Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into my_maxout_op

paddle/operators/beam_search_decode_op.cc

@@ -27,6 +27,7 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
   void Run(const framework::Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {
     framework::ExecutionContext ctx(*this, scope, dev_ctx);
+
     const LoDTensorArray* ids = ctx.Input<LoDTensorArray>("Ids");
     const LoDTensorArray* scores = ctx.Input<LoDTensorArray>("Scores");
     const size_t step_num = ids->size();

paddle/operators/l1_norm_op.h

@@ -29,7 +29,7 @@ class L1NormKernel : public framework::OpKernel<T> {
     Out->mutable_data<T>(context.GetPlace());
 
     auto x = framework::EigenVector<T>::Flatten(*X);
-    auto out = framework::EigenVector<T>::Flatten(*Out);
+    auto out = framework::EigenScalar<T>::From(*Out);
     auto place = context.GetEigenDevice<Place>();
 
     out.device(place) = x.abs().sum();

paddle/operators/squared_l2_norm_op.h

@@ -29,7 +29,7 @@ class SquaredL2NormKernel : public framework::OpKernel<T> {
     Out->mutable_data<T>(context.GetPlace());
 
     auto x = framework::EigenVector<T>::Flatten(*X);
-    auto out = framework::EigenVector<T>::Flatten(*Out);
+    auto out = framework::EigenScalar<T>::From(*Out);
     auto place = context.GetEigenDevice<Place>();
 
     out.device(place) = x.square().sum();

