diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp
--- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp
+++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp
@@ -241,6 +241,23 @@
   }
 };
 
+/// Sparse codegen rule for trivial tensor casts.
+class SparseCastConverter : public OpConversionPattern<tensor::CastOp> {
+public:
+  using OpConversionPattern::OpConversionPattern;
+  LogicalResult
+  matchAndRewrite(tensor::CastOp op, OpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+    // Only rewrite identically annotated source/dest.
+    auto encDst = getSparseTensorEncoding(op.getType());
+    auto encSrc = getSparseTensorEncoding(op.getSource().getType());
+    if (!encDst || encDst != encSrc)
+      return failure();
+    rewriter.replaceOp(op, adaptor.getOperands());
+    return success();
+  }
+};
+
 /// Sparse conversion rule for pointer accesses.
 class SparseToPointersConverter : public OpConversionPattern<ToPointersOp> {
 public:
@@ -314,7 +331,7 @@
 /// the sparsification of linear algebra operations.
 void mlir::populateSparseTensorCodegenPatterns(TypeConverter &typeConverter,
                                                RewritePatternSet &patterns) {
-  patterns.add<SparseReturnConverter, SparseDimOpConverter,
+  patterns.add<SparseReturnConverter, SparseDimOpConverter, SparseCastConverter,
                SparseToPointersConverter, SparseToIndicesConverter,
                SparseToValuesConverter>(typeConverter, patterns.getContext());
 }
diff --git a/mlir/test/Dialect/SparseTensor/codegen.mlir b/mlir/test/Dialect/SparseTensor/codegen.mlir
--- a/mlir/test/Dialect/SparseTensor/codegen.mlir
+++ b/mlir/test/Dialect/SparseTensor/codegen.mlir
@@ -36,12 +36,20 @@
 }>
 
 // CHECK-LABEL: func @sparse_nop(
-//  CHECK-SAME: %[[A:.*]]: tuple<memref<1xindex>, memref<?xi32>, memref<?xi64>, memref<?xf64>>) -> tuple<memref<1xindex>, memref<?xi32>, memref<?xi64>, memref<?xf64>>
+//  CHECK-SAME: %[[A:.*]]: tuple<memref<1xindex>, memref<?xi32>, memref<?xi64>, memref<?xf64>>)
 //       CHECK: return %[[A]] : tuple<memref<1xindex>, memref<?xi32>, memref<?xi64>, memref<?xf64>>
 func.func @sparse_nop(%arg0: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
   return %arg0 : tensor<?xf64, #SparseVector>
 }
 
+// CHECK-LABEL: func @sparse_nop_cast(
+//  CHECK-SAME: %[[A:.*]]: tuple<memref<1xindex>, memref<?xi32>, memref<?xi64>, memref<?xf32>>)
+//       CHECK: return %[[A]] : tuple<memref<1xindex>, memref<?xi32>, memref<?xi64>, memref<?xf32>>
+func.func @sparse_nop_cast(%arg0: tensor<64xf32, #SparseVector>) -> tensor<?xf32, #SparseVector> {
+  %0 = tensor.cast %arg0 : tensor<64xf32, #SparseVector> to tensor<?xf32, #SparseVector>
+  return %0 : tensor<?xf32, #SparseVector>
+}
+
 // CHECK-LABEL: func @sparse_dense_2d(
 //  CHECK-SAME: %[[A:.*]]: tuple<memref<2xindex>, memref<?xf64>>)
 func.func @sparse_dense_2d(%arg0: tensor<?x?xf64, #Dense2D>) {
@@ -71,7 +79,7 @@
 // fold using the original static dimension sizes.
 //
 // CHECK-LABEL: func @sparse_dense_3d(
-//  CHECK-SAME: %[[A:.*]]: tuple<memref<3xindex>, memref<6000xf64>>) -> index {
+//  CHECK-SAME: %[[A:.*]]: tuple<memref<3xindex>, memref<6000xf64>>)
 //       CHECK: %[[C:.*]] = arith.constant 20 : index
 //       CHECK: return %[[C]] : index
 func.func @sparse_dense_3d(%arg0: tensor<10x20x30xf64, #Dense3D>) -> index {
@@ -86,7 +94,7 @@
 // since the latter honors the dimOrdering.
 //
 // CHECK-LABEL: func @sparse_dense_3d_dyn(
-//  CHECK-SAME: %[[A:.*]]: tuple<memref<3xindex>, memref<?xf64>>) -> index {
+//  CHECK-SAME: %[[A:.*]]: tuple<memref<3xindex>, memref<?xf64>>)
 //       CHECK: %[[C:.*]] = arith.constant 2 : index
 //       CHECK: %[[F:.*]] = sparse_tensor.storage_get %[[A]][0] : tuple<memref<3xindex>, memref<?xf64>> to memref<3xindex>
 //       CHECK: %[[L:.*]] = memref.load %[[F]][%[[C]]] : memref<3xindex>