[Mlir-commits] [mlir] eae76fa - [mlir][Linalg] Retire C++ MatmulOp in favor of a linalg-ods-gen'd op.
Nicolas Vasilache
llvmlistbot at llvm.org
Tue Jun 16 07:47:05 PDT 2020
Author: Nicolas Vasilache
Date: 2020-06-16T10:46:35-04:00
New Revision: eae76faeeae7110389047c9d1ebe90990dec3497
URL: https://github.com/llvm/llvm-project/commit/eae76faeeae7110389047c9d1ebe90990dec3497
DIFF: https://github.com/llvm/llvm-project/commit/eae76faeeae7110389047c9d1ebe90990dec3497.diff
LOG: [mlir][Linalg] Retire C++ MatmulOp in favor of a linalg-ods-gen'd op.
Summary:
This revision replaces MatmulOp, now that DRR rules have been dropped.
This revision also fixes minor parsing bugs and a plugs a few holes to get e2e paths working (e.g. library call emission).
During the replacement the i32 version had to be dropped because only the EDSC operators +, *, etc support type inference.
Deciding on a type-polymorphic behavior, and implementing it, is left for future work.
Reviewers: aartbik
Subscribers: mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, stephenneuendorffer, Joonsoo, grosul1, frgossen, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D81935
Added:
Modified:
mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc
mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td
mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h
mlir/lib/Conversion/LinalgToStandard/LinalgToStandard.cpp
mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
mlir/lib/Dialect/Linalg/Transforms/Loops.cpp
mlir/test/Dialect/Linalg/affine.mlir
mlir/test/Dialect/Linalg/canonicalize.mlir
mlir/test/Dialect/Linalg/fusion-2-level.mlir
mlir/test/Dialect/Linalg/fusion.mlir
mlir/test/Dialect/Linalg/loops.mlir
mlir/test/Dialect/Linalg/promote.mlir
mlir/test/Dialect/Linalg/promotion_options.mlir
mlir/test/Dialect/Linalg/roundtrip.mlir
mlir/test/Dialect/Linalg/tile.mlir
mlir/test/Dialect/Linalg/tile_parallel_reduce.mlir
mlir/test/Dialect/Linalg/transform-patterns-matmul-to-vector.mlir
mlir/test/Dialect/Linalg/transform-patterns.mlir
mlir/test/mlir-cpu-runner/linalg_integration_test.mlir
mlir/tools/mlir-linalg-ods-gen/mlir-linalg-ods-gen.cpp
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc
index 9f9b53a22011..c510010acd0b 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOpsSpec.tc
@@ -1,3 +1,8 @@
+ods_def<MatmulOp>:
+def matmul(A: f32(M, K), B: f32(K, N)) -> (C: f32(M, N)) {
+ C(m, n) = std_addf<k>(std_mulf(A(m, k), B(k, n)));
+}
+
ods_def<BatchMatmulOp>:
def batch_matmul(A: f32(Batch, M, K), B: f32(Batch, K, N)) -> (C: f32(Batch, M, N)) {
C(b, m, n) = std_addf<k>(std_mulf(A(b, m, k), B(b, k, n)));
diff --git a/mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td b/mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td
index cc7eb5ce9d68..1292344484b0 100644
--- a/mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/IR/LinalgStructuredOps.td
@@ -225,36 +225,6 @@ def MatvecOp : LinalgStructured_Op<"matvec", [NInputs<2>, NOutputs<1>]> {
let hasFolder = 1;
}
-def MatmulOp : LinalgStructured_Op<"matmul", [NInputs<2>, NOutputs<1>]> {
-
- let arguments = (ins AnyStridedMemRefOfRank<2>,
- AnyStridedMemRefOfRank<2>,
- AnyStridedMemRefOfRank<2>);
-
- let extraClassDeclaration = libraryCallName # [{
- llvm::Optional<SmallVector<StringRef, 8>> referenceIterators() {
- return SmallVector<StringRef, 8>{
- getParallelIteratorTypeName(),
- getParallelIteratorTypeName(),
- getReductionIteratorTypeName()};
- }
-
- // A(i, r_k) * B(r_k, j) -> C(i, j)
- llvm::Optional<SmallVector<AffineMap, 8>> referenceIndexingMaps() {
- MLIRContext *context = getContext();
- AffineExpr i, j, r_k;
- bindDims(context, i, j, r_k);
- return SmallVector<AffineMap, 8>{
- AffineMap::get(3, 0, {i, r_k}, context),
- AffineMap::get(3, 0, {r_k, j},context),
- AffineMap::get(3, 0, {i, j}, context)
- };
- }
- }];
-
- let hasFolder = 1;
-}
-
/// A base class for pooling operation such as conv. The arguments must contain
/// optional arguments `strides`, `dilations` and `padding` with following type:
/// OptionalAttr<I64ArrayAttr>:$strides
diff --git a/mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h b/mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h
index 7bd57c832f77..b7be5d86f50c 100644
--- a/mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h
+++ b/mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h
@@ -165,19 +165,16 @@ Optional<LinalgOp> promoteSubViews(OpBuilder &b, LinalgOp op,
void vectorizeLinalgOp(OpBuilder &builder, Operation *op);
/// Emits a loop nest of `LoopTy` with the proper body for `op`.
-template <typename LoopTy, typename ConcreteOp>
+template <typename LoopTy>
Optional<LinalgLoops> linalgLowerOpToLoops(OpBuilder &builder, Operation *op);
/// Emits a loop nest of `scf.for` with the proper body for `op`.
-template <typename ConcreteOp>
LogicalResult linalgOpToLoops(OpBuilder &builder, Operation *op);
/// Emits a loop nest of `scf.parallel` with the proper body for `op`.
-template <typename ConcreteOp>
LogicalResult linalgOpToParallelLoops(OpBuilder &builder, Operation *op);
/// Emits a loop nest of `affine.for` with the proper body for `op`.
-template <typename ConcreteOp>
LogicalResult linalgOpToAffineLoops(OpBuilder &builder, Operation *op);
//===----------------------------------------------------------------------===//
@@ -419,12 +416,12 @@ template <typename OpTy> struct LinalgLoweringPattern : public RewritePattern {
// TODO: Move lowering to library calls here.
return failure();
} else if (loweringType == LinalgLoweringType::Loops) {
- if (failed(linalgOpToLoops<OpTy>(rewriter, op)))
+ if (failed(linalgOpToLoops(rewriter, op)))
return failure();
} else if (loweringType == LinalgLoweringType::AffineLoops) {
- if (failed(linalgOpToAffineLoops<OpTy>(rewriter, op)))
+ if (failed(linalgOpToAffineLoops(rewriter, op)))
return failure();
- } else if (failed(linalgOpToParallelLoops<OpTy>(rewriter, op))) {
+ } else if (failed(linalgOpToParallelLoops(rewriter, op))) {
return failure();
}
rewriter.eraseOp(op);
diff --git a/mlir/lib/Conversion/LinalgToStandard/LinalgToStandard.cpp b/mlir/lib/Conversion/LinalgToStandard/LinalgToStandard.cpp
index ca6ca8b24732..7b30646136cb 100644
--- a/mlir/lib/Conversion/LinalgToStandard/LinalgToStandard.cpp
+++ b/mlir/lib/Conversion/LinalgToStandard/LinalgToStandard.cpp
@@ -241,8 +241,11 @@ void mlir::populateLinalgToStandardConversionPatterns(
LinalgOpConversion<FillOp>,
LinalgOpConversion<GenericOp>,
LinalgOpConversion<IndexedGenericOp>,
- LinalgOpConversion<MatmulOp>,
LinalgOpConversion<MatvecOp>>(ctx);
+ // TODO: collect all auto-generated named ops with a tblgen directive.
+ patterns.insert<
+ LinalgOpConversion<BatchMatmulOp>,
+ LinalgOpConversion<MatmulOp>>(ctx);
// clang-format on
}
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index e9bd082fbcae..fe2f123a00cb 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -1128,10 +1128,6 @@ LogicalResult MatvecOp::fold(ArrayRef<Attribute>,
SmallVectorImpl<OpFoldResult> &) {
return foldMemRefCast(*this);
}
-LogicalResult MatmulOp::fold(ArrayRef<Attribute>,
- SmallVectorImpl<OpFoldResult> &) {
- return foldMemRefCast(*this);
-}
OpFoldResult ReshapeOp::fold(ArrayRef<Attribute>) {
if (succeeded(foldMemRefCast(*this)))
return getResult();
@@ -1193,7 +1189,7 @@ static void printNamedStructuredOp(OpAsmPrinter &p, NamedStructuredOpType op) {
p << op.getOperationName() << ' ';
p.printOptionalAttrDict(op.getAttrs(), silentAttrNames);
p << ' ' << op.getOperands();
- p << ": (" << op.getOperandTypes() << ")";
+ p << " : (" << op.getOperandTypes() << ")";
auto outputTensorTypes = op.getResultTypes();
if (!outputTensorTypes.empty())
p << " -> (" << outputTensorTypes << ")";
@@ -1205,8 +1201,8 @@ static ParseResult parseNamedStructuredOp(OpAsmParser &parser,
SmallVector<OpAsmParser::OperandType, 8> operandsInfo;
// Optional attributes may be added.
- if (parser.parseOptionalAttrDict(result.attributes) ||
- parser.parseOperandList(operandsInfo))
+ if (parser.parseOperandList(operandsInfo) ||
+ parser.parseOptionalAttrDict(result.attributes))
return failure();
SmallVector<Type, 8> operandTypes;
@@ -1242,3 +1238,7 @@ LogicalResult BatchMatmulOp::fold(ArrayRef<Attribute>,
SmallVectorImpl<OpFoldResult> &) {
return foldMemRefCast(*this);
}
+LogicalResult MatmulOp::fold(ArrayRef<Attribute>,
+ SmallVectorImpl<OpFoldResult> &) {
+ return foldMemRefCast(*this);
+}
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Loops.cpp b/mlir/lib/Dialect/Linalg/Transforms/Loops.cpp
index c1080ead1cf9..98baef105763 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Loops.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Loops.cpp
@@ -80,6 +80,8 @@ template <typename IndexedValueType, typename OpType>
static void inlineRegionAndEmitStore(OpType op, ArrayRef<Value> indexedValues,
ArrayRef<SmallVector<Value, 8>> indexing,
ArrayRef<Value> outputBuffers) {
+ assert(op.getOperation()->getNumRegions() == 1 &&
+ "Expected single region op");
auto &b = ScopedContext::getBuilderRef();
auto &block = op.region().front();
BlockAndValueMapping map;
@@ -150,276 +152,224 @@ namespace {
/// }
/// }
/// ```
-template <typename IndexedValueType, typename LinalgOpType>
-class LinalgScopedEmitter {
-public:
- static void emitScalarImplementation(ArrayRef<Value> allIvs,
- LinalgOpType linalgOp) {
- assert(linalgOp.hasBufferSemantics() &&
- "expected linalg op with buffer semantics");
- auto &b = ScopedContext::getBuilderRef();
- auto loc = ScopedContext::getLocation();
- unsigned nInputs = linalgOp.getNumInputs();
- unsigned nOutputs = linalgOp.getNumOutputs();
- SmallVector<Value, 4> indexedValues;
- indexedValues.reserve(nInputs + nOutputs);
-
- // TODO(mravishankar): Avoid the loads if the corresponding argument of the
- // region has no uses.
- // 1.a. Emit load from input views.
- for (unsigned i = 0; i < nInputs; ++i) {
- auto indexing = makeCanonicalAffineApplies(
- b, loc, linalgOp.getInputIndexingMap(i), allIvs);
- // Passing through IndexedValueType emits the proper load operation.
- indexedValues.push_back(IndexedValueType(linalgOp.getInput(i))(indexing));
- }
- // 1.b. Emit load from output views.
- for (unsigned i = 0; i < nOutputs; ++i) {
- auto indexing = makeCanonicalAffineApplies(
- b, loc, linalgOp.getOutputIndexingMap(i), allIvs);
- // Passing through IndexedValueType emits the proper load operation.
- indexedValues.push_back(
- IndexedValueType(linalgOp.getOutputBuffer(i))(indexing));
- }
+// TODO: need a LinalgStructuredOpInterface.
+template <typename IndexedValueType, typename LinalgStructuredOpType>
+void emitScalarImplementation(ArrayRef<Value> allIvs,
+ LinalgStructuredOpType linalgOp) {
+ assert(linalgOp.hasBufferSemantics() &&
+ "expected linalg op with buffer semantics");
+ auto &b = ScopedContext::getBuilderRef();
+ auto loc = ScopedContext::getLocation();
+ unsigned nInputs = linalgOp.getNumInputs();
+ unsigned nOutputs = linalgOp.getNumOutputs();
+ SmallVector<Value, 4> indexedValues;
+ indexedValues.reserve(nInputs + nOutputs);
+
+ // TODO(mravishankar): Avoid the loads if the corresponding argument of the
+ // region has no uses.
+ // 1.a. Emit load from input views.
+ for (unsigned i = 0; i < nInputs; ++i) {
+ auto indexing = makeCanonicalAffineApplies(
+ b, loc, linalgOp.getInputIndexingMap(i), allIvs);
+ // Passing through IndexedValueType emits the proper load operation.
+ indexedValues.push_back(IndexedValueType(linalgOp.getInput(i))(indexing));
+ }
+ // 1.b. Emit load from output views.
+ for (unsigned i = 0; i < nOutputs; ++i) {
+ auto indexing = makeCanonicalAffineApplies(
+ b, loc, linalgOp.getOutputIndexingMap(i), allIvs);
+ // Passing through IndexedValueType emits the proper load operation.
+ indexedValues.push_back(
+ IndexedValueType(linalgOp.getOutputBuffer(i))(indexing));
+ }
- // TODO(ntv): When a region inliner exists, use it.
- // 2. Inline region, currently only works for a single basic block.
- // 3. Emit store.
