Preserve shape information when passing SparseTensors to dataset functions

When we flatten SparseTensors into Tensors, the dense_shape of the SparseTensor is stored as a Tensor of dimensions instead of as a shape. Function tracing uses placeholder Tensors with no content, making it look as though all input SparseTensors have undefined shape.

This CL improves tracing by restoring SparseTensors' dense_shapes from their original SparseTensorSpecs.

PiperOrigin-RevId: 264927072

tensorflow/python/data/kernel_tests/map_test.py

@@ -733,6 +733,30 @@ class MapTest(test_base.DatasetTestBase, parameterized.TestCase):
         dataset,
         expected_output=[self.evaluate(_check(_sparse(i))) for i in range(10)])
 
+  def testSparseMapShapeInference(self):
+    if not context.executing_eagerly():
+      self.skipTest("SparseTensor shape inference requires eager mode")
+    row_lengths = np.random.randint(0, 4, size=128)
+    values = np.ones(np.sum(row_lengths))
+    sparse = ragged_tensor.RaggedTensor.from_row_lengths(
+        values, row_lengths).to_sparse()
+    dataset = dataset_ops.Dataset.from_tensor_slices(sparse)
+    dataset = dataset.batch(32, drop_remainder=True)
+    dataset = dataset.map(lambda x: x)
+    self.assertEqual((32, 3), dataset.element_spec.shape)
+
+  def testSparseMapShapeInferencePartial(self):
+    if not context.executing_eagerly():
+      self.skipTest("SparseTensor shape inference requires eager mode")
+    row_lengths = np.random.randint(0, 4, size=128)
+    values = np.ones(np.sum(row_lengths))
+    sparse = ragged_tensor.RaggedTensor.from_row_lengths(
+        values, row_lengths).to_sparse()
+    dataset = dataset_ops.Dataset.from_tensor_slices(sparse)
+    dataset = dataset.batch(32, drop_remainder=False)
+    dataset = dataset.map(lambda x: x)
+    self.assertEqual([None, 3], dataset.element_spec.shape.as_list())
+
   def testTensorArray(self):
 
     def _tensor_array(i):

tensorflow/python/framework/sparse_tensor.py

@@ -24,6 +24,7 @@ import numpy as np
 from tensorflow.python import pywrap_tensorflow
 from tensorflow.python import tf2
 from tensorflow.python.framework import composite_tensor
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_like
@@ -338,11 +339,28 @@ class SparseTensorSpec(type_spec.BatchableTypeSpec):
 
   def _from_compatible_tensor_list(self, tensor_list):
     tensor_list = gen_sparse_ops.deserialize_sparse(tensor_list[0], self._dtype)
-    result = SparseTensor(*tensor_list)
+    indices, values, dense_shape = tensor_list
     rank = self._shape.ndims
-    result.indices.set_shape([None, rank])
-    result.dense_shape.set_shape([rank])
-    return result
+    indices.set_shape([None, rank])
+    # We restore the dense_shape from the SparseTypeSpec. This is necessary
+    # for shape inference when using placeholder SparseTensors in function
+    # tracing.
+    if self._shape.is_fully_defined():
+      dense_shape = ops.convert_to_tensor(
+          self._shape, dtype=dtypes.int64, name="shape")
+    elif (self._shape.rank is not None and
+          any(dim.value is not None for dim in self._shape.dims)):
+      # array_ops imports sparse_tensor.py. Local import to avoid import cycle.
+      from tensorflow.python.ops import array_ops  # pylint: disable=g-import-not-at-top
+      pieces = array_ops.unstack(dense_shape, num=self._shape.rank)
+      for i, dim in enumerate(self._shape.dims):
+        if dim.value is not None:
+          pieces[i] = constant_op.constant(dim.value, dense_shape.dtype)
+      dense_shape = array_ops.stack(pieces)
+    else:
+      dense_shape.set_shape([rank])
+
+    return SparseTensor(indices, values, dense_shape)
 
   def _batch(self, batch_size):
     return SparseTensorSpec(