Merge pull request #1313 from emailweixu/memory.set_input

Make it possible to postpone setting the layer name for a memory.

python/paddle/trainer/config_parser.py

@@ -2222,7 +2222,10 @@ def Link(
 
 # memory for recurrent layer group.
 # *name* and *size* are actual layer's name and size.
-# will return name of the memory,
+# If *name* is None, need to provide *memory_name* and need to use
+# SetMemoryInput() later to specify the layer which this memory remembers.
+#
+# return the name of the memory,
 # use this name if you assign the memory as other layer's input
 #
 # boot frame of memory is zeroed by default,
@@ -2234,15 +2237,18 @@ def Link(
 # can only be initailized by a *boot_layer* which is a sequence.
 #
 @config_func
-def Memory(
-        name,
+def Memory(name,
            size,
            is_sequence=False,
            boot_layer=None,
            boot_bias=False,
            boot_bias_active_type="",
-        boot_with_const_id=None, ):
-    agent_name = name + "+delay1"
+           boot_with_const_id=None,
+           memory_name=None):
+    if not memory_name:
+        config_assert(name is not None, "name needs cannot be None")
+        memory_name = name + "+delay1"
+    agent_name = memory_name
     if is_sequence:
         agent_layer = SequenceAgentLayer(agent_name, size)
     else:
@@ -2250,6 +2256,7 @@ def Memory(
     config_assert(g_current_submodel.is_recurrent_layer_group,
                   'Memory should be used in recurrent layer group only')
     memory = g_current_submodel.memories.add()
+    if name is not None:
         memory.layer_name = MakeLayerNameInSubmodel(name)
     memory.link_name = MakeLayerNameInSubmodel(agent_name)
     memory.is_sequence = is_sequence
@@ -2274,6 +2281,17 @@ def Memory(
     return agent_name
 
 
+@config_func
+def SetMemoryInput(memory_name, layer_name):
+    memory_name = MakeLayerNameInSubmodel(memory_name)
+    layer_name = MakeLayerNameInSubmodel(layer_name)
+    for mem in g_current_submodel.memories:
+        if mem.link_name == memory_name:
+            mem.layer_name = layer_name
+            return
+    logger.fatal("Nonexistent memory name: " + memory_name)
+
+
 # Generator for recurrent layer group, to use it:
 #  1. define a id layer as output of layer group
 #  2. define a memory of this id layer, and assign a boot id(begin of sequence)

python/paddle/trainer_config_helpers/default_decorators.py

@@ -97,13 +97,13 @@ def reset_hook():
 register_parse_config_hook(reset_hook)
 
 
-def wrap_name_default(name_prefix=None):
+def wrap_name_default(name_prefix=None, name_param="name"):
     """
     Decorator to set "name" arguments default to "{name_prefix}_{invoke_count}".
 
     ..  code:: python
 
-        @default_name("some_name")
+        @wrap_name_default("some_name")
         def func(name=None):
             print name      # name will never be None. If name is not set,
                             # name will be "some_name_%d"
@@ -115,7 +115,7 @@ def wrap_name_default(name_prefix=None):
     """
     factory = DefaultNameFactory(name_prefix)
     _name_factories.append(factory)
-    return wrap_param_default(["name"], factory)
+    return wrap_param_default([name_param], factory)
 
 
 def wrap_param_attr_default(param_names=None, default_factory=None):

python/paddle/trainer_config_helpers/layers.py

@@ -288,6 +288,14 @@ class LayerOutput(object):
         """
         assert False, "this method should not be invoked"
 
+    def set_input(self, input):
+        """
+        Set the input for a memory layer. Can only be used for memory layer
+        """
+        assert isinstance(input, LayerOutput)
+        assert self.layer_type == LayerType.MEMORY
+        SetMemoryInput(self.name, input.name)
+
 
 ERROR_CLIPPING = 'error_clipping_threshold'
 DROPOUT = 'drop_rate'
@@ -2759,8 +2767,10 @@ def seq_concat_layer(a, b, act=None, name=None, layer_attr=None,
         size=a.size)
 
 
+@wrap_name_default("memory", "memory_name")
 def memory(name,
            size,
+           memory_name=None,
            is_seq=False,
            boot_layer=None,
            boot_bias=None,
@@ -2782,14 +2792,32 @@ def memory(name,
     If boot_layer is not null, the memory is just the boot_layer's output.
     Set :code:`is_seq` is true boot layer is sequence.
 
-
     The same name layer in recurrent group will set memory on each time
     step.
 
