PiperOrigin-RevId: 209600980
resolver support.
Still TODO are verifier support (to make sure you don't use an attribute for a
function in another module) and the TODO in ModuleParser::finalizeModule that I
will handle in the next patch.
PiperOrigin-RevId: 209361648
Collect loops through a post order walk instead of a pre-order so that loops
are collected from inner loops are collected before outer surrounding ones.
Add a complex test case.
PiperOrigin-RevId: 209041057
print floating point in a structured form that we know can round trip,
enumerate attributes in the visitor so we print affine mapping attributes
symbolically (the majority of the testcase updates).
We still have an issue where the hexadecimal floating point syntax is reparsed
as an integer, but that can evolve in subsequent patches.
PiperOrigin-RevId: 208828876
This patch passes the raw, unescaped value through to the rest of the stack. Partial escaping is a total pain to deal with, so we either need to implement escaping properly (ideally using a third party library like absl, I don't think LLVM has one that can handle the proper gamut of escape codes) or don't escape. I chose the latter for this patch.
PiperOrigin-RevId: 208608945
Prior to this CL, return statement had no explicit representation in MLIR. Now, it is represented as ReturnOp standard operation and is pretty printed according to the return statement syntax. This way statement walkers can process ML function return operands without making special case for them.
PiperOrigin-RevId: 208092424
- introduce affine integer sets into the IR
- parse and print affine integer sets (both inline or outlined) similar to
affine maps
- use integer set for IfStmt's conditional, and implement parsing of IfStmt's
conditional
- fixed an affine expr paren omission bug while one this.
TODO: parse/represent/print MLValue operands to affine integer set references.
PiperOrigin-RevId: 207779408
encapsulates an operation that is yet to be created. This is a patch towards
custom ops providing create methods that don't need to be templated, allowing
them to move out of line in the future.
PiperOrigin-RevId: 207725557
- fix/complete forStmt cloning for unrolling to work for outer loops
- create IV const's only when needed
- test outer loop unrolling by creating a short trip count unroll pass for
loops with trip counts <= <parameter>
- add unrolling test cases for multiple op results, outer loop unrolling
- fix/clean up StmtWalker class while on this
- switch unroll loop iterator values from i32 to affineint
PiperOrigin-RevId: 207645967
Unrelated minor change - remove OperationStmt::dropReferences(). Since MLFunction does not have cyclic operand references (it's an AST) destruction can be safely done w/o a special pass to drop references.
PiperOrigin-RevId: 207583024
- Implement a diagnostic hook in one of the paths in mlir-opt which
captures and reports the diagnostics nicely.
- Have the parser capture simple location information from the parser
indicating where each op came from in the source .mlir file.
- Add a verifyDominance() method to MLFuncVerifier to demo this, resolving b/112086163
- Add some PrettyStackTrace handlers to make crashes in the testsuite easier
to track down.
PiperOrigin-RevId: 207488548
- deal with non-operation stmt's (if/for stmt's) in loops being unrolled
(unrolling of non-innermost loops works).
- update uses in unrolled bodies to use results of new operations that may be
introduced in the unrolled bodies.
Unrolling now works for all kinds of loop nests - perfect nests, imperfect
nests, loops at any depth, and with any kind of operation in the body. (IfStmt
support not done, hence untested there).
Added missing dump/print method for StmtBlock.
TODO: add test case for outer loop unrolling.
PiperOrigin-RevId: 207314286
MLFunctions.
- MLStmt cloning and IV replacement
- While at this, fix the innermostLoopGatherer to actually gather all the
innermost loops (it was stopping its walk at the first innermost loop it
found)
- Improve comments for MLFunction statement classes, fix inheritance order.
- Fixed StmtBlock destructor.
PiperOrigin-RevId: 207049173
- simplify operations with identity elements (multiply by 1, add with 0).
- simplify successive add/mul: fold constants, propagate constants to the
right.
- simplify floordiv and ceildiv when divisors are constants, and the LHS is a
multiply expression with RHS constant.
- fix an affine expression printing bug on paren emission.
- while on this, fix affine-map test cases file (memref's using layout maps
that were duplicates of existing ones should be emitted pointing to the
unique'd one).
PiperOrigin-RevId: 207046738
generalize the asmprinters handling of pretty names to allow arbitrary sugar to
be dumped on various constructs. Give CFG function arguments nice "arg0" names
like MLFunctions get, and give constant integers pretty names like %c37 for a
constant 377
PiperOrigin-RevId: 206953080
Fix b/112039912 - we were recording 'i' instead of '%i' for loop induction variables causing "use of undefined SSA value" error.
PiperOrigin-RevId: 206884644
Two problems: 1) we didn't visit the types in ops correctly, and 2) the
general "T" version of the OpAsmPrinter inserter would match things like
MemRefType& and print it directly.
