chore: import upstream snapshot with attribution

tensorflow/python/framework/experimental/tape.py

@@ -0,0 +1,53 @@
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Experimental impl for GradientTape using unified APIs, for testing only."""
+
+from tensorflow.python.framework.experimental import _tape
+from tensorflow.python.framework.experimental import context_stack
+from tensorflow.python.framework.experimental import gradient_registry
+from tensorflow.python.util import nest
+
+
+class GradientTape(object):
+  """GradientTape using the unified API."""
+
+  def __init__(self, persistent=False):
+    self._c_tape = _tape.Tape(persistent)
+    ctx = context_stack.get_default()
+    self._tape_context = _tape.TapeContext(
+        ctx, self._c_tape, gradient_registry.get_global_registry())
+    self._ctx_manager = None
+
+  def watch(self, t):
+    self._c_tape.Watch(t)
+
+  # TODO(srbs): Add support for unconnected_gradients.
+  def gradient(self, targets, sources, output_gradients=None):
+    ctx = context_stack.get_default()
+    flat_targets = nest.flatten(targets)
+    flat_sources = nest.flatten(sources)
+    out_grads = self._c_tape.ComputeGradient(ctx, flat_targets, flat_sources,
+                                             output_gradients or [])
+    return nest.pack_sequence_as(sources, out_grads)
+
+  def __enter__(self):
+    """Enters a context inside which operations are recorded on this tape."""
+    self._ctx_manager = context_stack.set_default(self._tape_context)
+    self._ctx_manager.__enter__()
+    return self
+
+  def __exit__(self, typ, value, traceback):
+    self._ctx_manager.__exit__(typ, value, traceback)
+    self._ctx_manager = None