This revision removes all of the lingering usages of Type::getKind. A consequence of this is that FloatType is now split into 4 derived types that represent each of the possible float types(BFloat16Type, Float16Type, Float32Type, and Float64Type). Other than this split, this revision is NFC.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D85566
- Moved TypeRange into its own header/cpp file, and add hashing support.
- Change FunctionType::get() and TupleType::get() to use TypeRange
Differential Revision: https://reviews.llvm.org/D85075
Use direct vector constants for the 1-D case. This approach
scales much better than generating elaborate insertion operations
that are eventually folded into a constant. We could of course
generalize the 1-D case to higher ranks, but this simplification
already helps in scaling some microbenchmarks that would formerly
crash on the intermediate IR length.
Reviewed By: reidtatge
Differential Revision: https://reviews.llvm.org/D82144
This simplifies a lot of handling of BoolAttr/IntegerAttr. For example, a lot of places currently have to handle both IntegerAttr and BoolAttr. In other places, a decision is made to pick one which can lead to surprising results for users. For example, DenseElementsAttr currently uses BoolAttr for i1 even if the user initialized it with an Array of i1 IntegerAttrs.
Differential Revision: https://reviews.llvm.org/D81047
The current OpBuilder has a set of virtual functions required by the fact that the PatternRewriter inherits from it for convenience. The PatternRewriter is required to know about IR mutations for correctness. This revision changes the relationship to be explicit by having users register a listener with the builder instead of using inheritance/vtables. This still requires that users properly transfer the listener when creating new builders, but has several benefits:
* More than one builder can be created during pattern rewrites(assuming that the listener is properly forwarded)
* OpBuilder no longer requires a vtable, and thus does not incur the cost when a listener isn't present.
Differential Revision: https://reviews.llvm.org/D79206
Summary:
Modified AffineMap::get to remove support for the overload which allowed
an ArrayRef of AffineExpr but no context (and gathered the context from a
presumed first entry, resulting in bugs when there were 0 results).
Instead, we support only a ArrayRef and a context, and a version which
takes a single AffineExpr.
Additionally, removed some now needless case logic which previously
special cased which call to AffineMap::get to use.
Reviewers: flaub, bondhugula, rriddle!, nicolasvasilache, ftynse, ulysseB, mravishankar, antiagainst, aartbik
Subscribers: mehdi_amini, jpienaar, burmako, shauheen, antiagainst, arpith-jacob, mgester, lucyrfox, liufengdb, Joonsoo, bader, grosul1, frgossen, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D78226
Summary: Functional.h contains many different methods that have a direct, and more efficient, equivalent in LLVM. This revision replaces all usages with the LLVM equivalent, and removes the header. This is part of larger cleanup, pr45513, merging MLIR support facilities into LLVM.
Differential Revision: https://reviews.llvm.org/D78053
Summary:
This revision adds support to lower 1-D vector transfers to LLVM.
A mask of the vector length is created that compares the base offset + linear index to the dim of the vector.
In each position where this does not overflow (i.e. offset + vector index < dim), the mask is set to 1.
A notable fact is that the lowering uses llvm.dialect_cast to allow writing code in the simplest form by targeting the simplest mix of vector and LLVM dialects and
letting other conversions kick in.
Differential Revision: https://reviews.llvm.org/D77703
PatternRewriter and derived classes provide a set of virtual methods to
manipulate blocks, which ConversionPatternRewriter overrides to keep track of
the manipulations and undo them in case the conversion fails. However, one can
currently create a block only by splitting another block into two. This not
only makes the API inconsistent (`splitBlock` is allowed in conversion
patterns, but `createBlock` is not), but it also make it impossible for one to
create blocks with argument lists different from those of already existing
blocks since in-place block updates are not supported either. Such
functionality precludes dialect conversion infrastructure from being used more
extensively on region-containing ops, for example, for value-returning "if"
operations. At the same time, ConversionPatternRewriter already allows one to
undo block creation as block creation is one of the primitive operations in
already supported region inlining.
