227 Commits

Author SHA1 Message Date
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
315a466aed Rename BasicBlock and StmtBlock to Block, and make a pass cleaning it up. I did not make an effort to rename all of the 'bb' names in the codebase, since they are still correct and any specific missed once can be fixed up on demand.
The last major renaming is Statement -> Instruction, which is why Statement and
Stmt still appears in various places.

This is step 19/n towards merging instructions and statements, NFC.

PiperOrigin-RevId: 227163082
2019-03-29 14:43:58 -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
Chris Lattner
4c05f8cac6 Merge CFGFuncBuilder/MLFuncBuilder/FuncBuilder together into a single new
FuncBuilder class.  Also rename SSAValue.cpp to Value.cpp

This is step 12/n towards merging instructions and statements, NFC.

PiperOrigin-RevId: 227067644
2019-03-29 14:40:22 -07:00
Chris Lattner
3f190312f8 Merge SSAValue, CFGValue, and MLValue together into a single Value class, which
is the new base of the SSA value hierarchy.  This CL also standardizes all the
nomenclature and comments to use 'Value' where appropriate.  This also eliminates a large number of cast<MLValue>(x)'s, which is very soothing.

This is step 11/n towards merging instructions and statements, NFC.

PiperOrigin-RevId: 227064624
2019-03-29 14:40:06 -07:00
Chris Lattner
776b035646 Eliminate the Instruction, BasicBlock, CFGFunction, MLFunction, and ExtFunction classes, using the Statement/StmtBlock hierarchy and Function instead.
This *only* changes the internal data structures, it does not affect the user visible syntax or structure of MLIR code.  Function gets new "isCFG()" sorts of predicates as a transitional measure.

This patch is gross in a number of ways, largely in an effort to reduce the amount of mechanical churn in one go.  It introduces a bunch of using decls to keep the old names alive for now, and a bunch of stuff needs to be renamed.

This is step 10/n towards merging instructions and statements, NFC.

PiperOrigin-RevId: 227044402
2019-03-29 14:39:49 -07:00
Jacques Pienaar
58d50a6325 Rename convenience methods to make type explicit.
PiperOrigin-RevId: 226939383
2019-03-29 14:36:50 -07:00
Chris Lattner
87ce4cc501 Per review on the previous CL, drop MLFuncBuilder::createOperation, changing
clients to use OperationState instead.  This makes MLFuncBuilder more similiar
to CFGFuncBuilder.  This whole area will get tidied up more when cfg and ml
worlds get unified.  This patch is just gardening, NFC.

PiperOrigin-RevId: 226701959
2019-03-29 14:35:49 -07:00
Chris Lattner
49315c6f6b Give StmtBlocks a use-def list, and give OperationStmt's the ability to have
optional successor operands when they are terminator operations.

This isn't used yet, but is part 2/n towards merging BasicBlock into StmtBlock
and Instruction into OperationStmt.

PiperOrigin-RevId: 226684636
2019-03-29 14:35:34 -07:00
Alex Zinenko
df9bd857b1 Type system: replace Type::getBitWidth with getIntOrFloatBitWidth
As MLIR moves towards dialect-specific types, a generic Type::getBitWidth does
not make sense for all of them.  Even with the current type system, the bit
width is not defined (and causes the method in question to abort) for all
TensorFlow types.

This commit restricts the bit width definition to primitive standard types that
have a number of bits appearing verbatim in their type, i.e., integers and
floats.  As a side effect, it delegates the decision on the bit width of the
`index` to the backends.  Existing backends currently hardcode it to 64 bits.

The Type::getBitWidth method is replaced by Type::getIntOrFloatBitWidth that
only applies to integers and floats.  The call sites are updated to use the new
method, where applicable, or rewritten so as not rely on it.  Incidentally,
this fixes a utility method that did not account for memrefs being allowed to
have vectors as element types in the size computation.

As an observation, several places in the code use Type in places where a more
specific type could be used instead.  Some of those are fixed by this commit.