PiperOrigin-RevId: 206863642
This is doing it in a suboptimal manner by recombining [integer period literal] into a string literal and parsing that via to_float.
PiperOrigin-RevId: 206855106
This is still (intentionally) generating redundant parens for nested tightly
binding expressions, but I think that is reasonable for readability sake.
This also print x-y instead of x-(y*1)
PiperOrigin-RevId: 206847212
Induction variables are implemented by inheriting ForStmt from MLValue. ForStmt provides APIs that make this design decision invisible to the ForStmt users.
This CL in combination with cl/206253643 resolves http://b/111769060.
PiperOrigin-RevId: 206655937
and OtherType. Other type is now the thing that holds AffineInt, Control,
eventually Resource, Variant, String, etc. FloatType holds the floating point
types, and allows convenient query of isa<FloatType>().
This fixes issues where we allowed control to be the element type of tensor,
memref, vector. At the same time, ban AffineInt from being an element of a
vector/memref/tensor as well since we don't need it.
I updated the spec to match this as well.
PiperOrigin-RevId: 206361942
- Enhance memref type to allow omission of mappings and address
spaces (implying a default mapping).
- Fix printing of function types to properly recurse with printType
so mappings are printed by name.
- Simplify parsing of AffineMaps a bit now that we have
isSymbolicOrConstant()
PiperOrigin-RevId: 206039755
This regresses parser error recovery in some cases (in invalid.mlir) which I'll
consider in a follow-up patch. The important thing in this patch is that the
parse methods in StandardOps.cpp are nice and simple.
PiperOrigin-RevId: 206023308
- Implement a full loop unroll for innermost loops.
- Use it to implement a pass that unroll all the innermost loops of all
mlfunction's in a module. ForStmt's parsed currently have constant trip
counts (and constant loop bounds).
- Implement StmtVisitor based (Visitor pattern)
Loop IVs aren't currently parsed and represented as SSA values. Replacing uses
of loop IVs in unrolled bodies is thus a TODO. Class comments are sparse at some places - will add them after one round of comments.
A cmd-line flag triggers this for now.
Original:
mlfunc @loops() {
for x = 1 to 100 step 2 {
for x = 1 to 4 {
"Const"(){value: 1} : () -> ()
}
}
return
}
After unrolling:
mlfunc @loops() {
for x = 1 to 100 step 2 {
"Const"(){value: 1} : () -> ()
"Const"(){value: 1} : () -> ()
"Const"(){value: 1} : () -> ()
"Const"(){value: 1} : () -> ()
}
return
}
PiperOrigin-RevId: 205933235
This looks heavyweight but most of the code is in the massive number of operand accessors!
We need to be able to iterate over all operands to the condbr (all live-outs) but also just
the true/just the false operands too.
PiperOrigin-RevId: 205897704
While fixing this the parser-affine-map.mlir test started failing due to ordering of the printed affine maps. Even the existing CHECK-DAGs weren't enough to disambiguate; a partial match on one line precluded a total match on a following line.
The fix for this was easy - print the affine maps in reference order rather than in DenseMap iteration order.
PiperOrigin-RevId: 205843770
- Op classes can now provide customized matchers, allowing specializations
beyond just a name match.
- We now provide default implementations of verify/print hooks, so Op classes
only need to implement them if they're doing custom stuff, and only have to
implement the ones they're interested in.
- "Base" now takes a variadic list of template template arguments, allowing
concrete Op types to avoid passing the Concrete type multiple times.
- Add new ZeroOperands trait.
- Add verification hooks to Zero/One/Two operands and OneResult to check that
ops using them are correctly formed.
- Implement getOperand hooks to zero/one/two operand traits, and
getResult/getType hook to OneResult trait.
- Add a new "constant" op to show some of this off, with a specialization for
the constant case.
This patch also splits op validity checks out to a new test/IR/invalid-ops.mlir
file.
This stubs out support for default asmprinter support. My next planned patch
building on top of this will make asmprinter hooks real and will revise this.
PiperOrigin-RevId: 205833214
This patch adds support for basic block arguments including parsing and printing.
In doing so noticed that `ssa-id-and-type` is undefined in the MLIR spec; suggested an implementation in the spec doc.
PiperOrigin-RevId: 205593369
is still limited in several ways, which i'll build out in subsequent patches.
Rename the accessor for inst operands/results to make the Operand/Result
versions of these more obscure, allowing getOperand/getResult to traffic
in values (which is what - by far - most clients actually care about).
PiperOrigin-RevId: 205408439
- Drop sub-classing of affine binary op expressions.