python/paddle/trainer_config_helpers/layers.py

@@ -3592,10 +3592,9 @@ def lstm_step_layer(input,
     :type gate_act: BaseActivation
     :param state_act: State Activation Type. TanhActivation is the default.
     :type state_act: BaseActivation
-    :param bias_attr: The bias attribute. If the parameter is set to False or an object
-                      whose type is not ParameterAttribute, no bias is defined. If the
-                      parameter is set to True, the bias is initialized to zero.
-    :type bias_attr: ParameterAttribute | None | bool | Any
+    :param bias_attr: The parameter attribute for bias. If this parameter is
+                     set to True or None, the bias is initialized to zero.
+    :type bias_attr: ParameterAttribute | None | True
     :param layer_attr: layer's extra attribute.
     :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
@@ -3650,9 +3649,10 @@ def gru_step_layer(input,
     :param name: The name of this layer. It is optional.
     :param gate_act: Activation type of this layer's two gates. Default is Sigmoid.
     :type gate_act: BaseActivation
-    :param bias_attr: The bias attribute. If the parameter is set to False or an object
-                      whose type is not ParameterAttribute, no bias is defined. If the
-                      parameter is set to True, the bias is initialized to zero.
+    :param bias_attr: The parameter attribute for bias. If this parameter is set to
+                      False or an object whose type is not ParameterAttribute, no bias
+                      is defined. If this parameter is set to True,
+                      the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
     :param param_attr: the parameter_attribute for transforming the output_mem
                        from previous step.
@@ -3712,9 +3712,10 @@ def gru_step_naive_layer(input,
     :type act: BaseActivation
     :param gate_act: Activation type of this layer's two gates. Default is Sigmoid.
     :type gate_act: BaseActivation
-    :param bias_attr: The bias attribute. If the parameter is set to False or an object
-                      whose type is not ParameterAttribute, no bias is defined. If the
-                      parameter is set to True, the bias is initialized to zero.
+    :param bias_attr: The parameter attribute for bias. If this parameter is set to
+                      False or an object whose type is not ParameterAttribute, no bias
+                      is defined. If this parameter is set to True,
+                      the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
     :param param_attr:
     :param layer_attr:
@@ -3844,9 +3845,10 @@ def recurrent_layer(input,
     :type input: LayerOutput
     :param act: Activation type. TanhActivation is the default.
     :type act: BaseActivation
-    :param bias_attr: The bias attribute. If the parameter is set to False or an object
-                      whose type is not ParameterAttribute, no bias is defined. If the
-                      parameter is set to True, the bias is initialized to zero.
+    :param bias_attr: The parameter attribute for bias. If this parameter is set to 
+                      False or an object whose type is not ParameterAttribute,
+                      no bias is defined. If the parameter is set to True,
+                      the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
     :param param_attr: parameter attribute.
     :type param_attr: ParameterAttribute
@@ -4836,9 +4838,10 @@ def tensor_layer(a,
     :type act: BaseActivation
     :param param_attr: The Parameter Attribute.
     :type param_attr: ParameterAttribute
-    :param bias_attr: The bias attribute. If the parameter is set to False or an object
-                      whose type is not ParameterAttribute, no bias is defined. If the
-                      parameter is set to True, the bias is initialized to zero.
+    :param bias_attr: The parameter attribute for bias. If this parameter is set to
+                      False or an object whose type is not ParameterAttribute,
+                      no bias is defined. If this parameter is set to True,
+                      the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
     :param layer_attr: Extra Layer config.
     :type layer_attr: ExtraLayerAttribute | None
@@ -4900,9 +4903,10 @@ def selective_fc_layer(input,
     :type act: BaseActivation
     :param param_attr: The Parameter Attribute.
     :type param_attr: ParameterAttribute
-    :param bias_attr: The bias attribute. If the parameter is set to False or an object
-                      whose type is not ParameterAttribute, no bias is defined. If the
-                      parameter is set to True, the bias is initialized to zero.
+    :param bias_attr: The parameter attribute for bias. If this parameter is set to
+                      False or an object whose type is not ParameterAttribute,
+                      no bias is defined. If this parameter is set to True,
+                      the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
     :param layer_attr: Extra Layer config.
     :type layer_attr: ExtraLayerAttribute | None
@@ -5585,10 +5589,10 @@ def nce_layer(input,
                              to the num_classes. Each member of the list defines
                              the probability of a class given input x.
     :type neg_distribution: list | tuple | collections.Sequence | None
-    :param bias_attr: The attribute for bias. If this parameter is set False or
-                      any object whose type is not ParameterAttribute, no bias
-                      is added. If this parameter is set True, the bias is
-                      initialized to zero.
+    :param bias_attr: The parameter attribute for bias. If this parameter is set to
+                      False or an object whose type is not ParameterAttribute,
+                      no bias is defined. If this parameter is set to True,
+                      the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
     :param layer_attr: Extra Layer Attribute.
     :type layer_attr: ExtraLayerAttribute
@@ -6498,9 +6502,9 @@ def gated_unit_layer(input,
     :param gate_param_attr: The parameter attribute of the gate. See ParameterAttribute
                             for details.
     :type gate_param_attr: ParameterAttribute
-    :param gate_bias_attr: The bias attribute of the gate. If the parameter is set to False or
+    :param gate_bias_attr: The bias attribute of the gate. If this parameter is set to False or
                            an object whose type is not ParameterAttribute, no bias is defined.
-                           If the parameter is set to True, the bias is initialized to zero.
+                           If this parameter is set to True, the bias is initialized to zero.
     :type gate_bias_attr: ParameterAttribute | bool | None | Any
     :param inproj_attr: Extra layer attributes of the projection. See ExtraLayerAttribute for
                         details.
@@ -6508,9 +6512,9 @@ def gated_unit_layer(input,
     :param inproj_param_attr: The parameter attribute of the projection. See ParameterAttribute
                               for details.
     :type inproj_param_attr: ParameterAttribute
-    :param inproj_bias_attr: The bias attribute of the projection. If the parameter is set to False
+    :param inproj_bias_attr: The bias attribute of the projection. If this parameter is set to False
                              or an object whose type is not ParameterAttribute, no bias is defined.
-                             If the parameter is set to True, the bias is initialized to zero.
+                             If this parameter is set to True, the bias is initialized to zero.
     :type inproj_bias_attr: ParameterAttribute | bool | None | Any
     :param layer_attr: Extra layer attribute of the product. See ExtraLayerAttribute for
                        details.

python/paddle/trainer_config_helpers/networks.py

@@ -681,34 +681,42 @@ def lstmemory_unit(input,
                                    state_act=TanhActivation())
 
