Fixes issue where gradients are being quantized. In addition, because gradients are quantized, quantization fails for any node that is a producer of gradients since the _FollowedByFakeQuant method prevents any node followed by a fake quant from being properly quantized.

PiperOrigin-RevId: 209010415

2 files changed, 1 insertions, 29 deletions

diff --git a/tensorflow/contrib/quantize/python/quantize.py b/tensorflow/contrib/quantize/python/quantize.py
index 2ddbd73ea6..8ad5b42d18 100644
--- a/tensorflow/contrib/quantize/python/quantize.py
+++ b/tensorflow/contrib/quantize/python/quantize.py
@@ -198,7 +198,7 @@ def _FindLayersToQuantize(graph):
             |
     [post_conv_correction]
             |
-     [biasadd|folded_bias]
+     biasadd|folded_bias
             |
          [bypass]
             |
@@ -320,7 +320,6 @@ def _FindLayersToQuantize(graph):
               folded_bias_add_pattern,
               batch_norm_identity,
               bypass_pattern,
-              layer_pattern,
           ])
       ])
 
diff --git a/tensorflow/contrib/quantize/python/quantize_test.py b/tensorflow/contrib/quantize/python/quantize_test.py
index 212d902a3c..2369896562 100644
--- a/tensorflow/contrib/quantize/python/quantize_test.py
+++ b/tensorflow/contrib/quantize/python/quantize_test.py
@@ -194,33 +194,6 @@ class QuantizeTest(test_util.TensorFlowTestCase):
 
         self.assertNotIn('test/relu6', [c.name for c in consumers])
 
-  def testLayerActivationQuantized(self):
-    self._RunTestOverParameters(self._TestLayerActivationQuantized)
-
-  def _TestLayerActivationQuantized(self, is_training):
-    graph = ops.Graph()
-    with graph.as_default():
-      batch_size, height, width, depth = 5, 128, 128, 3
-      input1 = array_ops.zeros((batch_size, height, width, depth))
-      _ = conv2d(
-          input1,
-          32, [5, 5],
-          stride=2,
-          padding='SAME',
-          weights_initializer=self._WeightInit(0.09),
-          activation_fn=nn_ops.relu6,
-          biases_initializer=None,
-          scope='test')
-      # Ensure that both weights and output of activations are quantized
-      # when we have a conv->relu6 with no bias add
-      quantize.Quantize(graph, is_training, weight_bits=8, activation_bits=8)
-      activation_op = graph.get_operation_by_name('test/Relu6')
-      conv_op = graph.get_operation_by_name('test/Conv2D')
-      self.assertTrue('test/weights_quant/FakeQuantWithMinMaxVars:0' in
-                      [tensor_in.name for tensor_in in conv_op.inputs])
-      self.assertTrue('FakeQuantWithMinMaxVars' in
-                      [op.type for op in activation_op.outputs[0].consumers()])
-
   def testFinalLayerQuantized(self):
     self._RunTestOverParameters(self._TestFinalLayerQuantized)