diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp
--- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp
+++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp
@@ -388,20 +388,31 @@
 static void translateIndices(Location loc, ConversionPatternRewriter &rewriter,
                              ArrayRef<ReassociationIndices> reassociation,
                              TensorType dstTp, TensorType srcTp, Value dstIdx,
-                             Value srcIdx) {
+                             Value srcIdx, SmallVector<Value, 4> dstShape,
+                             SmallVector<Value, 4> srcShape) {
   unsigned dstRank = dstTp.getRank();
   unsigned srcRank = srcTp.getRank();
   unsigned start = 0;
   unsigned i = 0;
   bool isExpand = srcRank > dstRank;
+  bool isStatic = srcTp.hasStaticShape();
   ArrayRef<int64_t> shape = isExpand ? srcTp.getShape() : dstTp.getShape();
+  SmallVector<Value, 4> shape_dyn = isExpand ? srcShape : dstShape;
   // Iterate over reassociation map.
   for (const auto &map : llvm::enumerate(reassociation)) {
     // Prepare strides information in dimension slice.
     uint64_t linear = 1;
-    for (unsigned j = start, end = start + map.value().size(); j < end; j++) {
-      assert(!ShapedType::isDynamic(shape[j]));
-      linear *= shape[j];
+    Value linear_dyn;
+    if (isStatic) {
+      for (unsigned j = start, end = start + map.value().size(); j < end; j++) {
+        linear *= shape[j];
+      }
+    } else {
+      linear_dyn = constantIndex(rewriter, loc, 1);
+      for (unsigned j = start, end = start + map.value().size(); j < end; j++) {
+        linear_dyn = rewriter.create<arith::MulIOp>(
+          loc, linear_dyn, shape_dyn[j]);
+      }
     }
     // Start collapse.
     Value idx = constantIndex(rewriter, loc, i++);
@@ -410,21 +421,27 @@
       val = rewriter.create<memref::LoadOp>(loc, srcIdx, idx);
     // Iterate over dimension slice.
     for (unsigned j = start, end = start + map.value().size(); j < end; j++) {
-      linear /= shape[j];
-      Value stride = constantIndex(rewriter, loc, linear);
+      if (isStatic) {
+        linear /= shape[j];
+      } else {
+        linear_dyn = rewriter.create<arith::DivUIOp>(
+          loc, linear_dyn, shape_dyn[j]);
+      }
+      Value stride = isStatic ? constantIndex(rewriter, loc, linear)
+                              : linear_dyn;
       Value jdx = constantIndex(rewriter, loc, j);
       if (isExpand) {
         Value old = rewriter.create<memref::LoadOp>(loc, srcIdx, jdx);
-        Value mul = linear == 1
+        Value mul = isStatic && linear == 1
                         ? old
                         : rewriter.create<arith::MulIOp>(loc, old, stride);
         val = val ? rewriter.create<arith::AddIOp>(loc, val, mul) : mul;
       } else {
         Value old = val;
-        if (linear != 1)
+        if (!isStatic || linear != 1)
           val = rewriter.create<arith::DivUIOp>(loc, val, stride);
         rewriter.create<memref::StoreOp>(loc, val, dstIdx, jdx);
-        if (linear != 1)
+        if (!isStatic || linear != 1)
           val = rewriter.create<arith::RemUIOp>(loc, old, stride);
       }
     }
@@ -437,6 +454,30 @@
   assert((isExpand && i == dstRank) || (!isExpand && i == srcRank));
 }
 