- Drop affine expr op kind sub. Represent it as multiply by -1 and add. This
will also be in line with the math form when we'll need to represent a system of
linear equalities/inequalities: the negative number goes into the coefficient
of an affine form. (For eg. x_1 + (-1)*x_2 + 3*x_3 + (-2) >= 0). The folding
simplification will transparently deal with multiplying the -1 with any other
constants. This also means we won't need to simplify a multiply expression
like in x_1 + (-2)*x_2 to a subtract expression (x_1 - 2*x_2) for
canonicalization/uniquing.
- When we print the IR, we will still pretty print to a subtract when possible.
PiperOrigin-RevId: 205298958
Loop bounds and presumed to be constants for now and are stored in ForStmt as affine constant expressions. ML function arguments, return statement operands and loop variable name are dropped for now.
PiperOrigin-RevId: 205256208
- This introduces a new FunctionParser base class to handle logic common
between the kinds of functions we have, e.g. ssa operand/def parsing.
- This introduces a basic symbol table (without support for forward
references!) and links defs and uses.
- CFG functions now parse and build operand lists for operations. The printer
isn't set up for them yet tho.
PiperOrigin-RevId: 205246110
the instruction side of the house.
This has a number of limitations, including that we are still dropping
operands on the floor in the parser. Also, most of the convenience methods
aren't wired up yet. This is enough to get result type lists round tripping
through.
PiperOrigin-RevId: 205148223
Refactors operation parsing to share functionality between CFG and ML functions. ML function construction now goes through a builder, similar to the way it is done for
CFG functions.
PiperOrigin-RevId: 204779279
is no strong reason to prefer one or the other, but // is nice for consistency
given the rest of the compiler is written in C++.
PiperOrigin-RevId: 204628476
- fold constants when possible.
- for a mul expression, canonicalize to always keep the LHS as the
constant/symbolic term, and similarly, the RHS for an add expression to keep
it closer to the mathematical form. (Eg: f(x) = 3*x + 5)); other similar simplifications;
- verify binary op expressions at creation time.
TODO: we can completely drop AffineSubExpr, and instead use add and mul by -1.
This way something like x - 4 and -4 + x get canonicalized to x + -1 * 4
instead of being x - 4 and x + -4. (The other alternative if wanted to retain
AffineSubExpr would be to simplify x + -1*y to x - y and x + <neg number> to x
- <pos number>).
PiperOrigin-RevId: 204240258
- check for non-affine expressions
- handle negative numbers and negation of id's, expressions
- functions to check if a map is pure affine or semi-affine
- simplify/clean up affine map parsing code
- report more errors messages, more accurate error messages
PiperOrigin-RevId: 203773633
reducing the memory impact on Operation to one word instead of 3 from an
std::vector.
Implement Jacques' suggestion to merge OpImpl::Storage into OpImpl::Base.
PiperOrigin-RevId: 203426518
properties:
- They allow type checked dynamic casting from their base Operation.
- They allow nice accessors for C++ clients, e.g. a "getIndex()" method on
'dim' that returns an unsigned.
- They work with both OperationInst/OperationStmt (once OperationStmt is
implemented).
- They get custom printing logic. They will eventually get custom parsing,
verifier, and builder logic as well.
- Out of tree clients can register their own operation set without having to
change MLIR core, e.g. for TensorFlow or custom target instructions.
This registers addf and dim as examples.
PiperOrigin-RevId: 203382993
A recursive descent parser for affine maps/expressions with operator precedence and
associativity. (While on this, sketch out uniqui'ing functionality for affine maps
and affine binary op expressions (partly).)
PiperOrigin-RevId: 203222063
important for low-bitwidth inference cases and hardware synthesis targets.
Rename 'int' to 'affineint' to avoid confusion between "the integers" and "the int
type".
PiperOrigin-RevId: 202751508
Run test case:
$ mlir-opt test/IR/parser-affine-map.mlir
test/IR/parser-affine-map.mlir:3:30: error: expect '(' at start of map range
#hello_world2 (i, j) [s0] -> i+s0, j)
^
PiperOrigin-RevId: 202736856
class.
Introduce an Identifier class to MLIRContext to represent uniqued identifiers,
introduce string literal support to the lexer, introducing parser and printer
support etc.
PiperOrigin-RevId: 202592007
Add parsing tests with errors. Follows direct path of splitting file into test groups (using a marker) and parsing each section individually. The expected errors are checked using FileCheck and parser error does not result in terminating parsing the rest of the file if check-parser-error.
This is an interim approach until refactoring lexer/parser.
PiperOrigin-RevId: 201867941
This is pretty much minimal scaffolding for this step. Basic block arguments,
instructions, other terminators, a proper IR representation for
blocks/instructions, etc are all coming.
PiperOrigin-RevId: 201826439
Semi-affine maps and address spaces are not yet supported (someone want to take
this on?). We also don't generate IR objects for types yet, which I plan to
tackle next.
PiperOrigin-RevId: 201754283
Nicolas Vasilache