add mask

source/sample/transformer/T2TAttention.cpp

@@ -35,6 +35,7 @@ T2TAttention::T2TAttention()
     dk = -1;
     dv = -1;
     d  = -1;
+    isMasked = false;
 }
 
 /* deconstructor */
@@ -46,10 +47,11 @@ T2TAttention::~T2TAttention()
 initialize the model 
 >> argc - number of arguments
 >> argv - list of pointers to the arguments
+>> myIsMasked - indicates whether the attention is with a mask
 >> myDevID - device id
 >> myMem - the memory pool
 */
-void T2TAttention::InitModel(int argc, const char ** argv, int myDevID, XMem * myMem)
+void T2TAttention::InitModel(int argc, const char ** argv, bool myIsMasked, int myDevID, XMem * myMem)
 {
     devID = myDevID;
     mem = myMem;
@@ -82,9 +84,10 @@ make the network
        and H = vector size of each position
 >> q - queries
 >> v - values
+>> maske - as it is
 << return - multi-attention result
 */
-XTensor T2TAttention::Make(XTensor &k, XTensor &q, XTensor &v)
+XTensor T2TAttention::Make(XTensor &k, XTensor &q, XTensor &v, XTensor &mask)
 {
     XTensor k2;
     XTensor q2;
@@ -105,10 +108,14 @@ XTensor T2TAttention::Make(XTensor &k, XTensor &q, XTensor &v)
     vheads = Split(v2, v2.order - 1, nhead);
 
     XTensor att;
+    XTensor dot;
     XTensor scalar;
-
+    
     /* scalar = softmax(Q * K^T / sqrt(dk)) * V */
-    scalar = Softmax(Linear(BMMul(qheads, X_NOTRANS, kheads, X_TRANS), 1/(float)sqrt((float)dk)), -1);
+    dot = BMMul(qheads, X_NOTRANS, kheads, X_TRANS);
+    if(isMasked)
+        dot = dot + mask;
+    scalar = Softmax(Linear(dot, 1/(float)sqrt((float)dk)), -1);
     att = BMMul(scalar, vheads);
 
     /* concatenate the heads */

source/sample/transformer/T2TAttention.h

@@ -66,6 +66,9 @@ public:
     /* size of input Q, K and V */
     int d;
 
+    /* indicates whether the attention is masked */
+    bool isMasked;
+
 public:
     /* constructor */
     T2TAttention();
@@ -74,10 +77,10 @@ public:
     ~T2TAttention();
 
     /* initialize the model */
-    void InitModel(int argc, const char ** argv, int myDevID = -1, XMem * myMem = NULL);
+    void InitModel(int argc, const char ** argv, bool myIsMasked, int myDevID = -1, XMem * myMem = NULL);
 
     /* make the network */
-    XTensor Make(XTensor &k, XTensor &q, XTensor &v);
+    XTensor Make(XTensor &k, XTensor &q, XTensor &v, XTensor &mask);
 };
 
 }

source/sample/transformer/T2TEncoder.cpp

@@ -72,7 +72,7 @@ void AttEncoder::InitModel(int argc, const char ** argv, int myDevID, XMem * myM
 
     /* initialize the stacked layers */
     for(int i = 0; i < nlayer; i++){
-        attentions[i].InitModel(argc, argv, myDevID, myMem);
+        attentions[i].InitModel(argc, argv, false, myDevID, myMem);
         fnns[i].InitModel(argc, argv, myDevID, myMem);
         attLayerNorms[i].InitModel(argc, argv, myDevID, myMem);
         fnnLayerNorms[i].InitModel(argc, argv, myDevID, myMem);
@@ -82,9 +82,10 @@ void AttEncoder::InitModel(int argc, const char ** argv, int myDevID, XMem * myM
 /* 
 make the encoding network
 >> input - the input tensor of the encoder
+>> mask - the mask that indicate each position is valid
 << return - the output tensor of the encoder
 */
-XTensor AttEncoder::Make(XTensor &input)
+XTensor AttEncoder::Make(XTensor &input, XTensor &mask)
 {
     XTensor x;
 
