create the attention model

source/sample/transformer/T2TAttention.cpp

+/* NiuTrans.Tensor - an open-source tensor library
+ * Copyright (C) 2018, Natural Language Processing Lab, Northestern University. 
+ * All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2018-07-31
+ */
+
+#include "T2TAttention.h"
+#include "T2TUtility.h"
+
+namespace transformer
+{
+
+/* constructor */
+T2TAttention::T2TAttention()
+{
+    nhead = -1;
+    dk = -1;
+    dv = -1;
+    d  = -1;
+}
+
+/* deconstructor */
+T2TAttention::~T2TAttention()
+{
+}
+
+/* 
+initialize the model 
+>> argc - number of arguments
+>> argv - list pf pointers to the arguments
+>> devID - device id
+>> mem - the memory pool
+*/
+void T2TAttention::InitModel(int argc, const char ** argv, int devID, XMem * mem)
+{
+    float minmax = 0;
+
+    LoadParamInt(argc, argv, "nhead", &nhead, 8);
+    LoadParamInt(argc, argv, "dk", &dk, 512);
+    LoadParamInt(argc, argv, "dv", &dv, 512);
+    LoadParamInt(argc, argv, "d", &d, 512);
+    LoadParamFloat(argc, argv, "attminmax", &minmax, 0.08F);
+
+    InitTensor2D(&w, 3 * d, 2 * dk + dv, X_FLOAT, devID, mem);
+
+    w.SetDataRand(-minmax, minmax);
+}
+
+}
\ No newline at end of file

source/sample/transformer/T2TAttention.h

@@ -22,12 +22,47 @@
 #ifndef __T2TATTENTION_H__
 #define __T2TATTENTION_H__
 
+#include "../../network/XNet.h"
+
+using namespace nts;
+
 namespace transformer
 {
 
+/* 
+multi-head attention 
+y(Q, K, V) = cat(head_1, head_2, ..., head_n)
+where head_i = Attention(Q * w_i^Q, K * w_i^K, V * w_i^V)
+      attention(Q, K, V) = softmax(Q * K^T/d_k^0.5) V
+      d_k = dimension size of K
+*/
 class T2TAttention
 {
+public:
+    /* head number */
+    int nhead;
+
+    /* transformation matrix */
+    XTensor w;
+
+    /* size of transformed Q and K */
+    int dk;
+
+    /* size of transformed V */
+    int dv;
+
+    /* size of input Q, K and V */
+    int d;
+
+public:
+    /* constructor */
+    T2TAttention();
+
+    /* de-constructor */
+    ~T2TAttention();
 
+    /* initialize the model */
+    void InitModel(int argc, const char ** argv, int devID = -1, XMem * mem = NULL);
 };
 
 }

source/sample/transformer/T2TFNN.cpp

@@ -49,8 +49,8 @@ void T2TFNN::InitModel(int argc, const char ** argv, int devID, XMem * mem)
 {
     float minmax = 0;
 
-    LoadParamInt(argc, argv, "fnnin", &inSize, 512);
-    LoadParamInt(argc, argv, "fnnout", &outSize, 512);
+    LoadParamInt(argc, argv, "d", &inSize, 512);
+    LoadParamInt(argc, argv, "d", &outSize, 512);
     LoadParamInt(argc, argv, "fnnh", &hSize, 512);
     LoadParamFloat(argc, argv, "fnnminmax", &minmax, 0.08F);