PiperOrigin-RevId: 225844792
2019-03-29 14:30:43 -07:00
Jacques Pienaar
49c4d2a630 Fix builder getFloatAttr of double to use F64 type and use fltSemantics in FloatAttr.
Store FloatAttr using more appropriate fltSemantics (mostly fixing up F32/F64 storage, F16/BF16 pending). Previously F32 type was used incorrectly for double (the storage was double). Also add query method that returns fltSemantics for IEEE fp types and use that to verify that the APfloat given matches the type:
* FloatAttr created using APFloat is verified that the semantics of the type and APFloat matches;
* FloatAttr created using double has the APFloat created to match the semantics of the type;

Change parsing of tensor negative splat element to pass in the element type expected. Misc other changes to account for the storage type matching the attribute.

PiperOrigin-RevId: 225821834
2019-03-29 14:29:58 -07:00
Lei Zhang
1f5330ac90 Verify CmpIOp's result type to be bool-like
This CL added two new traits, SameOperandsAndResultShape and
ResultsAreBoolLike, and changed CmpIOp to embody these two
traits. As a consequence, CmpIOp's result type now is verified
to be bool-like.

PiperOrigin-RevId: 223208438
2019-03-29 14:11:53 -07:00
River Riddle
d34fcce2a7 [MLIR] Rename OperationInst to Instruction.
PiperOrigin-RevId: 221795407
2019-03-29 14:00:09 -07:00
Jacques Pienaar
711047c0cd Add Type to int/float attributes.
* Optionally attach the type of integer and floating point attributes to the attributes, this allows restricting a int/float to specific width.
  - Currently this allows suffixing int/float constant with type [this might be revised in future].
  - Default to i64 and f32 if not specified.
* For index types the APInt width used is 64.
* Change callers to request a specific attribute type.
* Store iN type with APInt of width N.
* This change does not handle the folding of constants of different types (e.g., doing int type promotions to support constant folding i3 and i32), and instead restricts the constant folding to only operate on the same types.

PiperOrigin-RevId: 221722699
2019-03-29 13:59:23 -07:00
River Riddle
c7df0651d3 [MLIR] Merge terminator and uses into BasicBlock operations list handling.
PiperOrigin-RevId: 221700132
2019-03-29 13:59:10 -07:00
River Riddle
503caf0722 Replace TerminatorInst with builtin terminator operations.
Note: Terminators will be merged into the operations list in a follow up patch.
PiperOrigin-RevId: 221670037
2019-03-29 13:58:55 -07:00
River Riddle
1807ba3c2c Add functionality for parsing/managing operation terminator successors.
Follow up patches will work to remove TerminatorInst.

PiperOrigin-RevId: 221640621
2019-03-29 13:58:27 -07:00
River Riddle
ce5ba22cd9 - Add support for fused locations.
These are locations that form a collection of other source locations with an optional metadata attribute.

- Add initial support for print/dump for locations.
Location Printing Examples:
* Unknown        : [unknown-location]
* FileLineColLoc : third_party/llvm/llvm/projects/google-mlir/test/TensorFlowLite/legalize.mlir:6:8
* FusedLoc       : <"tfl-legalize">[third_party/llvm/llvm/projects/google-mlir/test/TensorFlowLite/legalize.mlir:6:8, third_party/llvm/llvm/projects/google-mlir/test/TensorFlowLite/legalize.mlir:7:8]

- Add diagnostic support for fused locs.
* Prints the first location as the main location and the remaining as "fused from here" notes:
e.g.
third_party/llvm/llvm/projects/google-mlir/test/TensorFlowLite/legalize.mlir:6:8: error: This is an error.
  %1 = "tf.add"(%arg0, %0) : (i32, i32) -> i32
       ^
third_party/llvm/llvm/projects/google-mlir/test/TensorFlowLite/legalize.mlir:7:8: error: Fused from here.
  %2 = "tf.relu"(%1) : (i32) -> i32
       ^

PiperOrigin-RevId: 220835552
2019-03-29 13:53:42 -07:00
River Riddle
2fa4bc9fc8 Implement value type abstraction for locations.
Value type abstraction for locations differ from others in that a Location can NOT be null. NOTE: dyn_cast returns an Optional<T>.

