diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgInterfaces.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgInterfaces.cpp
--- a/mlir/lib/Dialect/Linalg/IR/LinalgInterfaces.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgInterfaces.cpp
@@ -431,5 +431,26 @@
     ++idx;
   }
 
+  // Check if shapes are valid
+  Optional<SmallVector<int64_t, 4>> loopRanges = linalgOp.getStaticLoopRanges();
+  if (!loopRanges)
+    return linalgOp.emitError("unable to find loop range for operation");
+  for (int64_t &range : *loopRanges)
+    range -= 1;
+  for (unsigned i = 0; i < nShapedOperands; i++) {
+    auto shapedOperand = linalgOp.getShapedOperandTypes()[i];
+    if (!shapedOperand.hasStaticShape())
+      continue;
+    auto indices = indexingMaps[i].compose(*loopRanges);
+    for (unsigned j = 0; j < shapedOperand.getRank(); j++) {
+      if (indices[j] >= shapedOperand.getDimSize(j)) {
+        return linalgOp.emitOpError("Out-of-bound memory access in operand #")
+               << i + 1 << ". The range of it's rank #" << j + 1
+               << " is from 0 through " << shapedOperand.getDimSize(j) - 1
+               << ", but the operand tries to access index #" << indices[j];
+      }
+    }
+  }
+
   return success();
 }
diff --git a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
--- a/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
+++ b/mlir/test/Dialect/Linalg/generalize-named-ops.mlir
@@ -1,7 +1,7 @@
 // RUN: mlir-opt %s -split-input-file -linalg-generalize-named-ops | FileCheck %s
 
-func @generalize_conv(%input : memref<1x225x225x3xf32>, %filter: memref<3x3x3x32xf32>, %output: memref<1x112x112x32xf32>) {
-  linalg.conv(%filter, %input, %output) {dilations = [2, 3], strides = [4, 5]} : memref<3x3x3x32xf32>, memref<1x225x225x3xf32>, memref<1x112x112x32xf32>
+func @generalize_conv(%input : memref<1x449x562x3xf32>, %filter: memref<3x3x3x32xf32>, %output: memref<1x112x112x32xf32>) {
+  linalg.conv(%filter, %input, %output) {dilations = [2, 3], strides = [4, 5]} : memref<3x3x3x32xf32>, memref<1x449x562x3xf32>, memref<1x112x112x32xf32>
   return
 }
 
@@ -10,7 +10,7 @@
 // CHECK: #[[OUTPUT_MAP:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3)>
 
 // CHECK: func @generalize_conv
-// CHECK-SAME:  %[[INPUT:.+]]: memref<1x225x225x3xf32>
+// CHECK-SAME:  %[[INPUT:.+]]: memref<1x449x562x3xf32>
 // CHECK-SAME: %[[FILTER:.+]]: memref<3x3x3x32xf32>
 // CHECK-SAME: %[[OUTPUT:.+]]: memref<1x112x112x32xf32>
 
diff --git a/mlir/test/Dialect/Linalg/reshape_linearization_fusion.mlir b/mlir/test/Dialect/Linalg/reshape_linearization_fusion.mlir
--- a/mlir/test/Dialect/Linalg/reshape_linearization_fusion.mlir
+++ b/mlir/test/Dialect/Linalg/reshape_linearization_fusion.mlir
@@ -168,8 +168,8 @@
 
 #map0 = affine_map<(d0, d1, d2) -> (d0)>
 #map1 = affine_map<(d0, d1, d2) -> (d1, d2)>
-#map2 = affine_map<(d0, d1, d2) -> (d1, d2, d0)>
-#map3 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+#map2 = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
+#map3 = affine_map<(d0, d1, d2) -> (d0, d2, d1)>
 func @generic_op_120_permultation_reshape_producer_fusion(%arg0 : tensor<3x35xf32>) -> tensor<5x7x3xf32> {
   %0 = linalg.tensor_reshape %arg0 [#map0, #map1] : tensor<3x35xf32> into tensor<3x5x7xf32>
   %1 = linalg.init_tensor [5, 7, 3] : tensor<5x7x3xf32>
@@ -183,8 +183,8 @@
     return %2 : tensor<5x7x3xf32>
 }
 
-//   CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d2, d0 * 7 + d1)>
-//   CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+//   CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d1, d0 * 7 + d2)>
+//   CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d0, d2, d1)>
 //       CHECK: func @generic_op_120_permultation_reshape_producer_fusion
 //   CHECK-NOT:   linalg.tensor_reshape
 //       CHECK:   linalg.generic
diff --git a/mlir/test/Dialect/Linalg/sparse_nd.mlir b/mlir/test/Dialect/Linalg/sparse_nd.mlir
--- a/mlir/test/Dialect/Linalg/sparse_nd.mlir
+++ b/mlir/test/Dialect/Linalg/sparse_nd.mlir
@@ -21,7 +21,7 @@
 
