[MLIR][Linalg] Add pass to convert linalg.generic back to named ops

mlir/include/mlir/Dialect/Linalg/Passes.td

@@ -104,6 +104,11 @@ def LinalgGeneralizeNamedOpsPass : Pass<"linalg-generalize-named-ops"> {
   let dependentDialects = ["linalg::LinalgDialect"];
 }
 
+def LinalgSpecializeGenericOpsPass : Pass<"linalg-specialize-generic-ops"> {
+  let summary = "Convert generic ops back to named ops";
+  let dependentDialects = ["linalg::LinalgDialect"];
+}
+
 def LinalgDetensorizePass : InterfacePass<"linalg-detensorize", "FunctionOpInterface"> {
   let summary = "Detensorize linalg ops";
   let dependentDialects = [];

mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h

@@ -1395,6 +1395,20 @@ struct LinalgGeneralizationPattern
   }
 };
 
+struct LinalgSpecializationPattern : public OpRewritePattern<GenericOp> {
+  using OpRewritePattern<GenericOp>::OpRewritePattern;
+
+  FailureOr<GenericOp>
+  returningMatchAndRewrite(GenericOp op, PatternRewriter &rewriter) const {
+    return specializeGenericOp(rewriter, op);
+  }
+
+  LogicalResult matchAndRewrite(GenericOp op,
+                                PatternRewriter &rewriter) const override {
+    return returningMatchAndRewrite(op, rewriter);
+  }
+};
+
 /// Vectorization pattern for memref::CopyOp.
 struct CopyVectorizationPattern : public OpRewritePattern<memref::CopyOp> {
   using OpRewritePattern<memref::CopyOp>::OpRewritePattern;
@@ -1546,6 +1560,15 @@ void populateLinalgTilingCanonicalizationPatterns(RewritePatternSet &patterns);
 /// linalg.generic ops.
 void populateLinalgNamedOpsGeneralizationPatterns(RewritePatternSet &patterns);
 
+/// Populates `patterns` with patterns to convert linalg.generic ops to named
+/// ops where possible. A linalg.generic can represent wide range and complex
+/// computations for which equivalent linalg named op may not exist e.g.
+/// linalg.generic that takes a tensor and computes a polynomial such as:
+///     p(x) = an*x^n + ... + a1x + a0
+/// There is no equivalent named op to convert to. Many such cases exist.
+void populateLinalgGenericOpsSpecializationPatterns(
+    RewritePatternSet &patterns);
+
 /// Linalg decompose convolutions patterns
 
 /// Populates patterns to decompose high-D convolution ops into low-D ones.

mlir/lib/Dialect/Linalg/IR/LinalgInterfaces.cpp

@@ -105,9 +105,9 @@ std::optional<Value> linalg::isaFillOpInterface(GenericOp genericOp) {
 static bool
 isaElemwiseSingleUnaryOrBinaryOpInterface(linalg::GenericOp genericOp,
                                           unsigned arity) {
-  // Check all loops are parallel, and have only tensor semantics.
+  // Check all loops are parallel.
   if (genericOp.getNumParallelLoops() != genericOp.getNumLoops() ||
-      genericOp.getNumLoops() < 1 || !genericOp.hasPureTensorSemantics())
+      genericOp.getNumLoops() < 1)
     return false;
 
   // Check there are arity-inputs, 1-output and all are identity-maps.

mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp

@@ -11,12 +11,22 @@
 //
 //===----------------------------------------------------------------------===//
 
+#include "mlir/Dialect/Complex/IR/Complex.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/IR/LinalgInterfaces.h"
+#include "mlir/Dialect/Linalg/Passes.h"
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 #include "mlir/Dialect/Math/IR/Math.h"
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/Support/TypeID.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 #include "llvm/Support/Debug.h"
 
+namespace mlir {
+#define GEN_PASS_DEF_LINALGSPECIALIZEGENERICOPSPASS
+#include "mlir/Dialect/Linalg/Passes.h.inc"
+} // namespace mlir
+
 #define DEBUG_TYPE "linalg-specialization"
 
 #define REPLACE_BINARY_OP(NEWOP, OPERANDS_SWAP)                                \
@@ -58,6 +68,197 @@ static bool areBinOpsSwapped(GenericOp genericOp) {
   return swapped;
 }
 
