Update ops-related pbtxt files.

PiperOrigin-RevId: 170240603

2 files changed, 368 insertions, 2 deletions

diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
index 8ca7a5f92e..8d4e182bf5 100644
--- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt
@@ -10402,6 +10402,172 @@ op {
   }
 }
 op {
+  name: "FusedBatchNormGradV2"
+  input_arg {
+    name: "y_backprop"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "scale"
+    type: DT_FLOAT
+  }
+  input_arg {
+    name: "reserve_space_1"
+    type_attr: "U"
+  }
+  input_arg {
+    name: "reserve_space_2"
+    type_attr: "U"
+  }
+  output_arg {
+    name: "x_backprop"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "scale_backprop"
+    type_attr: "U"
+  }
+  output_arg {
+    name: "offset_backprop"
+    type_attr: "U"
+  }
+  output_arg {
+    name: "reserve_space_3"
+    type_attr: "U"
+  }
+  output_arg {
+    name: "reserve_space_4"
+    type_attr: "U"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    name: "U"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    name: "epsilon"
+    type: "float"
+    default_value {
+      f: 0.0001
+    }
+  }
+  attr {
+    name: "data_format"
+    type: "string"
+    default_value {
+      s: "NHWC"
+    }
+  }
+  attr {
+    name: "is_training"
+    type: "bool"
+    default_value {
+      b: true
+    }
+  }
+}
+op {
+  name: "FusedBatchNormV2"
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "scale"
+    type_attr: "U"
+  }
+  input_arg {
+    name: "offset"
+    type_attr: "U"
+  }
+  input_arg {
+    name: "mean"
+    type_attr: "U"
+  }
+  input_arg {
+    name: "variance"
+    type_attr: "U"
+  }
+  output_arg {
+    name: "y"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "batch_mean"
+    type_attr: "U"
+  }
+  output_arg {
+    name: "batch_variance"
+    type_attr: "U"
+  }
+  output_arg {
+    name: "reserve_space_1"
+    type_attr: "U"
+  }
+  output_arg {
+    name: "reserve_space_2"
+    type_attr: "U"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    name: "U"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    name: "epsilon"
+    type: "float"
+    default_value {
+      f: 0.0001
+    }
+  }
+  attr {
+    name: "data_format"
+    type: "string"
+    default_value {
+      s: "NHWC"
+    }
+  }
+  attr {
+    name: "is_training"
+    type: "bool"
+    default_value {
+      b: true
+    }
+  }
+}
+op {
   name: "FusedPadConv2D"
   input_arg {
     name: "input"
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index a60ba0e37e..1fc7b932e5 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -9178,12 +9178,12 @@ op {
   }
   input_arg {
     name: "reserve_space_1"
-    description: "When is_training is True, a 1D Tensor for the computed batch mean\nto be reused in gradient computation.\nWhen is_training is False, a 1D Tensor for the population mean\nto be reused in both 1st and 2nd order gradient computation."
+    description: "When is_training is True, a 1D Tensor for the computed batch\nmean to be reused in gradient computation. When is_training is\nFalse, a 1D Tensor for the population mean to be reused in both\n1st and 2nd order gradient computation."
     type_attr: "T"
   }
   input_arg {
     name: "reserve_space_2"
-    description: "When is_training is True, a 1D Tensor for the computed batch variance\n(inverted variance in the cuDNN case) to be reused in gradient computation.\nWhen is_training is False, a 1D Tensor for the population variance\nto be reused in both 1st and 2nd order gradient computation."
+    description: "When is_training is True, a 1D Tensor for the computed batch\nvariance (inverted variance in the cuDNN case) to be reused in\ngradient computation. When is_training is False, a 1D Tensor\nfor the population variance to be reused in both 1st and 2nd\norder gradient computation."
     type_attr: "T"
   }
   output_arg {
@@ -9249,6 +9249,206 @@ op {
   description: "Note that the size of 4D Tensors are defined by either \"NHWC\" or \"NCHW\".\nThe size of 1D Tensors matches the dimension C of the 4D Tensors."
 }
 op {
+  name: "FusedBatchNormGradV2"
+  input_arg {
+    name: "y_backprop"
+    description: "A 4D Tensor for the gradient with respect to y."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "x"
+    description: "A 4D Tensor for input data."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "scale"
+    description: "A 1D Tensor for scaling factor, to scale the normalized x."
+    type: DT_FLOAT
+  }
+  input_arg {
+    name: "reserve_space_1"
+    description: "When is_training is True, a 1D Tensor for the computed batch\nmean to be reused in gradient computation. When is_training is\nFalse, a 1D Tensor for the population mean to be reused in both\n1st and 2nd order gradient computation."
+    type_attr: "U"
+  }
+  input_arg {
+    name: "reserve_space_2"
