[mlir][Linalg] Fix fusing of indexed linalg consumer with different axes

When fusing two `linalg.genericOp`, where the producer has index
semantics, invalid `affine.apply` ops can be generated where the number
of indices do not match the number of loops in the fused genericOp.

This patch fixes the issue by directly using the number of loops from the
generated fused op.

Author: simpel01

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

@@ -231,8 +231,7 @@ static void generateFusedElementwiseOpRegion(
   // `consumerToProducerLoopsMap` to map the producer indices.
   if (producer.hasIndexSemantics()) {
     // Add an index operation for every fused loop dimension.
-    unsigned numFusedOpLoops =
-        std::max(producer.getNumLoops(), consumer.getNumLoops());
+    unsigned numFusedOpLoops = fusedOp.getNumLoops();
     SmallVector<Value> fusedIndices;
     fusedIndices.reserve(numFusedOpLoops);
     llvm::transform(llvm::seq<uint64_t>(0, numFusedOpLoops),

mlir/test/Dialect/Linalg/fusion-elementwise-ops.mlir

@@ -860,6 +860,43 @@ func.func @fusion_different_axes(%arg0 : tensor<5000xi64>, %arg1 : tensor<5000xi
 
 // -----
 
+func.func @fusion_different_axes_indexed(%arg0: tensor<2x2xi32>) ->  tensor<2xi32> {
+  %0 = tensor.empty() : tensor<2x2xi32>
+  %1 = linalg.generic {
+        indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>],
+        iterator_types = ["parallel", "parallel"]}
+        ins(%arg0 : tensor<2x2xi32>) outs(%0 : tensor<2x2xi32>) {
+          ^bb0(%in: i32, %out: i32):
+            %2 = linalg.index 1 : index
+            %3 = arith.index_cast %2 : index to i32
+            linalg.yield %3 : i32
+        } -> tensor<2x2xi32>
+  %4 = tensor.empty() : tensor<2xi32>
+  %5 = linalg.generic {
+        indexing_maps = [affine_map<(d0) -> (d0, 1)>, affine_map<(d0) -> (d0)>],
+        iterator_types = ["parallel"]}
+        ins(%1 : tensor<2x2xi32>) outs(%4 : tensor<2xi32>) {
+          ^bb0(%in: i32, %out: i32):
+            linalg.yield %in : i32
+        } -> tensor<2xi32>
+  return %5 : tensor<2xi32>
+}
+
+//  CHECK-DAG: #[[MAP:.+]] = affine_map<(d0) -> (d0)>
+//      CHECK: func @fusion_different_axes_indexed(
+// CHECK-SAME:     %[[ARG0:.+]]: tensor<2x2xi32>
+//  CHECK-DAG:   %[[CST:.+]] = arith.constant 1 : i32
+//  CHECK-DAG:   %[[INIT:.+]] = tensor.empty() : tensor<2xi32>
+//      CHECK:   %[[RESULT:.+]] = linalg.generic
+// CHECK-SAME:       indexing_maps = [#[[MAP]]]
+// CHECK-SAME:       outs(%[[INIT]] :
+// CHECK-NEXT:   ^bb0(
+// CHECK-SAME:       %[[B0:.+]]: i32
+//      CHECK:     linalg.yield %[[CST]] : i32
+//      CHECK:   return %[[RESULT]]
+
+// -----
+
 // CHECK-LABEL: func @fold_fill_generic_basic
 //  CHECK-SAME: (%[[ARG0:.*]]: tensor<?xf32>) -> tensor<?xf32> {
 //   CHECK-NOT: linalg.fill