PiperOrigin-RevId: 220682078
2019-03-29 13:52:31 -07:00
Alex Zinenko
cc82a94aff Materialize IndexType in the API.
Previously, index (aka affint) type was hidden under OtherType in the type API.
We will need to identify and operate on values of index types in the upcoming
MLFunc->CFGFunc(->LLVM) lowering passes.  Materialize index type into a
separate class and make it visible to LLVM RTTI hierarchy directly.
Practically, index is an integer type of unknown bit width and is accetable in
most places where regular integer types are.  This is purely an API change that
does not affect the IR.

After IndexType is separated out from OtherType, the remaining "other types"
are, in fact, TF-specific types only.  Further renaming may be of interest.

PiperOrigin-RevId: 220614026
2019-03-29 13:51:04 -07:00
River Riddle
4c465a181d Implement value type abstraction for types.
This is done by changing Type to be a POD interface around an underlying pointer storage and adding in-class support for isa/dyn_cast/cast.

PiperOrigin-RevId: 219372163
2019-03-29 13:45:54 -07:00
Uday Bondhugula
ea65c695b9 Introduce integer set attribute
- add IntegerSetAttr to Attributes; add parsing and other support for it
  (builder, etc.).

PiperOrigin-RevId: 218804579
2019-03-29 13:40:50 -07:00
River Riddle
792d1c25e4 Implement value type abstraction for attributes.
This is done by changing Attribute to be a POD interface around an underlying pointer storage and adding in-class support for isa/dyn_cast/cast.

PiperOrigin-RevId: 218764173
2019-03-29 13:39:19 -07:00
Uday Bondhugula
80610c2f49 Introduce Fourier-Motzkin variable elimination + other cleanup/support
- 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
2019-03-29 13:38:24 -07:00
Feng Liu
3d7ab2d265 Add support to opaque elements attributes
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
2019-03-29 13:36:45 -07:00
Feng Liu
c5a3a5e4ca Use APFloat for FloatAttribute
We should be able to represent arbitrary precision Float-point values inside
the IR, so compiler optimizations, such as constant folding can be done
independently on the compiling platform.

This CL also added a new field, AttrValueGetter, to the Attr class definition
for TableGen. This field is used to customize which mlir::Attr getter method to
get the defined PrimitiveType.

PiperOrigin-RevId: 218034983
2019-03-29 13:34:09 -07:00
Feng Liu
03b48999b6 Add support to constant sparse tensor / vector attribute
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
2019-03-29 13:32:55 -07:00
Feng Liu
b5b90e5465 Add support to constant dense vector/tensor attribute.
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
2019-03-29 13:32:41 -07:00
Nicolas Vasilache
b04f881dcb [MLIR] IntegerSet value type
This CL applies the same pattern as AffineMap to IntegerSet: a simple struct
that acts as the storage is allocated in the bump pointer. The IntegerSet is
immutable and accessed everywhere by value.

Note that unlike AffineMap, it is not possible to remove the MLIRContext
parameter when constructing an IntegerSet for now. One possible way to achieve
this would be to add an enum to distinguish between the mathematically empty
set, the universe set and other sets.

This is left for future discussion.

PiperOrigin-RevId: 216545361
2019-03-29 13:27:19 -07:00
Feng Liu
5e3cca906a Add support to constant splat vector/tensor attribute.
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
2019-03-29 13:27:05 -07:00
Chris Lattner
fd06c6bc4e Change the representation of an operation name to be either an
AbstractOperation* or an Identifier.  This makes it possible to get to stuff in
AbstractOperation faster than going through a hash table lookup.  This makes
constant folding a bit faster now, but will become more important with
subsequent changes.

PiperOrigin-RevId: 216476772
2019-03-29 13:26:51 -07:00
Feng Liu
84a0c40261 Support getShape, hasStaticShape and getDimSize methods for all the Vector and Tensor Types.
PiperOrigin-RevId: 216447553
2019-03-29 13:26:38 -07:00
Nicolas Vasilache
1d3e7e2616 [MLIR] AffineMap value type
This CL applies the same pattern as AffineExpr to AffineMap: a simple struct
that acts as the storage is allocated in the bump pointer. The AffineMap is
immutable and accessed everywhere by value.

