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Differential D96401 Diff 323742 mlir/include/mlir/Dialect/Shape/IR/ShapeOps.td

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mlir/include/mlir/Dialect/Shape/IR/ShapeOps.td

Show First 20 Lines • Show All 184 Lines • ▼ Show 20 Linesdef Shape_FromExtentTensorOp : Shape_Op<"from_extent_tensor", [NoSideEffect]> {
}]; }];
let arguments = (ins 1DTensorOf<[Index]>:$input); let arguments = (ins 1DTensorOf<[Index]>:$input);
let results = (outs Shape_ShapeType:$result); let results = (outs Shape_ShapeType:$result);
let assemblyFormat = "$input attr-dict `:` type($input)"; let assemblyFormat = "$input attr-dict `:` type($input)";
} }
def Shape_IsBroadcastableOp : Shape_Op<"is_broadcastable", [Commutative]> { def Shape_IsBroadcastableOp : Shape_Op<"is_broadcastable",
let summary = "Determines if 2 shapes can be successfully broadcasted"; [Commutative, InferTypeOpInterface]> {
let summary = "Determines if 2+ shapes can be successfully broadcasted";
let description = [{ let description = [{
Given two input shapes or extent tensors, return a predicate specifying if Given multiple input shapes or extent tensors, return a predicate specifying
they are broadcastable. This broadcastable follows the same logic as what if they are broadcastable. This broadcastable follows the same logic as what
shape.broadcast documents. shape.broadcast documents.
Concretely, shape.is_broadcastable returning true implies that Concretely, shape.is_broadcastable returning true implies that
shape.broadcast will not give an error, and shape.cstr_broadcastable will shape.broadcast will not give an error, and shape.cstr_broadcastable will
not result in an assertion failure. Similarly, false implies an error or not result in an assertion failure. Similarly, false implies an error or
assertion failure. assertion failure.
Example: Example:
```mlir ```mlir
%true = shape.is_broadcastable [2,2], [3,1,2] %true = shape.is_broadcastable [2,2], [3,1,2]
%false = shape.is_broadcastable [2,2], [3,2] %false = shape.is_broadcastable [2,2], [3,2]
``` ```
}]; }];
let arguments = (ins Shape_ShapeOrExtentTensorType:$lhs, let arguments = (ins Variadic<Shape_ShapeOrExtentTensorType>:$shapes);
Shape_ShapeOrExtentTensorType:$rhs);
let results = (outs I1:$result); let results = (outs I1:$result);
let assemblyFormat = "$lhs `,` $rhs attr-dict `:` type($lhs) `,` type($rhs)"; let builders = [
OpBuilderDAG<(ins "::mlir::Value":$lhs, "::mlir::Value":$rhs),
[{ build($_builder, $_state, ::llvm::makeArrayRef({lhs, rhs})); }]>,
];
let extraClassDeclaration = [{
// TODO: This should really be automatic. Figure out how to not need this defined.
frgossenUnsubmitted
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Is the error something like type not buildable? In that case you could add the missing C++ for the type, I think.

frgossen: Is the error something like type not buildable? In that case you could add the missing C++ for…
tpoppAuthorUnsubmitted
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I was just getting compile time errors right after the table-gen'ed code. It wasn't at runtime. Any pointers to code snippets would be welcomed though. I couldn't figure out what to do.

tpopp: I was just getting compile time errors right after the table-gen'ed code. It wasn't at runtime.
frgossenUnsubmitted
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I meant BuildableType in mlir/include/mlir/IR/OpBase.td.
The I type in the same file is an example of a type that implements it so it's probably not the problem you ran into.

frgossen: I meant `BuildableType` in `mlir/include/mlir/IR/OpBase.td`. The `I` type in the same file is…
static ::mlir::LogicalResult inferReturnTypes(::mlir::MLIRContext *context,
::llvm::Optional<::mlir::Location> location, ::mlir::ValueRange operands,
::mlir::DictionaryAttr attributes, ::mlir::RegionRange regions,
::llvm::SmallVectorImpl<::mlir::Type>&inferredReturnTypes) {
inferredReturnTypes.push_back(::mlir::IntegerType::get(context,
/*width=*/1));
return success();
};
}];
let assemblyFormat = "$shapes attr-dict `:` type($shapes)";
let verifier = [{ return ::verify(*this); }];
rriddleUnsubmitted
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This should be emitting an error if there is a failure. Otherwise, it is silent with no indication to the user what is wrong. This points to a lack of test coverage.

