- Detect 'mod' to replace the combination of floordiv, mul, and subtract when
possible at construction time; when 'c' is a power of two, this reduces the number of
operations; also more compact and readable. Update simplifyAdd for this.
On a side note:
- with the affine expr flattening we have, a mod expression like d0 mod c
would be flattened into d0 - c * q, c * q <= d0 <= c*q + c - 1, with 'q'
being added as the local variable (q = d0 floordiv c); as a result, a mod
was turned into a floordiv whenever the expression was reconstructed back,
i.e., as d0 - c * (d0 floordiv c); as a result of this change, we recover
the mod back.
- rename SimplifyAffineExpr -> SimplifyAffineStructures (pass had been renamed but
the file hadn't been).
PiperOrigin-RevId: 228258120
getAffineBinaryOpExpr for consistency (NFC)
- this is consistent with the name of the class and getAffineDimExpr/ConstantExpr, etc.
PiperOrigin-RevId: 228164959
This impl class currently provides the following:
* auto definition of the 'ImplType = StorageClass'
* get/getChecked wrappers around TypeUniquer
* 'verifyConstructionInvariants' hook
- This hook verifies that the arguments passed into get/getChecked are valid
to construct a type instance with.
With this, all non-generic type uniquing has been moved out of MLIRContext.cpp
PiperOrigin-RevId: 227871108
symbols.
Included with this is some other infra:
- Testcases for other canonicalizations that I will implement next.
- Some helpers in AffineMap/Expr for doing simple walks without defining whole
visitor classes.
- A 'replaceDimsAndSymbols' facility that I'll be using to simplify maps and
exprs, e.g. to fold one constant into a mapping and to drop/renumber unused dims.
- Allow index (and everything else) to work in memref's, as we previously
discussed, to make the testcase easier to write.
- A "getAffineBinaryExpr" helper to produce a binop when you know the kind as
an enum.
This line of work will eventually subsume the ComposeAffineApply pass, but it is no where close to that yet :-)
PiperOrigin-RevId: 227852951
Dialect specific types are registered similarly to operations, i.e. registerType<...> within the dialect. Unlike operations, there is no notion of a "verbose" type, that is *all* types must be registered to a dialect. Casting support(isa/dyn_cast/etc.) is implemented by reserving a range of type kinds in the top level Type class as opposed to string comparison like operations.
To support derived types a few hooks need to be implemented:
In the concrete type class:
- static char typeID;
* A unique identifier for the type used during registration.
In the Dialect:
- typeParseHook and typePrintHook must be implemented to provide parser support.
The syntax for dialect extended types is as follows:
dialect-type: '!' dialect-namespace '<' '"' type-specific-data '"' '>'
The 'type-specific-data' is information used to identify different types within the dialect, e.g:
- !tf<"variant"> // Tensor Flow Variant Type
- !tf<"string"> // Tensor Flow String Type
TensorFlow/TensorFlowControl types are now implemented as dialect specific types as a proof
of concept.
PiperOrigin-RevId: 227580052
Moving forward dialect namespaces cannot contain '.' characters.
This cl also standardizes that operation names must begin with the dialect namespace followed by a '.'.
PiperOrigin-RevId: 227532193
The NameLoc can be used to represent a variable, node or method. The
CallSiteLoc has two fields, one represents the concrete location and another
one represents the caller's location. Multiple CallSiteLocs can be chained as
a call stack.
For example, the following call stack
```
AAA
at file1:1
at file2:135
at file3:34
```
can be formed by call0:
```
auto name = NameLoc::get("AAA");
auto file1 = FileLineColLoc::get("file1", 1);
auto file2 = FileLineColLoc::get("file2", 135);
auto file3 = FileLineColLoc::get("file3", 34);
auto call2 = CallSiteLoc::get(file2, file3);
auto call1 = CallSiteLoc::get(file1, call2);
auto call0 = CallSiteLoc::get(name, call1);
```
PiperOrigin-RevId: 226941797
This allows for us to decouple type uniquing/construction from MLIRContext and pave the way for dialect specific types.