+//===----------------------------------------------------------------------===//
+// Specialize linalg generic to matmul variants.
+//===----------------------------------------------------------------------===//
+/// Identifies linalg.generic that is essentially named op of the form:
+//    ` linalg.{batch_}?matmul{_transpose_a | _transpose_b}? `
+//
+// It is possible that a linalg.generic may be implementing a matmul but not
+// in a straight-forward way e.g. below is matrix multiply over some slice
+// ```
+//  %0 = linalg.generic {
+//          indexing_maps = [affine_map<(d0, d1, d2) -> (3, d1, d0)>,
+//                           affine_map<(d0, d1, d2) -> (d0, 5, d2)>,
+//                           affine_map<(d0, d1, d2) -> (d2, d1, 13)>],
+//          iterator_types = ["parallel", "parallel", "parallel"]}
+//          ins(%A, %B : tensor<20x20x20xf32>,  tensor<20x20x20xf32>)
+//          outs(%C : tensor<20x20x20xf32>) {
+//             ^bb0(%a: f32, %b: f32, %c : f32):
+//                %mul = arith.mulf %a, %b : f32
+//                %add = arith.addf %mul, %c : f32
+//                linalg.yield %add : f32
+//       } -> tensor<20x20x20xf32>
+// ```
+// It is not possible to represent above as named op.
+// e.g. linalg.batch_matmul(%A, %B :  tensor<20x20x20xf32>, ...) is
+// not  the same as linalg.generic above.
+namespace {
+enum class IndexMatchResult {
+  Match = 0,  // identity map.
+  Transposed, // transposed map.
+  Mismatch    // none of the above.
+};
+
+// Checks whether the input Affine `map` contains two consecutive dims that
+// can be interpreted as accessing a 2D matrix. It is assumed that the row
+// column dimension are adjacent axis (in this order) and start at
+// `rowDimIdx` in the input map.
+//
+//  e.g. consider A matrix in `C[M,N] = A[M,K] * B[K,N]`. We will check
+//  whether the map of A is identity (match), transposed, or something
+//  completely different (mis-match). Similar for B and C.
+static IndexMatchResult matchOperandMap(AffineMap map, unsigned rowDimIdx,
+                                        unsigned expectedPosOfRowDim,
+                                        unsigned expectedPosOfColDim) {
+  // Get the matrix multiply indices. They are past the batch indices.
+  auto exprOfRowDim = map.getResults()[rowDimIdx];
+  auto exprOfColDim = map.getResults()[rowDimIdx + 1];
+
+  // They should be pure dimension ids.
+  if (exprOfRowDim.getKind() != AffineExprKind::DimId ||
+      exprOfColDim.getKind() != AffineExprKind::DimId)
+    return IndexMatchResult::Mismatch;
+
+  auto posRowDim = cast<AffineDimExpr>(exprOfRowDim).getPosition();
+  auto posColDim = cast<AffineDimExpr>(exprOfColDim).getPosition();
+
+  if (expectedPosOfRowDim == posRowDim && expectedPosOfColDim == posColDim)
+    return IndexMatchResult::Match;
+
+  if (expectedPosOfRowDim == posColDim && expectedPosOfColDim == posRowDim)
+    return IndexMatchResult::Transposed;
+
+  return IndexMatchResult::Mismatch;
+}
+
+// Replaces genericOp with `NamedOpTy` op, supplied as a template arg.
+//  All the variants expressed as pseudo regular expression:
+//      `linalg.{batch_}?matmul{_transpose_a | _transpose_b}?`
+//  have same number of ins/out, so its easy to stamp different versions.
+template <typename NamedOpTy>
+static LinalgOp replaceWithMatmulVariant(RewriterBase &rewriter, GenericOp op) {
+  LinalgOp namedOp = rewriter.replaceOpWithNewOp<NamedOpTy>(
+      op, ValueRange{op.getDpsInputs()[0], op.getDpsInputs()[1]},
+      ValueRange{op.getDpsInits()[0]});
+  return namedOp;
+}
+
+// Converts linalg.generic to named linalg.*matmul* where possible.
+static FailureOr<LinalgOp> specializeLinalgContractions(RewriterBase &rewriter,
+                                                        GenericOp genericOp) {
+  if (genericOp.getNumDpsInputs() != 2 || genericOp.getNumDpsInits() != 1)
+    return failure();
+
+  // Early exit if not projected permutations.
+  auto mapRange = genericOp.getIndexingMapsArray();
+  if (llvm::any_of(mapRange,
+                   [](AffineMap m) { return !m.isProjectedPermutation(); }))
+    return failure();
+
+  // Linalg generic contraction can be across multiple axis e.g.
+  // ```
+  //      linalg.generic
+  //           {indexing_maps = [affine_map<(m, n, k1, k2) -> (m, k1, k2)>,
+  //                             affine_map<(m, n, k1, k2) -> (k2, k1, n)>,
+  //                             affine_map<(m, n, k1, k2) -> (m, n)>],
+  //           iterator_types = ["parallel", "parallel",
+  //                             "reduction", "reduction"]}
+  //           ins(%A, %B : tensor<10x20x30xf32>, tensor<30x20x40xf32>)
+  //           outs(%C : tensor<10x40xf32>) {
+  //           ^bb0(%a: f32, %b: f32, %c: f32):
+  //                 %1 = arith.mulf %a, %b : f32
+  //                 %2 = arith.addf %c, %1 : f32
+  //                 linalg.yield %2 : f32