- SmallVector<SmallVector<Value, 8>, 8> indexing;
- SmallVector<Value, 8> outputBuffers;
- for (unsigned i = 0; i < nOutputs; ++i) {
- indexing.push_back(makeCanonicalAffineApplies(
- b, loc, linalgOp.getOutputIndexingMap(i), allIvs));
- outputBuffers.push_back(linalgOp.getOutputBuffer(i));
- }
- inlineRegionAndEmitStore<IndexedValueType>(linalgOp, indexedValues,
- indexing, outputBuffers);
+ // TODO(ntv): When a region inliner exists, use it.
+ // 2. Inline region, currently only works for a single basic block.
+ // 3. Emit store.
+ SmallVector<SmallVector<Value, 8>, 8> indexing;
+ SmallVector<Value, 8> outputBuffers;
+ for (unsigned i = 0; i < nOutputs; ++i) {
+ indexing.push_back(makeCanonicalAffineApplies(
+ b, loc, linalgOp.getOutputIndexingMap(i), allIvs));
+ outputBuffers.push_back(linalgOp.getOutputBuffer(i));
}
-};
+ inlineRegionAndEmitStore<IndexedValueType>(linalgOp, indexedValues, indexing,
+ outputBuffers);
+}
template <typename IndexedValueType>
-class LinalgScopedEmitter<IndexedValueType, CopyOp> {
-public:
- static void emitScalarImplementation(ArrayRef<Value> allIvs, CopyOp copyOp) {
- assert(copyOp.hasBufferSemantics() &&
- "expected linalg op with buffer semantics");
- auto nPar = copyOp.getNumParallelLoops();
- assert(nPar == allIvs.size());
- auto inputIvs =
- permuteIvs(allIvs.take_front(nPar), copyOp.inputPermutation());
- auto outputIvs =
- permuteIvs(allIvs.take_front(nPar), copyOp.outputPermutation());
- SmallVector<Value, 8> iivs(inputIvs.begin(), inputIvs.end());
- SmallVector<Value, 8> oivs(outputIvs.begin(), outputIvs.end());
- IndexedValueType O(copyOp.getOutputBuffer(0)), I(copyOp.getInput(0));
- // Emit the proper scalar assignment, whether we are dealing with a 0-D or
- // an n-D loop nest; with or without permutations.
- // clang-format off
+void emitScalarImplementation(ArrayRef<Value> allIvs, CopyOp copyOp) {
+ assert(copyOp.hasBufferSemantics() &&
+ "expected linalg op with buffer semantics");
+ auto nPar = copyOp.getNumParallelLoops();
+ assert(nPar == allIvs.size());
+ auto inputIvs =
+ permuteIvs(allIvs.take_front(nPar), copyOp.inputPermutation());
+ auto outputIvs =
+ permuteIvs(allIvs.take_front(nPar), copyOp.outputPermutation());
+ SmallVector<Value, 8> iivs(inputIvs.begin(), inputIvs.end());
+ SmallVector<Value, 8> oivs(outputIvs.begin(), outputIvs.end());
+ IndexedValueType O(copyOp.getOutputBuffer(0)), I(copyOp.getInput(0));
+ // Emit the proper scalar assignment, whether we are dealing with a 0-D or
+ // an n-D loop nest; with or without permutations.
+ // clang-format off
nPar > 0 ? O(oivs) = I(iivs) :
O() = I();
- // clang-format on
- }
-};
-
-template <typename IndexedValueType>
-class LinalgScopedEmitter<IndexedValueType, FillOp> {
-public:
- static void emitScalarImplementation(ArrayRef<Value> allIvs, FillOp fillOp) {
- assert(fillOp.hasBufferSemantics() &&
- "expected linalg op with buffer semantics");
- auto nPar = fillOp.getNumParallelLoops();
- assert(nPar == allIvs.size());
- auto ivs = SmallVector<Value, 4>(allIvs.begin(), allIvs.begin() + nPar);
- IndexedValueType O(fillOp.getOutputBuffer(0));
- // Emit the proper scalar assignment, whether we are dealing with a 0-D or
- // an n-D loop nest; with or without permutations.
- nPar > 0 ? O(ivs) = fillOp.value() : O() = fillOp.value();
- }
-};
+ // clang-format on
+}
template <typename IndexedValueType>
-class LinalgScopedEmitter<IndexedValueType, DotOp> {
-public:
- static void emitScalarImplementation(ArrayRef<Value> allIvs, DotOp dotOp) {
- assert(dotOp.hasBufferSemantics() &&
- "expected linalg op with buffer semantics");
- assert(allIvs.size() == 1);
- Value r_i(allIvs[0]);
- IndexedValueType A(dotOp.getInput(0)), B(dotOp.getInput(1)),
- C(dotOp.getOutputBuffer(0));
- // Emit scalar form.
- C() = C() + A(r_i) * B(r_i);
- }
-};
+void emitScalarImplementation(ArrayRef<Value> allIvs, FillOp fillOp) {
+ assert(fillOp.hasBufferSemantics() &&
+ "expected linalg op with buffer semantics");
+ auto nPar = fillOp.getNumParallelLoops();
+ assert(nPar == allIvs.size());
+ auto ivs = SmallVector<Value, 4>(allIvs.begin(), allIvs.begin() + nPar);
+ IndexedValueType O(fillOp.getOutputBuffer(0));
+ // Emit the proper scalar assignment, whether we are dealing with a 0-D or
+ // an n-D loop nest; with or without permutations.
+ nPar > 0 ? O(ivs) = fillOp.value() : O() = fillOp.value();
+}
template <typename IndexedValueType>
-class LinalgScopedEmitter<IndexedValueType, MatvecOp> {
-public:
- static void emitScalarImplementation(ArrayRef<Value> allIvs,
- MatvecOp matvecOp) {
- assert(matvecOp.hasBufferSemantics() &&
- "expected linalg op with buffer semantics");
- assert(allIvs.size() == 2);
- Value i(allIvs[0]), r_j(allIvs[1]);
- IndexedValueType A(matvecOp.getInput(0)), B(matvecOp.getInput(1)),
- C(matvecOp.getOutputBuffer(0));
- // Emit scalar form.
- C(i) = C(i) + A(i, r_j) * B(r_j);
- }
-};
-
+void emitScalarImplementation(ArrayRef<Value> allIvs, DotOp dotOp) {
+ assert(dotOp.hasBufferSemantics() &&
+ "expected linalg op with buffer semantics");
+ assert(allIvs.size() == 1);
+ Value r_i(allIvs[0]);
+ IndexedValueType A(dotOp.getInput(0)), B(dotOp.getInput(1)),
+ C(dotOp.getOutputBuffer(0));
+ // Emit scalar form.
+ C() = C() + A(r_i) * B(r_i);
+}
template <typename IndexedValueType>
-class LinalgScopedEmitter<IndexedValueType, MatmulOp> {
-public:
- static void emitScalarImplementation(ArrayRef<Value> allIvs,
- MatmulOp matmulOp) {
- assert(matmulOp.hasBufferSemantics() &&
- "expected linalg op with buffer semantics");
- assert(allIvs.size() == 3);
- Value i(allIvs[0]), j(allIvs[1]), r_k(allIvs[2]);
- IndexedValueType A(matmulOp.getInput(0)), B(matmulOp.getInput(1)),
- C(matmulOp.getOutputBuffer(0));
- // Emit scalar form.
- C(i, j) = C(i, j) + A(i, r_k) * B(r_k, j);
- }
-};
+void emitScalarImplementation(ArrayRef<Value> allIvs, MatvecOp matvecOp) {
+ assert(matvecOp.hasBufferSemantics() &&
+ "expected linalg op with buffer semantics");
+ assert(allIvs.size() == 2);
+ Value i(allIvs[0]), r_j(allIvs[1]);
+ IndexedValueType A(matvecOp.getInput(0)), B(matvecOp.getInput(1)),
+ C(matvecOp.getOutputBuffer(0));
+ // Emit scalar form.
+ C(i) = C(i) + A(i, r_j) * B(r_j);
+}
template <typename IndexedValueType>
-class LinalgScopedEmitter<IndexedValueType, ConvOp> {
-public:
- /// Returns the input value of convOp. If the indices in `imIdx` is out of
- /// boundary, returns 0 instead.
- static Value getConvOpInput(ConvOp convOp, StdIndexedValue im,
- MutableArrayRef<Value> imIdx) {
- // TODO(ntv): add a level of indirection to linalg.generic.
- if (!convOp.padding())
- return im(imIdx);
-
- auto *context = ScopedContext::getContext();
- Value zeroIndex = std_constant_index(0);
- SmallVector<Value, 8> conds;
- SmallVector<Value, 8> clampedImIdx;
- for (auto iter : llvm::enumerate(imIdx)) {
- int idx = iter.index();
- auto dim = iter.value();
- // Only need to iterate over the window dimensions.
- if (idx == 0 || idx == static_cast<int>(imIdx.size()) - 1) {
- clampedImIdx.push_back(dim);
- continue;
- }
-
- using edsc::op::operator<;
- using edsc::op::operator>=;
- using edsc::op::operator||;
- Value leftOutOfBound = dim < zeroIndex;
- if (conds.empty())
- conds.push_back(leftOutOfBound);
- else
- conds.push_back(conds.back() || leftOutOfBound);
- Value rightBound = std_dim(convOp.input(), idx);
- conds.push_back(conds.back() || (dim >= rightBound));
-
- // When padding is involved, the indices will only be shifted to negative,
- // so having a max op is enough.
- auto maxMap = AffineMap::get(/*dimCount=*/1, 0,
- {getAffineDimExpr(/*position=*/0, context),
- getAffineConstantExpr(0, context)},
- context);
- clampedImIdx.push_back(
- affine_max(dim.getType(), maxMap, ValueRange{dim}));
+Value getConvOpInput(ConvOp convOp, StdIndexedValue im,
+ MutableArrayRef<Value> imIdx) {
+ // TODO(ntv): add a level of indirection to linalg.generic.
+ if (!convOp.padding())
+ return im(imIdx);
+
+ auto *context = ScopedContext::getContext();
+ Value zeroIndex = std_constant_index(0);
+ SmallVector<Value, 8> conds;
+ SmallVector<Value, 8> clampedImIdx;
+ for (auto iter : llvm::enumerate(imIdx)) {
+ int idx = iter.index();
+ auto dim = iter.value();
+ // Only need to iterate over the window dimensions.
+ if (idx == 0 || idx == static_cast<int>(imIdx.size()) - 1) {
+ clampedImIdx.push_back(dim);
+ continue;
}
- auto &b = ScopedContext::getBuilderRef();
- Type type = convOp.input().getType().cast<MemRefType>().getElementType();
- Value zero = std_constant(type, b.getZeroAttr(type));
- Value readInput = im(clampedImIdx);
- return conds.empty() ? readInput
- : (Value)std_select(conds.back(), zero, readInput);
+ using edsc::op::operator<;
+ using edsc::op::operator>=;
+ using edsc::op::operator||;
+ Value leftOutOfBound = dim < zeroIndex;
+ if (conds.empty())
+ conds.push_back(leftOutOfBound);
+ else
+ conds.push_back(conds.back() || leftOutOfBound);
+ Value rightBound = std_dim(convOp.input(), idx);
+ conds.push_back(conds.back() || (dim >= rightBound));
+
+ // When padding is involved, the indices will only be shifted to negative,
+ // so having a max op is enough.
+ auto maxMap = AffineMap::get(/*dimCount=*/1, 0,
+ {getAffineDimExpr(/*position=*/0, context),
+ getAffineConstantExpr(0, context)},
+ context);
+ clampedImIdx.push_back(affine_max(dim.getType(), maxMap, ValueRange{dim}));
}
- /// Returns true is `convOp` has a non-zero padding.
- static bool hasPadding(ConvOp convOp) {
- for (unsigned i = 0, e = convOp.getNumSpatialDimensions(); i < e; ++i) {
- if (convOp.getLowPad(i) > 0 || convOp.getHighPad(i) > 0)
- return true;
- }
- return false;
- }
+ auto &b = ScopedContext::getBuilderRef();
+ Type type = convOp.input().getType().cast<MemRefType>().getElementType();
+ Value zero = std_constant(type, b.getZeroAttr(type));
+ Value readInput = im(clampedImIdx);
+ return conds.empty() ? readInput
+ : (Value)std_select(conds.back(), zero, readInput);
+}
- static void emitScalarImplementation(ArrayRef<Value> allIvs, ConvOp convOp) {
- assert(convOp.hasBufferSemantics() &&
- "expected linalg op with buffer semantics");
- auto &b = ScopedContext::getBuilderRef();
- auto loc = ScopedContext::getLocation();
- auto mapsRange = convOp.indexing_maps().getAsRange<AffineMapAttr>();
- auto maps = llvm::to_vector<8>(llvm::map_range(
- mapsRange, [](AffineMapAttr a) { return a.getValue(); }));
- SmallVector<Value, 8> fIdx(
- makeCanonicalAffineApplies(b, loc, maps[0], allIvs));
- SmallVector<Value, 8> imIdx(
- makeCanonicalAffineApplies(b, loc, maps[1], allIvs));
- SmallVector<Value, 8> oIdx(
- makeCanonicalAffineApplies(b, loc, maps[2], allIvs));
-
- IndexedValueType F(convOp.filter()), O(convOp.output());
-
- // Emit scalar form. Padded conv involves an affine.max in the memory access
- // which is not allowed by affine.load. Override to use an StdIndexedValue
- // when there is non-zero padding.
- if (hasPadding(convOp)) {
- StdIndexedValue I(convOp.input());
- Value paddedInput = getConvOpInput(convOp, I, imIdx);
- O(oIdx) += F(fIdx) * paddedInput;
- } else {
- IndexedValueType I(convOp.input());
- O(oIdx) += F(fIdx) * I(imIdx);
- }
+/// Returns true is `convOp` has a non-zero padding.
+static bool hasPadding(ConvOp convOp) {
+ for (unsigned i = 0, e = convOp.getNumSpatialDimensions(); i < e; ++i) {
+ if (convOp.getLowPad(i) > 0 || convOp.getHighPad(i) > 0)
+ return true;
}
-};
+ return false;
+}
template <typename IndexedValueType>
-class LinalgScopedEmitter<IndexedValueType, PoolingMaxOp> {
-public:
- static void emitScalarImplementation(ArrayRef<Value> allIvs,
- PoolingMaxOp op) {
- auto indices = getInputAndOutputIndices(allIvs, op);
- // Emit scalar form.