PiperOrigin-RevId: 216445930
2019-03-29 13:26:24 -07:00
Nicolas Vasilache
8ebb6ff171 [MLIR] Sketch AffineExpr value type
This CL sketches what it takes for AffineExpr to fully have by-value semantics
and not be a not-so-smart pointer anymore.

This essentially makes the underyling class a simple storage struct and
implements the operations on the value type directly. Since there is no
forwarding of operations anymore, we can full isolate the storage class and
make a hard visibility barrier by moving detail::AffineExpr into
AffineExprDetail.h.

AffineExprDetail.h is only included where storage-related information is
needed.

PiperOrigin-RevId: 216385459
2019-03-29 13:25:42 -07:00
MLIR Team
c386143834 Address comments from previous CL/216216446
PiperOrigin-RevId: 216298139
2019-03-29 13:25:28 -07:00
Nicolas Vasilache
6707c7bea1 [MLIR] AffineExpr final cleanups
This CL:
1. performs the global codemod AffineXExpr->AffineXExprClass and
AffineXExprRef -> AffineXExpr;
2. simplifies function calls by removing the redundant MLIRContext parameter;
3. adds missing binary operator versions of scalar op AffineExpr where it
makes sense.

PiperOrigin-RevId: 216242674
2019-03-29 13:25:14 -07:00
MLIR Team
fe490043b0 Affine map composition.
*) Implements AffineValueMap forward substitution for AffineApplyOps.
*) Adds ComposeAffineMaps transformation pass, which composes affine maps for all loads/stores in an MLFunction.
*) Adds multiple affine map composition tests.

PiperOrigin-RevId: 216216446
2019-03-29 13:24:59 -07:00
Nicolas Vasilache
ce2edea135 [MLIR] Cleanup AffineExpr
This CL introduces a series of cleanups for AffineExpr value types:
1. to make it clear that the value types should be used, the pointer
AffineExpr types are put in the detail namespace. Unfortunately, since the
value type operator-> only forwards to the underlying pointer type, we
still
need to expose this in the include file for now;
2. AffineExprKind is ok to use, it thus comes out of detail and thus of
AffineExpr
3. getAffineDimExpr, getAffineSymbolExpr, getAffineConstantExpr are
similarly
extracted as free functions and their naming is mande consistent across
Builder, MLContext and AffineExpr
4. AffineBinaryOpEx::simplify functions are made into static free
functions.
In particular it is moved away from AffineMap.cpp where it does not belong
5. operator AffineExprType is made explicit
6. uses the binary operators everywhere possible
7. drops the pointer usage everywhere outside of AffineExpr.cpp,
MLIRContext.cpp and AsmPrinter.cpp

PiperOrigin-RevId: 216207212
2019-03-29 13:24:45 -07:00
Chris Lattner
d2d89cbc19 Rename affineint type to index type. The name 'index' may not be perfect, but is better than the old name. Here is some justification:
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
2019-03-29 13:24:16 -07:00
Uday Bondhugula
6cfdb756b1 Introduce memref replacement/rewrite support: to replace an existing memref
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
2019-03-29 13:23:19 -07:00
Nicolas Vasilache
b55b407601 [RFC][MLIR] Use AffineExprRef in place of AffineExpr* in IR
This CL starts by replacing AffineExpr* with value-type AffineExprRef in a few
places in the IR. By a domino effect that is pretty telling of the
inconsistencies in the codebase, const is removed where it makes sense.

The rationale is that the decision was concisously made that unique'd types
have pointer semantics without const specifier. This is fine but we should be
consistent. In the end, the only logical invariant is that there should never
be such a thing as a const AffineExpr*, const AffineMap* or const IntegerSet*
in our codebase.

This CL takes a number of shortcuts to killing const with fire, in particular
forcing const AffineExprRef to return the underlying non-const
AffineExpr*. This will be removed once AffineExpr* has disappeared in
containers but for now such shortcuts allow a bit of sanity in this long quest
for cleanups.