+    description: "When is_training is True, a 1D Tensor for the computed batch\nvariance (inverted variance in the cuDNN case) to be reused in\ngradient computation. When is_training is False, a 1D Tensor\nfor the population variance to be reused in both 1st and 2nd\norder gradient computation."
+    type_attr: "U"
+  }
+  output_arg {
+    name: "x_backprop"
+    description: "A 4D Tensor for the gradient with respect to x."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "scale_backprop"
+    description: "A 1D Tensor for the gradient with respect to scale."
+    type_attr: "U"
+  }
+  output_arg {
+    name: "offset_backprop"
+    description: "A 1D Tensor for the gradient with respect to offset."
+    type_attr: "U"
+  }
+  output_arg {
+    name: "reserve_space_3"
+    description: "Unused placeholder to match the mean input in FusedBatchNorm."
+    type_attr: "U"
+  }
+  output_arg {
+    name: "reserve_space_4"
+    description: "Unused placeholder to match the variance input\nin FusedBatchNorm."
+    type_attr: "U"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    description: "The data type for the elements of input and output Tensors."
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    name: "U"
+    type: "type"
+    description: "The data type for the scale, offset, mean, and variance."
+    allowed_values {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    name: "epsilon"
+    type: "float"
+    default_value {
+      f: 0.0001
+    }
+    description: "A small float number added to the variance of x."
+  }
+  attr {
+    name: "data_format"
+    type: "string"
+    default_value {
+      s: "NHWC"
+    }
+    description: "The data format for y_backprop, x, x_backprop.\nEither \"NHWC\" (default) or \"NCHW\"."
+  }
+  attr {
+    name: "is_training"
+    type: "bool"
+    default_value {
+      b: true
+    }
+    description: "A bool value to indicate the operation is for training (default)\nor inference."
+  }
+  summary: "Gradient for batch normalization."
+  description: "Note that the size of 4D Tensors are defined by either \"NHWC\" or \"NCHW\".\nThe size of 1D Tensors matches the dimension C of the 4D Tensors."
+}
+op {
+  name: "FusedBatchNormV2"
+  input_arg {
+    name: "x"
+    description: "A 4D Tensor for input data."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "scale"
+    description: "A 1D Tensor for scaling factor, to scale the normalized x."
+    type_attr: "U"
+  }
+  input_arg {
+    name: "offset"
+    description: "A 1D Tensor for offset, to shift to the normalized x."
+    type_attr: "U"
+  }
+  input_arg {
+    name: "mean"
+    description: "A 1D Tensor for population mean. Used for inference only;\nmust be empty for training."
+    type_attr: "U"
+  }
+  input_arg {
+    name: "variance"
+    description: "A 1D Tensor for population variance. Used for inference only;\nmust be empty for training."
+    type_attr: "U"
+  }
+  output_arg {
+    name: "y"
+    description: "A 4D Tensor for output data."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "batch_mean"
+    description: "A 1D Tensor for the computed batch mean, to be used by TensorFlow\nto compute the running mean."
+    type_attr: "U"
+  }
+  output_arg {
+    name: "batch_variance"
+    description: "A 1D Tensor for the computed batch variance, to be used by\nTensorFlow to compute the running variance."
+    type_attr: "U"
+  }
+  output_arg {
+    name: "reserve_space_1"
+    description: "A 1D Tensor for the computed batch mean, to be reused\nin the gradient computation."
+    type_attr: "U"
+  }
+  output_arg {
+    name: "reserve_space_2"
+    description: "A 1D Tensor for the computed batch variance (inverted variance\nin the cuDNN case), to be reused in the gradient computation."
+    type_attr: "U"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    description: "The data type for the elements of input and output Tensors."
+    allowed_values {
+      list {
+        type: DT_HALF
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    name: "U"
+    type: "type"
+    description: "The data type for the scale, offset, mean, and variance."
+    allowed_values {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    name: "epsilon"
+    type: "float"
+    default_value {
+      f: 0.0001
+    }
+    description: "A small float number added to the variance of x."
+  }
+  attr {
+    name: "data_format"
+    type: "string"
+    default_value {
+      s: "NHWC"
+    }
+    description: "The data format for x and y. Either \"NHWC\" (default) or \"NCHW\"."
+  }
+  attr {
+    name: "is_training"
+    type: "bool"
+    default_value {
+      b: true
+    }
+    description: "A bool value to indicate the operation is for training (default)\nor inference."
+  }
+  summary: "Batch normalization."
+  description: "Note that the size of 4D Tensors are defined by either \"NHWC\" or \"NCHW\".\nThe size of 1D Tensors matches the dimension C of the 4D Tensors."
+}
+op {
   name: "FusedPadConv2D"
   input_arg {
     name: "input"