Switch ops in functional_ops, math_ops, state_ops, string_ops,

contrib/quantization, and contrib/layers to use C++ shape inference functions.

Implement Betainc C++ shape inference function; it's a little different from
the python one, mainly because propagating an unknown input (a_shape in this
case) when all are unknown is not correct for C++ (in case we later backwards
bind the value), since it could end up being a broadcasted scalar.

Change ReduceJoin's C++ shape inference function to match python.

Fix SymbolicGradient's shape function in C++ - there are actually more inputs
than outputs.

Changes QuantizedBiasAdd to be more precise (the C++ shape fn invokes the common bias_add to compute the shape of the first output).
Change: 132688147

2 files changed, 5 insertions, 63 deletions

diff --git a/tensorflow/contrib/quantization/python/math_ops.py b/tensorflow/contrib/quantization/python/math_ops.py
index 43c1409358..d4fabbd36b 100644
--- a/tensorflow/contrib/quantization/python/math_ops.py
+++ b/tensorflow/contrib/quantization/python/math_ops.py
@@ -23,16 +23,6 @@ from tensorflow.contrib.quantization.ops import gen_math_ops
 from tensorflow.contrib.quantization.ops.gen_math_ops import *
 from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
 
 
-# QuantizedMatMul* ops.
-@ops.RegisterShape("QuantizedMatMul")
-def _QuantizedMatMulShape(op):
-  unused_a_min = op.inputs[2].get_shape().merge_with(tensor_shape.scalar())
-  unused_a_max = op.inputs[3].get_shape().merge_with(tensor_shape.scalar())
-  unused_b_min = op.inputs[4].get_shape().merge_with(tensor_shape.scalar())
-  unused_b_max = op.inputs[5].get_shape().merge_with(tensor_shape.scalar())
-  result = common_shapes.matmul_shape(op)
-  result.extend([tensor_shape.scalar(), tensor_shape.scalar()])
-  return result
+ops.RegisterShape("QuantizedMatMul")(common_shapes.call_cpp_shape_fn)
diff --git a/tensorflow/contrib/quantization/python/nn_ops.py b/tensorflow/contrib/quantization/python/nn_ops.py
index 122d93fd23..d31f1d4e68 100644
--- a/tensorflow/contrib/quantization/python/nn_ops.py
+++ b/tensorflow/contrib/quantization/python/nn_ops.py
@@ -23,60 +23,12 @@ from tensorflow.contrib.quantization.ops import gen_nn_ops
 from tensorflow.contrib.quantization.ops.gen_nn_ops import *
 from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
-
-
-# QuantizedAvgPool* ops.
-@ops.RegisterShape("QuantizedAvgPool")
-def _QuantizedAvgPoolShape(op):
-  return [common_shapes.avg_pool_shape(op)[0], tensor_shape.scalar(),
-          tensor_shape.scalar()]
-
-
-# QuantizedBiasAdd op.
-@ops.RegisterShape("QuantizedBiasAdd")
-def _QuantizedBiasAddShape(op):
-  """Returns the same shape as the input, plus min and max scalar values.
-
-  Args:
-    op: Input operation.
-  Returns:
-    Shape of ops first input, plus min and max tensors.
-  """
-  unused_input_min = op.inputs[2].get_shape().merge_with(tensor_shape.scalar())
-  unused_input_max = op.inputs[3].get_shape().merge_with(tensor_shape.scalar())
-  unused_bias_min = op.inputs[4].get_shape().merge_with(tensor_shape.scalar())
-  unused_bias_max = op.inputs[5].get_shape().merge_with(tensor_shape.scalar())
-  return [op.inputs[0].get_shape(), tensor_shape.scalar(),
-          tensor_shape.scalar()]
-
-
-# QuantizedConv2D* ops.
-@ops.RegisterShape("QuantizedConv2D")
-def _QuantizedConv2DShape(op):
-  """Returns the same shape as Conv2D, plus min and max scalar values.
-
-  Args:
-    op: Input operation.
-  Returns:
-    Shape of float Conv2D, plus min and max tensors.
-  """
-  unused_input_min = op.inputs[2].get_shape().merge_with(tensor_shape.scalar())
-  unused_input_max = op.inputs[3].get_shape().merge_with(tensor_shape.scalar())
-  unused_filter_min = op.inputs[4].get_shape().merge_with(tensor_shape.scalar())
-  unused_filter_max = op.inputs[5].get_shape().merge_with(tensor_shape.scalar())
-  result = common_shapes.conv2d_shape(op)
-  result.extend([tensor_shape.scalar(), tensor_shape.scalar()])
-  return result
-
-
-# QuantizedMaxPool* ops.
-@ops.RegisterShape("QuantizedMaxPool")
-def _QuantizedMaxPoolShape(op):
-  return [common_shapes.max_pool_shape(op)[0], tensor_shape.scalar(),
-          tensor_shape.scalar()]
 
 
+ops.RegisterShape("QuantizedAvgPool")(common_shapes.call_cpp_shape_fn)
+ops.RegisterShape("QuantizedBiasAdd")(common_shapes.call_cpp_shape_fn)
+ops.RegisterShape("QuantizedConv2D")(common_shapes.call_cpp_shape_fn)
+ops.RegisterShape("QuantizedMaxPool")(common_shapes.call_cpp_shape_fn)
 ops.RegisterShape("QuantizedRelu")(common_shapes.call_cpp_shape_fn)
 ops.RegisterShape("QuantizedRelu6")(common_shapes.call_cpp_shape_fn)
 ops.RegisterShape("QuantizedReluX")(common_shapes.call_cpp_shape_fn)