 // CHECK-LABEL:   func @mul(
 // CHECK-SAME:              %[[VAL_0:.*0]]: tensor<10x20x30x40x50x60x70x80xf32>,
-// CHECK-SAME:              %[[VAL_1:.*1]]: tensor<10x20x30x40x50x60x70x80xf32>,
+// CHECK-SAME:              %[[VAL_1:.*1]]: tensor<80x70x60x50x40x30x20x10xf32>,
 // CHECK-SAME:              %[[VAL_2:.*2]]: tensor<10x20x30x40x50x60x70x80xf32>) -> tensor<10x20x30x40x50x60x70x80xf32> {
 // CHECK:           %[[VAL_3:.*]] = constant 3 : index
 // CHECK:           %[[VAL_4:.*]] = constant 4 : index
@@ -34,11 +34,11 @@
 // CHECK:           %[[VAL_11:.*]] = constant 0 : index
 // CHECK:           %[[VAL_12:.*]] = constant 1 : index
 // CHECK:           %[[VAL_13:.*]] = tensor_to_memref %[[VAL_0]] : memref<10x20x30x40x50x60x70x80xf32>
-// CHECK:           %[[VAL_14:.*]] = linalg.sparse_pointers %[[VAL_1]], %[[VAL_3]] : tensor<10x20x30x40x50x60x70x80xf32> to memref<?xindex>
-// CHECK:           %[[VAL_15:.*]] = linalg.sparse_indices %[[VAL_1]], %[[VAL_3]] : tensor<10x20x30x40x50x60x70x80xf32> to memref<?xindex>
-// CHECK:           %[[VAL_16:.*]] = linalg.sparse_pointers %[[VAL_1]], %[[VAL_4]] : tensor<10x20x30x40x50x60x70x80xf32> to memref<?xindex>
-// CHECK:           %[[VAL_17:.*]] = linalg.sparse_indices %[[VAL_1]], %[[VAL_4]] : tensor<10x20x30x40x50x60x70x80xf32> to memref<?xindex>
-// CHECK:           %[[VAL_18:.*]] = linalg.sparse_values %[[VAL_1]] : tensor<10x20x30x40x50x60x70x80xf32> to memref<?xf32>
+// CHECK:           %[[VAL_14:.*]] = linalg.sparse_pointers %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32> to memref<?xindex>
+// CHECK:           %[[VAL_15:.*]] = linalg.sparse_indices %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32> to memref<?xindex>
+// CHECK:           %[[VAL_16:.*]] = linalg.sparse_pointers %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32> to memref<?xindex>
+// CHECK:           %[[VAL_17:.*]] = linalg.sparse_indices %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32> to memref<?xindex>
+// CHECK:           %[[VAL_18:.*]] = linalg.sparse_values %[[VAL_1]] : tensor<80x70x60x50x40x30x20x10xf32> to memref<?xf32>
 // CHECK:           %[[VAL_19:.*]] = tensor_to_memref %[[VAL_2]] : memref<10x20x30x40x50x60x70x80xf32>
 // CHECK:           %[[VAL_20:.*]] = alloc() : memref<10x20x30x40x50x60x70x80xf32>
 // CHECK:           linalg.copy(%[[VAL_19]], %[[VAL_20]]) : memref<10x20x30x40x50x60x70x80xf32>, memref<10x20x30x40x50x60x70x80xf32>
@@ -84,12 +84,12 @@
 // CHECK:           return %[[VAL_50]] : tensor<10x20x30x40x50x60x70x80xf32>
 // CHECK:         }
 func @mul(%arga: tensor<10x20x30x40x50x60x70x80xf32>,
-          %argb: tensor<10x20x30x40x50x60x70x80xf32>,
+          %argb: tensor<80x70x60x50x40x30x20x10xf32>,
           %argx: tensor<10x20x30x40x50x60x70x80xf32>)
 	      -> tensor<10x20x30x40x50x60x70x80xf32> {
   %0 = linalg.generic #trait_mul
     ins(%arga, %argb: tensor<10x20x30x40x50x60x70x80xf32>,
-                      tensor<10x20x30x40x50x60x70x80xf32>)
+                      tensor<80x70x60x50x40x30x20x10xf32>)
     outs(%argx: tensor<10x20x30x40x50x60x70x80xf32>) {
       ^bb(%a: f32, %b: f32, %x: f32):
         %0 = mulf %a, %b : f32