Support block creation in conversion patterns by hooking `createBlock` on the
block action undo mechanism. This requires to make `Builder::createBlock`
virtual, similarly to Op insertion. This is a minimal change to the Builder
infrastructure that will later help support additional use cases such as block
signature changes. `createBlock` now additionally takes the types of the block
arguments that are added immediately so as to avoid in-place argument list
manipulation that would be illegal in conversion patterns.
Builder::get{I32,I64}VectorAttr are actually of limited applicability since
vector types can't have zero elements, whereas many uses of this kind of
attribute (such as dimension lists for "transpose"-like and other tensor
ops) often can result in empty lists.
Differential Revision: https://reviews.llvm.org/D76403
The current mechanism for identifying is a bit hacky and extremely adhoc, i.e. we explicit check 1-result, 0-operand, no side-effect, and always foldable and then assume that this is a constant. Adding a trait adds structure to this, and makes checking for a constant much more efficient as we can guarantee that all of these things have already been verified.
Differential Revision: https://reviews.llvm.org/D76020
Thus far IntegerType has been signless: a value of IntegerType does
not have a sign intrinsically and it's up to the specific operation
to decide how to interpret those bits. For example, std.addi does
two's complement arithmetic, and std.divis/std.diviu treats the first
bit as a sign.
This design choice was made some time ago when we did't have lots
of dialects and dialects were more rigid. Today we have much more
extensible infrastructure and different dialect may want different
modelling over integer signedness. So while we can say we want
signless integers in the standard dialect, we cannot dictate for
others. Requiring each dialect to model the signedness semantics
with another set of custom types is duplicating the functionality
everywhere, considering the fundamental role integer types play.
This CL extends the IntegerType with a signedness semantics bit.
This gives each dialect an option to opt in signedness semantics
if that's what they want and helps code sharing. The parser is
modified to recognize `si[1-9][0-9]*` and `ui[1-9][0-9]*` as
signed and unsigned integer types, respectively, leaving the
original `i[1-9][0-9]*` to continue to mean no indication over
signedness semantics. All existing dialects are not affected (yet)
as this is a feature to opt in.
More discussions can be found at:
https://groups.google.com/a/tensorflow.org/d/msg/mlir/XmkV8HOPWpo/7O4X0Nb_AQAJ
Differential Revision: https://reviews.llvm.org/D72533
Summary: Introduce m_Constant() which allows matching a constant operation without forcing the user also to capture the attribute value.
Differential Revision: https://reviews.llvm.org/D72397
This is an initial step to refactoring the representation of OpResult as proposed in: https://groups.google.com/a/tensorflow.org/g/mlir/c/XXzzKhqqF_0/m/v6bKb08WCgAJ
This change will make it much simpler to incrementally transition all of the existing code to use value-typed semantics.
PiperOrigin-RevId: 286844725
This change allows for DialectConversion to attempt folding as a mechanism to legalize illegal operations. This also expands folding support in OpBuilder::createOrFold to generate new constants when folding, and also enables it to work in the context of a PatternRewriter.
PiperOrigin-RevId: 285448440
It is sometimes useful to create operations separately from the builder before insertion as it may be easier to erase them in isolation if necessary. One example use case for this is folding, as we will only want to insert newly generated constant operations on success. This has the added benefit of fixing some silent PatternRewriter failures related to cloning, as the OpBuilder 'clone' methods don't call createOperation.
PiperOrigin-RevId: 285086242
This change allows for adding additional nested references to a SymbolRefAttr to allow for further resolving a symbol if that symbol also defines a SymbolTable. If a referenced symbol also defines a symbol table, a nested reference can be used to refer to a symbol within that table. Nested references are printed after the main reference in the following form:
symbol-ref-attribute ::= symbol-ref-id (`::` symbol-ref-id)*
Example:
module @reference {
func @nested_reference()
}
my_reference_op @reference::@nested_reference
Given that SymbolRefAttr is now more general, the existing functionality centered around a single reference is moved to a derived class FlatSymbolRefAttr. Followup commits will add support to lookups, rauw, etc. for scoped references.
PiperOrigin-RevId: 279860501
This CL allows binary operations on n-D vector types to be lowered to LLVMIR by performing an (n-1)-D extractvalue, 1-D vector operation and an (n-1)-D insertvalue.