 
-    :param input: input layer.
+    :param input: Input layer.
     :type input: LayerOutput
-    :param out_memory: output of previous time step
+    :param out_memory: The output of previous time step.
     :type out_memory: LayerOutput | None
-    :param name: lstmemory unit name.
+    :param name: The lstmemory unit name.
     :type name: basestring
-    :param size: lstmemory unit size.
+    :param size: The lstmemory unit size.
     :type size: int
-    :param param_attr: parameter attribute, None means default attribute.
+    :param param_attr: The parameter attribute for the weights in
+                     input to hidden projection.
+                     None means default attribute.
     :type param_attr: ParameterAttribute
-    :param act: last activiation type of lstm.
+    :param act: The last activiation type of lstm.
     :type act: BaseActivation
-    :param gate_act: gate activiation type of lstm.
+    :param gate_act: The gate activiation type of lstm.
     :type gate_act: BaseActivation
-    :param state_act: state activiation type of lstm.
+    :param state_act: The state activiation type of lstm.
     :type state_act: BaseActivation
-    :param input_proj_bias_attr: bias attribute for input to hidden projection.
-                False means no bias, None means default bias.
-    :type input_proj_bias_attr: ParameterAttribute|False|None
-    :param input_proj_layer_attr: extra layer attribute for input to hidden
-                projection of the LSTM unit, such as dropout, error clipping.
+    :param input_proj_bias_attr: The parameter attribute for the bias in
+                      input to hidden projection.
+                      False or None means no bias.
+                      If this parameter is set to True,
+                      the bias is initialized to zero.
+    :type input_proj_bias_attr: ParameterAttribute|bool|None
+    :param input_proj_layer_attr: The extra layer attribute for
+                     input to hidden projection of the LSTM unit,
+                     such as dropout, error clipping.
     :type input_proj_layer_attr: ExtraLayerAttribute
-    :param lstm_bias_attr: bias parameter attribute of lstm layer.
-                False means no bias, None means default bias.
-    :type lstm_bias_attr: ParameterAttribute|False|None
-    :param lstm_layer_attr: extra attribute of lstm layer.
+    :param lstm_bias_attr: The parameter attribute for the bias in lstm layer.
+                      False or None means no bias.
+                      If this parameter is set to True,
+                      the bias is initialized to zero.
+    :type lstm_bias_attr: ParameterAttribute|True|None
+    :param lstm_layer_attr: The extra attribute of lstm layer.
     :type lstm_layer_attr: ExtraLayerAttribute
-    :return: lstmemory unit name.
+    :return: The lstmemory unit name.
     :rtype: LayerOutput
     """
     if size is None:
@@ -786,34 +794,42 @@ def lstmemory_group(input,
                                     gate_act=SigmoidActivation(),
                                     state_act=TanhActivation())
 
-    :param input: input layer.
+    :param input: Input layer.
     :type input: LayerOutput
-    :param size: lstmemory group size.
+    :param size: The lstmemory group size.
     :type size: int
-    :param name: name of lstmemory group.
+    :param name: The name of lstmemory group.
     :type name: basestring
-    :param out_memory: output of previous time step.
+    :param out_memory: The output of previous time step.
     :type out_memory: LayerOutput | None
-    :param reverse: process the input in a reverse order or not.
+    :param reverse: Process the input in a reverse order or not.
     :type reverse: bool
-    :param param_attr: parameter attribute, None means default attribute.
+    :param param_attr: The parameter attribute for the weights in
+                     input to hidden projection.
+                     None means default attribute.
     :type param_attr: ParameterAttribute
-    :param act: last activiation type of lstm.
+    :param act: The last activiation type of lstm.
     :type act: BaseActivation
-    :param gate_act: gate activiation type of lstm.
+    :param gate_act: The gate activiation type of lstm.
     :type gate_act: BaseActivation
-    :param state_act: state activiation type of lstm.
+    :param state_act: The state activiation type of lstm.
     :type state_act: BaseActivation
-    :param lstm_bias_attr: bias parameter attribute of lstm layer.
-                           False means no bias, None means default bias.
-    :type lstm_bias_attr: ParameterAttribute|False|None
-    :param input_proj_bias_attr: bias attribute for input to hidden projection.
-                False means no bias, None means default bias.
-    :type input_proj_bias_attr: ParameterAttribute|False|None
-    :param input_proj_layer_attr: extra layer attribute for input to hidden
-                projection of the LSTM unit, such as dropout, error clipping.
+    :param input_proj_bias_attr: The parameter attribute for the bias in
+                      input to hidden projection.
+                      False or None means no bias.
+                      If this parameter is set to True,
+                      the bias is initialized to zero.
+    :type input_proj_bias_attr: ParameterAttribute|bool|None
+    :param input_proj_layer_attr: The extra layer attribute for
+                     input to hidden projection of the LSTM unit,
+                     such as dropout, error clipping.
     :type input_proj_layer_attr: ExtraLayerAttribute
-    :param lstm_layer_attr: lstm layer's extra attribute.
+    :param lstm_bias_attr: The parameter attribute for the bias in lstm layer.
+                      False or None means no bias.
+                      If this parameter is set to True,
+                      the bias is initialized to zero.
+    :type lstm_bias_attr: ParameterAttribute|True|None
+    :param lstm_layer_attr: The extra attribute of lstm layer.
     :type lstm_layer_attr: ExtraLayerAttribute
     :return: the lstmemory group.
     :rtype: LayerOutput

python/paddle/v2/framework/layers.py

@@ -839,6 +839,23 @@ def batch_norm(input,
     return helper.append_activation(batch_norm_out)
 
