[mlir][sparse] Fix a bug in concatenate operator rewriting.

When calculating the dynamic dimensions for the concatenate result, we
shouldn't accumulate the sizes for the non-concatenating dimensions.

Reviewed By: aartbik, Peiming

Differential Revision: https://reviews.llvm.org/D138436

diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
index 1472b67..7b9eba9 100644 (file)
--- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
+++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
@@ -469,9 +469,12 @@ struct ConcatenateRewriter : public OpRewritePattern<ConcatenateOp> {
           Value v =
               createOrFoldDimOp(rewriter, loc, op.getOperand(0), d.index());
           rewriter.create<tensor::DimOp>(loc, op.getOperand(0), d.index());
-          for (const auto &opnd : op.getOperands().drop_front()) {
-            Value t = createOrFoldDimOp(rewriter, loc, opnd, d.index());
-            v = rewriter.create<arith::AddIOp>(loc, v, t);
+          if (conDim == d.index()) {
+            // Adding the size of the concatenating dimension.
+            for (const auto &opnd : op.getOperands().drop_front()) {
+              Value t = createOrFoldDimOp(rewriter, loc, opnd, d.index());
+              v = rewriter.create<arith::AddIOp>(loc, v, t);
+            }
           }
           dynSizes.push_back(v);
         }

diff --git a/mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir b/mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir
index c2a1fd8..9d26f3b 100644 (file)
--- a/mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir
+++ b/mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir
@@ -1,5 +1,5 @@
 // RUN: mlir-opt %s --post-sparsification-rewrite="enable-runtime-library=false enable-convert=false" \
-// RUN: --sparsification | FileCheck %s
+// RUN: | FileCheck %s
 