To accomplish this we two new classes, TypeUniquer and TypeStorageAllocator.
* TypeUniquer is now responsible for all construction and uniquing of types.
* TypeStorageAllocator is a utility used by derived type storage objects to allocate memory within an MLIRContext.
This cl also standardizes what a derived type storage class needs to provide:
- Define a type alias, KeyTy, to a type that uniquely identifies the
instance of the type within its kind.
* The key type must be constructible from the values passed into the
detail::TypeUniquer::get call after the type kind.
* The key type must have a llvm::DenseMapInfo specialization for
hashing.
- Provide a method, 'KeyTy getKey() const', to construct the key type
from an existing storage instance.
- Provide a construction method:
'DerivedStorage *construct(TypeStorageAllocator &, ...)'
that builds a unique instance of the derived storage. The arguments
after the TypeStorageAllocator must correspond with the values passed
into the detail::TypeUniquer::get call after the type kind.
PiperOrigin-RevId: 226507184
Existing implementation always uses 64 bits to store floating point values in
DenseElementsAttr. This was due to FloatAttrs always a `double` for storage
independently of the actual type. Recent commits added support for FloatAttrs
with the proper f32 type and floating semantics and changed the bitwidth
reporting on FloatType.
Use the existing infrastructure for densely storing 16 and 32-bit values in
DenseElementsAttr storage to store f16 and f32 values. Move floating semantics
definition to the FloatType level. Properly support f16 / IEEEhalf semantics
at the FloatAttr level and in the builder.
Note that bf16 is still stored as a 64-bit value with IEEEdouble semantics
because APFloat does not have first-class support for bf16 types.
PiperOrigin-RevId: 225981289
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
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
An extensive discussion demonstrated that it is difficult to support `index`
types as elements of compound (vector, memref, tensor) types. In particular,
their size is unknown until the target-specific lowering takes place. MLIR may
need to store constants of the fixed-shape compound types (e.g.,
vector<4 x index>) internally and must know the size of the element type and
data layout constraints. The same information is necessary for target-specific
lowering and translation to reliably support compound types with `index`
elements, but MLIR does not have a dedicated target description mechanism yet.
The uses cases for compound types with `index` elements, should they appear,
can be handled via an `index_cast` operation that converts between `index` and
fixed-size integer types at the SSA value level instead of the type level.
PiperOrigin-RevId: 225064373
This simplifies call-sites returning true after emitting an error. After the
conversion, dropped braces around single statement blocks as that seems more
common.
Also, switched to emitError method instead of emitting Error kind using the
emitDiagnostic method.
TESTED with existing unit tests
PiperOrigin-RevId: 224527868
Ensures both hash values returned are the same. Tested by triggering resize of map/set and verifying failure before change.
PiperOrigin-RevId: 223651443
This reverts the previous method which needs to create a new dialect with the
constant fold hook from TensorFlow. This new method uses a function object in
dialect to store the constant fold hook. Once a hook is registered to the
dialect, this function object will be assigned when the dialect is added to the
MLIRContext.
For the operations which are not registered, a new method getRegisteredDialects
is added to the MLIRContext to query the dialects which matches their op name
prefixes.
PiperOrigin-RevId: 222310149
* 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
Similarly to other types, introduce "get" and "getChecked" static member
functions for IntegerType. The latter emits errors to the error handler
registered with the MLIR context and returns a null type for the caller to
handle errors gracefully. This deduplicates type consistency checks between
the parser and the builder. Update the parser to call IntegerType::getChecked
for error reporting instead of the builder that would simply assert.
This CL completes the type system error emission refactoring: the parser now
only emits syntax-related errors for types while type factory systems may emit
type consistency errors.
PiperOrigin-RevId: 221165207
Change the storage type to APInt from int64_t for IntegerAttr (following the change to APFloat storage in FloatAttr). Effectively a direct change from int64_t to 64-bit APInt throughout (the bitwidth hardcoded). This change also adds a getInt convenience method to IntegerAttr and replaces previous getValue calls with getInt calls.
While this changes updates the storage type, it does not update all constant folding calls.