@@ -97,7 +98,7 @@ XTensor AttEncoder::Make(XTensor &input)
         XTensor res;
 
         /* self attention */
-        att = attentions[i].Make(x, x, x);
+        att = attentions[i].Make(x, x, x, mask);
 
         /* residual connection */
         res = Sum(att, x);

source/sample/transformer/T2TEncoder.h

@@ -40,7 +40,7 @@ class T2TEncoder
 {
 public:
     virtual
-    XTensor Make(XTensor &input) = 0;
+    XTensor Make(XTensor &input, XTensor &mask) = 0;
 };
 
 /* 
@@ -49,7 +49,7 @@ the encoder based on RNN
 class RNNEncoder : T2TEncoder
 {
 public:
-    XTensor Make(XTensor &input);
+    XTensor Make(XTensor &input, XTensor &mask);
 };
 
 
@@ -109,7 +109,7 @@ public:
     void InitModel(int argc, const char ** argv, int myDevID = -1, XMem * myMem = NULL);
 
     /* make the encoding network */
-    XTensor Make(XTensor &input);
+    XTensor Make(XTensor &input, XTensor &mask);
 };
 
 

source/sample/transformer/T2TLayerNormal.cpp

@@ -96,7 +96,7 @@ XTensor T2TLN::Make(XTensor &input)
     standardFilled = Unsqueeze(standard, x.order - 1, x.GetDim(-1));
 
     /* x' = (x - \mu)/standard */
-    xn = (x - meanFilled)/standardFilled ;
+    xn = (x - meanFilled)/standardFilled;
 
     /* result = x' * w + b   */
     return MMul(xn, w) + b;

source/sample/transformer/T2TModel.cpp

@@ -68,11 +68,12 @@ void T2TModel::InitModel(int argc, const char ** argv)
 /* 
 make the encoding network
 >> input - input tensor
+>> mask - the mask for positions that are/not involved in computation
 << return - encoding result
 */
-XTensor T2TModel::MakeEncoding(XTensor &input)
+XTensor T2TModel::MakeEncoding(XTensor &input, XTensor &mask)
 {
-    return encoder.Make(input);
+    return encoder.Make(input, mask);
 }
 
 /* 
@@ -85,7 +86,15 @@ void T2TModel::Make(XTensor &input, XTensor &output)
     XTensor encoding;
     
     if(isLM){
-        encoding = MakeEncoding(input);
+        /* generate mask to see "previous" words only */
+        int len = input.GetDim(input.order - 2);
+        int dims[MAX_TENSOR_DIM_NUM];
+        for(int i = 0; i < input.order; i++)
+            dims[i] = input.GetDim(i);
+        dims[input.order - 1] = len;
+        XTensor mask(input.order, dims, X_FLOAT, 1.0F, input.devID, input.mem);
+
+        encoding = MakeEncoding(input, mask);
         outputLayer.Make(encoding, output);
     }
     else{

source/sample/transformer/T2TModel.h

@@ -66,7 +66,7 @@ public:
     void InitModel(int argc, const char ** argv);
 
     /* make the encoding network */
-    XTensor MakeEncoding(XTensor &input);
+    XTensor MakeEncoding(XTensor &input, XTensor &mask);
 
     /* make the entire network (with the output softmax layer) */
     void Make(XTensor &input, XTensor &output);

source/sample/transformer/T2TUtility.cpp

@@ -100,7 +100,9 @@ void ShowParams(int argc, const char ** argv)
 {
     fprintf(stderr, "args:\n");
     for(int i = 0; i < argc; i++){
-        if(argv[i][0] == '-'){
+        if(argv[i][1] == 0)
+            continue;
+        if(argv[i][0] == '-' && (argv[i][1] < '1' || argv[i][1] > '9')){
             if(i + 1 < argc && argv[i + 1][0] != '-')
                 fprintf(stderr, " %s=%s\n", argv[i], argv[i + 1]);
             else