-    :param name: memory's name.
+    .. code-block:: python
+
+       mem = memory(size=256, name='state')
+       state = fc_layer(input=mem, size=256, name='state')
+
+    If you do not want to specify the name, you can equivalently use set_input()
+    to specify the layer needs to be remembered as the following:
+
+    .. code-block:: python
+       mem = memory(size=256)
+       state = fc_layer(input=mem, size=256)
+       mem.set_input(mem)
+
+
+    :param name: the name of the layer which this memory remembers.
+                 If name is None, user should call set_input() to specify the
+                 name of the layer which this memory remembers.
     :type name: basestring
     :param size: size of memory.
     :type size: int
+    :param memory_name: the name of the memory.
+                        It is ignored when name is provided.
+    :type memory_name: basestring
     :param is_seq: is sequence for boot_layer
     :type is_seq: bool
     :param boot_layer: boot layer of memory.
@@ -2811,13 +2839,21 @@ def memory(name,
         boot_bias = ParamAttr.to_bias(boot_bias)
 
     assert boot_layer is None or isinstance(boot_layer, LayerOutput)
+    if name is not None:
+        memory_name = None
 
-    agent_name = Memory(name, size, is_seq, boot_layer.name
-                        if boot_layer is not None else None, boot_bias,
-                        boot_bias_active_type.name, boot_with_const_id)
+    memory_name = Memory(
+        name,
+        size,
+        is_sequence=is_seq,
+        boot_layer=boot_layer.name if boot_layer is not None else None,
+        boot_bias=boot_bias,
+        boot_bias_active_type=boot_bias_active_type.name,
+        boot_with_const_id=boot_with_const_id,
+        memory_name=memory_name)
 
     lout = LayerOutput(
-        name=agent_name,
+        name=memory_name,
         size=size,
         layer_type=LayerType.MEMORY,
         parents=[boot_layer] if boot_layer is not None else None)
@@ -3565,7 +3601,7 @@ def __cost_input__(input, label, weight=None):
     ipts = [Input(input.name), Input(label.name)]
     parents = [input, label]
     if weight is not None:
-        assert weight.layer_type == LayerType.DATA
+        assert weight.size == 1
         ipts.append(Input(weight.name))
         parents.append(weight)
     return ipts, parents
@@ -4946,7 +4982,12 @@ def lambda_cost(input,
 
 @wrap_name_default()
 @layer_support()
-def cross_entropy(input, label, name=None, coeff=1.0, layer_attr=None):
+def cross_entropy(input,
+                  label,
+                  name=None,
+                  coeff=1.0,
+                  weight=None,
+                  layer_attr=None):
     """
     A loss layer for multi class entropy.
 
@@ -4961,22 +5002,27 @@ def cross_entropy(input, label, name=None, coeff=1.0, layer_attr=None):
     :type input: LayerOutput.
     :param name: The name of this layers. It is not necessary.
     :type name: None|basestring.
-    :param coeff: The coefficient affects the gradient in the backward.
+    :param coeff: The cost is multiplied with coeff.
+                  The coefficient affects the gradient in the backward.
     :type coeff: float.
+    :param weight: The cost of each sample is multiplied with each weight.
+                   The weight should be a layer with size=1. Note that gradient
+                   will not be calculated for weight.
+    :type weight: LayerOutout
     :param layer_attr: Extra Layer Attribute.
     :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput.
     """
 
+    ipts, parents = __cost_input__(input, label, weight)
     Layer(
         name=name,
         type=LayerType.CROSS_ENTROPY,
-        inputs=[input.name, label.name],
+        inputs=ipts,
         coeff=coeff,
         **ExtraLayerAttribute.to_kwargs(layer_attr))
-    return LayerOutput(
-        name, LayerType.CROSS_ENTROPY, parents=[input, label], size=1)
+    return LayerOutput(name, LayerType.CROSS_ENTROPY, parents=parents, size=1)
 
 
 @wrap_name_default()

