(A previous version of this, dd2c639c3c, was
reverted.)
Introduce op trait PolyhedralScope for ops to define a new scope for
polyhedral optimization / affine dialect purposes, thus generalizing
such scopes beyond FuncOp. Ops to which this trait is attached will
define a new scope for the consideration of SSA values as valid symbols
for the purposes of polyhedral analysis and optimization. Update methods
that check for dim/symbol validity to work based on this trait.
Differential Revision: https://reviews.llvm.org/D79060
Introduce op trait `PolyhedralScope` for ops to define a new scope for
polyhedral optimization / affine dialect purposes, thus generalizing
such scopes beyond FuncOp. Ops to which this trait is attached will
define a new scope for the consideration of SSA values as valid symbols
for the purposes of polyhedral analysis and optimization. Update methods
that check for dim/symbol validity to work based on this trait.
Differential Revision: https://reviews.llvm.org/D78863
Summary:
These have been replaced from attributes to operations gpu.module and
gpu.func respectively.
Differential Revision: https://reviews.llvm.org/D78803
Fix affine dialect documentation on valid dimensional values: they also
include affine.parallel IVs.
Differential Revision: https://reviews.llvm.org/D78855
Similarly to actual LLVM IR, and to `llvm.mlir.func`, allow the custom syntax
of `llvm.mlir.global` to omit the linkage keyword. If omitted, the linkage is
assumed to be external. This makes the modeling of globals in the LLVM dialect
more consistent, both within the dialect and with LLVM IR.
Differential Revision: https://reviews.llvm.org/D78096
This commit added stride support in runtime array types. It also
adjusted the assembly form for the stride from `[N]` to `stride=N`.
This makes the IR more readable, especially for the cases where
one mix array types and struct types.
Differential Revision: https://reviews.llvm.org/D78034
Summary:
This revision adds a tool that generates the ODS and C++ implementation for "named" Linalg ops according to the [RFC discussion](https://llvm.discourse.group/t/rfc-declarative-named-ops-in-the-linalg-dialect/745).
While the mechanisms and language aspects are by no means set in stone, this revision allows connecting the pieces end-to-end from a mathematical-like specification.
Some implementation details and short-term decisions taken for the purpose of bootstrapping and that are not set in stone include:
1. using a "[Tensor Comprehension](https://arxiv.org/abs/1802.04730)-inspired" syntax
2. implicit and eager discovery of dims and symbols when parsing
3. using EDSC ops to specify the computation (e.g. std_addf, std_mul_f, ...)
A followup revision will connect this tool to tablegen mechanisms and allow the emission of named Linalg ops that automatically lower to various loop forms and run end to end.
For the following "Tensor Comprehension-inspired" string:
```
def batch_matmul(A: f32(Batch, M, K), B: f32(K, N)) -> (C: f32(Batch, M, N)) {
C(b, m, n) = std_addf<k>(std_mulf(A(b, m, k), B(k, n)));
}
```
With -gen-ods-decl=1, this emits (modulo formatting):
```
def batch_matmulOp : LinalgNamedStructured_Op<"batch_matmul", [
NInputs<2>,
NOutputs<1>,
NamedStructuredOpTraits]> {
let arguments = (ins Variadic<LinalgOperand>:$views);
let results = (outs Variadic<AnyRankedTensor>:$output_tensors);
let extraClassDeclaration = [{
llvm::Optional<SmallVector<StringRef, 8>> referenceIterators();
llvm::Optional<SmallVector<AffineMap, 8>> referenceIndexingMaps();
void regionBuilder(ArrayRef<BlockArgument> args);
}];
let hasFolder = 1;
}
```
With -gen-ods-impl, this emits (modulo formatting):
```
llvm::Optional<SmallVector<StringRef, 8>> batch_matmul::referenceIterators() {
return SmallVector<StringRef, 8>{ getParallelIteratorTypeName(),
getParallelIteratorTypeName(),
getParallelIteratorTypeName(),
getReductionIteratorTypeName() };
}
llvm::Optional<SmallVector<AffineMap, 8>> batch_matmul::referenceIndexingMaps()
{
MLIRContext *context = getContext();
AffineExpr d0, d1, d2, d3;
bindDims(context, d0, d1, d2, d3);
return SmallVector<AffineMap, 8>{
AffineMap::get(4, 0, {d0, d1, d3}),
AffineMap::get(4, 0, {d3, d2}),
AffineMap::get(4, 0, {d0, d1, d2}) };
}
void batch_matmul::regionBuilder(ArrayRef<BlockArgument> args) {
using namespace edsc;
using namespace intrinsics;
ValueHandle _0(args[0]), _1(args[1]), _2(args[2]);
ValueHandle _4 = std_mulf(_0, _1);
ValueHandle _5 = std_addf(_2, _4);
(linalg_yield(ValueRange{ _5 }));
}
```
Differential Revision: https://reviews.llvm.org/D77067
Summary:
LLVM IR functions can have arbitrary attributes attached to them, some of which
affect may affect code transformations. Until we can model all attributes
consistently, provide a pass-through mechanism that forwards attributes from
the LLVMFuncOp in MLIR to LLVM IR functions during translation. This mechanism
relies on LLVM IR being able to recognize string representations of the
attributes and performs some additional checking to avoid hitting assertions
within LLVM code.
