Add tf.quantize op, which is the same as tf.quantize_v2.

PiperOrigin-RevId: 173735986

tensorflow/core/kernels/quantize_op.cc

@@ -250,5 +250,4 @@ REGISTER_KERNEL_BUILDER(
 REGISTER_KERNEL_BUILDER(
     Name("QuantizeV2").Device(DEVICE_CPU).TypeConstraint<qint32>("T"),
     QuantizeV2Op<CPUDevice, qint32>);
-
 }  // namespace tensorflow

tensorflow/core/ops/array_ops.cc

@@ -4902,10 +4902,10 @@ with the range of qint8.
 If the mode is 'MIN_FIRST', then this approach is used:
 
 ```
-number_of_steps = 1 << (# of bits in T)
-range_adjust = number_of_steps / (number_of_steps - 1)
+num_discrete_values = 1 << (# of bits in T)
+range_adjust = num_discrete_values / (num_discrete_values - 1)
 range = (range_max - range_min) * range_adjust
-range_scale = number_of_steps / range
+range_scale = num_discrete_values / range
 quantized = round(input * range_scale) - round(range_min * range_scale) +
   numeric_limits<T>::min()
 quantized = max(quantized, numeric_limits<T>::min())
@@ -5017,10 +5017,10 @@ each value by 128 prior to casting.
 If the mode is 'MIN_FIRST', then this approach is used:
 
 ```c++
-number_of_steps = 1 << (# of bits in T)
-range_adjust = number_of_steps / (number_of_steps - 1)
+num_discrete_values = 1 << (# of bits in T)
+range_adjust = num_discrete_values / (num_discrete_values - 1)
 range = (range_max - range_min) * range_adjust
-range_scale = range / number_of_steps
+range_scale = range / num_discrete_values
 const double offset_input = static_cast<double>(input) - lowest_quantized;
 result = range_min + ((input - numeric_limits<T>::min()) * range_scale)
 ```

tensorflow/python/ops/array_ops.py

@@ -66,6 +66,7 @@ See the @{$python/array_ops} guide.
 @@one_hot
 @@sequence_mask
 @@dequantize
+@@quantize
 @@quantize_v2
 @@quantized_concat
 @@setdiff1d
@@ -2525,7 +2526,10 @@ gather.__doc__ = gen_array_ops.gather_v2.__doc__
 
 # Define quantize_v2 here in order to make name the second-to-last attribute,
 # because round_mode was added later.
-def quantize_v2(input,
+@deprecation.deprecated(
+    "2017-10-25",
+    "`tf.quantize_v2` is deprecated, please use `tf.quantize` instead.")
+def quantize_v2(input,  # pylint: disable=redefined-builtin
                 min_range,
                 max_range,
                 T,
@@ -2541,4 +2545,26 @@ def quantize_v2(input,
                                    round_mode=round_mode)
 
 
-quantize_v2.__doc__ = gen_array_ops.quantize_v2.__doc__
+quantize_v2.__doc__ = """Please use `tf.quantize` instead."""
+
+
+# We want to expose tf.quantize instead of tf.quantize_v2; we can deprecate
+# tf.quantize_v2 in next version of TensorFlow.
+def quantize(input,  # pylint: disable=redefined-builtin
+             min_range,
+             max_range,
+             T,
+             mode="MIN_COMBINED",
+             round_mode="HALF_AWAY_FROM_ZERO",
+             name=None):
+  return gen_array_ops.quantize_v2(
+      input,
+      min_range,
+      max_range,
+      T,
+      mode=mode,
+      round_mode=round_mode,
+      name=name)
+
+
+quantize.__doc__ = gen_array_ops.quantize_v2.__doc__

tensorflow/tools/api/golden/tensorflow.pbtxt

@@ -1480,6 +1480,10 @@ tf_module {
     name: "qr"
     argspec: "args=[\'input\', \'full_matrices\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], "
   }
+  member_method {
+    name: "quantize"
+    argspec: "args=[\'input\', \'min_range\', \'max_range\', \'T\', \'mode\', \'round_mode\', \'name\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'HALF_AWAY_FROM_ZERO\', \'None\'], "
+  }
   member_method {
     name: "quantize_v2"
     argspec: "args=[\'input\', \'min_range\', \'max_range\', \'T\', \'mode\', \'name\', \'round_mode\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'None\', \'HALF_AWAY_FROM_ZERO\'], "