This CL adds support for and a vectorization test to perform scalar 2-D addf.
The support extension notably comprises:
1. extend vectorizable test to exclude vector_transfer operations and
expose them to LoopAnalysis where they are needed. This is a temporary
solution a concrete MLIR Op exists;
2. add some more functional sugar mapKeys, apply and ScopeGuard (which became
relevant again);
3. fix improper shifting during coarsening;
4. rename unaligned load/store to vector_transfer_read/write and simplify the
design removing the unnecessary AllocOp that were introduced prematurely:
vector_transfer_read currently has the form:
(memref<?x?x?xf32>, index, index, index) -> vector<32x64x256xf32>
vector_transfer_write currently has the form:
(vector<32x64x256xf32>, memref<?x?x?xf32>, index, index, index) -> ()
5. adds vectorizeOperations which traverses the operations in a ForStmt and
rewrites them to their vector form;
6. add support for vector splat from a constant.
The relevant tests are also updated.
PiperOrigin-RevId: 221421426
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
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
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