+  //      } -> tensor<10x40xf32>
+  //  ```
+  //  In above contraction, there are two reduction dimensions {k1, k2}
+  //  and although a valid linalg contraction, it is not a named-op
+  //  matrix multiply kind. Therefore, reject multi-dim reduction.
+  auto res = inferContractionDims(genericOp);
+  if (!succeeded(res))
+    return failure();
+  auto dims = *res;
+  if (dims.m.size() != 1 || dims.n.size() != 1 || dims.k.size() != 1)
+    return failure();
+
+  if (!mlir::linalg::detail::isContractionBody(
+          *genericOp.getBlock(), [](Operation *first, Operation *second) {
+            if ((isa<arith::MulFOp>(first) && isa<arith::AddFOp>(second)) ||
+                (isa<arith::MulIOp>(first) && isa<arith::AddIOp>(second)) ||
+                (isa<complex::MulOp>(first) && isa<complex::AddOp>(second)))
+              return true;
+            return false;
+          }))
+    return failure();
+
+  // Check rank of operands
+  auto indexingMaps = genericOp.getIndexingMapsArray();
+  if (llvm::any_of(indexingMaps, [&dims](AffineMap m) {
+        return m.getResults().size() !=
+               dims.batch.size() + 2 /* any two of {m,n,k} */;
+      }))
+    return failure();
+
+  auto numOfBatchDims = dims.batch.size();
+  if (indexingMaps[0].getNumDims() != numOfBatchDims + 3)
+    return failure();
+
+  if (numOfBatchDims) {
+    // Each operand in a linalg generic contraction  could express different
+    // permutations for its batch dimension. But for named op it must be
+    // identity since separate maps are not specified.
+    if (llvm::any_of(indexingMaps, [numOfBatchDims](AffineMap m) {
+          for (unsigned i = 0; i < numOfBatchDims; ++i) {
+            auto expr = m.getResults()[i];
+            if (expr.getKind() != AffineExprKind::DimId ||
+                cast<AffineDimExpr>(expr).getPosition() != i)
+              return true;
+          }
+          return false;
+        }))
+      return failure();
+  }
+
+  auto a =
+      matchOperandMap(indexingMaps[0], numOfBatchDims, dims.m[0], dims.k[0]);
+  auto b =
+      matchOperandMap(indexingMaps[1], numOfBatchDims, dims.k[0], dims.n[0]);
+  auto c =
+      matchOperandMap(indexingMaps[2], numOfBatchDims, dims.m[0], dims.n[0]);
+
+  if (llvm::any_of(ArrayRef<IndexMatchResult>{a, b, c}, [](IndexMatchResult r) {
+        return r == IndexMatchResult::Mismatch;
+      }))
+    return failure();
+
+  if (c != IndexMatchResult::Match ||
+      (a == IndexMatchResult::Transposed && b == IndexMatchResult::Transposed))
+    return failure();
+
+  /// Codegen the different matmul variants.
+  if (numOfBatchDims) {
+    if (a == IndexMatchResult::Transposed)
+      return replaceWithMatmulVariant<BatchMatmulTransposeAOp>(rewriter,
+                                                               genericOp);
+    if (b == IndexMatchResult::Transposed)
+      return replaceWithMatmulVariant<BatchMatmulTransposeBOp>(rewriter,
+                                                               genericOp);
+    return replaceWithMatmulVariant<BatchMatmulOp>(rewriter, genericOp);
+  }
+
+  if (a == IndexMatchResult::Transposed)
+    return replaceWithMatmulVariant<MatmulTransposeAOp>(rewriter, genericOp);
+  if (b == IndexMatchResult::Transposed)
+    return replaceWithMatmulVariant<MatmulTransposeBOp>(rewriter, genericOp);
+  return replaceWithMatmulVariant<MatmulOp>(rewriter, genericOp);
+}
+
+} // namespace
+
+//===----------------------------------------------------------------------===//
+// Categorize linalg generic to named op where possible.
+//===----------------------------------------------------------------------===//
 FailureOr<LinalgOp> mlir::linalg::specializeGenericOp(RewriterBase &rewriter,
                                                       GenericOp genericOp) {
   if (isaCopyOpInterface(genericOp)) {
@@ -100,5 +301,33 @@ FailureOr<LinalgOp> mlir::linalg::specializeGenericOp(RewriterBase &rewriter,
       return namedOp;
     }
   }
+
+  if (isaContractionOpInterface(genericOp)) {
+    return specializeLinalgContractions(rewriter, genericOp);
+  }
   return failure();
 }
+
+namespace {
+struct LinalgSpecializeGenericOpsPass
+    : public impl::LinalgSpecializeGenericOpsPassBase<
+          LinalgSpecializeGenericOpsPass> {
+
+  using impl::LinalgSpecializeGenericOpsPassBase<
+      LinalgSpecializeGenericOpsPass>::LinalgSpecializeGenericOpsPassBase;
+  void runOnOperation() override;
+};
+} // namespace
+
+void LinalgSpecializeGenericOpsPass::runOnOperation() {
+  RewritePatternSet patterns(&getContext());
+  populateLinalgGenericOpsSpecializationPatterns(patterns);
+
+  if (failed(applyPatternsAndFoldGreedily(getOperation(), std::move(patterns))))
+    signalPassFailure();
+}
+
+void mlir::linalg::populateLinalgGenericOpsSpecializationPatterns(
+    RewritePatternSet &patterns) {
+  patterns.add<LinalgSpecializationPattern>(patterns.getContext());
+}