 #DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
 
@@ -87,3 +87,90 @@ func.func @concat_sparse_sparse(%arg0: tensor<2x4xf64, #DCSR>,
            tensor<4x4xf64, #DCSR> to tensor<9x4xf64, #DCSR>
     return %0 : tensor<9x4xf64, #DCSR>
 }
+
+// CHECK-LABEL: @concat_sparse_sparse_dynamic(
+//  CHECK-SAME:  %[[TMP_arg0:.*]]: tensor<2x4xf64, #sparse_tensor
+//  CHECK-SAME:  %[[TMP_arg1:.*]]: tensor<3x4xf64, #sparse_tensor
+//  CHECK-SAME:  %[[TMP_arg2:.*]]: tensor<4x4xf64, #sparse_tensor
+//   CHECK-DAG:  %[[TMP_c0:.*]] = arith.constant 0 : index
+//   CHECK-DAG:  %[[TMP_c1:.*]] = arith.constant 1 : index
+//   CHECK-DAG:  %[[TMP_c5:.*]] = arith.constant 5 : index
+//   CHECK-DAG:  %[[TMP_c2:.*]] = arith.constant 2 : index
+//   CHECK-DAG:  %[[TMP_c9:.*]] = arith.constant 9 : index
+//   CHECK-DAG:  %[[TMP_c4:.*]] = arith.constant 4 : index
+//       CHECK:  %[[TMP_0:.*]] = bufferization.alloc_tensor(%[[TMP_c9]], %[[TMP_c4]]) : tensor<?x?xf64, #sparse_tensor
+//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]] : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
+//       CHECK:  %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
+//       CHECK:  %[[RET_1:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] iter_args(%[[A0:.*]] = %[[TMP_0]]) 
+//       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_2]][%[[TMP_arg3]]] : memref<?xindex>
+//   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_3]][%[[TMP_arg3]]] : memref<?xindex>
+//   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
+//       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_3]][%[[TMP_24]]] : memref<?xindex>
+//       CHECK:    %[[RET_4:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A1:.*]] = %[[A0]]) 
+//       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_4]][%[[TMP_arg4]]] : memref<?xindex>
+//       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_5]][%[[TMP_arg4]]] : memref<?xf64>
+//       CHECK:      %[[NEW_1:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A1]][%[[TMP_23]], %[[TMP_27]]] : tensor<?x?xf64, #sparse_tensor
+//       CHECK:      scf.yield %[[NEW_1]]
+//       CHECK:    }
+//       CHECK:    scf.yield %[[RET_4]]
+//       CHECK:  }
+//       CHECK:  %[[TMP_8:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_9:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_10:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_11:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_12:.*]] = sparse_tensor.values %[[TMP_arg1]] : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_13:.*]] = memref.load %[[TMP_8]][%[[TMP_c0]]] : memref<?xindex>
+//       CHECK:  %[[TMP_14:.*]] = memref.load %[[TMP_8]][%[[TMP_c1]]] : memref<?xindex>
+//       CHECK:  %[[RET_2:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]] iter_args(%[[A2:.*]] = %[[RET_1]]) 
+//       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_9]][%[[TMP_arg3]]] : memref<?xindex>
+//   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_10]][%[[TMP_arg3]]] : memref<?xindex>
+//   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
+//       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_10]][%[[TMP_24]]] : memref<?xindex>
+//       CHECK:    %[[RET_5:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A3:.*]] = %[[A2]]) 
+//       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_11]][%[[TMP_arg4]]] : memref<?xindex>
+//       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_12]][%[[TMP_arg4]]] : memref<?xf64>
+//       CHECK:      %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c2]] : index
+//       CHECK:      %[[NEW_2:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A3]][%[[TMP_29]], %[[TMP_27]]] : tensor<?x?xf64, #sparse_tensor
+//       CHECK:      scf.yield %[[NEW_2]]
+//       CHECK:    }
+//       CHECK:    scf.yield %[[RET_5]]
+//       CHECK:  }
+//       CHECK:  %[[TMP_15:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_16:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_17:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_18:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_19:.*]] = sparse_tensor.values %[[TMP_arg2]] : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_20:.*]] = memref.load %[[TMP_15]][%[[TMP_c0]]] : memref<?xindex>
+//       CHECK:  %[[TMP_21:.*]] = memref.load %[[TMP_15]][%[[TMP_c1]]] : memref<?xindex>
+//       CHECK:  %[[RET_3:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]] iter_args(%[[A4:.*]] = %[[RET_2]]) 
+//       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_16]][%[[TMP_arg3]]] : memref<?xindex>
+//       CHECK:    %[[TMP_25:.*]] = memref.load %[[TMP_17]][%[[TMP_arg3]]] : memref<?xindex>
+//       CHECK:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
+//       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_17]][%[[TMP_24]]] : memref<?xindex>
+//       CHECK:    %[[RET_6:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A5:.*]] = %[[A4]]) 
+//       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_18]][%[[TMP_arg4]]] : memref<?xindex>
+//       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_19]][%[[TMP_arg4]]] : memref<?xf64>
+//       CHECK:      %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c5]] : index
+//       CHECK:      %[[NEW_3:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A5]][%[[TMP_29]], %[[TMP_27]]] : tensor<?x?xf64, #sparse_tensor
+//       CHECK:      scf.yield %[[NEW_3]]
+//       CHECK:    }
+//       CHECK:    scf.yield %[[RET_6]]
+//       CHECK:  }
+//       CHECK:  %[[TMP_23:.*]] = sparse_tensor.load %[[RET_3]] hasInserts
+//       CHECK:  %[[TMP_22:.*]] = sparse_tensor.convert %[[TMP_23]] : tensor<?x?xf64, #sparse_tensor
+//       CHECK:  return %[[TMP_22]] : tensor<?x?xf64, #sparse_tensor
+func.func @concat_sparse_sparse_dynamic(%arg0: tensor<2x4xf64, #DCSR>,
+                                %arg1: tensor<3x4xf64, #DCSR>,
+                                %arg2: tensor<4x4xf64, #DCSR>)
+                                -> tensor<?x?xf64, #DCSR> {
+    %0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
+         : tensor<2x4xf64, #DCSR>,
+           tensor<3x4xf64, #DCSR>,
+           tensor<4x4xf64, #DCSR> to tensor<?x?xf64, #DCSR>
+    return %0 : tensor<?x?xf64, #DCSR>
+}
\ No newline at end of file