update doc

docs/ContribOperators.md

@@ -6,6 +6,7 @@ Do not modify directly.*
   * <a href="#com.microsoft.Attention">com.microsoft.Attention</a>
   * <a href="#com.microsoft.AttnLSTM">com.microsoft.AttnLSTM</a>
   * <a href="#com.microsoft.BeamSearch">com.microsoft.BeamSearch</a>
+  * <a href="#com.microsoft.BiasAdd">com.microsoft.BiasAdd</a>
   * <a href="#com.microsoft.BiasDropout">com.microsoft.BiasDropout</a>
   * <a href="#com.microsoft.BiasGelu">com.microsoft.BiasGelu</a>
   * <a href="#com.microsoft.BiasSoftmax">com.microsoft.BiasSoftmax</a>
@@ -468,6 +469,40 @@ This version of the operator has been available since version 1 of the 'com.micr
 </dl>
 
 
+### <a name="com.microsoft.BiasAdd"></a><a name="com.microsoft.biasadd">**com.microsoft.BiasAdd**</a>
+
+  Add input with bias, then add residual inputs.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'com.microsoft' operator set.
+
+#### Inputs
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input tensor. Dimensions are (N, S, C), where N is the batch size, S is image size H*W, and C is number of channels</dd>
+<dt><tt>bias</tt> : T</dt>
+<dd>Bias tensor. Dimensions are (C)</dd>
+<dt><tt>skip</tt> : T</dt>
+<dd>Residual tensor. Dimensions are (N, S, C)</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>The output tensor with dimensions (N, S, C)</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float)</dt>
+<dd>Constrain input and output types to float tensors.</dd>
+</dl>
+
+
 ### <a name="com.microsoft.BiasDropout"></a><a name="com.microsoft.biasdropout">**com.microsoft.BiasDropout**</a>
 
   output, dropout_mask = Dropout(data + bias, ratio) + residual, Intended to specialize the dropout pattern commonly found in transformer models.

docs/OperatorKernels.md

@@ -787,6 +787,7 @@ Do not modify directly.*
 |**Operator Domain:** *com.microsoft*||||
 |Attention|*in* input:**T**<br> *in* weights:**T**<br> *in* bias:**T**<br> *in* mask_index:**M**<br> *in* past:**T**<br> *in* relative_position_bias:**T**<br> *in* past_sequence_length:**M**<br> *out* output:**T**<br> *out* present:**T**|1+|**T** = tensor(float), tensor(float16)|
 |BeamSearch|*in* input_ids:**I**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* num_beams:**I**<br> *in* num_return_sequences:**I**<br> *in* length_penalty:**T**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**M**<br> *in* prefix_vocab_mask:**M**<br> *in* attention_mask:**I**<br> *out* sequences:**I**<br> *out* sequences_scores:**T**<br> *out* scores:**T**|1+|**T** = tensor(float), tensor(float16)|
+|BiasAdd|*in* X:**T**<br> *in* bias:**T**<br> *in* skip:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
 |BiasDropout|*in* data:**T**<br> *in* bias:**T**<br> *in* residual:**T**<br> *in* ratio:**T1**<br> *in* training_mode:**T2**<br> *out* output:**T**<br> *out* mask:**T2**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
 |BiasGelu|*in* A:**T**<br> *in* B:**T**<br> *out* C:**T**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
 |BiasSoftmax|*in* data:**T**<br> *in* bias:**T**<br> *out* output:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|