PiperOrigin-RevId: 264339118
Verification complained when using zero-dimensional memrefs in
affine.load, affine.store, std.load and std.store. This PR extends
verification so that those memrefs can be used.
Closestensorflow/mlir#58
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/58 from dcaballe:dcaballe/zero-dim 49bcdcd45c52c48beca776431328e5ce551dfa9e
PiperOrigin-RevId: 262164916
This CL introduces a linalg.generic op to represent generic tensor contraction operations on views.
A linalg.generic operation requires a numbers of attributes that are sufficient to emit the computation in scalar form as well as compute the appropriate subviews to enable tiling and fusion.
These attributes are very similar to the attributes for existing operations such as linalg.matmul etc and existing operations can be implemented with the generic form.
In the future, most existing operations can be implemented using the generic form.
This CL starts by splitting out most of the functionality of the linalg::NInputsAndOutputs trait into a ViewTrait that queries the per-instance properties of the op. This allows using the attribute informations.
This exposes an ordering of verifiers issue where ViewTrait::verify uses attributes but the verifiers for those attributes have not been run. The desired behavior would be for the verifiers of the attributes specified in the builder to execute first but it is not the case atm. As a consequence, to emit proper error messages and avoid crashing, some of the
linalg.generic methods are defensive as such:
```
unsigned getNumInputs() {
// This is redundant with the `n_views` attribute verifier but ordering of verifiers
// may exhibit cases where we crash instead of emitting an error message.
if (!getAttr("n_views") || n_views().getValue().size() != 2)
return 0;
```
In pretty-printed form, the specific attributes required for linalg.generic are factored out in an independent dictionary named "_". When parsing its content is flattened and the "_name" is dropped. This allows using aliasing for reducing boilerplate at each linalg.generic invocation while benefiting from the Tablegen'd verifier form for each named attribute in the dictionary.
For instance, implementing linalg.matmul in terms of linalg.generic resembles:
```
func @mac(%a: f32, %b: f32, %c: f32) -> f32 {
%d = mulf %a, %b: f32
%e = addf %c, %d: f32
return %e: f32
}
#matmul_accesses = [
(m, n, k) -> (m, k),
(m, n, k) -> (k, n),
(m, n, k) -> (m, n)
]
#matmul_trait = {
doc = "C(m, n) += A(m, k) * B(k, n)",
fun = @mac,
indexing_maps = #matmul_accesses,
library_call = "linalg_matmul",
n_views = [2, 1],
n_loop_types = [2, 1, 0]
}
```
And can be used in multiple places as:
```
linalg.generic #matmul_trait %A, %B, %C [other-attributes] :
!linalg.view<?x?xf32>, !linalg.view<?x?xf32>, !linalg.view<?x?xf32>
```
In the future it would be great to have a mechanism to alias / register a new
linalg.op as a pair of linalg.generic, #trait.
Also, note that with one could theoretically only specify the `doc` string and parse all the attributes from it.
PiperOrigin-RevId: 261338740
This field wasn't updated as the insertion point changed, making it potentially dangerous given the multi-level of MLIR(e.g. 'createBlock' would always insert the new block in 'region'). This also allows for building an OpBuilder with just a context.
PiperOrigin-RevId: 257829135
This allows for the attribute to hold symbolic references to other operations than FuncOp. This also allows for removing the dependence on FuncOp from the base Builder.
PiperOrigin-RevId: 257650017
As with Functions, Module will soon become an operation, which are value-typed. This eases the transition from Module to ModuleOp. A new class, OwningModuleRef is provided to allow for owning a reference to a Module, and will auto-delete the held module on destruction.
PiperOrigin-RevId: 256196193
Move the data members out of Function and into a new impl storage class 'FunctionStorage'. This allows for Function to become value typed, which will greatly simplify the transition of Function to FuncOp(given that FuncOp is also value typed).
PiperOrigin-RevId: 255983022
* 'get' methods that allow constructing from an ArrayRef of integer or floating point values.
* A 'reshape' method to allow for changing the shape without changing the underlying data.
PiperOrigin-RevId: 252067898
River Riddle