- Value lhs = std_load(op.output(), indices.outputs);
- Value rhs = std_load(op.input(), indices.inputs);
- using edsc::op::operator>;
- Value maxValue = std_select(lhs > rhs, lhs, rhs);
- std_store(maxValue, op.output(), indices.outputs);
+static void emitScalarImplementation(ArrayRef<Value> allIvs, ConvOp convOp) {
+ assert(convOp.hasBufferSemantics() &&
+ "expected linalg op with buffer semantics");
+ auto &b = ScopedContext::getBuilderRef();
+ auto loc = ScopedContext::getLocation();
+ auto mapsRange = convOp.indexing_maps().getAsRange<AffineMapAttr>();
+ auto maps = llvm::to_vector<8>(
+ llvm::map_range(mapsRange, [](AffineMapAttr a) { return a.getValue(); }));
+ SmallVector<Value, 8> fIdx(
+ makeCanonicalAffineApplies(b, loc, maps[0], allIvs));
+ SmallVector<Value, 8> imIdx(
+ makeCanonicalAffineApplies(b, loc, maps[1], allIvs));
+ SmallVector<Value, 8> oIdx(
+ makeCanonicalAffineApplies(b, loc, maps[2], allIvs));
+
+ IndexedValueType F(convOp.filter()), O(convOp.output());
+
+ // Emit scalar form. Padded conv involves an affine.max in the memory access
+ // which is not allowed by affine.load. Override to use an StdIndexedValue
+ // when there is non-zero padding.
+ if (hasPadding(convOp)) {
+ StdIndexedValue I(convOp.input());
+ Value paddedInput = getConvOpInput<IndexedValueType>(convOp, I, imIdx);
+ O(oIdx) += F(fIdx) * paddedInput;
+ } else {
+ IndexedValueType I(convOp.input());
+ O(oIdx) += F(fIdx) * I(imIdx);
}
-};
+}
template <typename IndexedValueType>
-class LinalgScopedEmitter<IndexedValueType, PoolingMinOp> {
-public:
- static void emitScalarImplementation(ArrayRef<Value> allIvs,
- PoolingMinOp op) {
- auto indices = getInputAndOutputIndices(allIvs, op);
- // Emit scalar form.
- Value lhs = std_load(op.output(), indices.outputs);
- Value rhs = std_load(op.input(), indices.inputs);
- using edsc::op::operator<;
- Value minValue = std_select(lhs < rhs, lhs, rhs);
- std_store(minValue, op.output(), indices.outputs);
- }
-};
-
+void emitScalarImplementation(ArrayRef<Value> allIvs, PoolingMaxOp op) {
+ auto indices = getInputAndOutputIndices(allIvs, op);
+ // Emit scalar form.
+ Value lhs = std_load(op.output(), indices.outputs);
+ Value rhs = std_load(op.input(), indices.inputs);
+ using edsc::op::operator>;
+ Value maxValue = std_select(lhs > rhs, lhs, rhs);
+ std_store(maxValue, op.output(), indices.outputs);
+}
template <typename IndexedValueType>
-class LinalgScopedEmitter<IndexedValueType, PoolingSumOp> {
-public:
- static void emitScalarImplementation(ArrayRef<Value> allIvs,
- PoolingSumOp op) {
- auto indices = getInputAndOutputIndices(allIvs, op);
- IndexedValueType input(op.input()), output(op.output());
-
- // Emit scalar form.
- output(indices.outputs) += input(indices.inputs);
- }
-};
+void emitScalarImplementation(ArrayRef<Value> allIvs, PoolingMinOp op) {
+ auto indices = getInputAndOutputIndices(allIvs, op);
+ // Emit scalar form.
+ Value lhs = std_load(op.output(), indices.outputs);
+ Value rhs = std_load(op.input(), indices.inputs);
+ using edsc::op::operator<;
+ Value minValue = std_select(lhs < rhs, lhs, rhs);
+ std_store(minValue, op.output(), indices.outputs);
+}
+template <typename IndexedValueType>
+void emitScalarImplementation(ArrayRef<Value> allIvs, PoolingSumOp op) {
+ auto indices = getInputAndOutputIndices(allIvs, op);
+ IndexedValueType input(op.input()), output(op.output());
+ // Emit scalar form.
+ output(indices.outputs) += input(indices.inputs);
+}
/// Emits the MLIR for the scalar part of the indexed generic op by:
/// 1. Emitting load ops for each input and output view in order. This is
/// achieved by applying the appropriate input or output map to the
@@ -451,55 +401,52 @@ class LinalgScopedEmitter<IndexedValueType, PoolingSumOp> {
/// }
/// ```
template <typename IndexedValueType>
-class LinalgScopedEmitter<IndexedValueType, IndexedGenericOp> {
-public:
- static void emitScalarImplementation(ArrayRef<Value> allIvs,
- IndexedGenericOp indexedGenericOp) {
- assert(indexedGenericOp.hasBufferSemantics() &&
- "expected linalg op with buffer semantics");
- auto &b = ScopedContext::getBuilderRef();
- auto loc = ScopedContext::getLocation();
- unsigned nInputs = indexedGenericOp.getNumInputs();
- unsigned nOutputs = indexedGenericOp.getNumOutputs();
- unsigned nLoops = allIvs.size();
- SmallVector<Value, 4> indexedValues;
- indexedValues.reserve(nLoops + nInputs + nOutputs);
- for (unsigned i = 0; i < nLoops; ++i)
- indexedValues.push_back(allIvs[i]);
-
- // TODO(mravishankar): Avoid the loads if the corresponding argument of the
- // region has no uses.
- // 1.a. Emit load from input views.
- for (unsigned i = 0; i < nInputs; ++i) {
- auto indexing = makeCanonicalAffineApplies(
- b, loc, indexedGenericOp.getInputIndexingMap(i), allIvs);
- // Pass input i through IndexedValueType emits the proper load operation.
- indexedValues.push_back(
- IndexedValueType(indexedGenericOp.getInput(i))(indexing));
- }
- // 1.b. Emit load from output views.
- for (unsigned i = 0; i < nOutputs; ++i) {
- auto indexing = makeCanonicalAffineApplies(
- b, loc, indexedGenericOp.getOutputIndexingMap(i), allIvs);
- // Pass output i through IndexedValueType emits the proper load operation.
- indexedValues.push_back(
- IndexedValueType(indexedGenericOp.getOutputBuffer(i))(indexing));
- }
+static void emitScalarImplementation(ArrayRef<Value> allIvs,
+ IndexedGenericOp indexedGenericOp) {
+ assert(indexedGenericOp.hasBufferSemantics() &&
+ "expected linalg op with buffer semantics");
+ auto &b = ScopedContext::getBuilderRef();
+ auto loc = ScopedContext::getLocation();
+ unsigned nInputs = indexedGenericOp.getNumInputs();
+ unsigned nOutputs = indexedGenericOp.getNumOutputs();
+ unsigned nLoops = allIvs.size();
+ SmallVector<Value, 4> indexedValues;
+ indexedValues.reserve(nLoops + nInputs + nOutputs);
+ for (unsigned i = 0; i < nLoops; ++i)
+ indexedValues.push_back(allIvs[i]);
+
+ // TODO(mravishankar): Avoid the loads if the corresponding argument of the
+ // region has no uses.
+ // 1.a. Emit load from input views.
+ for (unsigned i = 0; i < nInputs; ++i) {
+ auto indexing = makeCanonicalAffineApplies(
+ b, loc, indexedGenericOp.getInputIndexingMap(i), allIvs);
+ // Pass input i through IndexedValueType emits the proper load operation.
+ indexedValues.push_back(
+ IndexedValueType(indexedGenericOp.getInput(i))(indexing));
+ }
+ // 1.b. Emit load from output views.
+ for (unsigned i = 0; i < nOutputs; ++i) {
+ auto indexing = makeCanonicalAffineApplies(
+ b, loc, indexedGenericOp.getOutputIndexingMap(i), allIvs);
+ // Pass output i through IndexedValueType emits the proper load operation.
+ indexedValues.push_back(
+ IndexedValueType(indexedGenericOp.getOutputBuffer(i))(indexing));
+ }
- // TODO(ntv): When a region inliner exists, use it.
- // 2. Inline region, currently only works for a single basic block.
- // 3. Emit store.
- SmallVector<SmallVector<Value, 8>, 8> indexing;
- SmallVector<Value, 8> outputBuffers;
- for (unsigned i = 0; i < nOutputs; ++i) {
- indexing.push_back(makeCanonicalAffineApplies(
- b, loc, indexedGenericOp.getOutputIndexingMap(i), allIvs));
- outputBuffers.push_back(indexedGenericOp.getOutputBuffer(i));
- }
- inlineRegionAndEmitStore<IndexedValueType>(indexedGenericOp, indexedValues,
- indexing, outputBuffers);
+ // TODO(ntv): When a region inliner exists, use it.
+ // 2. Inline region, currently only works for a single basic block.
+ // 3. Emit store.
+ SmallVector<SmallVector<Value, 8>, 8> indexing;
+ SmallVector<Value, 8> outputBuffers;
+ for (unsigned i = 0; i < nOutputs; ++i) {
+ indexing.push_back(makeCanonicalAffineApplies(
+ b, loc, indexedGenericOp.getOutputIndexingMap(i), allIvs));
+ outputBuffers.push_back(indexedGenericOp.getOutputBuffer(i));
}
-};
+ inlineRegionAndEmitStore<IndexedValueType>(indexedGenericOp, indexedValues,
+ indexing, outputBuffers);
+}
template <typename LoopTy, typename ConcreteOpTy>
Optional<LinalgLoops> linalgOpToLoopsImpl(Operation *op, OpBuilder &builder) {
@@ -524,8 +471,7 @@ Optional<LinalgLoops> linalgOpToLoopsImpl(Operation *op, OpBuilder &builder) {
if (!invertedMap)
return {};
if (invertedMap.isEmpty()) {
- LinalgScopedEmitter<IndexedValueTy, ConcreteOpTy>::emitScalarImplementation(
- {}, linalgOp);
+ emitScalarImplementation<IndexedValueTy>({}, linalgOp);
return LinalgLoops();
}
@@ -537,9 +483,7 @@ Optional<LinalgLoops> linalgOpToLoopsImpl(Operation *op, OpBuilder &builder) {
GenerateLoopNest<LoopTy>::doit(
allIvs, loopRanges, linalgOp.iterator_types().getValue(), [&] {
SmallVector<Value, 4> allIvValues(allIvs.begin(), allIvs.end());
- LinalgScopedEmitter<IndexedValueTy,
- ConcreteOpTy>::emitScalarImplementation(allIvValues,
- linalgOp);
+ emitScalarImplementation<IndexedValueTy>(allIvValues, linalgOp);
});
// Number of loop ops might be
diff erent from the number of ivs since some
// loops like affine.parallel and scf.parallel have multiple ivs.
@@ -573,32 +517,15 @@ class LinalgRewritePattern : public RewritePattern {
}
};
-/// Helper classes for type list expansion.
-template <typename LoopType, typename... LinalgOps>
-class RewritePatternList;
-
-template <typename LoopType>
-class RewritePatternList<LoopType> {
-public:
- static void build(OwningRewritePatternList &patterns, MLIRContext *ctx) {}
-};
-
-template <typename LoopType, typename ConcreteOp, typename... LinalgOps>
-class RewritePatternList<LoopType, ConcreteOp, LinalgOps...> {
-public:
- static void build(OwningRewritePatternList &patterns, MLIRContext *ctx) {
- patterns.insert<LinalgRewritePattern<LoopType, ConcreteOp>>(ctx);
- RewritePatternList<LoopType, LinalgOps...>::build(patterns, ctx);
- }
-};
+template <typename LoopType, typename ConcreteOp>
+void insertOnePattern(OwningRewritePatternList &patterns, MLIRContext *ctx) {
+ patterns.insert<LinalgRewritePattern<LoopType, ConcreteOp>>(ctx);
+}
-/// Populate the given list with patterns that convert from Linalg to loops.
-template <typename LoopType>
-void FillRewritePatterns(OwningRewritePatternList &patterns, MLIRContext *ctx) {
- RewritePatternList<LoopType,
-#define GET_OP_LIST
-#include "mlir/Dialect/Linalg/IR/LinalgStructuredOps.cpp.inc"
- >::build(patterns, ctx);
+template <typename LoopType, typename... Args>
+void insertPatterns(OwningRewritePatternList &patterns, MLIRContext *ctx) {
+ (void)std::initializer_list<int>{
+ 0, (insertOnePattern<LoopType, Args>(patterns, ctx), 0)...};
}
/// Local folding pattern for AffineApplyOp that we can apply greedily.
@@ -640,17 +567,21 @@ struct FoldAffineOp : public RewritePattern {
} // namespace
template <typename LoopType>
-static void lowerLinalgToLoopsImpl(Operation *op, MLIRContext *context) {
+static void lowerLinalgToLoopsImpl(FuncOp funcOp, MLIRContext *context) {
OwningRewritePatternList patterns;
// Canonicalization and folding patterns applied greedily allow cleaning up
// the emitted IR on the fly.
// TODO(ntv) fold view and subview ops?
- FillRewritePatterns<LoopType>(patterns, context);
+ insertPatterns<LoopType,
+#define GET_OP_LIST
+#include "mlir/Dialect/Linalg/IR/LinalgStructuredOps.cpp.inc"
+ >(patterns, context);
+
DimOp::getCanonicalizationPatterns(patterns, context);
AffineApplyOp::getCanonicalizationPatterns(patterns, context);
patterns.insert<FoldAffineOp>(context);
// Just apply the patterns greedily.
- applyPatternsAndFoldGreedily(op, patterns);
+ applyPatternsAndFoldGreedily(funcOp, patterns);
}
namespace {
@@ -687,60 +618,74 @@ mlir::createConvertLinalgToAffineLoopsPass() {
return std::make_unique<LowerToAffineLoops>();
}
+// TODO: gradually remove this layer as more ops become "named".