The **only** places where const AffineExpr*, const AffineMap* or const
IntegerSet* may still appear is by transitive needs from containers,
comparison operators etc.

There is still one major thing remaining here: figure out why cast/dyn_cast
return me a const AffineXXX*, which in turn requires a bunch of ugly
const_casts. I suspect this is due to the classof
taking const AffineXXXExpr*. I wonder whether this is a side effect of 1., if
it is coming from llvm itself (I'd doubt it) or something else (clattner@?)

In light of this, the whole discussion about const makes total sense to me now
and I would systematically apply the rule that in the end, we should never
have any const XXX in our codebase for unique'd types (assuming we can remove
them all in containers and no additional constness constraint is added on us
from the outside world).

PiperOrigin-RevId: 215811554
2019-03-29 13:23:05 -07:00
Nicolas Vasilache
5b8017db18 [MLIR] Templated AffineExprBaseRef
This CL implements AffineExprBaseRef as a templated type to allow LLVM-style
casts to work properly. This also allows making AffineExprBaseRef::expr
private.

To achieve this, it is necessary to use llvm::simplify_type and make
AffineConstExpr derive from both AffineExpr and llvm::simplify<AffineExprRef>.
Note that llvm::simplify_type is just an interface to enable the proper
template resolution of isa/cast/dyn_cast but it otherwise holds no value.

Lastly note that certain dyn_cast operations wanted the const AffineExpr* form
of AffineExprBaseRef so I made the implicit constructor take that by default
and documented the immutable behavior. I think this is consistent with the
decision to make unique'd type immutable by convention and never use const on
them.

PiperOrigin-RevId: 215642247
2019-03-29 13:22:49 -07:00
Nicolas Vasilache
544f5e7a9b [MLIR] Remove uses of AffineExpr* outside of IR
This CL uniformizes the uses of AffineExprWrap outside of IR.
The public API of AffineExpr builder is modified to only use AffineExprWrap.
A few places access AffineExprWrap.expr, this is only while the API is in
transition to easily keep track (i.e. make expr private and let the compiler
track the errors).

Parser.cpp exhibits patterns that are dependent on nullptr values so
converting it is left for another CL.

PiperOrigin-RevId: 215642005
2019-03-29 13:22:35 -07:00
Nicolas Vasilache
9ef87c4b6b [MLIR] AffineExpr lightweight value type for operators
This CL proposes adding MLIRContext* to AffineExpr as discussed previously.
This allows the value class to not require the context in its constructor and
makes it a POD that it makes sense to pass by value everywhere.
A list of other RFC CLs will build on this. The RFC CLs are small incremental
pushes of the API which would be a pretty big change otherwise.

Pushing the thinking a little bit more it seems reasonable to use implicit
cast/constructor to/from AffineExpr*.
As this thing evolves, it looks to me like IR (and
probably Parser, for not so good reasons) want to operate on AffineExpr* and
the rest of the code wants to operate on the value type.

For this reason I think AffineExprImpl*/AffineExpr may also make sense but I
do not have a particular naming preference.
The jury is still out for naming decision between the above and
AffineExprBase*/AffineExpr or AffineExpr*/AffineExprRef.

PiperOrigin-RevId: 215641596
2019-03-29 13:22:21 -07:00
Nicolas Vasilache
4805e629c5 [MLIR] Use chainable ligthweight wrapper for AffineExpr
This CL argues that the builder API for AffineExpr should be used
with a lightweight wrapper that supports operators chaining.
This CL takes the ill-named AffineExprWrap and proposes a simple
set of operators with builtin constant simplifications.

This allows:
1. removing the getAddMulPureAffineExpr function;
2. avoiding concerns about constant vs non-constant simplifications
at **every call site**;
3. writing the mathematical expressions we want to write without unnecessary
obfuscations.

The points above represent pure technical debt that we don't want to carry on.
It is important to realize that this is not a mere convenience or "just sugar"
but reduction in cognitive overhead.

