Automated rollback of commit 4cd79b3f6361b6518463349a51fe33f7520f3b49

PiperOrigin-RevId: 211832421

2 files changed, 17 insertions, 63 deletions

diff --git a/tensorflow/contrib/opt/python/training/lazy_adam_optimizer.py b/tensorflow/contrib/opt/python/training/lazy_adam_optimizer.py
index f026f437dc..72117c1e81 100644
--- a/tensorflow/contrib/opt/python/training/lazy_adam_optimizer.py
+++ b/tensorflow/contrib/opt/python/training/lazy_adam_optimizer.py
@@ -25,11 +25,9 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.training import adam
 
@@ -48,12 +46,7 @@ class LazyAdamOptimizer(adam.AdamOptimizer):
   may lead to different empirical results.
   """
 
-  def _apply_sparse_shared(self,
-                           grad,
-                           var,
-                           indices,
-                           scatter_update,
-                           scatter_sub):
+  def _apply_sparse(self, grad, var):
     beta1_power, beta2_power = self._get_beta_accumulators()
     beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype)
     beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype)
@@ -65,51 +58,23 @@ class LazyAdamOptimizer(adam.AdamOptimizer):
 
     # \\(m := beta1 * m + (1 - beta1) * g_t\\)
     m = self.get_slot(var, "m")
-    m_t = scatter_update(m, indices,
-                         beta1_t * array_ops.gather(m, indices) +
-                         (1 - beta1_t) * grad)
+    m_t = state_ops.scatter_update(m, grad.indices,
+                                   beta1_t * array_ops.gather(m, grad.indices) +
+                                   (1 - beta1_t) * grad.values,
+                                   use_locking=self._use_locking)
 
     # \\(v := beta2 * v + (1 - beta2) * (g_t * g_t)\\)
     v = self.get_slot(var, "v")
-    v_t = scatter_update(v, indices,
-                         beta2_t * array_ops.gather(v, indices) +
-                         (1 - beta2_t) * math_ops.square(grad))
+    v_t = state_ops.scatter_update(v, grad.indices,
+                                   beta2_t * array_ops.gather(v, grad.indices) +
+                                   (1 - beta2_t) * math_ops.square(grad.values),
+                                   use_locking=self._use_locking)
 
     # \\(variable -= learning_rate * m_t / (epsilon_t + sqrt(v_t))\\)
-    m_t_slice = array_ops.gather(m_t, indices)
-    v_t_slice = array_ops.gather(v_t, indices)
+    m_t_slice = array_ops.gather(m_t, grad.indices)
+    v_t_slice = array_ops.gather(v_t, grad.indices)
     denominator_slice = math_ops.sqrt(v_t_slice) + epsilon_t
-    var_update = scatter_sub(var, indices,
-                             lr * m_t_slice / denominator_slice)
+    var_update = state_ops.scatter_sub(var, grad.indices,
+                                       lr * m_t_slice / denominator_slice,
+                                       use_locking=self._use_locking)
     return control_flow_ops.group(var_update, m_t, v_t)
-
-  def _apply_sparse(self, grad, var):
-    return self._apply_sparse_shared(
-        grad.values, var, grad.indices,
-        self._scatter_update,
-        self._scatter_sub)
-
-  def _resource_apply_sparse(self, grad, var, indices):
-    return self._apply_sparse_shared(
-        grad, var, indices,
-        self._resource_scatter_update,
-        self._resource_scatter_sub)
-
-  # Utility functions for updating resource or non-resource variables.
-  def _scatter_update(self, x, i, v):
-    return state_ops.scatter_update(
-        x, i, v, use_locking=self._use_locking)
-
-  def _scatter_sub(self, x, i, v):
-    return state_ops.scatter_sub(
-        x, i, v, use_locking=self._use_locking)
-
-  def _resource_scatter_update(self, x, i, v):
-    update_op = resource_variable_ops.resource_scatter_update(x.handle, i, v)
-    with ops.control_dependencies([update_op]):
-      return x.value()
-
-  def _resource_scatter_sub(self, x, i, v):
-    sub_op = resource_variable_ops.resource_scatter_sub(x.handle, i, v)
-    with ops.control_dependencies([sub_op]):
-      return x.value()
diff --git a/tensorflow/contrib/opt/python/training/lazy_adam_optimizer_test.py b/tensorflow/contrib/opt/python/training/lazy_adam_optimizer_test.py
index d3e9e89502..dc4c462ce4 100644
--- a/tensorflow/contrib/opt/python/training/lazy_adam_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/lazy_adam_optimizer_test.py
@@ -27,7 +27,6 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 
@@ -52,7 +51,7 @@ def adam_update_numpy(param,
 
 class AdamOptimizerTest(test.TestCase):
 
-  def doTestSparse(self, use_resource=False):
+  def testSparse(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
       with self.cached_session():
         # Initialize variables for numpy implementation.
@@ -62,12 +61,8 @@ class AdamOptimizerTest(test.TestCase):
         var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
         grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
 
-        if use_resource:
-          var0 = resource_variable_ops.ResourceVariable(var0_np)
-          var1 = resource_variable_ops.ResourceVariable(var1_np)
-        else:
-          var0 = variables.Variable(var0_np)
-          var1 = variables.Variable(var1_np)
+        var0 = variables.Variable(var0_np)
+        var1 = variables.Variable(var1_np)
         grads0_np_indices = np.array([0, 1], dtype=np.int32)
         grads0 = ops.IndexedSlices(
             constant_op.constant(grads0_np),
@@ -99,12 +94,6 @@ class AdamOptimizerTest(test.TestCase):
           self.assertAllCloseAccordingToType(var0_np, var0.eval())
           self.assertAllCloseAccordingToType(var1_np, var1.eval())
 
-  def testSparse(self):
-    self.doTestSparse(use_resource=False)
-
-  def testResourceSparse(self):
-    self.doTestSparse(use_resource=True)
-
   def testSparseDevicePlacement(self):
     for index_dtype in [dtypes.int32, dtypes.int64]:
       with self.test_session(force_gpu=test.is_gpu_available()):