+template <typename LoopTy>
+Optional<LinalgLoops> linalgOpToLoopsImplSwitch(Operation *op,
+ OpBuilder &builder) {
+ assert(isa<LinalgOp>(op) && "LinalgOp expected");
+ if (isa<CopyOp>(op))
+ return linalgOpToLoopsImpl<LoopTy, CopyOp>(op, builder);
+ if (isa<FillOp>(op))
+ return linalgOpToLoopsImpl<LoopTy, FillOp>(op, builder);
+ if (isa<DotOp>(op))
+ return linalgOpToLoopsImpl<LoopTy, DotOp>(op, builder);
+ if (isa<MatvecOp>(op))
+ return linalgOpToLoopsImpl<LoopTy, MatvecOp>(op, builder);
+ if (isa<ConvOp>(op))
+ return linalgOpToLoopsImpl<LoopTy, ConvOp>(op, builder);
+ if (isa<PoolingMaxOp>(op))
+ return linalgOpToLoopsImpl<LoopTy, PoolingMaxOp>(op, builder);
+ if (isa<PoolingMinOp>(op))
+ return linalgOpToLoopsImpl<LoopTy, PoolingMinOp>(op, builder);
+ if (isa<PoolingSumOp>(op))
+ return linalgOpToLoopsImpl<LoopTy, PoolingSumOp>(op, builder);
+ if (isa<IndexedGenericOp>(op))
+ return linalgOpToLoopsImpl<LoopTy, IndexedGenericOp>(op, builder);
+
+ // TODO: Cases below are generic and need a LinalgStructuredOpInterface.
+ if (isa<GenericOp>(op))
+ return linalgOpToLoopsImpl<LoopTy, GenericOp>(op, builder);
+ if (isa<MatmulOp>(op))
+ return linalgOpToLoopsImpl<LoopTy, MatmulOp>(op, builder);
+ if (isa<BatchMatmulOp>(op))
+ return linalgOpToLoopsImpl<LoopTy, BatchMatmulOp>(op, builder);
+ llvm_unreachable("Unexpected op in linalgOpToLoopsImpl");
+}
+
/// Emits a loop nest with the proper body for `op`.
-template <typename LoopTy, typename ConcreteOp>
+template <typename LoopTy>
Optional<LinalgLoops> mlir::linalg::linalgLowerOpToLoops(OpBuilder &builder,
Operation *op) {
- return linalgOpToLoopsImpl<LoopTy, ConcreteOp>(op, builder);
+ return linalgOpToLoopsImplSwitch<LoopTy>(op, builder);
}
-/// Emits a loop nest of `scf.for` with the proper body for `op`.
-template <typename ConcreteOp>
-LogicalResult mlir::linalg::linalgOpToLoops(OpBuilder &builder, Operation *op) {
- Optional<LinalgLoops> loops =
- linalgLowerOpToLoops<scf::ForOp, ConcreteOp>(builder, op);
- return loops ? success() : failure();
-}
+template Optional<LinalgLoops>
+mlir::linalg::linalgLowerOpToLoops<AffineForOp>(OpBuilder &builder,
+ Operation *op);
+template Optional<LinalgLoops>
+mlir::linalg::linalgLowerOpToLoops<scf::ForOp>(OpBuilder &builder,
+ Operation *op);
+template Optional<LinalgLoops>
+mlir::linalg::linalgLowerOpToLoops<scf::ParallelOp>(OpBuilder &builder,
+ Operation *op);
/// Emits a loop nest of `affine.for` with the proper body for `op`.
-template <typename ConcreteOp>
LogicalResult mlir::linalg::linalgOpToAffineLoops(OpBuilder &builder,
Operation *op) {
- Optional<LinalgLoops> loops =
- linalgLowerOpToLoops<AffineForOp, ConcreteOp>(builder, op);
+ Optional<LinalgLoops> loops = linalgLowerOpToLoops<AffineForOp>(builder, op);
+ return loops ? success() : failure();
+}
+
+/// Emits a loop nest of `scf.for` with the proper body for `op`.
+LogicalResult mlir::linalg::linalgOpToLoops(OpBuilder &builder, Operation *op) {
+ Optional<LinalgLoops> loops = linalgLowerOpToLoops<scf::ForOp>(builder, op);
return loops ? success() : failure();
}
/// Emits a loop nest of `scf.parallel` with the proper body for `op`.
-template <typename ConcreteOp>
LogicalResult mlir::linalg::linalgOpToParallelLoops(OpBuilder &builder,
Operation *op) {
Optional<LinalgLoops> loops =
- linalgLowerOpToLoops<scf::ParallelOp, ConcreteOp>(builder, op);
+ linalgLowerOpToLoops<scf::ParallelOp>(builder, op);
return loops ? success() : failure();
}
-
-// TODO Need to make these instantiations more future-proof to avoid the need to
-// update as soon as we add new ops.
-#define INSTANTIATE_LINALG_OP_TO_LOOPS(OP_TYPE) \
- template LogicalResult mlir::linalg::linalgOpToLoops<OP_TYPE>( \
- OpBuilder & builder, Operation * op); \
- template LogicalResult mlir::linalg::linalgOpToAffineLoops<OP_TYPE>( \
- OpBuilder & builder, Operation * op); \
- template LogicalResult mlir::linalg::linalgOpToParallelLoops<OP_TYPE>( \
- OpBuilder & builder, Operation * op); \
- template Optional<LinalgLoops> \
- mlir::linalg::linalgLowerOpToLoops<scf::ParallelOp, OP_TYPE>( \
- OpBuilder & builder, Operation * op);
-
-INSTANTIATE_LINALG_OP_TO_LOOPS(CopyOp)
-INSTANTIATE_LINALG_OP_TO_LOOPS(FillOp)
-INSTANTIATE_LINALG_OP_TO_LOOPS(DotOp)
-INSTANTIATE_LINALG_OP_TO_LOOPS(MatvecOp)
-INSTANTIATE_LINALG_OP_TO_LOOPS(MatmulOp)
-INSTANTIATE_LINALG_OP_TO_LOOPS(ConvOp)
-INSTANTIATE_LINALG_OP_TO_LOOPS(PoolingMaxOp)
-INSTANTIATE_LINALG_OP_TO_LOOPS(PoolingMinOp)
-INSTANTIATE_LINALG_OP_TO_LOOPS(PoolingSumOp)
-INSTANTIATE_LINALG_OP_TO_LOOPS(GenericOp)
-INSTANTIATE_LINALG_OP_TO_LOOPS(IndexedGenericOp)
diff --git a/mlir/test/Dialect/Linalg/affine.mlir b/mlir/test/Dialect/Linalg/affine.mlir
index 5d20b0b4e0a9..cb2064602c47 100644
--- a/mlir/test/Dialect/Linalg/affine.mlir
+++ b/mlir/test/Dialect/Linalg/affine.mlir
@@ -15,7 +15,7 @@ func @matmul(%arg0: memref<?xi8>, %M: index, %N: index, %K: index) {
%A = view %arg0[%c0][%M, %K] : memref<?xi8> to memref<?x?xf32>
%B = view %arg0[%c0][%K, %N] : memref<?xi8> to memref<?x?xf32>
%C = view %arg0[%c0][%M, %N] : memref<?xi8> to memref<?x?xf32>
- linalg.matmul(%A, %B, %C) : memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>
+ linalg.matmul %A, %B, %C : (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>)
return
}
diff --git a/mlir/test/Dialect/Linalg/canonicalize.mlir b/mlir/test/Dialect/Linalg/canonicalize.mlir
index 914c7b7ce345..9cb7df05d63e 100644
--- a/mlir/test/Dialect/Linalg/canonicalize.mlir
+++ b/mlir/test/Dialect/Linalg/canonicalize.mlir
@@ -14,8 +14,8 @@ func @memref_cast(%a: index, %b: index) -> memref<?x?xf32> {
// CHECK: linalg.slice {{.*}} : memref<16x16xf32>, !linalg.range, !linalg.range, memref<?x?xf32>
%4 = linalg.slice %3[%r0, %r0] : memref<?x?xf32>, !linalg.range, !linalg.range, memref<?x?xf32>
- // CHECK: linalg.matmul{{.*}}: memref<16x16xf32>, memref<16x16xf32>, memref<16x16xf32>
- linalg.matmul(%3, %3, %3) : memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>
+ // CHECK: linalg.matmul{{.*}}: (memref<16x16xf32>, memref<16x16xf32>, memref<16x16xf32>)
+ linalg.matmul %3, %3, %3 : (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>)
return %4: memref<?x?xf32>
}
diff --git a/mlir/test/Dialect/Linalg/fusion-2-level.mlir b/mlir/test/Dialect/Linalg/fusion-2-level.mlir
index 7be54f45b473..44dd268998d2 100644
--- a/mlir/test/Dialect/Linalg/fusion-2-level.mlir
+++ b/mlir/test/Dialect/Linalg/fusion-2-level.mlir
@@ -12,7 +12,7 @@ func @f1(%A: memref<?x?xf32, offset: ?, strides: [?, 1]>, %B: memref<?x?xf32, of
%0 = dim %C, %c0 : memref<?x?xf32, offset: ?, strides: [?, 1]>
%1 = dim %C, %c1 : memref<?x?xf32, offset: ?, strides: [?, 1]>
%2 = dim %D, %c1 : memref<?x?xf32, offset: ?, strides: [?, 1]>
- linalg.matmul(%A, %B, %C) : memref<?x?xf32, offset: ?, strides: [?, 1]>, memref<?x?xf32, offset: ?, strides: [?, 1]>, memref<?x?xf32, offset: ?, strides: [?, 1]>
+ linalg.matmul %A, %B, %C : (memref<?x?xf32, offset: ?, strides: [?, 1]>, memref<?x?xf32, offset: ?, strides: [?, 1]>, memref<?x?xf32, offset: ?, strides: [?, 1]>)
scf.for %arg5 = %c0 to %0 step %c20 {
scf.for %arg6 = %c0 to %2 step %c30 {
scf.for %arg7 = %c0 to %1 step %c40 {
@@ -28,7 +28,7 @@ func @f1(%A: memref<?x?xf32, offset: ?, strides: [?, 1]>, %B: memref<?x?xf32, of
%14 = std.subview %5[%arg8, %arg10][%c2, %c4][%c1, %c1] : memref<?x?xf32, offset: ?, strides: [?, ?]> to memref<?x?xf32, offset: ?, strides: [?, ?]>
%16 = std.subview %7[%arg10, %arg9][%c4, %c3][%c1, %c1]: memref<?x?xf32, offset: ?, strides: [?, ?]> to memref<?x?xf32, offset: ?, strides: [?, ?]>
%17 = std.subview %8[%arg8, %arg9][%c2, %c4][%c1, %c1] : memref<?x?xf32, offset: ?, strides: [?, ?]> to memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%14, %16, %17) : memref<?x?xf32, offset: ?, strides: [?, ?]>, memref<?x?xf32, offset: ?, strides: [?, ?]>, memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %14, %16, %17 : (memref<?x?xf32, offset: ?, strides: [?, ?]>, memref<?x?xf32, offset: ?, strides: [?, ?]>, memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
diff --git a/mlir/test/Dialect/Linalg/fusion.mlir b/mlir/test/Dialect/Linalg/fusion.mlir
index da6bd26ef1de..7fb5a7ab4e85 100644
--- a/mlir/test/Dialect/Linalg/fusion.mlir
+++ b/mlir/test/Dialect/Linalg/fusion.mlir
@@ -14,10 +14,10 @@ func @f1(%A: memref<?x?xf32, offset: 0, strides: [?, 1]>,
%0 = dim %A, %c0 : memref<?x?xf32, offset: 0, strides: [?, 1]>
%1 = dim %A, %c1 : memref<?x?xf32, offset: 0, strides: [?, 1]>
%2 = dim %B, %c1 : memref<?x?xf32, offset: 0, strides: [?, 1]>
- linalg.matmul(%A, %B, %C) :
- memref<?x?xf32, offset: 0, strides: [?, 1]>,
- memref<?x?xf32, offset: 0, strides: [?, 1]>,
- memref<?x?xf32, offset: 0, strides: [?, 1]>
+ linalg.matmul %A, %B, %C :
+ (memref<?x?xf32, offset: 0, strides: [?, 1]>,
+ memref<?x?xf32, offset: 0, strides: [?, 1]>,
+ memref<?x?xf32, offset: 0, strides: [?, 1]>)
scf.for %arg5 = %c0 to %0 step %c2 {
scf.for %arg6 = %c0 to %2 step %c3 {
scf.for %arg7 = %c0 to %1 step %c4 {
@@ -30,10 +30,10 @@ func @f1(%A: memref<?x?xf32, offset: 0, strides: [?, 1]>,
%8 = std.subview %C[%arg5, %arg6][%c2, %c3][%c1, %c1] :
memref<?x?xf32, offset: 0, strides: [?, 1]> to
memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%5, %7, %8) :
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %5, %7, %8 :
+ (memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
@@ -61,10 +61,10 @@ func @f2(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%c4 = constant 4 : index
%c3 = constant 3 : index
%c2 = constant 2 : index
- linalg.matmul(%A, %B, %C) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
+ linalg.matmul %A, %B, %C :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
%0 = dim %C, %c0 : memref<?x?xf32, offset: 0, strides: [?, ?]>
%1 = dim %C, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
%2 = dim %D, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
@@ -80,10 +80,10 @@ func @f2(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%8 = std.subview %E[%arg5, %arg6][%c2, %c3][%c1, %c1] :
memref<?x?xf32, offset: 0, strides: [?, ?]> to
memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%5, %7, %8) :
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %5, %7, %8 :
+ (memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
@@ -113,10 +113,10 @@ func @f3(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%c4 = constant 4 : index
%c3 = constant 3 : index
%c2 = constant 2 : index
- linalg.matmul(%A, %B, %C) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
+ linalg.matmul %A, %B, %C :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
%0 = dim %D, %c0 : memref<?x?xf32, offset: 0, strides: [?, ?]>
%1 = dim %D, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
%2 = dim %C, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
@@ -132,10 +132,10 @@ func @f3(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%8 = std.subview %E[%arg5, %arg6][%c2, %c3][%c1, %c1] :
memref<?x?xf32, offset: 0, strides: [?, ?]> to
memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%5, %7, %8) :