This thinking can be pushed significantly further, I have added some comments
with some basic ideas but we could make AffineMap, AffineApply and other
objects that use map applications more functional and value-based.

I am putting this out to get a first batch of reviews and see what people
think.

I think in my preferred design I would have the Builder directly return such
AffineExprPtr objects by value everywhere and avoid the boilerplate explicit
creations that I am doing by hand at this point.

Yes this AffineExprPtr would implicitly convert to AffineExpr* because that is
what it is.

PiperOrigin-RevId: 215641317
2019-03-29 13:22:07 -07:00
Uday Bondhugula
041817a45e Introduce loop body skewing / loop pipelining / loop shifting utility.
- loopBodySkew shifts statements of a loop body by stmt-wise delays, and is
  typically meant to be used to:
  - allow overlap of non-blocking start/wait until completion operations with
    other computation
  - allow shifting of statements (for better register
    reuse/locality/parallelism)
  - software pipelining (when applied to the innermost loop)
- an additional argument specifies whether to unroll the prologue and epilogue.
- add method to check SSA dominance preservation.
- add a fake loop pipeline pass to test this utility.

Sample input/output are below. While on this, fix/add following:

- fix minor bug in getAddMulPureAffineExpr
- add additional builder methods for common affine map cases
- fix const_operand_iterator's for ForStmt, etc. When there is no such thing
  as 'const MLValue', the iterator shouldn't be returning const MLValue's.
  Returning MLValue is const correct.

Sample input/output examples:

1) Simplest case: shift second statement by one.

Input:

for %i = 0 to 7 {
  %y = "foo"(%i) : (affineint) -> affineint
  %x = "bar"(%i) : (affineint) -> affineint
}

Output:

#map0 = (d0) -> (d0 - 1)
mlfunc @loop_nest_simple1() {
  %c8 = constant 8 : affineint
  %c0 = constant 0 : affineint
  %0 = "foo"(%c0) : (affineint) -> affineint
  for %i0 = 1 to 7 {
    %1 = "foo"(%i0) : (affineint) -> affineint
    %2 = affine_apply #map0(%i0)
    %3 = "bar"(%2) : (affineint) -> affineint
  }
  %4 = affine_apply #map0(%c8)
  %5 = "bar"(%4) : (affineint) -> affineint
  return
}

2) DMA overlap: shift dma.wait and compute by one.

Input
  for %i = 0 to 7 {
    %pingpong = affine_apply (d0) -> (d0 mod 2) (%i)
    "dma.enqueue"(%pingpong) : (affineint) -> affineint
    %pongping = affine_apply (d0) -> (d0 mod 2) (%i)
    "dma.wait"(%pongping) : (affineint) -> affineint
    "compute1"(%pongping) : (affineint) -> affineint
  }

Output

#map0 = (d0) -> (d0 mod 2)
#map1 = (d0) -> (d0 - 1)
#map2 = ()[s0] -> (s0 + 7)
mlfunc @loop_nest_dma() {
  %c8 = constant 8 : affineint
  %c0 = constant 0 : affineint
  %0 = affine_apply #map0(%c0)
  %1 = "dma.enqueue"(%0) : (affineint) -> affineint
  for %i0 = 1 to 7 {
    %2 = affine_apply #map0(%i0)
    %3 = "dma.enqueue"(%2) : (affineint) -> affineint
    %4 = affine_apply #map1(%i0)
    %5 = affine_apply #map0(%4)
    %6 = "dma.wait"(%5) : (affineint) -> affineint
    %7 = "compute1"(%5) : (affineint) -> affineint
  }
  %8 = affine_apply #map1(%c8)
  %9 = affine_apply #map0(%8)
  %10 = "dma.wait"(%9) : (affineint) -> affineint
  %11 = "compute1"(%9) : (affineint) -> affineint
  return
}

3) With arbitrary affine bound maps:

Shift last two statements by two.

