resovle merge conflicts

official/nlp/modeling/README.md

 # NLP Modeling Library
 
-This libary provides a set of Keras primitives (Layers, Networks, and Models)
+This library provides a set of Keras primitives (Layers, Networks, and Models)
 that can be assembled into transformer-based models. They are
 flexible, validated, interoperable, and both TF1 and TF2 compatible.
 
@@ -16,6 +16,11 @@ standardized configuration.
 
 * [`losses`](losses) contains common loss computation used in NLP tasks.
 
+Please see the colab
+[nlp_modeling_library_intro.ipynb]
+(https://colab.sandbox.google.com/github/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb)
+for how to build transformer-based NLP models using above primitives.
+
 Besides the pre-defined primitives, it also provides scaffold classes to allow
 easy experimentation with noval achitectures, e.g., you don’t need to fork a whole Transformer object to try a different kind of attention primitive, for instance.
 
@@ -33,11 +38,9 @@ embedding subnetwork (which will replace the standard embedding logic) and/or a
 custom hidden layer (which will replace the Transformer instantiation in the
 encoder).
 
-BERT and ALBERT models in this repo are implemented using this library. Code examples can be found in the corresponding model folder.
-
-
-
-
-
-
+Please see the colab
+[customize_encoder.ipynb]
+(https://colab.sandbox.google.com/github/tensorflow/models/blob/master/official/colab/nlp/customize_encoder.ipynb)
+for how to use scaffold classes to build noval achitectures.
 
+BERT and ALBERT models in this repo are implemented using this library. Code examples can be found in the corresponding model folder.

official/nlp/modeling/layers/README.md

@@ -3,11 +3,6 @@
 Layers are the fundamental building blocks for NLP models. They can be used to
 assemble new layers, networks, or models.
 
-*   [DenseEinsum](dense_einsum.py) implements a feedforward network using
-    tf.einsum. This layer contains the einsum op, the associated weight, and the
-    logic required to generate the einsum expression for the given
-    initialization parameters.
-
 *   [MultiHeadAttention](attention.py) implements an optionally masked attention
     between query, key, value tensors as described in
     ["Attention Is All You Need"](https://arxiv.org/abs/1706.03762). If

official/nlp/modeling/layers/dense_einsum.py

@@ -21,6 +21,8 @@ from __future__ import print_function
 
 import tensorflow as tf
 
+from tensorflow.python.util import deprecation
+
 _CHR_IDX = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m"]
 
 
@@ -57,6 +59,9 @@ class DenseEinsum(tf.keras.layers.Layer):
       `(batch_size, units)`.
   """
 
+  @deprecation.deprecated(
+      None, "DenseEinsum is deprecated. Please use "
+      "tf.keras.experimental.EinsumDense layer instead.")
   def __init__(self,
                output_shape,
                num_summed_dimensions=1,

official/nlp/modeling/layers/multi_channel_attention_test.py

@@ -48,7 +48,11 @@ class MultiChannelAttentionTest(tf.test.TestCase):
     mask_data = np.random.randint(2, size=(3, num_docs, 4, 2))
     doc_probs = np.random.randint(
         2, size=(3, num_heads, 4, num_docs)).astype(float)
-    outputs = attention_layer([from_data, to_data, doc_probs], mask_data)
+    outputs = attention_layer(
+        query=from_data,
+        value=to_data,
+        context_attention_weights=doc_probs,
+        attention_mask=mask_data)
     self.assertEqual(outputs.shape, (3, 4, 8))