14 Commits

Author SHA1 Message Date
Alex Zinenko
5a0d3d0204 Basic conversion of MLFunctions to CFGFunctions.
Implement a pass converting a subset of MLFunctions to CFGFunctions.  Currently
supports arbitrarily complex imperfect loop nests with statically constant
(i.e., not affine map) bounds filled with operations.  Does NOT support
branches and non-constant loop bounds.

Conversion is performed per-function and the function names are preserved to
avoid breaking any external references to the current module.  In-memory IR is
updated to point to the right functions in direct calls and constant loads.
This behavior is tested via a really hidden flag that enables function
renaming.

Inside each function, the control flow conversion is based on single-entry
single-exit regions, i.e. subgraphs of the CFG that have exactly one incoming
and exactly one outgoing edge.  Since an MLFunction must have a single "return"
statement as per MLIR spec, it constitutes an SESE region.  Individual
operations are appended to this region.  Control flow statements are
recursively converted into such regions that are concatenated with the current
region.  Bodies of the compound statement also form SESE regions, which allows
to nest control flow statements easily.  Note that SESE regions are not
materialized in the code.  It is sufficent to keep track of the end of the
region as the current instruction insertion point as long as all recursive
calls update the insertion point in the end.

The converter maintains a mapping between SSA values in ML functions and their
CFG counterparts.  The mapping is used to find the operands for each operation
and is updated to contain the results of each operation as the conversion
continues.

PiperOrigin-RevId: 221162602
2019-03-29 13:55:22 -07:00
Jacques Pienaar
cc9a6ed09d Initialize Pass with PassID.
The passID is not currently stored in Pass but this avoids the unused variable warning. The passID is used to uniquely identify passes, currently this is only stored/used in PassInfo.

PiperOrigin-RevId: 220485662
2019-03-29 13:50:34 -07:00
Jacques Pienaar
6f0fb22723 Add static pass registration
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
2019-03-29 13:49:34 -07:00
Uday Bondhugula
8201e19e3d Introduce memref bound checking.
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
2019-03-29 13:46:08 -07:00
Feng Liu
7e004efae2 Add function attributes for ExtFunction, CFGFunction and MLFunction.
PiperOrigin-RevId: 213540509
2019-03-29 13:15:35 -07:00
Jacques Pienaar
fb3116f59e Add PassResult and have passes return PassResult to indicate failure/success.
For FunctionPass's for passes that want to stop upon error encountered.

PiperOrigin-RevId: 213058651
2019-03-29 13:13:55 -07:00
Chris Lattner
348f31a4fa Add location specifier to MLIR Functions, and:
- 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
2019-03-29 13:10:55 -07:00
Chris Lattner
dfc58848e3 Two unrelated API cleanups: remove the location processing stuff from custom op
parser hooks, as it has been subsumed by a simpler and cleaner mechanism.
Second, remove the "Inst" suffixes from a few methods in CFGFuncBuilder since
they are redundant and this is inconsistent with the other builders.  NFC.

PiperOrigin-RevId: 210006263
2019-03-29 13:04:47 -07:00
Chris Lattner
956e0f7e21 Push location information more tightly into the IR, providing space for every
operation and statement to have a location, and make it so a location is
required to be specified whenever you make one (though a null location is still
allowed).  This is to encourage compiler authors to propagate loc info
properly, allowing our failability story to work well.

This is still a WIP - it isn't clear if we want to continue abusing Attribute
for location information, or whether we should introduce a new class heirarchy
to do so.  This is good step along the way, and unblocks some of the tf/xla
work that builds upon it.

PiperOrigin-RevId: 210001406
2019-03-29 13:04:33 -07:00
Chris Lattner
ae79d69922 Implement a module-level symbol table for functions, enforcing uniqueness of
names across the module and auto-renaming conflicts.  Have the parser reject
malformed modules that have redefinitions.

PiperOrigin-RevId: 209227560
2019-03-29 13:02:30 -07:00
Uday Bondhugula
3e92be9c71 Move Pass.{h,cpp} from lib/IR/ to lib/Transforms/.
PiperOrigin-RevId: 208571437
2019-03-29 12:59:07 -07:00
Chris Lattner
12adbeb872 Prepare for implementation of TensorFlow passes:
- Sketch out a TensorFlow/IR directory that will hold op definitions and common TF support logic.  We will eventually have TensorFlow/TF2HLO, TensorFlow/Grappler, TensorFlow/TFLite, etc.
 - Add sketches of a Switch/Merge op definition, including some missing stuff like the TwoResults trait.  Add a skeleton of a pass to raise this form.
 - Beef up the Pass/FunctionPass definitions slightly, moving the common code out of LoopUnroll.cpp into a new IR/Pass.cpp file.
 - Switch ConvertToCFG.cpp to be a ModulePass.
 - Allow _ to start bare identifiers, since this is important for TF attributes.

PiperOrigin-RevId: 206502517
2019-03-29 12:47:25 -07:00
Chris Lattner
f964bad6d1 Implement a proper function list in module, which auto-maintain the parent
pointer, and ensure that functions are deleted when the module is destroyed.

This exposed the fact that MLFunction had no dtor, and that the dtor in
CFGFunction was broken with cyclic references.  Fix both of these problems.

PiperOrigin-RevId: 206051666
2019-03-29 12:43:57 -07:00
Tatiana Shpeisman
1b24c48b91 Scaffolding for convertToCFG pass that replaces all instances of ML functions with equivalent CFG functions. Traverses module MLIR, generates CFG functions (empty for now) and removes ML functions. Adds Transforms library and tests.
PiperOrigin-RevId: 205848367
2019-03-29 12:41:15 -07:00