Input:

  for %i = %N to ()[s0] -> (s0 + 7)()[%N] {
    %y = "foo"(%i) : (affineint) -> affineint
    %x = "bar"(%i) : (affineint) -> affineint
    %z = "foo_bar"(%i) : (affineint) -> (affineint)
    "bar_foo"(%i) : (affineint) -> (affineint)
  }

Output

#map0 = ()[s0] -> (s0 + 1)
#map1 = ()[s0] -> (s0 + 2)
#map2 = ()[s0] -> (s0 + 7)
#map3 = (d0) -> (d0 - 2)
#map4 = ()[s0] -> (s0 + 8)
#map5 = ()[s0] -> (s0 + 9)

  for %i0 = %arg0 to #map0()[%arg0] {
    %0 = "foo"(%i0) : (affineint) -> affineint
    %1 = "bar"(%i0) : (affineint) -> affineint
  }
  for %i1 = #map1()[%arg0] to #map2()[%arg0] {
    %2 = "foo"(%i1) : (affineint) -> affineint
    %3 = "bar"(%i1) : (affineint) -> affineint
    %4 = affine_apply #map3(%i1)
    %5 = "foo_bar"(%4) : (affineint) -> affineint
    %6 = "bar_foo"(%4) : (affineint) -> affineint
  }
  for %i2 = #map4()[%arg0] to #map5()[%arg0] {
    %7 = affine_apply #map3(%i2)
    %8 = "foo_bar"(%7) : (affineint) -> affineint
    %9 = "bar_foo"(%7) : (affineint) -> affineint
  }

4) Shift one by zero, second by one, third by two

  for %i = 0 to 7 {
    %y = "foo"(%i) : (affineint) -> affineint
    %x = "bar"(%i) : (affineint) -> affineint
    %z = "foobar"(%i) : (affineint) -> affineint
  }

#map0 = (d0) -> (d0 - 1)
#map1 = (d0) -> (d0 - 2)
#map2 = ()[s0] -> (s0 + 7)

  %c9 = constant 9 : affineint
  %c8 = constant 8 : affineint
  %c1 = constant 1 : affineint
  %c0 = constant 0 : affineint
  %0 = "foo"(%c0) : (affineint) -> affineint
  %1 = "foo"(%c1) : (affineint) -> affineint
  %2 = affine_apply #map0(%c1)
  %3 = "bar"(%2) : (affineint) -> affineint
  for %i0 = 2 to 7 {
    %4 = "foo"(%i0) : (affineint) -> affineint
    %5 = affine_apply #map0(%i0)
    %6 = "bar"(%5) : (affineint) -> affineint
    %7 = affine_apply #map1(%i0)
    %8 = "foobar"(%7) : (affineint) -> affineint
  }
  %9 = affine_apply #map0(%c8)
  %10 = "bar"(%9) : (affineint) -> affineint
  %11 = affine_apply #map1(%c8)
  %12 = "foobar"(%11) : (affineint) -> affineint
  %13 = affine_apply #map1(%c9)
  %14 = "foobar"(%13) : (affineint) -> affineint

5) SSA dominance violated; no shifting if a shift is specified for the second
statement.

  for %i = 0 to 7 {
    %x = "foo"(%i) : (affineint) -> affineint
    "bar"(%x) : (affineint) -> affineint
  }

PiperOrigin-RevId: 214975731
2019-03-29 13:21:26 -07:00
Feng Liu
430172ab47 Add support to TF f32_ref type in MLIR
PiperOrigin-RevId: 214722005
2019-03-29 13:20:32 -07:00
Feng Liu
948dea045b Supports TF Complex64/Complex128 types in the tf/mlir roundtrip pass.
Alternatively, we can defined a TFComplexType with a width parameter in the
mlir, then both types can be converted to the same mlir type with different width (like IntegerType).
We chose to use a direct mapping because there are only two TF Complex types.

PiperOrigin-RevId: 213856651
2019-03-29 13:17:02 -07:00
Feng Liu
5f69643cbf Support TF Variant type in the tf/mlir roundtrip pass.
PiperOrigin-RevId: 213748573
2019-03-29 13:16:18 -07:00
Feng Liu
4bc5dc9602 Handle the TF resource data type in the TF/XLA roundtrip pass.
PiperOrigin-RevId: 213650346
2019-03-29 13:16:03 -07:00