mlir/test/Dialect/Linalg/roundtrip-linalg-named-ops.mlir

@@ -0,0 +1,52 @@
+// The following test examples of linalg named ops lowered to linalg.generic and then
+// lifted back up to named op.
+// RUN: mlir-opt %s -linalg-generalize-named-ops | mlir-opt --linalg-specialize-generic-ops | FileCheck %s
+
+func.func @unary_exp(%A: memref<7x14x21xf32>, %Out: memref<7x14x21xf32>) {
+  linalg.exp ins(%A : memref<7x14x21xf32>) outs(%Out : memref<7x14x21xf32>)
+  return
+}
+
+// CHECK-LABEL: unary_exp
+// CHECK-SAME: %[[A:.+]]: memref<7x14x21xf32>, %[[Out:.+]]: memref<7x14x21xf32>)
+// CHECK-NOT: linalg.generic
+// CHECK: linalg.exp ins(%[[A]] : memref<7x14x21xf32>) outs(%[[Out]] : memref<7x14x21xf32>)
+
+// -----
+
+func.func @binary_add(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>, %Out: tensor<?x?xf32>) -> tensor<?x?xf32> {
+  %0 = linalg.add ins(%A, %B : tensor<?x?xf32>, tensor<?x?xf32>) outs(%Out : tensor<?x?xf32>) -> tensor<?x?xf32>
+  return %0 : tensor<?x?xf32>
+}
+
+// CHECK-LABEL: binary_add
+// CHECK-SAME: %[[A:.+]]: tensor<?x?xf32>, %[[B:.+]]: tensor<?x?xf32>,  %[[Out:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK-NOT: linalg.generic
+// CHECK: linalg.add ins(%[[A]], %[[B]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[Out]] : tensor<?x?xf32>) -> tensor<?x?xf32>
+
+// -----
+
+func.func @matmul(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>, %Out: tensor<?x?xf32>) -> tensor<?x?xf32> {
+  %0 = linalg.matmul ins(%A, %B : tensor<?x?xf32>, tensor<?x?xf32>) outs(%Out : tensor<?x?xf32>) -> tensor<?x?xf32>
+  return %0 : tensor<?x?xf32>
+}
+
+// CHECK-LABEL: @matmul
+// CHECK-SAME: %[[A:.+]]: tensor<?x?xf32>, %[[B:.+]]: tensor<?x?xf32>, %[[Out:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK-NOT: linalg.generic
+// CHECK: linalg.matmul ins(%[[A]], %[[B]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[Out]] : tensor<?x?xf32>) -> tensor<?x?xf32>
+
+// -----
+
+func.func @mixed_named_ops(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>,
+                                   %C: tensor<?x?xf32>, %Out: tensor<?x?xf32>) -> tensor<?x?xf32> {
+  %AB = linalg.matmul ins(%A, %B : tensor<?x?xf32>, tensor<?x?xf32>) outs(%Out : tensor<?x?xf32>) -> tensor<?x?xf32>
+  %1 = linalg.add ins(%AB, %C : tensor<?x?xf32>, tensor<?x?xf32>) outs(%Out : tensor<?x?xf32>) -> tensor<?x?xf32>
+  return %1 : tensor<?x?xf32>
+}
+
+// CHECK-LABEL: @mixed_named_ops
+// CHECK-SAME: %[[A:.+]]: tensor<?x?xf32>, %[[B:.+]]: tensor<?x?xf32>, %[[C:.+]]: tensor<?x?xf32>, %[[Out:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK-NOT: linalg.generic
+// CHECK: %[[AB:.+]] = linalg.matmul ins(%[[A]], %[[B]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[Out]] : tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK: linalg.add ins(%[[AB]], %[[C]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[Out]] : tensor<?x?xf32>) -> tensor<?x?xf32>