diff --git a/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp b/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
--- a/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
@@ -115,12 +115,27 @@
     if (tensorResultType.getRank() == 0) {
       // 0-d collapses must go through a different op builder.
       auto bufferType = buffer.getType().cast<MemRefType>();
-      // Assume identity layout: No offset.
-      assert(bufferType.getLayout().isIdentity() &&
-             "non-zero offset for 0-d collapse not supported");
-      MemRefLayoutAttrInterface layout;
-      auto resultType = MemRefType::get({}, tensorResultType.getElementType(),
-                                        layout, bufferType.getMemorySpace());
+      MemRefType resultType;
+
+      if (bufferType.getLayout().isIdentity()) {
+        // Standard layout: result type has no offset.
+        MemRefLayoutAttrInterface layout;
+        resultType = MemRefType::get({}, tensorResultType.getElementType(),
+                                     layout, bufferType.getMemorySpace());
+      } else {
+        // Source memref has a layout map: result type has the same offset as
+        // the source type.
+        SmallVector<int64_t> strides;
+        int64_t offset;
+        if (failed(getStridesAndOffset(bufferType, strides, offset)))
+          return failure();
+        AffineMap resultLayout =
+            makeStridedLinearLayoutMap({}, offset, op->getContext());
+        resultType =
+            MemRefType::get({}, tensorResultType.getElementType(), resultLayout,
+                            bufferType.getMemorySpaceAsInt());
+      }
+
       replaceOpWithNewBufferizedOp<memref::CollapseShapeOp>(
           rewriter, op, resultType, buffer, collapseShapeOp.reassociation());
       return success();
diff --git a/mlir/test/Dialect/Tensor/bufferize.mlir b/mlir/test/Dialect/Tensor/bufferize.mlir
--- a/mlir/test/Dialect/Tensor/bufferize.mlir
+++ b/mlir/test/Dialect/Tensor/bufferize.mlir
@@ -3,6 +3,8 @@
 // CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1)[s0, s1] -> (d0 * s1 + s0 + d1)>
 // CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1)[s0] -> (d0 * 20 + s0 + d1)>
 // CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3)[s0] -> (d0 * 140 + d1 * 20 + d2 * 5 + d3 + s0)>
+// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0) -> (d0 + 1)>
+// CHECK-DAG: #[[$MAP4:.*]] = affine_map<() -> (1)>
 
 // CHECK-LABEL:   func @dim(
 // CHECK-SAME:              %[[TENSOR:.*]]: tensor<f32>,
@@ -361,3 +363,12 @@
   // CHECK: return %[[r]]
   return %0 : tensor<f32>
 }
+
+// CHECK-LABEL: func @tensor.collapse_shape_of_slice(
+func @tensor.collapse_shape_of_slice(%arg0: tensor<2xi32>) -> tensor<i32> {
+  // CHECK: memref.subview %{{.*}}[1] [1] [1] : memref<2xi32> to memref<1xi32, #[[$MAP3]]>
+  %0 = tensor.extract_slice %arg0[1] [1] [1] : tensor<2xi32> to tensor<1xi32>
+  // CHECK: memref.collapse_shape %{{.*}} [] : memref<1xi32, #[[$MAP3]]> into memref<i32, #[[$MAP4]]>
+  %1 = tensor.collapse_shape %0 [] : tensor<1xi32> into tensor<i32>
+  return %1 : tensor<i32>
+}