Differential Revision: https://reviews.llvm.org/D77072
Summary:
This revision performs a lot of different cleanups on operation documentation to ensure that they are consistent, e.g. using mlir code blocks, formatting, etc.
This revision also includes the auto-generated documentation into the hand-written documentation for the dialects that have a specific top-level dialect file. This updates the documentation for all dialects aside from SPIRV and STD. These dialects will be updated in a followup.
Differential Revision: https://reviews.llvm.org/D76734
Summary: This revision updates the dialect documentation to use the auto-generated markdown for operations. This allows for updating some out-of-date bits of documentation, and allows for displaying a large of number of newly added operations that did not have a counter part in Standard.md.
Differential Revision: https://reviews.llvm.org/D76743
Summary:
This revisions performs several cleanups to the generated dialect documentation:
* Standardizes format of attributes/operands/results sections
* Splits out operation/type/dialect documentation generation to allow for composing generated and hand-written documentation
* Add section for declarative assembly syntax and successors
* General cleanup
Differential Revision: https://reviews.llvm.org/D76573
Previously we only look at the directly passed-in op for a potential
spv.target_env attribute. This commit switches to use a larger range
and recursively check enclosing symbol tables.
Differential Revision: https://reviews.llvm.org/D75869
We also need the (version, capabilities, extensions) triple on the
spv.module op. Thus far we have been using separate 'extensions'
and 'capabilities' attributes there and 'version' is missing. Creating
a separate attribute for the trip allows us to reuse the assembly
form and verification.
Differential Revision: https://reviews.llvm.org/D75868
This revision takes advantage of the empty AffineMap to specify the
0-D edge case. This allows removing a bunch of annoying corner cases
that ended up impacting users of Linalg.
Differential Revision: https://reviews.llvm.org/D75831
This commit updates SPIR-V dialect to support integer signedness
by relaxing various checks for signless to just normal integers.
The hack for spv.Bitcast can now be removed.
Differential Revision: https://reviews.llvm.org/D75611
We have one title in every doc which corresponds to `#`, in the some
there are multiple and it is expected to be h1 headers (visual elements
rather than organizational). Indent every nesting by one in all of the
docs with multiple titles.
Also fixing trailing whitespace.
We were using normal dictionary attribute for target environment
specification. It becomes cumbersome with more and more fields.
This commit changes the modelling to a dialect-specific attribute,
where we can have control over its storage and assembly form.
Differential Revision: https://reviews.llvm.org/D73959
This commit defines a new SPIR-V dialect attribute for specifying
a SPIR-V target environment. It is a dictionary attribute containing
the SPIR-V version, supported extension list, and allowed capability
list. A SPIRVConversionTarget subclass is created to take in the
target environment and sets proper dynmaically legal ops by querying
the op availability interface of SPIR-V ops to make sure they are
available in the specified target environment. All existing conversions
targeting SPIR-V is changed to use this SPIRVConversionTarget. It
probes whether the input IR has a `spv.target_env` attribute,
otherwise, it uses the default target environment: SPIR-V 1.0 with
Shader capability and no extra extensions.
Differential Revision: https://reviews.llvm.org/D72256
I used the codemod python tool to do this with the following commands:
codemod 'tensorflow/mlir/blob/master/include' 'llvm/llvm-project/blob/master/mlir/include'
codemod 'tensorflow/mlir/blob/master' 'llvm/llvm-project/blob/master/mlir'
codemod 'tensorflow/mlir' 'llvm-project/llvm'
Differential Revision: https://reviews.llvm.org/D72244
Summary: The current syntax for AffineMapAttr and IntegerSetAttr conflict with function types, making it currently impossible to round-trip function types(and e.g. FuncOp) in the IR. This revision changes the syntax for the attributes by wrapping them in a keyword. AffineMapAttr is wrapped with `affine_map<>` and IntegerSetAttr is wrapped with `affine_set<>`.
Reviewed By: nicolasvasilache, ftynse
Differential Revision: https://reviews.llvm.org/D72429
Summary:
This commit fixes links to code directories and uses doc links on
mlir.llvm.org where possible. The docs in TableGen dialect definition
is also updated to reflect recent developments.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D72051
This commit expands on the steps of defining a new SPIR-V op and
also provides pointers on how to define a new SPIR-V specific type.
Differential Revision: https://reviews.llvm.org/D71928
Summary:
This commit updates links to SPIR-V dialect code to LLVM monorepo
on GitHub. It also points to the operation doc on mlir.llvm.org.
Reviewers: mravishankar, denis13, ftynse
Reviewed By: ftynse
Subscribers: merge_guards_bot, mehdi_amini, rriddle, jpienaar, burmako, shauheen, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D71926
Uday Bondhugula