Merge pull request #1255 from sneaxiy/dam_develop

Modify dam model

fluid/deep_attention_matching_net/model.py

@@ -137,8 +137,8 @@ class Net(object):
                 r_a_t = fluid.layers.concat(input=r_a_t_stack, axis=1)
 
             # sim shape: [batch_size, 2*(stack_num+2), max_turn_len, max_turn_len]    
-            sim = fluid.layers.matmul(x=t_a_r, y=r_a_t, transpose_y=True)
-            sim = fluid.layers.scale(x=sim, scale=1 / np.sqrt(200.0))
+            sim = fluid.layers.matmul(
+                x=t_a_r, y=r_a_t, transpose_y=True, alpha=1 / np.sqrt(200.0))
             sim_turns.append(sim)
 
         if self.use_stack_op:

fluid/deep_attention_matching_net/utils/layers.py

@@ -72,8 +72,8 @@ def dot_product_attention(query,
                         type is dot.
     """
 
-    logits = fluid.layers.matmul(x=query, y=key, transpose_y=True)
-    logits = logits * (d_key**(-0.5))
+    logits = fluid.layers.matmul(
+        x=query, y=key, transpose_y=True, alpha=d_key**(-0.5))
 
     if (q_mask is not None) and (k_mask is not None):
         if mask_cache is not None and q_mask.name in mask_cache and k_mask.name in mask_cache[
@@ -81,9 +81,12 @@ def dot_product_attention(query,
             mask, another_mask = mask_cache[q_mask.name][k_mask.name]
         else:
             mask = fluid.layers.matmul(x=q_mask, y=k_mask, transpose_y=True)
-            another_mask = (1 - mask) * (-2**32 + 1)
+            another_mask = fluid.layers.scale(
+                mask, scale=2**32 - 1, bias=-1, bias_after_scale=False)
             if mask_cache is not None:
-                mask_cache[q_mask.name] = dict()
+                if q_mask.name not in mask_cache:
+                    mask_cache[q_mask.name] = dict()
+
                 mask_cache[q_mask.name][k_mask.name] = [mask, another_mask]
 
         logits = mask * logits + another_mask