From 2cdd6cfac57cd0c3a71989f971112247b2532cd3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 17 Dec 2020 18:28:15 -0800
Subject: [PATCH] Op documentation update. 	update of g3doc/tf_ops.md 
 update of g3doc/includes/tf_passes.md

PiperOrigin-RevId: 348130050
Change-Id: I75a1e19967d7af7f7a90ffbee82b364320788589
---
 .../compiler/mlir/g3doc/includes/tf_passes.md | 30 +++++++++++++++++++
 1 file changed, 30 insertions(+)

diff --git a/tensorflow/compiler/mlir/g3doc/includes/tf_passes.md b/tensorflow/compiler/mlir/g3doc/includes/tf_passes.md
index 40d59bba9c4..a344655a324 100644
--- a/tensorflow/compiler/mlir/g3doc/includes/tf_passes.md
+++ b/tensorflow/compiler/mlir/g3doc/includes/tf_passes.md
@@ -1,4 +1,34 @@
 <!-- Autogenerated by mlir-tblgen; don't manually edit -->
+### `-tf-device-constant-sinking`: Sinks constants implicitly captured in a tf_device.cluster region.
+This pass sinks implicitly captured constants (`tf.Const` ops) used by and into
+a `tf_device.cluster` region. Performing this prior to outlining will reduce the
+number of arguments of the outlined function.
+
+For example, the following:
+
+```mlir
+func @cluster() -> tensor<i32> {
+  %const = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
+  %cluster = "tf_device.cluster"() ( {
+    %identity = "tf.Identity"(%const) : (tensor<i32>) -> tensor<i32>
+    tf_device.return %identity : tensor<i32>
+  }) : () -> (tensor<i32>)
+  return %cluster : tensor<i32>
+}
+```
+
+will be transformed into:
+
+```mlir
+func @cluster() -> tensor<i32> {
+  %cluster = "tf_device.cluster"() ( {
+    %const = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
+    %identity = "tf.Identity"(%const) : (tensor<i32>) -> tensor<i32>
+    tf_device.return %identity : tensor<i32>
+  }) : () -> (tensor<i32>)
+  return %cluster : tensor<i32>
+}
+```
 ### `-tf-executor-graph-pruning`: Prunes unreachable ops in a tf_executor.graph
 This pass removes ops from a `tf_executor.graph` that are not transitively, via
 data or control dependencies, connected to the associated `tf_executor.fetch`