python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr

@@ -331,6 +331,54 @@ layers {
   }
   trans_type: "non-seq"
 }
+layers {
+  name: "__recurrent_group_3__"
+  type: "recurrent_layer_group"
+  active_type: ""
+}
+layers {
+  name: "seq_input@__recurrent_group_3__"
+  type: "scatter_agent"
+  size: 100
+  active_type: ""
+}
+layers {
+  name: "__memory_6__@__recurrent_group_3__"
+  type: "agent"
+  size: 200
+  active_type: ""
+}
+layers {
+  name: "__fc_layer_0__@__recurrent_group_3__"
+  type: "fc"
+  size: 200
+  active_type: "tanh"
+  inputs {
+    input_layer_name: "seq_input@__recurrent_group_3__"
+    input_parameter_name: "___fc_layer_0__@__recurrent_group_3__.w0"
+  }
+  inputs {
+    input_layer_name: "__memory_6__@__recurrent_group_3__"
+    input_parameter_name: "___fc_layer_0__@__recurrent_group_3__.w1"
+  }
+  bias_parameter_name: "___fc_layer_0__@__recurrent_group_3__.wbias"
+}
+layers {
+  name: "__fc_layer_0__"
+  type: "gather_agent"
+  size: 200
+  active_type: ""
+}
+layers {
+  name: "__last_seq_4__"
+  type: "seqlastins"
+  size: 200
+  active_type: "linear"
+  inputs {
+    input_layer_name: "__fc_layer_0__"
+  }
+  trans_type: "non-seq"
+}
 parameters {
   name: "___mixed_0__.w0"
   size: 40000
@@ -481,6 +529,36 @@ parameters {
   initial_strategy: 0
   initial_smart: false
 }
+parameters {
+  name: "___fc_layer_0__@__recurrent_group_3__.w0"
+  size: 20000
+  initial_mean: 0.0
+  initial_std: 0.1
+  dims: 100
+  dims: 200
+  initial_strategy: 0
+  initial_smart: true
+}
+parameters {
+  name: "___fc_layer_0__@__recurrent_group_3__.w1"
+  size: 40000
+  initial_mean: 0.0
+  initial_std: 0.0707106781187
+  dims: 200
+  dims: 200
+  initial_strategy: 0
+  initial_smart: true
+}
+parameters {
+  name: "___fc_layer_0__@__recurrent_group_3__.wbias"
+  size: 200
+  initial_mean: 0.0
+  initial_std: 0.0
+  dims: 1
+  dims: 200
+  initial_strategy: 0
+  initial_smart: false
+}
 input_layer_names: "seq_input"
 input_layer_names: "sub_seq_input"
 output_layer_names: "__last_seq_0__"
@@ -488,6 +566,7 @@ output_layer_names: "__first_seq_0__"
 output_layer_names: "__last_seq_1__"
 output_layer_names: "__last_seq_2__"
 output_layer_names: "__last_seq_3__"
+output_layer_names: "__last_seq_4__"
 sub_models {
   name: "root"
   layer_names: "seq_input"
@@ -510,6 +589,9 @@ sub_models {
   layer_names: "__gru_group_0___recurrent_group"
   layer_names: "__gru_group_0__"
   layer_names: "__last_seq_3__"
+  layer_names: "__recurrent_group_3__"
+  layer_names: "__fc_layer_0__"
+  layer_names: "__last_seq_4__"
   input_layer_names: "seq_input"
   input_layer_names: "sub_seq_input"
   output_layer_names: "__last_seq_0__"
@@ -517,6 +599,7 @@ sub_models {
   output_layer_names: "__last_seq_1__"
   output_layer_names: "__last_seq_2__"
   output_layer_names: "__last_seq_3__"
+  output_layer_names: "__last_seq_4__"
   is_recurrent_layer_group: false
 }
 sub_models {
@@ -647,4 +730,28 @@ sub_models {
   }
   target_inlinkid: -1
 }
+sub_models {
+  name: "__recurrent_group_3__"
+  layer_names: "seq_input@__recurrent_group_3__"
+  layer_names: "__memory_6__@__recurrent_group_3__"
+  layer_names: "__fc_layer_0__@__recurrent_group_3__"
+  is_recurrent_layer_group: true
+  reversed: false
+  memories {
+    layer_name: "__fc_layer_0__@__recurrent_group_3__"
+    link_name: "__memory_6__@__recurrent_group_3__"
+    is_sequence: false
+  }
+  in_links {
+    layer_name: "seq_input"
+    link_name: "seq_input@__recurrent_group_3__"
+    has_subseq: false
+  }
+  out_links {
+    layer_name: "__fc_layer_0__@__recurrent_group_3__"
+    link_name: "__fc_layer_0__"
+    has_subseq: false
+  }
+  target_inlinkid: -1
+}
 

python/paddle/trainer_config_helpers/tests/configs/test_rnn_group.py

@@ -16,6 +16,16 @@ def generate_rnn_simple(name):
     return rnn_simple
 
 
+def generate_rnn_simple_no_name():
+    def rnn_simple(s):
+        m = memory(name=None, size=200)
+        fc = fc_layer(input=[s, m], size=200)
+        m.set_input(fc)
+        return fc
+
+    return rnn_simple
+
+
 with mixed_layer() as lstm_param:  # test lstm unit, rnn group
     lstm_param += full_matrix_projection(input=seq, size=100 * 4)
 
@@ -33,4 +43,6 @@ outputs(
     last_seq(input=lstmemory_group(
         input=lstm_param, size=100)),
     last_seq(input=gru_group(
-        input=gru_param, size=100)))
+        input=gru_param, size=100)),
+    last_seq(input=recurrent_group(
+        step=generate_rnn_simple_no_name(), input=seq)), )