Differential D95636 Diff 319931 mlir/include/mlir/Dialect/Linalg/IR/LinalgOps.td

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mlir/include/mlir/Dialect/Linalg/IR/LinalgOps.td

Show First 20 Lines • Show All 188 Lines • ▼ Show 20 Lines	let extraClassDeclaration = [{
static StringRef getStaticLowAttrName() {		static StringRef getStaticLowAttrName() {
return "static_low";		return "static_low";
}		}

static StringRef getStaticHighAttrName() {		static StringRef getStaticHighAttrName() {
return "static_high";		return "static_high";
}		}

RankedTensorType getSourceType() {
return source().getType().cast<RankedTensorType>();
}
RankedTensorType getResultType() {
return getResult().getType().cast<RankedTensorType>();
}

// Infer the shape of the result tensor given the static shapes		// Infer the shape of the result tensor given the static shapes
// and element type of the result tensor.		// and element type of the result tensor.
static RankedTensorType inferResultType(RankedTensorType sourceType,		static RankedTensorType inferResultType(RankedTensorType sourceType,
ArrayRef<int64_t> staticLow,		ArrayRef<int64_t> staticLow,
ArrayRef<int64_t> staticHigh);		ArrayRef<int64_t> staticHigh);

// Return a PadTensorOp that pads `source` to `type` size where the static		// Return a PadTensorOp that pads `source` to `type` size where the static
// sizes are assumed to be greater than the dynamic sizes. The op performs		// sizes are assumed to be greater than the dynamic sizes. The op performs
▲ Show 20 Lines • Show All 277 Lines • ▼ Show 20 Lines	SmallVector<Value, 8> getRanges() {
res.push_back(operand);		res.push_back(operand);
return res;		return res;
}		}
}];		}];

let hasFolder = 1;		let hasFolder = 1;
}		}

		def Linalg_SimplePadOp : Linalg_Op<"simple_pad", [NoSideEffect]> {
		let summary = "TODO: replace with pad_tensors when ready.";

		let description = [{
		`linalg.simple_pad` is a tmp placeholder for padding and packing on tensors.
		Its semantics are to pad a partially dynamic tensor to a fully static tensor
		where the static sizes are assumed to be greater than the dynamic sizes. The
		op perforrms "high" padding (i.e. it adds trailing padding values until the
		desired size is met).
		}];

		let arguments = (ins AnyRankedTensor:$tensor, AnyType:$padding);
		let results = (outs AnyRankedTensor:$result);

		// TODO: verify all static result, some dynamic input, static shapes match,
		// element types match, ranks match etc. Use pad_tensors when ready but for
		// now just let it ne fully specified by traits.
		let verifier = ?;

		let extraClassDeclaration = [{
		RankedTensorType getSourceType() {
		return tensor().getType().cast<RankedTensorType>(); }
		RankedTensorType getResultType() {
		return getResult().getType().cast<RankedTensorType>(); }
		}];

		let assemblyFormat = [{
		$tensor `pad` $padding attr-dict `:`
		type($tensor) `to` type($result) `pad` type($padding)
		}];
		}

def Linalg_YieldOp : Linalg_Op<"yield", [NoSideEffect, ReturnLike, Terminator]>,		def Linalg_YieldOp : Linalg_Op<"yield", [NoSideEffect, ReturnLike, Terminator]>,
Arguments<(ins Variadic<AnyType>:$values)> {		Arguments<(ins Variadic<AnyType>:$values)> {
let summary = "Linalg yield operation";		let summary = "Linalg yield operation";
let description = [{		let description = [{
`linalg.yield` is a special terminator operation for blocks inside regions		`linalg.yield` is a special terminator operation for blocks inside regions
in `linalg` generic ops. It returns values to the immediately enclosing		in `linalg` generic ops. It returns values to the immediately enclosing
`linalg` generic op.		`linalg` generic op.

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