Merge pull request #4924 from ranqiu92/attention

Add the configuration helper for multi-head attention.

Author: lcy-seso

python/paddle/trainer_config_helpers/networks.py

@@ -11,7 +11,7 @@
 # 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.
-
+import math
 
 from activations import LinearActivation, ReluActivation, SoftmaxActivation, \
     IdentityActivation, TanhActivation, SequenceSoftmaxActivation
@@ -26,9 +26,9 @@
     'sequence_conv_pool', 'simple_lstm', "simple_img_conv_pool",
     "img_conv_bn_pool", 'lstmemory_group', 'lstmemory_unit', 'small_vgg',
     'img_conv_group', 'vgg_16_network', 'gru_unit', 'gru_group', 'simple_gru',
-    'simple_attention', 'dot_product_attention', 'simple_gru2',
-    'bidirectional_gru', 'text_conv_pool', 'bidirectional_lstm', 'inputs',
-    'outputs'
+    'simple_attention', 'dot_product_attention', 'multi_head_attention',
+    'simple_gru2', 'bidirectional_gru', 'text_conv_pool', 'bidirectional_lstm',
+    'inputs', 'outputs'
 ]
 
 ######################################################
@@ -1496,6 +1496,134 @@ def dot_product_attention(encoded_sequence,
         input=scaled, pooling_type=SumPooling(), name="%s_pooling" % name)
 
 
+@wrap_name_default()
+def multi_head_attention(query,
+                         key,
+                         value,
+                         key_proj_size,
+                         value_proj_size,
+                         head_num,
+                         attention_type,
+                         softmax_param_attr=None,
+                         name=None):
+    """
+    Calculate and return a context vector with dot-product attention mechanism.
+    The dimension of the context vector equals to value_proj_size * head_num.
+
+    Please refer to **Attention Is All You Need** for more details. The link is
+    as follows:
+    https://arxiv.org/abs/1706.03762.
+
+    The example usage is:
+
+    ..  code-block:: python
+
+        context = multi_head_attention(query=decoder_state,
+                                       key=enc_seq,
+                                       value=enc_seq,
+                                       key_proj_size=64,
+                                       value_pro_size=64,
+                                       head_num=8,
+                                       attention_type='dot-product attention')
+
+    :param name: A prefix attached to the name of each layer that defined inside
+                 the multi_head_attention.
+    :type name: basestring
+    :param softmax_param_attr: The parameter attribute of sequence softmax
+                               that is used to produce attention weight.
+    :type softmax_param_attr: ParameterAttribute
+    :param query: query is used to calculate attention weights over values at current step.
+    :type query: LayerOutput
+    :param key: key is used to calculate the attention weight of the corresponding value.
+    :type key: LayerOutput
+    :param value: value is the sequence to be attended.
+    :type value: LayerOutput
+    :param key_proj_size: The dimension of the linear projection performed on key and query.
+    :type key_proj_size: int
+    :param value_proj_size: The dimension of the linear projection performed on value.
+    :type value_proj_size: int
+    :param head_num: The number of attention heads.
+    :type head_num: int
+    :param attention_type: The type of the attention mechanism used in each attention
+                           heads. Now, we only support scaled dot-product attention and
+                           additive attention.
+    :type attention_type: basestring
+    :return: The context vector.
+    :rtype: LayerOutput
+    """
+    assert attention_type in ['dot-product attention', 'additive attention']
+
+    with mixed_layer(
+            size=key_proj_size * head_num,
+            name='%s_query_proj' % name) as query_proj:
+        query_proj += full_matrix_projection(query)
+    query_proj = expand_layer(input=query_proj, expand_as=key)
+
+    with mixed_layer(
+            size=key_proj_size * head_num,
+            name='%s_key_proj' % name) as key_proj:
+        key_proj += full_matrix_projection(key)
+
+    with mixed_layer(
+            size=value_proj_size * head_num,
+            name='%s_value_proj' % name) as value_proj:
+        value_proj += full_matrix_projection(value)
+
+    head_list = []
+    for i in range(head_num):
+        with mixed_layer(size=key_proj_size) as sub_query_proj:
+            sub_query_proj += identity_projection(
+                query_proj, offset=key_proj_size * i, size=key_proj_size)
+
+        with mixed_layer(size=key_proj_size) as sub_key_proj:
+            sub_key_proj += identity_projection(
+                key_proj, offset=key_proj_size * i, size=key_proj_size)
+
+        with mixed_layer(size=value_proj_size) as sub_value_proj:
+            sub_value_proj += identity_projection(
+                value_proj, offset=value_proj_size * i, size=value_proj_size)
+
+        if attention_type == 'dot-product attention':
+            m = dot_prod_layer(
+                input1=sub_query_proj,
+                input2=sub_key_proj,
+                name='%s_dot-product_%d' % (name, i))
+            m = slope_intercept_layer(
+                input=m,
+                slope=math.sqrt(1.0 / key_proj_size),
+                name='%s_dot-product_scaling_%d' % (name, i))
+        else:
+            with mixed_layer(
+                    size=key_proj_size,
+                    act=TanhActivation(),
+                    name='%s_combine_%d' % (name, i)) as m:
+                m += identity_projection(sub_query_proj)
+                m += identity_projection(sub_key_proj)
+
+        attention_weight = fc_layer(
+            input=m,
+            size=1,
+            act=SequenceSoftmaxActivation(),
+            param_attr=softmax_param_attr,
+            name="%s_softmax_%d" % (name, i),
+            bias_attr=False)
+
+        scaled = scaling_layer(
+            weight=attention_weight,
+            input=sub_value_proj,
+            name='%s_scaling_%d' % (name, i))
+        head = pooling_layer(
+            input=scaled,
+            pooling_type=SumPooling(),
+            name="%s_pooling_%d" % (name, i))
+
+        head_list.append(head)
+
+    attended = concat_layer(head_list)
+
+    return attended
+
+
 def inputs(layers, *args):
     """
     Declare the inputs of network. The order of input should be as same as