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %5, %7, %8 :
+ (memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
@@ -165,14 +165,14 @@ func @f4(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%c4 = constant 4 : index
%c3 = constant 3 : index
%c2 = constant 2 : index
- linalg.matmul(%A, %B, %C) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
- linalg.matmul(%A, %B, %D) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
+ linalg.matmul %A, %B, %C :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
+ linalg.matmul %A, %B, %D :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
%0 = dim %C, %c0 : memref<?x?xf32, offset: 0, strides: [?, ?]>
%1 = dim %C, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
%2 = dim %D, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
@@ -188,10 +188,10 @@ func @f4(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%8 = std.subview %E[%arg5, %arg6][%c2, %c3][%c1, %c1] :
memref<?x?xf32, offset: 0, strides: [?, ?]> to
memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%5, %7, %8) :
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %5, %7, %8 :
+ (memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
@@ -227,14 +227,14 @@ func @f5(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%0 = dim %B, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
%1 = dim %D, %c0 : memref<?x?xf32, offset: 0, strides: [?, ?]>
%2 = dim %D, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
- linalg.matmul(%A, %B, %C) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
- linalg.matmul(%C, %B, %D) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
+ linalg.matmul %A, %B, %C :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
+ linalg.matmul %C, %B, %D :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
scf.for %arg5 = %c0 to %1 step %c2 {
scf.for %arg6 = %c0 to %0 step %c3 {
scf.for %arg7 = %c0 to %2 step %c4 {
@@ -247,10 +247,10 @@ func @f5(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%8 = std.subview %E[%arg5, %arg6][%c2, %c3][%c1, %c1] :
memref<?x?xf32, offset: 0, strides: [?, ?]> to
memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%5, %7, %8) :
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %5, %7, %8 :
+ (memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
@@ -275,9 +275,9 @@ func @f5(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
// CHECK-DAG: %[[A_I0:.*]] = subview %[[A]][%[[I]], %{{.*}}]
// CHECK-DAG: %[[B_00:.*]] = subview %[[B]][%{{.*}}, %{{.*}}]
// CHECK-DAG: %[[C_I0_:.*]] = subview %[[C]][%[[I]], %{{.*}}]
-// CHECK: linalg.matmul(%[[A_I0]], %[[B_00]], %[[C_I0_]])
-// CHECK: linalg.matmul(%[[C_I0]], %[[B_0K]], %[[D_IK_]])
-// CHECK: linalg.matmul(%[[D_IK]], %[[B_KJ]], %[[E_IJ]])
+// CHECK: linalg.matmul %[[A_I0]], %[[B_00]], %[[C_I0_]]
+// CHECK: linalg.matmul %[[C_I0]], %[[B_0K]], %[[D_IK_]]
+// CHECK: linalg.matmul %[[D_IK]], %[[B_KJ]], %[[E_IJ]]
// -----
@@ -297,14 +297,14 @@ func @f6(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%c3 = constant 3 : index
%c2 = constant 2 : index
%0 = dim %C, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
- linalg.matmul(%A, %B, %C) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
- linalg.matmul(%A, %C, %E) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
+ linalg.matmul %A, %B, %C :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
+ linalg.matmul %A, %C, %E :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
%1 = dim %C, %c0 : memref<?x?xf32, offset: 0, strides: [?, ?]>
%2 = dim %D, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
scf.for %arg5 = %c0 to %1 step %c2 {
@@ -322,10 +322,10 @@ func @f6(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%8 = std.subview %E[%arg5, %arg6][%c2, %c3][%c1, %c1] :
memref<?x?xf32, offset: 0, strides: [?, ?]> to
memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%5, %7, %8) :
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %5, %7, %8 :
+ (memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
@@ -359,14 +359,14 @@ func @f7(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%2 = dim %C, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
%3 = dim %C, %c0 : memref<?x?xf32, offset: 0, strides: [?, ?]>
%4 = dim %D, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
- linalg.matmul(%A, %C, %E) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
- linalg.matmul(%A, %B, %C) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
+ linalg.matmul %A, %C, %E :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
+ linalg.matmul %A, %B, %C :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
scf.for %arg5 = %c0 to %0 step %c2 {
scf.for %arg6 = %c0 to %2 step %c3 {
scf.for %arg7 = %c0 to %1 step %c4 {
@@ -379,10 +379,10 @@ func @f7(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%10 = std.subview %E[%arg5, %arg6][%c2, %c3][%c1, %c1] :
memref<?x?xf32, offset: 0, strides: [?, ?]> to
memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%7, %9, %10) :
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %7, %9, %10 :
+ (memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
@@ -398,10 +398,10 @@ func @f7(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%10 = std.subview %E[%arg5, %arg6][%c2, %c3][%c1, %c1] :
memref<?x?xf32, offset: 0, strides: [?, ?]> to
memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%7, %9, %10) :
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %7, %9, %10 :
+ (memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
@@ -414,7 +414,7 @@ func @f7(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
// CHECK: %[[C_1:.*]] = dim %[[C]], %c1{{_[0-9]*}} : memref<?x?xf32, #[[$strided2D]]>
// CHECK: %[[C_0:.*]] = dim %[[C]], %c0{{_[0-9]*}} : memref<?x?xf32, #[[$strided2D]]>
// CHECK: %[[D_1:.*]] = dim %[[D]], %c1{{_[0-9]*}} : memref<?x?xf32, #[[$strided2D]]>
-// CHECK: linalg.matmul(%[[A]], %[[C]], %[[E]])
+// CHECK: linalg.matmul %[[A]], %[[C]], %[[E]]
// CHECK: scf.for %{{.*}} = %{{.*}} to %[[A_0]] step %{{.*}} {
// CHECK: scf.for %{{.*}} = %{{.*}} to %[[C_1]] step %{{.*}} {
// CHECK: scf.for %{{.*}} = %{{.*}} to %[[A_1]] step %{{.*}} {
@@ -445,14 +445,14 @@ func @f8(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%c2 = constant 2 : index
%0 = dim %A, %c0 : memref<?x?xf32, offset: 0, strides: [?, ?]>
%1 = dim %A, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
- linalg.matmul(%A, %C, %D) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
- linalg.matmul(%A, %B, %C) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
+ linalg.matmul %A, %C, %D :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
+ linalg.matmul %A, %B, %C :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
%2 = dim %D, %c1 : memref<?x?xf32, offset: 0, strides: [?, ?]>
scf.for %arg5 = %c0 to %0 step %c2 {
scf.for %arg6 = %c0 to %2 step %c3 {
@@ -469,10 +469,10 @@ func @f8(%A: memref<?x?xf32, offset: 0, strides: [?, ?]>,
%8 = std.subview %E[%arg5, %arg6][%c2, %c3][%c1, %c1] :
memref<?x?xf32, offset: 0, strides: [?, ?]> to
memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%5, %7, %8) :
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %5, %7, %8 :
+ (memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
@@ -742,10 +742,10 @@ func @accept_
diff erent_alloc_ops(%dim: index, %s0 : index, %s1: index) {
%B = alloca(%dim, %dim)[%s0, %s1] : memref<?x?xf32, offset: 0, strides: [?, ?]>
%C = alloc(%dim, %dim)[%s0, %s1] : memref<?x?xf32, offset: 0, strides: [?, ?]>
- linalg.matmul(%A, %B, %C) :
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>,
- memref<?x?xf32, offset: 0, strides: [?, ?]>
+ linalg.matmul %A, %B, %C :
+ (memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>,
+ memref<?x?xf32, offset: 0, strides: [?, ?]>)
scf.for %i = %c0 to %dim step %c2 {
scf.for %j = %c0 to %dim step %c3 {
@@ -759,10 +759,10 @@ func @accept_
diff erent_alloc_ops(%dim: index, %s0 : index, %s1: index) {
%2 = std.subview %C[%i, %j][%c2, %c3][%c1, %c1] :
memref<?x?xf32, offset: 0, strides: [?, ?]> to
memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%0, %1, %2) :
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %0, %1, %2 :
+ (memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
diff --git a/mlir/test/Dialect/Linalg/loops.mlir b/mlir/test/Dialect/Linalg/loops.mlir
index d1e86ba361c6..020e43da00b4 100644
--- a/mlir/test/Dialect/Linalg/loops.mlir
+++ b/mlir/test/Dialect/Linalg/loops.mlir
@@ -33,7 +33,7 @@ func @matmul(%arg0: memref<?xi8>, %M: index, %N: index, %K: index) {
%A = view %arg0[%c0][%M, %K] : memref<?xi8> to memref<?x?xf32>
%B = view %arg0[%c0][%K, %N] : memref<?xi8> to memref<?x?xf32>
%C = view %arg0[%c0][%M, %N] : memref<?xi8> to memref<?x?xf32>
- linalg.matmul(%A, %B, %C) : memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>
+ linalg.matmul %A, %B, %C : (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>)
return
}
// CHECKLOOP-LABEL: func @matmul(%{{.*}}: memref<?xi8>,
diff --git a/mlir/test/Dialect/Linalg/promote.mlir b/mlir/test/Dialect/Linalg/promote.mlir
index c4c5e00c42e7..fe36755345ef 100644
--- a/mlir/test/Dialect/Linalg/promote.mlir
+++ b/mlir/test/Dialect/Linalg/promote.mlir
@@ -26,7 +26,10 @@ func @matmul_f32(%A: memref<?xi8>, %M: index, %N: index, %K: index) {
%11 = std.subview %3[%arg4, %arg6][%c2, %c4][1, 1] : memref<?x?xf32> to memref<?x?xf32, offset: ?, strides: [?, 1]>
%14 = std.subview %4[%arg6, %arg5][%c4, %c3][1, 1] : memref<?x?xf32> to memref<?x?xf32, offset: ?, strides: [?, 1]>
%17 = std.subview %5[%arg4, %arg5][%c2, %c3][1, 1] : memref<?x?xf32> to memref<?x?xf32, offset: ?, strides: [?, 1]>
- linalg.matmul(%11, %14, %17) : memref<?x?xf32, offset: ?, strides: [?, 1]>, memref<?x?xf32, offset: ?, strides: [?, 1]>, memref<?x?xf32, offset: ?, strides: [?, 1]>
+ linalg.matmul %11, %14, %17 :
+ (memref<?x?xf32, offset: ?, strides: [?, 1]>,
+ memref<?x?xf32, offset: ?, strides: [?, 1]>,
+ memref<?x?xf32, offset: ?, strides: [?, 1]>)
}
}
}
@@ -60,9 +63,14 @@ func @matmul_f32(%A: memref<?xi8>, %M: index, %N: index, %K: index) {
// CHECK: linalg.copy(%[[vB]], %[[partialB]]) : memref<?x?xf32, #[[$strided2D]]>, memref<?x?xf32, #[[$strided2D_dynamic]]>
// CHECK: linalg.copy(%[[vC]], %[[partialC]]) : memref<?x?xf32, #[[$strided2D]]>, memref<?x?xf32, #[[$strided2D_dynamic]]>
//
-// CHECK: linalg.matmul(%[[partialA]], %[[partialB]], %[[partialC]]) : memref<?x?xf32, #[[$strided2D_dynamic]]>, memref<?x?xf32, #[[$strided2D_dynamic]]>, memref<?x?xf32, #[[$strided2D_dynamic]]>
+// CHECK: linalg.matmul %[[partialA]], %[[partialB]], %[[partialC]] :
+// CHECK: memref<?x?xf32, #[[$strided2D_dynamic]]>,
+// CHECK: memref<?x?xf32, #[[$strided2D_dynamic]]>,
+// CHECK: memref<?x?xf32, #[[$strided2D_dynamic]]>
//
-// CHECK: linalg.copy(%[[partialC]], %[[vC]]) : memref<?x?xf32, #[[$strided2D_dynamic]]>, memref<?x?xf32, #[[$strided2D]]>
+// CHECK: linalg.copy(%[[partialC]], %[[vC]]) :
+// CHECK: memref<?x?xf32, #[[$strided2D_dynamic]]>,
+// CHECK: memref<?x?xf32, #[[$strided2D]]>
//
// CHECK: dealloc %[[tmpA]] : memref<32xi8>