+/// Helper method to compute the shape of destination tensor of a reshape
+/// operator. This is only used when operands have dynamic shape.
+static SmallVector<Value, 4> genDstShape(
+    Location loc,
+    ConversionPatternRewriter &rewriter,
+    SmallVector<Value, 4> srcShape,
+    ArrayRef<ReassociationIndices> reassociation) {
+  // TODO: Support computing dst shape for expand_shape operator.
+  assert(reassociation.size() <= srcShape.size());
+  SmallVector<Value, 4> dstShape;
+  unsigned start = 0;
+  for (const auto &map : llvm::enumerate(reassociation)) {
+    auto dstDim = constantIndex(rewriter, loc, 1);
+    for (unsigned i = start; i < start + map.value().size(); i++) {
+      dstDim = rewriter.create<arith::MulIOp>(loc, dstDim, srcShape[i]);
+    }
+    dstShape.push_back(dstDim);
+    start = start + map.value().size();
+  }
+  // Sanity.
+  assert(start == srcShape.size());
+  return dstShape;
+}
+
 /// Generate code for a general sparse to sparse reshaping operation.
 /// Note that unlike dense reshaping (which can be done with a "cheap"
 /// change of view), sparse reshaping is currently done with actual
@@ -468,17 +509,21 @@
   auto noPerm = SparseTensorEncodingAttr::get(
       op->getContext(), encSrc.getDimLevelType(), AffineMap(),
       encSrc.getPointerBitWidth(), encSrc.getIndexBitWidth());
-  SmallVector<Value, 4> sizes;
+  SmallVector<Value, 4> srcSizes;
   SmallVector<Value, 8> params;
-  sizesFromPtr(rewriter, sizes, op, noPerm, srcTp, src);
-  newParams(rewriter, params, op, srcTp, noPerm, Action::kToIterator, sizes,
+  sizesFromPtr(rewriter, srcSizes, op, noPerm, srcTp, src);
+  newParams(rewriter, params, op, srcTp, noPerm, Action::kToIterator, srcSizes,
             src);
   Value iter = genNewCall(rewriter, op, params);
   // Start a new COO for the destination tensor.
-  sizes.clear();
+  SmallVector<Value, 4> dstSizes;
   params.clear();
-  sizesFromPtr(rewriter, sizes, op, encDst, dstTp, src);
-  newParams(rewriter, params, op, dstTp, encDst, Action::kEmptyCOO, sizes);
+  if (srcTp.hasStaticShape()) {
+    sizesFromPtr(rewriter, dstSizes, op, encDst, dstTp, src);
+  } else {
+    dstSizes = genDstShape(loc, rewriter, srcSizes, reassociation);
+  }
+  newParams(rewriter, params, op, dstTp, encDst, Action::kEmptyCOO, dstSizes);
   Value coo = genNewCall(rewriter, op, params);
   Value dstPerm = params[2];
   // Construct a while loop over the iterator.
@@ -496,7 +541,8 @@
   // not need to store the value in elemPtr, as the value is still there.
   Block *after = rewriter.createBlock(&whileOp.getAfter(), {}, noTypes);
   rewriter.setInsertionPointToStart(after);
-  translateIndices(loc, rewriter, reassociation, dstTp, srcTp, dstIdx, srcIdx);
+  translateIndices(loc, rewriter, reassociation, dstTp, srcTp, dstIdx, srcIdx,
+    dstSizes, srcSizes);
   genAddEltCall(rewriter, op, elemTp, coo, elemPtr, dstIdx, dstPerm);
   rewriter.create<scf::YieldOp>(loc);
   // Final call to construct sparse tensor storage and free temporary resources.
diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_reshape.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_reshape.mlir
--- a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_reshape.mlir
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_reshape.mlir
@@ -135,6 +135,11 @@
     return %0 : tensor<?x?xf64, #SparseMatrix>
   }
 
+  func.func @collapse_sparse2sparse_dyn(%arg0: tensor<?x?x?x?xf64, #Sparse4dTensor>) -> tensor<?x?xf64, #SparseMatrix> {
+    %0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?x?x?xf64, #Sparse4dTensor> into tensor<?x?xf64, #SparseMatrix>
+    return %0 : tensor<?x?xf64, #SparseMatrix>
+  }
+
   //
   // Main driver.
   //
@@ -189,6 +194,7 @@
     %collapse8 = call @collapse_dense_dyn(%dn) : (tensor<?x?x?x?xf64>) -> tensor<?x?xf64>
     %collapse9 = call @collapse_from_sparse_dyn(%sdn) : (tensor<?x?x?x?xf64, #Sparse4dTensor>) -> tensor<?x?xf64>
     %collapse10 = call @collapse_to_sparse_dyn(%dn) : (tensor<?x?x?x?xf64>) -> tensor<?x?xf64, #SparseMatrix>
+    %collapse11 = call @collapse_sparse2sparse_dyn(%sdn) : (tensor<?x?x?x?xf64, #Sparse4dTensor>) -> tensor<?x?xf64, #SparseMatrix>
 
     //
     // Verify results of expand
@@ -250,6 +256,7 @@
     // CHECK-NEXT: ( ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ( 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ), ( 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 ), ( 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 ), ( 41, 42, 43, 44, 45, 26, 47, 48, 49, 50 ), ( 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 ) )
     // CHECK-NEXT: ( ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ( 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ), ( 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 ), ( 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 ), ( 41, 42, 43, 44, 45, 26, 47, 48, 49, 50 ), ( 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 ) )
     // CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 26, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, -1, -1, -1, -1 )
+    // CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 26, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, -1, -1, -1, -1 )
     //
 
     %v0 = vector.transfer_read %collapse0[%c0], %df: tensor<12xf64>, vector<12xf64>
@@ -281,6 +288,10 @@
     %b10 = sparse_tensor.values %collapse10 : tensor<?x?xf64, #SparseMatrix> to memref<?xf64>
     %v10 = vector.transfer_read %b10[%c0], %df: memref<?xf64>, vector<64xf64>
     vector.print %v10 : vector<64xf64>
+    %b11 = sparse_tensor.values %collapse11 : tensor<?x?xf64, #SparseMatrix> to memref<?xf64>
+    %v11 = vector.transfer_read %b11[%c0], %df: memref<?xf64>, vector<64xf64>
+    vector.print %v11 : vector<64xf64>
+
 
     // Release sparse resources.
     bufferization.dealloc_tensor %sv : tensor<12xf64, #SparseVector>
@@ -298,6 +309,7 @@
     bufferization.dealloc_tensor %collapse6 : tensor<6x10xf64, #SparseMatrix>
     bufferization.dealloc_tensor %collapse7 : tensor<6x10xf64, #SparseMatrix>
     bufferization.dealloc_tensor %collapse10 : tensor<?x?xf64, #SparseMatrix>
+    bufferization.dealloc_tensor %collapse11 : tensor<?x?xf64, #SparseMatrix>
 
     // Release dense resources.
     bufferization.dealloc_tensor %expand1 : tensor<3x4xf64>