PiperOrigin-RevId: 221082788
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
This CL introduces the following related changes:
- move tensor element type validity checking to a static member function
TensorType::isValidElementType
- introduce get/getChecked similarly to MemRefType, where the checked function
emits errors and returns nullptrs;
- remove duplicate element type validity checking from the parser and rely on
the type constructor to emit errors instead.
PiperOrigin-RevId: 220694831
This CL introduces the following related changes:
- factor out element type validity checking to a static member function
VectorType::isValidElementType;
- introduce get/getChecked similarly to MemRefType, where the checked function
emits errors and returns nullptrs;
- remove duplicate element type validity checking from the parser and rely on
the type constructor to emit errors instead.
PiperOrigin-RevId: 220693828
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
It is unclear why vector types were not allowed to have "index" as element
type. Index values are integers, although of unknown bit width, and should
behave as such. Vectors of integers are allowed and so are tensors of indices
(for indirection purposes), it is more consistent to also have vectors of
indices.
PiperOrigin-RevId: 220630123
Arithmetic and comparison instructions are necessary to implement, e.g.,
control flow when lowering MLFunctions to CFGFunctions. (While it is possible
to replace some of the arithmetics by affine_apply instructions for loop
bounds, it is still necessary for loop bounds checking, steps, if-conditions,
non-trivial memref subscripts, etc.) Furthermore, working with indirect
accesses in, e.g., lookup tables for large embeddings, may require operating on
tensors of indexes. For example, the equivalents to C code "LUT[Index[i]]" or
"ResultIndex[i] = i + j" where i, j are loop induction variables require the
arithmetics on indices as well as the possibility to operate on tensors
thereof. Allow arithmetic and comparison operations to apply to index types by
declaring them integer-like. Allow tensors whose element type is index for
indirection purposes.
The absence of vectors with "index" element type is explicitly tested, but the
only justification for this restriction in the CL introducing the test is
"because we don't need them". Do NOT enable vectors of index types, although
it makes vector and tensor types inconsistent with respect to allowed element
types.
PiperOrigin-RevId: 220614055
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
Introduce a new public static member function, MemRefType::getChecked, intended
for the users that want detailed error messages to be emitted during MemRefType
construction and can gracefully handle these errors. This function takes a
Location of the "MemRef" token if known. The parser is one user of getChecked
that has location information, it outputs errors as compiler diagnostics.
Other users may pass in an instance of UnknownLoc and still have error messages
emitted. Compiler-internal users not expecting the MemRefType construction to
fail should call MemRefType::get, which now aborts on failure with a generic
message.
Both "getChecked" and "get" call to a static free function that does actual
construction with well-formedness checks, optionally emits errors and returns
nullptr on failure.
The location information passed to getChecked has voluntarily coarse precision.
The error messages are intended for compiler engineers and do not justify
heavier API than a single location. The text of the messages can be written so
that it pinpoints the actual location of the error within a MemRef declaration.
PiperOrigin-RevId: 219765902
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
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
The documentation for MLIRContext::registerDiagnosticHandler describing the
arguments of the diagnostic handler is inconsistent with the code. It also
mentions LLVM context rather than MLIR context, likely a typo. Fix both
issues.
PiperOrigin-RevId: 219120954
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
make operations provide a list of canonicalizations that can be applied to
them. This allows canonicalization to be general to any IR definition.
As part of this, sink PatternMatch.h/cpp down to the IR library to fix a
layering problem.
PiperOrigin-RevId: 218773981
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
- 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
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
a step forward because now every AbstractOperation knows which Dialect it is
associated with, enabling things in the future like "constant folding
hooks" which will be important for layering. This is also a bit nicer on
the registration side of things.
PiperOrigin-RevId: 218104230
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
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
* Move Return, Constant and AffineApply out into BuiltinOps;
* BuiltinOps are always registered, while StandardOps follow the same dynamic registration;
* Kept isValidX in MLValue as we don't have a verify on AffineMap so need to keep it callable from Parser (I wanted to move it to be called in verify instead);
PiperOrigin-RevId: 216592527
