4 Commits

Author SHA1 Message Date
River Riddle
b6eb26fd0e [mlir][NFC] Move around the code related to PatternRewriting to improve layering
There are several pieces of pattern rewriting infra in IR/ that really shouldn't be there. This revision moves those pieces to a better location such that they are easier to evolve in the future(e.g. with PDL). More concretely this revision does the following:

* Create a Transforms/GreedyPatternRewriteDriver.h and move the apply*andFold methods there.
The definitions for these methods are already in Transforms/ so it doesn't make sense for the declarations to be in IR.

* Create a new lib/Rewrite library and move PatternApplicator there.
This new library will be focused on applying rewrites, and will also include compiling rewrites with PDL.

Differential Revision: https://reviews.llvm.org/D89103
2020-10-26 18:01:06 -07:00
Jakub Lichman
0b17d4754a [mlir][Linalg] Tile sizes for Conv ops vectorization added as pass arguments
Current setup for conv op vectorization does not enable user to specify tile
sizes as well as dimensions for vectorization. In this commit we change that by
adding tile sizes as pass arguments. Every dimension with corresponding tile
size > 1 is automatically vectorized.

Differential Revision: https://reviews.llvm.org/D88533
2020-09-30 11:31:28 +00:00
Jakub Lichman
347d59b16c [mlir][Linalg] Convolution tiling added to ConvOp vectorization pass
ConvOp vectorization supports now only convolutions of static shapes with dimensions
of size either 3(vectorized) or 1(not) as underlying vectors have to be of static
shape as well. In this commit we add support for convolutions of any size as well as
dynamic shapes by leveraging existing matmul infrastructure for tiling of both input
and kernel to sizes accepted by the previous version of ConvOp vectorization.
In the future this pass can be extended to take "tiling mask" as a user input which
will enable vectorization of user specified dimensions.

Differential Revision: https://reviews.llvm.org/D87676
2020-09-17 09:39:41 +00:00
Jakub Lichman
67b37f571c [mlir] Conv ops vectorization pass
In this commit a new way of convolution ops lowering is introduced.
The conv op vectorization pass lowers linalg convolution ops
into vector contractions. This lowering is possible when conv op
is first tiled by 1 along specific dimensions which transforms
it into dot product between input and kernel subview memory buffers.
This pass converts such conv op into vector contraction and does
all necessary vector transfers that make it work.

Differential Revision: https://reviews.llvm.org/D86619
2020-09-08 08:47:42 +00:00