// CHECK: dealloc %[[tmpB]] : memref<48xi8>
@@ -88,7 +96,10 @@ func @matmul_f64(%A: memref<?xi8>, %M: index, %N: index, %K: index) {
%11 = std.subview %3[%arg4, %arg6][%c2, %c4][1, 1] : memref<?x?xf64> to memref<?x?xf64, offset: ?, strides: [?, 1]>
%14 = std.subview %4[%arg6, %arg5][%c4, %c3][1, 1] : memref<?x?xf64> to memref<?x?xf64, offset: ?, strides: [?, 1]>
%17 = std.subview %5[%arg4, %arg5][%c2, %c3][1, 1] : memref<?x?xf64> to memref<?x?xf64, offset: ?, strides: [?, 1]>
- linalg.matmul(%11, %14, %17) : memref<?x?xf64, offset: ?, strides: [?, 1]>, memref<?x?xf64, offset: ?, strides: [?, 1]>, memref<?x?xf64, offset: ?, strides: [?, 1]>
+ linalg.matmul %11, %14, %17 :
+ (memref<?x?xf64, offset: ?, strides: [?, 1]>,
+ memref<?x?xf64, offset: ?, strides: [?, 1]>,
+ memref<?x?xf64, offset: ?, strides: [?, 1]>)
}
}
}
@@ -122,72 +133,15 @@ func @matmul_f64(%A: memref<?xi8>, %M: index, %N: index, %K: index) {
// CHECK: linalg.copy(%[[vB_f64]], %[[partialB_f64]]) : memref<?x?xf64, #[[$strided2D]]>, memref<?x?xf64, #[[$strided2D_dynamic]]>
// CHECK: linalg.copy(%[[vC_f64]], %[[partialC_f64]]) : memref<?x?xf64, #[[$strided2D]]>, memref<?x?xf64, #[[$strided2D_dynamic]]>
//
-// CHECK: linalg.matmul(%[[partialA_f64]], %[[partialB_f64]], %[[partialC_f64]]) : memref<?x?xf64, #[[$strided2D_dynamic]]>, memref<?x?xf64, #[[$strided2D_dynamic]]>, memref<?x?xf64, #[[$strided2D_dynamic]]>
+// CHECK: linalg.matmul %[[partialA_f64]], %[[partialB_f64]], %[[partialC_f64]] :
+// CHECK: memref<?x?xf64, #[[$strided2D_dynamic]]>,
+// CHECK: memref<?x?xf64, #[[$strided2D_dynamic]]>,
+// CHECK: memref<?x?xf64, #[[$strided2D_dynamic]]>
//
-// CHECK: linalg.copy(%[[partialC_f64]], %[[vC_f64]]) : memref<?x?xf64, #[[$strided2D_dynamic]]>, memref<?x?xf64, #[[$strided2D]]>
+// CHECK: linalg.copy(%[[partialC_f64]], %[[vC_f64]]) :
+// CHECK: memref<?x?xf64, #[[$strided2D_dynamic]]>,
+// CHECK: memref<?x?xf64, #[[$strided2D]]>
//
// CHECK: dealloc %[[tmpA_f64]] : memref<64xi8>
// CHECK: dealloc %[[tmpB_f64]] : memref<96xi8>
// CHECK: dealloc %[[tmpC_f64]] : memref<48xi8>
-
-// -----
-
-func @matmul_i32(%A: memref<?xi8>, %M: index, %N: index, %K: index) {
- %c4 = constant 4 : index
- %c3 = constant 3 : index
- %c2 = constant 2 : index
- %c0 = constant 0 : index
- %c1 = constant 1 : index
- %3 = view %A[%c0][%M, %K] : memref<?xi8> to memref<?x?xi32>
- %4 = view %A[%c0][%K, %N] : memref<?xi8> to memref<?x?xi32>
- %5 = view %A[%c0][%M, %N] : memref<?xi8> to memref<?x?xi32>
- %6 = dim %3, %c0 : memref<?x?xi32>
- %7 = dim %3, %c1 : memref<?x?xi32>
- %8 = dim %4, %c1 : memref<?x?xi32>
- scf.for %arg4 = %c0 to %6 step %c2 {
- scf.for %arg5 = %c0 to %8 step %c3 {
- scf.for %arg6 = %c0 to %7 step %c4 {
- %11 = std.subview %3[%arg4, %arg6][%c2, %c4][1, 1] : memref<?x?xi32> to memref<?x?xi32, offset: ?, strides: [?, 1]>
- %14 = std.subview %4[%arg6, %arg5][%c4, %c3][1, 1] : memref<?x?xi32> to memref<?x?xi32, offset: ?, strides: [?, 1]>
- %17 = std.subview %5[%arg4, %arg5][%c2, %c3][1, 1] : memref<?x?xi32> to memref<?x?xi32, offset: ?, strides: [?, 1]>
- linalg.matmul(%11, %14, %17) : memref<?x?xi32, offset: ?, strides: [?, 1]>, memref<?x?xi32, offset: ?, strides: [?, 1]>, memref<?x?xi32, offset: ?, strides: [?, 1]>
- }
- }
- }
- return
-}
-
-// CHECK-LABEL: func @matmul_i32(%{{.*}}: memref<?xi8>, %{{.*}}: index, %{{.*}}: index, %{{.*}}: index) {
-// CHECK: scf.for %{{.*}} = %{{.*}} to %{{.*}} step %{{.*}} {
-// CHECK: scf.for %{{.*}} = %{{.*}} to %{{.*}} step %{{.*}} {
-// CHECK: scf.for %{{.*}} = %{{.*}} to %{{.*}} step %{{.*}} {
-// CHECK: %[[vA_i32:.*]] = subview {{.*}} : memref<?x?xi32>
-// CHECK: %[[vB_i32:.*]] = subview {{.*}} : memref<?x?xi32>
-// CHECK: %[[vC_i32:.*]] = subview {{.*}} : memref<?x?xi32>
-///
-// CHECK: %[[tmpA_i32:.*]] = alloc() : memref<32xi8>
-// CHECK: %[[fullA_i32:.*]] = std.view %[[tmpA_i32]][{{.*}}][{{.*}}] : memref<32xi8> to memref<?x?xi32>
-// DYNAMIC: std.view %{{.*}}[{{.*}}][{{.*}}] : memref<?xi8> to memref<?x?xi32>
-// CHECK: %[[partialA_i32:.*]] = subview %[[fullA_i32]][%{{.*}}, %{{.*}}] : memref<?x?xi32> to memref<?x?xi32, #[[$strided2D_dynamic]]>
-///
-// CHECK: %[[tmpB_i32:.*]] = alloc() : memref<48xi8>
-// CHECK: %[[fullB_i32:.*]] = std.view %[[tmpB_i32]][{{.*}}][{{.*}}] : memref<48xi8> to memref<?x?xi32>
-// DYNAMIC: std.view %{{.*}}[{{.*}}][{{.*}}] : memref<?xi8> to memref<?x?xi32>
-// CHECK: %[[partialB_i32:.*]] = subview %[[fullB_i32]][%{{.*}}, %{{.*}}] : memref<?x?xi32> to memref<?x?xi32, #[[$strided2D_dynamic]]>
-///
-// CHECK: %[[tmpC_i32:.*]] = alloc() : memref<24xi8>
-// CHECK: %[[fullC_i32:.*]] = std.view %[[tmpC_i32]][{{.*}}][{{.*}}] : memref<24xi8> to memref<?x?xi32>
-// DYNAMIC: std.view %{{.*}}[{{.*}}][{{.*}}] : memref<?xi8> to memref<?x?xi32>
-// CHECK: %[[partialC_i32:.*]] = subview %[[fullC_i32]][%{{.*}}, %{{.*}}] : memref<?x?xi32> to memref<?x?xi32, #[[$strided2D_dynamic]]>
-
-// CHECK: linalg.copy(%[[vA_i32]], %[[partialA_i32]]) : memref<?x?xi32, #[[$strided2D]]>, memref<?x?xi32, #[[$strided2D_dynamic]]>
-// CHECK: linalg.copy(%[[vB_i32]], %[[partialB_i32]]) : memref<?x?xi32, #[[$strided2D]]>, memref<?x?xi32, #[[$strided2D_dynamic]]>
-// CHECK: linalg.copy(%[[vC_i32]], %[[partialC_i32]]) : memref<?x?xi32, #[[$strided2D]]>, memref<?x?xi32, #[[$strided2D_dynamic]]>
-//
-// CHECK: linalg.matmul(%[[partialA_i32]], %[[partialB_i32]], %[[partialC_i32]]) : memref<?x?xi32, #[[$strided2D_dynamic]]>, memref<?x?xi32, #[[$strided2D_dynamic]]>, memref<?x?xi32, #[[$strided2D_dynamic]]>
-//
-// CHECK: linalg.copy(%[[partialC_i32]], %[[vC_i32]]) : memref<?x?xi32, #[[$strided2D_dynamic]]>, memref<?x?xi32, #[[$strided2D]]>
-//
-// CHECK: dealloc %[[tmpA_i32]] : memref<32xi8>
-// CHECK: dealloc %[[tmpB_i32]] : memref<48xi8>
-// CHECK: dealloc %[[tmpC_i32]] : memref<24xi8>
diff --git a/mlir/test/Dialect/Linalg/promotion_options.mlir b/mlir/test/Dialect/Linalg/promotion_options.mlir
index e6c8e2158fc3..270a63cf8609 100644
--- a/mlir/test/Dialect/Linalg/promotion_options.mlir
+++ b/mlir/test/Dialect/Linalg/promotion_options.mlir
@@ -2,8 +2,8 @@
func @gemm(%a : memref<?x?xf32>, %b : memref<?x?xf32>, %c : memref<?x?xf32>)
{
- linalg.matmul(%a, %b, %c) {__internal_linalg_transform__ = "START"}
- : memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>
+ linalg.matmul %a, %b, %c {__internal_linalg_transform__ = "START"}
+ : (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>)
return
}
@@ -26,7 +26,7 @@ func @gemm(%a : memref<?x?xf32>, %b : memref<?x?xf32>, %c : memref<?x?xf32>)
// CHECK: linalg.copy(%[[T7]], %[[T19]])
// CHECK: linalg.fill(%[[T21]], %[[C42]])
// CHECK: linalg.copy(%[[T17]], %[[T21]])
-// CHECK: linalg.matmul(%[[T19]], %[[T12]], %[[T21]])
+// CHECK: linalg.matmul %[[T19]], %[[T12]], %[[T21]]
// CHECK-NOT: linalg.fill
// CHECK: linalg.copy(%[[T21]], %[[T17]])
// CHECK: dealloc %[[T18]]
diff --git a/mlir/test/Dialect/Linalg/roundtrip.mlir b/mlir/test/Dialect/Linalg/roundtrip.mlir
index 6fded85c504a..aaa2890060e6 100644
--- a/mlir/test/Dialect/Linalg/roundtrip.mlir
+++ b/mlir/test/Dialect/Linalg/roundtrip.mlir
@@ -83,9 +83,9 @@ func @ops(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>,
%arg1: memref<?xf32, offset: ?, strides: [1]>,
%arg2: memref<?xf32, offset: ?, strides: [1]>,
%arg3: memref<f32>) {
- linalg.matmul(%arg0, %arg0, %arg0) : memref<?x?xf32, offset: ?, strides: [?, 1]>,
+ linalg.matmul %arg0, %arg0, %arg0 : (memref<?x?xf32, offset: ?, strides: [?, 1]>,
memref<?x?xf32, offset: ?, strides: [?, 1]>,
- memref<?x?xf32, offset: ?, strides: [?, 1]>
+ memref<?x?xf32, offset: ?, strides: [?, 1]>)
linalg.matvec(%arg0, %arg1, %arg2) : memref<?x?xf32, offset: ?, strides: [?, 1]>,
memref<?xf32, offset: ?, strides: [1]>,
memref<?xf32, offset: ?, strides: [1]>
@@ -95,10 +95,10 @@ func @ops(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>,
return
}
// CHECK-LABEL: func @ops(%
-// CHECK-NEXT: linalg.matmul(%{{.*}}, %{{.*}}, %{{.*}}) :
+// CHECK-NEXT: linalg.matmul %{{.*}}, %{{.*}}, %{{.*}} :
+// CHECK-SAME: (memref<?x?xf32, #[[$strided2D]]>,
// CHECK-SAME: memref<?x?xf32, #[[$strided2D]]>,
-// CHECK-SAME: memref<?x?xf32, #[[$strided2D]]>,
-// CHECK-SAME: memref<?x?xf32, #[[$strided2D]]>
+// CHECK-SAME: memref<?x?xf32, #[[$strided2D]]>)
// CHECK-NEXT: linalg.matvec(%{{.*}}, %{{.*}}, %{{.*}}) :
// CHECK-SAME: memref<?x?xf32, #[[$strided2D]]>,
// CHECK-SAME: memref<?xf32, #[[$strided1D]]>,
diff --git a/mlir/test/Dialect/Linalg/tile.mlir b/mlir/test/Dialect/Linalg/tile.mlir
index f55e20fe76c9..a36adf242d63 100644
--- a/mlir/test/Dialect/Linalg/tile.mlir
+++ b/mlir/test/Dialect/Linalg/tile.mlir
@@ -20,12 +20,21 @@
// TILE-234-DAG: #[[$bound_map_3:.*]] = affine_map<(d0)[s0] -> (3, -d0 + s0)>
// TILE-234-DAG: #[[$bound_map_4:.*]] = affine_map<(d0)[s0] -> (4, -d0 + s0)>
+// TILE-2-DAG: #[[$bound_map_static:.*]] = affine_map<(d0) -> (2, -d0 + 10)>
+// TILE-02-DAG: #[[$bound_map_static:.*]] = affine_map<(d0) -> (2, -d0 + 12)>
+// TILE-002-DAG: #[[$bound_map_static:.*]] = affine_map<(d0) -> (2, -d0 + 16)>
+
// TILE-2-DAG: #[[$stride_99_1_layout_map:.*]] = affine_map<(d0, d1)[s0] -> (d0 * 99 + s0 + d1)>
// TILE-02-DAG: #[[$stride_99_1_layout_map:.*]] = affine_map<(d0, d1)[s0] -> (d0 * 99 + s0 + d1)>
// TILE-234-DAG: #[[$stride_99_1_layout_map:.*]] = affine_map<(d0, d1)[s0] -> (d0 * 99 + s0 + d1)>
-func @matmul(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>, %arg1: memref<?x?xf32, offset: ?, strides: [?, 1]>, %arg2: memref<?x?xf32, offset: ?, strides: [?, 1]>) {
- linalg.matmul(%arg0, %arg1, %arg2) : memref<?x?xf32, offset: ?, strides: [?, 1]>, memref<?x?xf32, offset: ?, strides: [?, 1]>, memref<?x?xf32, offset: ?, strides: [?, 1]>
+func @matmul(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>,
+ %arg1: memref<?x?xf32, offset: ?, strides: [?, 1]>,
+ %arg2: memref<?x?xf32, offset: ?, strides: [?, 1]>) {
+ linalg.matmul %arg0, %arg1, %arg2 :
+ (memref<?x?xf32, offset: ?, strides: [?, 1]>,
+ memref<?x?xf32, offset: ?, strides: [?, 1]>,
+ memref<?x?xf32, offset: ?, strides: [?, 1]>)
return
}
// TILE-2-LABEL: func @matmul(
@@ -41,7 +50,10 @@ func @matmul(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>, %arg1: memref<?
// TILE-2: %[[szK:.*]] = affine.min #[[$bound_map]](%[[I]])[%[[localK]]]
// TILE-2: %[[N:.*]] = dim %{{.*}}, %c1 : memref<?x?xf32, #[[$strided2D]]>
// TILE-2: %[[sCi:.*]] = subview %{{.*}}[%[[I]], 0] [%[[szK]], %[[N]]] [1, 1] : memref<?x?xf32, #[[$strided2D]]> to memref<?x?xf32, #[[$strided2D]]>
-// TILE-2: linalg.matmul(%[[sAi]], %{{.*}}, %[[sCi]]) : memref<?x?xf32, #[[$strided2D]]>, memref<?x?xf32, #[[$strided2D]]>, memref<?x?xf32, #[[$strided2D]]>
+// TILE-2: linalg.matmul %[[sAi]], %{{.*}}, %[[sCi]] :
+// TILE-2: (memref<?x?xf32, #[[$strided2D]]>,
+// TILE-2: memref<?x?xf32, #[[$strided2D]]>,
+// TILE-2: memref<?x?xf32, #[[$strided2D]]>)
// TILE-02-LABEL: func @matmul(
// TILE-02-DAG: %[[C0:.*]] = constant 0 : index
@@ -56,7 +68,10 @@ func @matmul(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>, %arg1: memref<?
