diff --git a/mlir/include/mlir/Dialect/Bufferization/IR/BufferizationOps.td b/mlir/include/mlir/Dialect/Bufferization/IR/BufferizationOps.td
--- a/mlir/include/mlir/Dialect/Bufferization/IR/BufferizationOps.td
+++ b/mlir/include/mlir/Dialect/Bufferization/IR/BufferizationOps.td
@@ -51,6 +51,11 @@
     If neither `copy` nor `memory_space` is specified, the default memory space
     is used during bufferization.
 
+    The optional `size_hint` operand specifies the number of non-zero elements
+    for sparse tensors. The value of `size_hint` should be not less than 1 and
+    not larger than the linear size of the corresponding dense tensor type. If
+    this requirement is not met, the behavior of the operator is not defined.
+
     Both dense and sparse tensor types are supported. The result of a
     `bufferization.alloc_tensor` is a tensor value that can be used like any
     other tensor value. In practice, it is often used as the "out" operand of
@@ -66,10 +71,16 @@
       outs(%c: tensor<?x?xf32, #SparseMatrix>) -> tensor<?x?xf32, #SparseMatrix>
     return %0 : tensor<?x?xf32, #SparseMatrix>
     ```
+
+    ```mlir
+    %c = bufferization.alloc_tensor(%d1, %d2) size_hint = %noe
+      : tensor<?x?xf32, #SparseMatrix>
+    ```
   }];
 
   let arguments = (ins Variadic<Index>:$dynamic_sizes,
                        Optional<AnyTensor>:$copy,
+                       Optional<Index>:$size_hint,
                        OptionalAttr<UI64Attr>:$memory_space);
 
   let results = (outs AnyTensor:$result);
@@ -129,12 +140,16 @@
   }];
 
   let builders = [
-    // Build an op without `copy` or `memory_space`.
+    // Build an op without `copy` or `memory_space` or `size_hint`.
     OpBuilder<(ins "RankedTensorType":$type, "ValueRange":$dynamicSizes)>,
 
-    // Build an op without `memory_space`.
+    // Build an op without `memory_space` or `size_hint`.
     OpBuilder<(ins "RankedTensorType":$type, "ValueRange":$dynamicSizes,
                    "Value":$copy)>,
+
+    // Build an op without `size_hint`.
+    OpBuilder<(ins "TensorType":$type, "ValueRange":$dynamicSizes,
+                   "Value":$copy, "IntegerAttr":$memory_space)>,
   ];
 
   let hasCanonicalizer = 1;
diff --git a/mlir/lib/Dialect/Bufferization/IR/BufferizationOps.cpp b/mlir/lib/Dialect/Bufferization/IR/BufferizationOps.cpp
--- a/mlir/lib/Dialect/Bufferization/IR/BufferizationOps.cpp
+++ b/mlir/lib/Dialect/Bufferization/IR/BufferizationOps.cpp
@@ -282,16 +282,24 @@
 void AllocTensorOp::build(OpBuilder &builder, OperationState &result,
                           RankedTensorType type, ValueRange dynamicSizes) {
   build(builder, result, type, dynamicSizes, /*copy=*/Value(),
+        /*size_hint=*/Value(),
         /*memory_space=*/IntegerAttr());
 }
 
 void AllocTensorOp::build(OpBuilder &builder, OperationState &result,
                           RankedTensorType type, ValueRange dynamicSizes,
                           Value copy) {
-  build(builder, result, type, dynamicSizes, copy,
+  build(builder, result, type, dynamicSizes, copy, /*size_hint=*/Value(),
         /*memory_space=*/IntegerAttr());
 }
 
+void AllocTensorOp::build(OpBuilder &builder, OperationState &result,
+                          TensorType type, ValueRange dynamicSizes, Value copy,
+                          IntegerAttr memorySpace) {
+  build(builder, result, type, dynamicSizes, copy, /*size_hint=*/Value(),
+        memorySpace);
+}
+
 namespace {
 /// Change the type of the result of a `bufferization.alloc_tensor` by making
 /// the result type statically sized along dimension that in the original
@@ -383,6 +391,11 @@
     if (parser.parseLParen() || parser.parseOperand(copyOperand) ||
         parser.parseRParen())
       return failure();
+  ParseResult sizeHintKeyword = parser.parseOptionalKeyword("size_hint");
+  OpAsmParser::UnresolvedOperand sizeHintOperand;
+  if (sizeHintKeyword.succeeded())
+    if (parser.parseEqual() || parser.parseOperand(sizeHintOperand))
+      return failure();
   if (parser.parseOptionalAttrDict(result.attributes) || parser.parseColon())
     return failure();
 
@@ -397,10 +410,14 @@
   if (copyKeyword.succeeded())
     if (parser.resolveOperand(copyOperand, type, result.operands))
       return failure();
+  if (sizeHintKeyword.succeeded())
+    if (parser.resolveOperand(sizeHintOperand, indexType, result.operands))
+      return failure();
   result.addAttribute(AllocTensorOp::getOperandSegmentSizeAttr(),
                       parser.getBuilder().getDenseI32ArrayAttr(
                           {static_cast<int32_t>(dynamicSizesOperands.size()),
-                           static_cast<int32_t>(copyKeyword.succeeded())}));
+                           static_cast<int32_t>(copyKeyword.succeeded()),
+                           static_cast<int32_t>(sizeHintKeyword.succeeded())}));
   return success();
 }
 
@@ -408,6 +425,8 @@
   p << "(" << getDynamicSizes() << ")";
   if (getCopy())
     p << " copy(" << getCopy() << ")";
+  if (getSizeHint())
+    p << " size_hint=" << getSizeHint();
   p.printOptionalAttrDict((*this)->getAttrs(), /*elidedAttrs=*/{
                               AllocTensorOp::getOperandSegmentSizeAttr()});
   p << " : ";
diff --git a/mlir/test/Dialect/Bufferization/ops.mlir b/mlir/test/Dialect/Bufferization/ops.mlir
--- a/mlir/test/Dialect/Bufferization/ops.mlir
+++ b/mlir/test/Dialect/Bufferization/ops.mlir
@@ -1,6 +1,10 @@
 // RUN: mlir-opt %s | mlir-opt | FileCheck %s
 // RUN: mlir-opt %s --mlir-print-op-generic | mlir-opt | FileCheck %s
 
+#CSR = #sparse_tensor.encoding<{
+  dimLevelType = ["dense", "compressed"]
+}>
+
 // CHECK-LABEL: func @test_clone
 func.func @test_clone(%buf : memref<*xf32>) -> memref<*xf32> {
   %clone = bufferization.clone %buf : memref<*xf32> to memref<*xf32>
@@ -39,6 +43,9 @@
   %4 = bufferization.alloc_tensor() copy(%t) {escape = true} : tensor<?x5xf32>
   // CHECK: bufferization.alloc_tensor() copy(%{{.*}}) {escape = false} : tensor<?x5xf32>
   %5 = bufferization.alloc_tensor() copy(%t) {escape = false} : tensor<?x5xf32>
+  %c100 = arith.constant 100 : index
+  // CHECK: bufferization.alloc_tensor() size_hint=
+  %6 = bufferization.alloc_tensor() size_hint=%c100 : tensor<100x100xf64, #CSR>
   return %1 : tensor<?x5xf32>
 }