 
+def beam_search_decode(ids, scores, main_program=None, startup_program=None):
+    helper = LayerHelper('beam_search_decode', **locals())
+    sentence_ids = helper.create_tmp_variable(dtype=ids.data_type)
+    sentence_scores = helper.create_tmp_variable(dtype=ids.data_type)
+
+    helper.append_op(
+        type="beam_search_decode",
+        inputs={"Ids": ids,
+                "Scores": scores},
+        outputs={
+            "SentenceIds": sentence_ids,
+            "SentenceScores": sentence_scores
+        })
+
+    return sentence_ids, sentence_scores
+
+
 class BlockGuard(object):
     """
     BlockGuard class.

python/paddle/v2/framework/tests/test_beam_search_decode_op.py

+import unittest
+
+import numpy as np
+import paddle.v2.framework.core as core
+from paddle.v2.framework.op import Operator
+
+
+class TestBeamSearchDecodeOp(unittest.TestCase):
+    def setUp(self):
+        self.scope = core.Scope()
+        self.cpu_place = core.CPUPlace()
+
+    def append_lod_tensor(self, tensor_array, lod, data):
+        lod_tensor = core.LoDTensor()
+        lod_tensor.set_lod(lod)
+        lod_tensor.set(data, self.cpu_place)
+        tensor_array.append(lod_tensor)
+
+    def test_get_set(self):
+        ids = self.scope.var("ids").get_lod_tensor_array()
+        self.append_lod_tensor(
+            ids, [[0, 3, 6], [0, 1, 2, 3, 4, 5, 6]],
+            np.array(
+                [1, 2, 3, 4, 5, 6], dtype="int64"))
+        self.append_lod_tensor(
+            ids, [[0, 3, 6], [0, 1, 1, 3, 5, 5, 6]],
+            np.array(
+                [0, 1, 2, 3, 4, 5], dtype="int64"))
+        self.append_lod_tensor(
+            ids, [[0, 3, 6], [0, 0, 1, 2, 3, 4, 5]],
+            np.array(
+                [0, 1, 2, 3, 4], dtype="int64"))
+
+        scores = self.scope.var("scores").get_lod_tensor_array()
+        self.append_lod_tensor(
+            scores, [[0, 3, 6], [0, 1, 2, 3, 4, 5, 6]],
+            np.array(
+                [1, 2, 3, 4, 5, 6], dtype="float32"))
+        self.append_lod_tensor(
+            scores, [[0, 3, 6], [0, 1, 1, 3, 5, 5, 6]],
+            np.array(
+                [0, 1, 2, 3, 4, 5], dtype="float32"))
+        self.append_lod_tensor(
+            scores, [[0, 3, 6], [0, 0, 1, 2, 3, 4, 5]],
+            np.array(
+                [0, 1, 2, 3, 4], dtype="float32"))
+
+        sentence_ids = self.scope.var("sentence_ids").get_tensor()
+        sentence_scores = self.scope.var("sentence_scores").get_tensor()
+
+        beam_search_decode_op = Operator(
+            "beam_search_decode",
+            # inputs
+            Ids="ids",
+            Scores="scores",
+            # outputs
+            SentenceIds="sentence_ids",
+            SentenceScores="sentence_scores")
+
+        ctx = core.DeviceContext.create(self.cpu_place)
+        beam_search_decode_op.run(self.scope, ctx)
+
+        expected_lod = [[0, 4, 8], [0, 1, 3, 6, 9, 10, 13, 16, 19]]
+        self.assertEqual(sentence_ids.lod(), expected_lod)
+        self.assertEqual(sentence_scores.lod(), expected_lod)
+
+        expected_data = np.array(
+            [2, 1, 0, 3, 1, 0, 3, 2, 1, 5, 4, 3, 2, 4, 4, 3, 6, 5, 4], "int64")
+        self.assertTrue(np.array_equal(np.array(sentence_ids), expected_data))
+        self.assertTrue(
+            np.array_equal(np.array(sentence_scores), expected_data))
+
+
+if __name__ == '__main__':
+    unittest.main()