Special case tfnp ndarrays in some places, and minor improvements to tfnp array

functions

PiperOrigin-RevId: 317735476
Change-Id: I58123c5fb64257975e220df4e57f2a1f74856da7

tensorflow/python/eager/BUILD

@@ -560,7 +560,6 @@ py_library(
         "//tensorflow/python:graph_to_function_def",
         "//tensorflow/python:pywrap_tf_session",
         "//tensorflow/python:util",
-        "//tensorflow/python/ops/numpy_ops:numpy",
         "//third_party/py/numpy",
         "@six_archive//:six",
     ],

tensorflow/python/eager/function.py

@@ -81,9 +81,6 @@ from tensorflow.python.util import tf_inspect
 ag_ctx = lazy_loader.LazyLoader(
     "ag_ctx", globals(),
     "tensorflow.python.autograph.core.ag_ctx")
-np_arrays = lazy_loader.LazyLoader(
-    "np_arrays", globals(),
-    "tensorflow.python.ops.numpy_ops.np_arrays")
 
 
 FORWARD_FUNCTION_ATTRIBUTE_NAME = "forward_function_name"
@@ -1487,11 +1484,6 @@ class ConcreteFunction(object):
     self._func_graph = func_graph
     self._captured_inputs = self._func_graph.external_captures
     self._captured_closures = self._func_graph.deferred_external_captures
-    structured_outputs = self._func_graph.structured_outputs
-    self._ndarrays_list = (
-        isinstance(structured_outputs, (list, tuple)) and
-        all([isinstance(o, np_arrays.ndarray) for o in structured_outputs]))
-    self._ndarray_singleton = isinstance(structured_outputs, np_arrays.ndarray)
 
     # function_spec defines the structured signature.
     self._set_function_spec(function_spec)
@@ -2158,15 +2150,9 @@ class ConcreteFunction(object):
     if self._func_graph.structured_outputs is None:
       return result
 
-    if result:
-      if self._ndarrays_list:
-        return [np_arrays.tensor_to_ndarray(o) for o in result]
-      elif self._ndarray_singleton:
-        return np_arrays.tensor_to_ndarray(result[0])
-
     # Replace outputs with results, skipping over any 'None' values.
-    outputs_list = nest.flatten(
-        self._func_graph.structured_outputs, expand_composites=True)
+    outputs_list = nest.flatten(self._func_graph.structured_outputs,
+                                expand_composites=True)
     j = 0
     for i, o in enumerate(outputs_list):
       if o is not None:

tensorflow/python/ops/numpy_ops/np_arrays.py

@@ -149,23 +149,10 @@ class ndarray(composite_tensor.CompositeTensor):  # pylint: disable=invalid-name
     if dtype and dtype != buffer.dtype:
       buffer = array_ops.bitcast(buffer, dtype)
     self._data = buffer
-    self._type_spec_internal = None
-
-  @classmethod
-  def from_tensor(cls, tensor):
-    o = cls.__new__(cls, None)
-    # pylint: disable=protected-access
-    o._data = tensor
-    o._type_spec_internal = None
-    # pylint: enable=protected-access
-    return o
 
   @property
   def _type_spec(self):
-    if self._type_spec_internal is None:
-      self._type_spec_internal = NdarraySpec(
-          type_spec.type_spec_from_value(self._data))
-    return self._type_spec_internal
+    return NdarraySpec(type_spec.type_spec_from_value(self._data))
 
   @property
   def data(self):
@@ -312,7 +299,7 @@ class ndarray(composite_tensor.CompositeTensor):  # pylint: disable=invalid-name
 
 
 def tensor_to_ndarray(tensor):
-  return ndarray.from_tensor(tensor)
+  return ndarray(tensor._shape_tuple(), dtype=tensor.dtype, buffer=tensor)  # pylint: disable=protected-access
 
 
 def ndarray_to_tensor(arr, dtype=None, name=None, as_ref=False):

tensorflow/python/ops/numpy_ops/np_interop_test.py

@@ -64,9 +64,7 @@ class InteropTest(test.TestCase):
 
     dx, dy = t.gradient([xx, yy], [x, y])
 
-    # # TODO(nareshmodi): Figure out a way to rewrap ndarray as tensors.
-    # self.assertIsInstance(dx, np_arrays.ndarray)
-    # self.assertIsInstance(dy, np_arrays.ndarray)
+    # TODO(nareshmodi): Gradient tape returns tensors. Is it possible to rewrap?
     self.assertAllClose(dx, 2.0)
     self.assertAllClose(dy, 3.0)