// TILE-02: %[[localK:.*]] = dim %{{.*}}, %c1
// TILE-02: %[[szK:.*]] = affine.min #[[$bound_map]](%[[J]])[%[[localK]]]
// TILE-02: %[[sCj:.*]] = subview %{{.*}}[0, %[[J]]] [%[[M]], %[[szK]]] [1, 1] : memref<?x?xf32, #[[$strided2D]]> to memref<?x?xf32, #[[$strided2D]]>
-// TILE-02: linalg.matmul(%{{.*}}, %[[sBj]], %[[sCj]]) : memref<?x?xf32, #[[$strided2D]]>, memref<?x?xf32, #[[$strided2D]]>, memref<?x?xf32, #[[$strided2D]]>
+// TILE-02: linalg.matmul %{{.*}}, %[[sBj]], %[[sCj]] :
+// TILE-02: (memref<?x?xf32, #[[$strided2D]]>,
+// TILE-02: memref<?x?xf32, #[[$strided2D]]>,
+// TILE-02: memref<?x?xf32, #[[$strided2D]]>)
// TILE-002-LABEL: func @matmul(
// TILE-002-DAG: %[[C0:.*]] = constant 0 : index
@@ -71,7 +86,10 @@ func @matmul(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>, %arg1: memref<?
// TILE-002: %[[szK:.*]] = affine.min #[[$bound_map]](%[[K]])[%[[localK]]]
// TILE-002: %[[N:.*]] = dim %{{.*}}, %c1 : memref<?x?xf32, #[[$strided2D]]>
// TILE-002: %[[sBj:.*]] = subview %{{.*}}[%[[K]], 0] [%[[szK]], %[[N]]] [1, 1] : memref<?x?xf32, #[[$strided2D]]> to memref<?x?xf32, #[[$strided2D]]>
-// TILE-002: linalg.matmul(%[[sAj]], %[[sBj]], %{{.*}}) : memref<?x?xf32, #[[$strided2D]]>, memref<?x?xf32, #[[$strided2D]]>, memref<?x?xf32, #[[$strided2D]]>
+// TILE-002: linalg.matmul %[[sAj]], %[[sBj]], %{{.*}} :
+// TILE-002: (memref<?x?xf32, #[[$strided2D]]>,
+// TILE-002: memref<?x?xf32, #[[$strided2D]]>,
+// TILE-002: memref<?x?xf32, #[[$strided2D]]>)
// TILE-234-LABEL: func @matmul(
// TILE-234-DAG: %[[C0:.*]] = constant 0 : index
@@ -100,14 +118,22 @@ func @matmul(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>, %arg1: memref<?
// TILE-234: %[[szN:.*]] = affine.min #[[$bound_map_3]](%[[J]])[%[[localN]]]
// TILE-234: %[[sCij:.*]] = subview %{{.*}}[%[[I]], %[[J]]] [%[[szM]], %[[szN]]] [1, 1] : memref<?x?xf32, #[[$strided2D]]> to memref<?x?xf32, #[[$strided2D]]>
//
-// TILE-234: linalg.matmul(%[[sAik]], %[[sBkj]], %[[sCij]]) : memref<?x?xf32, #[[$strided2D]]>, memref<?x?xf32, #[[$strided2D]]>, memref<?x?xf32, #[[$strided2D]]>
+// TILE-234: linalg.matmul %[[sAik]], %[[sBkj]], %[[sCij]] :
+// TILE-234: (memref<?x?xf32, #[[$strided2D]]>,
+// TILE-234: memref<?x?xf32, #[[$strided2D]]>,
+// TILE-234: memref<?x?xf32, #[[$strided2D]]>)
// When the buffer shapes are known at compile time, it is possible to avoid
// the "min" in subview size computation. This test uses buffer sizes divisible
// by respective tile sizes (M=10 divisble by 2, N=12 divisible by 2 and 3,
// K=16 divisble by 2 and 4).
-func @matmul_static(%arg0: memref<10x16xf32, offset: ?, strides: [?, 1]>, %arg1: memref<16x12xf32, offset: ?, strides: [?, 1]>, %arg2: memref<10x12xf32, offset: ?, strides: [?, 1]>) {
- linalg.matmul(%arg0, %arg1, %arg2) : memref<10x16xf32, offset: ?, strides: [?, 1]>, memref<16x12xf32, offset: ?, strides: [?, 1]>, memref<10x12xf32, offset: ?, strides: [?, 1]>
+func @matmul_static(%arg0: memref<10x16xf32, offset: ?, strides: [?, 1]>,
+ %arg1: memref<16x12xf32, offset: ?, strides: [?, 1]>,
+ %arg2: memref<10x12xf32, offset: ?, strides: [?, 1]>) {
+ linalg.matmul %arg0, %arg1, %arg2 :
+ (memref<10x16xf32, offset: ?, strides: [?, 1]>,
+ memref<16x12xf32, offset: ?, strides: [?, 1]>,
+ memref<10x12xf32, offset: ?, strides: [?, 1]>)
return
}
// TILE-2-LABEL: func @matmul_static(
@@ -118,33 +144,39 @@ func @matmul_static(%arg0: memref<10x16xf32, offset: ?, strides: [?, 1]>, %arg1:
// TILE-2-DAG: %[[C2:.*]] = constant 2 : index
// TILE-2-DAG: %[[M:.*]] = constant 10 : index
// TILE-2: scf.for %[[I:.*]] = %{{.*}} to %[[M]] step %{{.*}} {
-// TILE-2: %[[MIN2:.*]] = affine.min #map2(%[[I]])
+// TILE-2: %[[MIN2:.*]] = affine.min #[[$bound_map_static]](%[[I]])
// TILE-2: %[[sAi:.*]] = subview %{{.*}}[%[[I]], 0] [%[[MIN2]], 16] [1, 1] : memref<10x16xf32, #[[$strided2D]]> to memref<?x16xf32, #[[$strided2D]]>
-// TILE-2: %[[MIN22:.*]] = affine.min #map2(%[[I]])
+// TILE-2: %[[MIN22:.*]] = affine.min #[[$bound_map_static]](%[[I]])
// TILE-2: %[[sCi:.*]] = subview %{{.*}}[%[[I]], 0] [%[[MIN22]], 12] [1, 1] : memref<10x12xf32, #[[$strided2D]]> to memref<?x12xf32, #[[$strided2D]]>
-// TILE-2: linalg.matmul(%[[sAi]], %{{.*}}, %[[sCi]])
+// TILE-2: linalg.matmul %[[sAi]], %{{.*}}, %[[sCi]]
// TILE-02-LABEL: func @matmul_static(
// TILE-02-DAG: %[[C0:.*]] = constant 0 : index
// TILE-02-DAG: %[[C2:.*]] = constant 2 : index
// TILE-02-DAG: %[[N:.*]] = constant 12 : index
// TILE-02: scf.for %[[J:.*]] = %{{.*}} to %[[N]] step %{{.*}} {
-// TILE-02: %[[MIN2:.*]] = affine.min #map2(%[[J]])
+// TILE-02: %[[MIN2:.*]] = affine.min #[[$bound_map_static]](%[[J]])
// TILE-02: %[[sBj:.*]] = subview %{{.*}}[0, %[[J]]] [16, %[[MIN2]]] [1, 1] : memref<16x12xf32, #[[$strided2D]]> to memref<16x?xf32, #[[$strided2D]]>
-// TILE-02: %[[MIN22:.*]] = affine.min #map2(%[[J]])
+// TILE-02: %[[MIN22:.*]] = affine.min #[[$bound_map_static]](%[[J]])
// TILE-02: %[[sCj:.*]] = subview %{{.*}}[0, %[[J]]] [10, %[[MIN22]]] [1, 1] : memref<10x12xf32, #[[$strided2D]]> to memref<10x?xf32, #[[$strided2D]]>
-// TILE-02: linalg.matmul(%{{.*}}, %[[sBj]], %[[sCj]]) : memref<10x16xf32, #[[$strided2D]]>, memref<16x?xf32, #[[$strided2D]]>, memref<10x?xf32, #[[$strided2D]]>
+// TILE-02: linalg.matmul %{{.*}}, %[[sBj]], %[[sCj]] :
+// TILE-02: (memref<10x16xf32, #[[$strided2D]]>,
+// TILE-02: memref<16x?xf32, #[[$strided2D]]>,
+// TILE-02: memref<10x?xf32, #[[$strided2D]]>)
// TILE-002-LABEL: func @matmul_static(
// TILE-002-DAG: %[[C0:.*]] = constant 0 : index
// TILE-002-DAG: %[[C2:.*]] = constant 2 : index
// TILE-002-DAG: %[[C16:.*]] = constant 16 : index
// TILE-002: scf.for %[[K:.*]] = %{{.*}}{{.*}} to %[[C16]] step %{{.*}} {
-// TILE-002: %[[MIN2:.*]] = affine.min #map2(%[[K]])
+// TILE-002: %[[MIN2:.*]] = affine.min #[[$bound_map_static]](%[[K]])
// TILE-002: %[[sAj:.*]] = subview %{{.*}}[0, %[[K]]] [10, %[[MIN2]]] [1, 1] : memref<10x16xf32, #[[$strided2D]]> to memref<10x?xf32, #[[$strided2D]]>
-// TILE-002: %[[MIN22:.*]] = affine.min #map2(%[[K]])
+// TILE-002: %[[MIN22:.*]] = affine.min #[[$bound_map_static]](%[[K]])
// TILE-002: %[[sBj:.*]] = subview %{{.*}}[%[[K]], 0] [%[[MIN22]], 12] [1, 1] : memref<16x12xf32, #[[$strided2D]]> to memref<?x12xf32, #[[$strided2D]]>
-// TILE-002: linalg.matmul(%[[sAj]], %[[sBj]], %{{.*}}) : memref<10x?xf32, #[[$strided2D]]>, memref<?x12xf32, #[[$strided2D]]>, memref<10x12xf32, #[[$strided2D]]>
+// TILE-002: linalg.matmul %[[sAj]], %[[sBj]], %{{.*}} :
+// TILE-002: (memref<10x?xf32, #[[$strided2D]]>,
+// TILE-002: memref<?x12xf32, #[[$strided2D]]>,
+// TILE-002: memref<10x12xf32, #[[$strided2D]]>)
// TILE-234-LABEL: func @matmul_static(
// TILE-234-DAG: %[[C0:.*]] = constant 0 : index
@@ -161,7 +193,10 @@ func @matmul_static(%arg0: memref<10x16xf32, offset: ?, strides: [?, 1]>, %arg1:
// TILE-234: %[[sBkj:.*]] = subview %{{.*}}[%[[K]], %[[J]]] [%{{.*}}, %{{.*}}] [1, 1] : memref<16x12xf32, #[[$strided2D]]> to memref<?x?xf32, #[[$strided2D]]>
// TILE-234: %[[sCij:.*]] = subview %{{.*}}[%[[I]], %[[J]]] [%{{.*}}, %{{.*}}] [1, 1] : memref<10x12xf32, #[[$strided2D]]> to memref<?x?xf32, #[[$strided2D]]>
//
-// TILE-234: linalg.matmul(%[[sAik]], %[[sBkj]], %[[sCij]]) : memref<?x?xf32, #[[$strided2D]]>, memref<?x?xf32, #[[$strided2D]]>, memref<?x?xf32, #[[$strided2D]]>
+// TILE-234: linalg.matmul %[[sAik]], %[[sBkj]], %[[sCij]] :
+// TILE-234: (memref<?x?xf32, #[[$strided2D]]>,
+// TILE-234: memref<?x?xf32, #[[$strided2D]]>,
+// TILE-234: memref<?x?xf32, #[[$strided2D]]>)
func @matvec(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>, %arg1: memref<?xf32, offset: ?, strides: [1]>, %arg2: memref<?xf32, offset: ?, strides: [1]>) {
linalg.matvec(%arg0, %arg1, %arg2) : memref<?x?xf32, offset: ?, strides: [?, 1]>, memref<?xf32, offset: ?, strides: [1]>, memref<?xf32, offset: ?, strides: [1]>
diff --git a/mlir/test/Dialect/Linalg/tile_parallel_reduce.mlir b/mlir/test/Dialect/Linalg/tile_parallel_reduce.mlir
index bfa14570aef1..9d86e5e3f50c 100644
--- a/mlir/test/Dialect/Linalg/tile_parallel_reduce.mlir
+++ b/mlir/test/Dialect/Linalg/tile_parallel_reduce.mlir
@@ -6,8 +6,8 @@ func @gemm(%arg0 : memref<?x?xf32>,
%arg1 : memref<?x?xf32>,
%arg2 : memref<?x?xf32>)
{
- linalg.matmul(%arg0, %arg1, %arg2)
- : memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>
+ linalg.matmul %arg0, %arg1, %arg2
+ : (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>)
return
}
// CHECK-LABEL: func @gemm
@@ -21,7 +21,7 @@ func @gemm(%arg0 : memref<?x?xf32>,
// CHECK: %[[SV1:.*]] = subview %{{.*}}[%[[ARG3]], %[[ARG5]]]
// CHECK: %[[SV2:.*]] = subview %{{.*}}[%[[ARG5]], %[[ARG4]]]
// CHECK: %[[SV3:.*]] = subview %{{.*}}[%[[ARG3]], %[[ARG4]]]
-// CHECK: linalg.matmul(%[[SV1]], %[[SV2]], %[[SV3]])
+// CHECK: linalg.matmul %[[SV1]], %[[SV2]], %[[SV3]]
// TILE1-LABEL: func @gemm
// TILE1-DAG: %[[C2:.*]] = constant 2 : index
@@ -30,7 +30,7 @@ func @gemm(%arg0 : memref<?x?xf32>,
// TILE1: %[[SV1:.*]] = subview %{{.*}}[%[[ARG3]], 0]
// TILE1: %[[SV3:.*]] = subview %{{.*}}[%[[ARG3]], 0]
// TILE1-NOT: subview
-// TILE1: linalg.matmul(%[[SV1]], %{{.*}}, %[[SV3]])
+// TILE1: linalg.matmul %[[SV1]], %{{.*}}, %[[SV3]]
// TILE2-LABEL: func @gemm
// TILE2-DAG: %[[C2:.*]] = constant 2 : index
@@ -40,7 +40,7 @@ func @gemm(%arg0 : memref<?x?xf32>,
// TILE2: %[[SV1:.*]] = subview %{{.*}}[%[[ARG3]], 0]
// TILE2: %[[SV2:.*]] = subview %{{.*}}[0, %[[ARG4]]]
// TILE2: %[[SV3:.*]] = subview %{{.*}}[%[[ARG3]], %[[ARG4]]]
-// TILE2: linalg.matmul(%[[SV1]], %[[SV2]], %[[SV3]])
+// TILE2: linalg.matmul %[[SV1]], %[[SV2]], %[[SV3]]