rriddle: This should be emitting an error if there is a failure. Otherwise, it is silent with no…
} }
def Shape_RankOp : Shape_Op<"rank", [NoSideEffect]> { def Shape_RankOp : Shape_Op<"rank", [NoSideEffect]> {
let summary = "Gets the rank of a shape"; let summary = "Gets the rank of a shape";
let description = [{ let description = [{
Returns the rank of the shape or extent tensor, i.e. the number of extents. Returns the rank of the shape or extent tensor, i.e. the number of extents.
}]; }];
▲ Show 20 Lines • Show All 462 Lines • ▼ Show 20 Linesdef Shape_AssumingYieldOp : Shape_Op<"assuming_yield",
let arguments = (ins Variadic<AnyType>:$operands); let arguments = (ins Variadic<AnyType>:$operands);
let builders = [OpBuilderDAG<(ins), [{ /* nothing to do */ }]>]; let builders = [OpBuilderDAG<(ins), [{ /* nothing to do */ }]>];
let assemblyFormat = "attr-dict ($operands^ `:` type($operands))?"; let assemblyFormat = "attr-dict ($operands^ `:` type($operands))?";
} }
def Shape_CstrBroadcastableOp : Shape_Op<"cstr_broadcastable", [Commutative]> { def Shape_CstrBroadcastableOp : Shape_Op<"cstr_broadcastable",
let summary = "Determines if 2 shapes can be successfully broadcasted"; [Commutative, InferTypeOpInterface]> {
let summary = "Determines if 2+ shapes can be successfully broadcasted";
let description = [{ let description = [{
Given two input shapes or extent tensors, return a witness specifying if Given input shapes or extent tensors, return a witness specifying if they
they are broadcastable. This broadcastable follows the same logic as what are broadcastable. This broadcastable follows the same logic as what
shape.broadcast documents. shape.broadcast documents.
"cstr" operations represent runtime assertions. "cstr" operations represent runtime assertions.
Example: Example:
```mlir ```mlir
%w0 = shape.cstr_broadcastable [2,2], [3,1,2] // Passing %w0 = shape.cstr_broadcastable [2,2], [3,1,2] // Passing
%w1 = shape.cstr_broadcastable [2,2], [3,2] // Failure %w1 = shape.cstr_broadcastable [2,2], [3,2] // Failure
``` ```
}]; }];
let arguments = (ins Shape_ShapeOrExtentTensorType:$lhs, let arguments = (ins Variadic<Shape_ShapeOrExtentTensorType>:$shapes);
Shape_ShapeOrExtentTensorType:$rhs);
let results = (outs Shape_WitnessType:$result); let results = (outs Shape_WitnessType:$result);
let assemblyFormat = "$lhs `,` $rhs attr-dict `:` type($lhs) `,` type($rhs)"; let assemblyFormat = "$shapes attr-dict `:` type($shapes)";
let builders = [
OpBuilderDAG<(ins "::mlir::Value":$lhs, "::mlir::Value":$rhs),
[{ build($_builder, $_state, ::llvm::makeArrayRef({lhs, rhs})); }]>,
];
let extraClassDeclaration = [{
// TODO: This should really be automatic. Figure out how to not need this defined.
static ::mlir::LogicalResult inferReturnTypes(::mlir::MLIRContext *context,
::llvm::Optional<::mlir::Location> location, ::mlir::ValueRange operands,
::mlir::DictionaryAttr attributes, ::mlir::RegionRange regions,
::llvm::SmallVectorImpl<::mlir::Type>&inferredReturnTypes) {
inferredReturnTypes.push_back(::mlir::shape::WitnessType::get(context));
return success();
};
}];
let hasCanonicalizer = 1; let hasCanonicalizer = 1;
let hasFolder = 1; let hasFolder = 1;
let verifier = [{ return ::verify(*this); }];
} }
rriddleUnsubmitted
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Same here.

rriddle: Same here.
def Shape_CstrEqOp : Shape_Op<"cstr_eq", [Commutative]> { def Shape_CstrEqOp : Shape_Op<"cstr_eq", [Commutative]> {
let summary = "Determines if all input shapes are equal"; let summary = "Determines if all input shapes are equal";
let description = [{ let description = [{
Given 1 or more input shapes, determine if all shapes are the exact same. Given 1 or more input shapes, determine if all shapes are the exact same.
"cstr" operations represent runtime assertions. "cstr" operations represent runtime assertions.
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