// -----
diff --git a/mlir/test/Dialect/Linalg/transform-patterns-matmul-to-vector.mlir b/mlir/test/Dialect/Linalg/transform-patterns-matmul-to-vector.mlir
index 73c72ba1c6ef..cf75ee5691d0 100644
--- a/mlir/test/Dialect/Linalg/transform-patterns-matmul-to-vector.mlir
+++ b/mlir/test/Dialect/Linalg/transform-patterns-matmul-to-vector.mlir
@@ -4,10 +4,10 @@
func @matmul(%A: memref<1584x1584xf32, offset: 0, strides: [1584, 1]>,
%B: memref<1584x1584xf32, offset: 0, strides: [1584, 1]>,
%C: memref<1584x1584xf32, offset: 0, strides: [1584, 1]>) {
- linalg.matmul(%A, %B, %C) {__internal_linalg_transform__ = "START"} :
- memref<1584x1584xf32, offset: 0, strides: [1584, 1]>,
- memref<1584x1584xf32, offset: 0, strides: [1584, 1]>,
- memref<1584x1584xf32, offset: 0, strides: [1584, 1]>
+ linalg.matmul %A, %B, %C {__internal_linalg_transform__ = "START"} :
+ (memref<1584x1584xf32, offset: 0, strides: [1584, 1]>,
+ memref<1584x1584xf32, offset: 0, strides: [1584, 1]>,
+ memref<1584x1584xf32, offset: 0, strides: [1584, 1]>)
return
}
diff --git a/mlir/test/Dialect/Linalg/transform-patterns.mlir b/mlir/test/Dialect/Linalg/transform-patterns.mlir
index 7ea28a274e05..f2ae0ba76ed0 100644
--- a/mlir/test/Dialect/Linalg/transform-patterns.mlir
+++ b/mlir/test/Dialect/Linalg/transform-patterns.mlir
@@ -53,10 +53,10 @@ func @matvec(%A: memref<?x?xf32, offset: ?, strides: [?, 1]>,
func @matmul(%A: memref<?x?xf32, offset: ?, strides: [?, 1]>,
%B: memref<?x?xf32, offset: ?, strides: [?, 1]>,
%C: memref<?x?xf32, offset: ?, strides: [?, 1]>) {
- linalg.matmul(%A, %B, %C) { __internal_linalg_transform__ = "MEM" } :
- memref<?x?xf32, offset: ?, strides: [?, 1]>,
- memref<?x?xf32, offset: ?, strides: [?, 1]>,
- memref<?x?xf32, offset: ?, strides: [?, 1]>
+ linalg.matmul %A, %B, %C { __internal_linalg_transform__ = "MEM" } :
+ (memref<?x?xf32, offset: ?, strides: [?, 1]>,
+ memref<?x?xf32, offset: ?, strides: [?, 1]>,
+ memref<?x?xf32, offset: ?, strides: [?, 1]>)
return
}
// CHECK-LABEL: func @matmul
@@ -85,7 +85,10 @@ func @matmul(%A: memref<?x?xf32, offset: ?, strides: [?, 1]>,
// CHECK: scf.for {{.*}} = %[[c0]] to {{.*}} step %[[c2]] {
// CHECK: scf.for {{.*}} = %[[c0]] to {{.*}} step %[[c3]] {
// CHECK: scf.for {{.*}} = %[[c0]] to {{.*}} step %[[c4]] {
-// CHECK: linalg.matmul({{.*}}, {{.*}}, {{.*}}) : memref<?x?xf32, #[[$STRIDED_2D]]>, memref<?x?xf32, #[[$STRIDED_2D]]>, memref<?x?xf32, #[[$STRIDED_2D]]>
+// CHECK: linalg.matmul {{.*}}, {{.*}}, {{.*}} : (
+// CHECK: memref<?x?xf32, #[[$STRIDED_2D]]>,
+// CHECK: memref<?x?xf32, #[[$STRIDED_2D]]>,
+// CHECK: memref<?x?xf32, #[[$STRIDED_2D]]>)
#matmul_trait = {
args_in = 2,
@@ -117,8 +120,8 @@ func @vectorization_test(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
func @vectorization_test_2(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
%C: memref<8x32xf32>) {
- linalg.matmul(%A, %B, %C) { __internal_linalg_transform__ = "VECTORIZE"} :
- memref<8x16xf32>, memref<16x32xf32>, memref<8x32xf32>
+ linalg.matmul %A, %B, %C { __internal_linalg_transform__ = "VECTORIZE"} :
+ (memref<8x16xf32>, memref<16x32xf32>, memref<8x32xf32>)
return
}
// CHECK-LABEL: func @vectorization_test_2
@@ -216,10 +219,10 @@ func @matvec_perm(%A: memref<?x?xf32, offset: ?, strides: [?, 1]>,
func @matmul_perm(%A: memref<?x?xf32, offset: ?, strides: [?, 1]>,
%B: memref<?x?xf32, offset: ?, strides: [?, 1]>,
%C: memref<?x?xf32, offset: ?, strides: [?, 1]>) {
- linalg.matmul(%A, %B, %C) {__internal_linalg_transform__ = "__with_perm__"} :
- memref<?x?xf32, offset: ?, strides: [?, 1]>,
- memref<?x?xf32, offset: ?, strides: [?, 1]>,
- memref<?x?xf32, offset: ?, strides: [?, 1]>
+ linalg.matmul %A, %B, %C {__internal_linalg_transform__ = "__with_perm__"} :
+ (memref<?x?xf32, offset: ?, strides: [?, 1]>,
+ memref<?x?xf32, offset: ?, strides: [?, 1]>,
+ memref<?x?xf32, offset: ?, strides: [?, 1]>)
return
}
// CHECK-LABEL: func @matmul_perm
@@ -242,7 +245,10 @@ func @matmul_perm(%A: memref<?x?xf32, offset: ?, strides: [?, 1]>,
// CHECK: scf.for {{.*}} = %[[c0]] to {{.*}} step %[[c20]] {
// CHECK: scf.for {{.*}} = %[[c0]] to {{.*}} step %[[c30]] {
// CHECK: scf.for {{.*}} = %[[c0]] to {{.*}} step %[[c40]] {
-// CHECK: linalg.matmul({{.*}}, {{.*}}, {{.*}}) : memref<?x?xf32, #[[$STRIDED_2D]]>, memref<?x?xf32, #[[$STRIDED_2D]]>, memref<?x?xf32, #[[$STRIDED_2D]]>
+// CHECK: linalg.matmul {{.*}}, {{.*}}, {{.*}} : (
+// CHECK: memref<?x?xf32, #[[$STRIDED_2D]]>,
+// CHECK: memref<?x?xf32, #[[$STRIDED_2D]]>,
+// CHECK: memref<?x?xf32, #[[$STRIDED_2D]]>)
func @promote_subview_matmul(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>,
%arg1: memref<?x?xf32, offset: ?, strides: [?, 1]>,
@@ -264,10 +270,10 @@ func @promote_subview_matmul(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>,
memref<?x?xf32, offset: ?, strides: [?, 1]> to memref<?x?xf32, offset: ?, strides: [?, ?]>
%5 = subview %arg2[%arg3, %arg4][%c2000, %c3000][%c1, %c1] :
memref<?x?xf32, offset: ?, strides: [?, 1]> to memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%3, %4, %5) {__internal_linalg_transform__ = "_promote_views_"} :
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %3, %4, %5 {__internal_linalg_transform__ = "_promote_views_"} :
+ (memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
@@ -296,7 +302,8 @@ func @promote_subview_matmul(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>,
// CHECK: linalg.copy(%[[s0]], %[[l0]]) : memref<?x?xf32, #map{{.*}}>, memref<?x?xf32, #map{{.*}}>
// CHECK: linalg.copy(%[[s1]], %[[l1]]) : memref<?x?xf32, #map{{.*}}>, memref<?x?xf32, #map{{.*}}>
// CHECK: linalg.copy(%[[s2]], %[[l2]]) : memref<?x?xf32, #map{{.*}}>, memref<?x?xf32, #map{{.*}}>
-// CHECK: linalg.matmul(%[[v0]], %[[v1]], %[[v2]]) : memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>
+// CHECK: linalg.matmul %[[v0]], %[[v1]], %[[v2]] :
+// CHECK: (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>)
func @promote_first_subview_matmul(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>,
%arg1: memref<?x?xf32, offset: ?, strides: [?, 1]>,
@@ -318,10 +325,10 @@ func @promote_first_subview_matmul(%arg0: memref<?x?xf32, offset: ?, strides: [?
memref<?x?xf32, offset: ?, strides: [?, 1]> to memref<?x?xf32, offset: ?, strides: [?, ?]>
%5 = std.subview %arg2[%arg3, %arg4][%c2000, %c3000][%c1, %c1] :
memref<?x?xf32, offset: ?, strides: [?, 1]> to memref<?x?xf32, offset: ?, strides: [?, ?]>
- linalg.matmul(%3, %4, %5) {__internal_linalg_transform__ = "_promote_first_view_"} :
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>,
- memref<?x?xf32, offset: ?, strides: [?, ?]>
+ linalg.matmul %3, %4, %5 {__internal_linalg_transform__ = "_promote_first_view_"} :
+ (memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>,
+ memref<?x?xf32, offset: ?, strides: [?, ?]>)
}
}
}
@@ -350,7 +357,10 @@ func @promote_first_subview_matmul(%arg0: memref<?x?xf32, offset: ?, strides: [?
// CHECK: linalg.copy(%[[s0]], %[[l0]]) : memref<?x?xf32, #map{{.*}}>, memref<?x?xf32, #map{{.*}}>
// CHECK-NOT: linalg.copy(%[[s1]], %[[l1]]) : memref<?x?xf32, #map{{.*}}>, memref<?x?xf32, #map{{.*}}>
// CHECK-NOT: linalg.copy(%[[s2]], %[[l2]]) : memref<?x?xf32, #map{{.*}}>, memref<?x?xf32, #map{{.*}}>^
-// CHECK: linalg.matmul(%[[v0]], %[[s1]], %[[s2]]) : memref<?x?xf32>, memref<?x?xf32, #[[$STRIDED_2D]]>, memref<?x?xf32, #[[$STRIDED_2D]]>
+// CHECK: linalg.matmul %[[v0]], %[[s1]], %[[s2]] :
+// CHECK: (memref<?x?xf32>,
+// CHECK: memref<?x?xf32, #[[$STRIDED_2D]]>,
+// CHECK: memref<?x?xf32, #[[$STRIDED_2D]]>)
func @aligned_promote_fill(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>) {
%c2000 = constant 2000 : index
@@ -377,8 +387,8 @@ func @aligned_promote_fill(%arg0: memref<?x?xf32, offset: ?, strides: [?, 1]>) {
func @tile_permute_parallel_loop(%arg0: memref<?x?xf32>,
%arg1: memref<?x?xf32>,
%arg2: memref<?x?xf32>) {
- linalg.matmul(%arg0, %arg1, %arg2) {__internal_linalg_transform__ = "par__with_perm__"}
- : memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>
+ linalg.matmul %arg0, %arg1, %arg2 {__internal_linalg_transform__ = "par__with_perm__"}
+ : (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>)
return
}
// CHECK-LABEL: func @tile_permute_parallel_loop
diff --git a/mlir/test/mlir-cpu-runner/linalg_integration_test.mlir b/mlir/test/mlir-cpu-runner/linalg_integration_test.mlir
index 43641fd1ab7f..dd6feb96240e 100644
--- a/mlir/test/mlir-cpu-runner/linalg_integration_test.mlir
+++ b/mlir/test/mlir-cpu-runner/linalg_integration_test.mlir
@@ -83,7 +83,7 @@ func @matmul() -> f32 {
%B = view %bB[%c0][%c16, %c2] : memref<?xi8> to memref<?x?xf32>
%C = view %bC[%c0][%c2, %c2] : memref<?xi8> to memref<?x?xf32>
- linalg.matmul(%A, %B, %C) : memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>
+ linalg.matmul %A, %B, %C : (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>)
%res = load %C[%c0, %c1] : memref<?x?xf32>
dealloc %bC : memref<?xi8>
diff --git a/mlir/tools/mlir-linalg-ods-gen/mlir-linalg-ods-gen.cpp b/mlir/tools/mlir-linalg-ods-gen/mlir-linalg-ods-gen.cpp
index c417995e67ec..12e6aeef9162 100644
--- a/mlir/tools/mlir-linalg-ods-gen/mlir-linalg-ods-gen.cpp
+++ b/mlir/tools/mlir-linalg-ods-gen/mlir-linalg-ods-gen.cpp
@@ -1474,6 +1474,10 @@ void TCParser::printODS(llvm::raw_ostream &os, StringRef cppOpName,
TypeRange inputTypes, TypeRange outputTypes);
static void regionBuilder(Block &block);
+
+ std::string getLibraryCallName() {{
+ return generateLibraryCallName(getOperation());
+ }
}];
})FMT";
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