API/OP（sequence_first_step，sequence_last_step，sequence_mask，beam_search，beam_s...

API/OP（sequence_first_step，sequence_last_step，sequence_mask，beam_search，beam_search_decode） error message enhancement ,test=release/1.8 (#24668)

paddle/fluid/operators/beam_search_decode_op.cc

@@ -128,14 +128,31 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
     const LoDTensorArray* ids = ctx.Input<LoDTensorArray>("Ids");
     const LoDTensorArray* scores = ctx.Input<LoDTensorArray>("Scores");
     const size_t step_num = ids->size();
-    PADDLE_ENFORCE_GT(step_num, 0UL,
-                      "beam search steps should be larger than 0");
+    PADDLE_ENFORCE_GT(
+        step_num, 0UL,
+        platform::errors::InvalidArgument(
+            "beam search steps, which is the"
+            "size of Input(Ids) LoDTensorArray. beam search steps should "
+            "be larger than 0, but received %d. ",
+            step_num));
     const size_t source_num = ids->at(0).lod().at(0).size() - 1;
-    PADDLE_ENFORCE_GT(source_num, 0UL, "source num should be larger than 0");
+    PADDLE_ENFORCE_GT(
+        source_num, 0UL,
+        platform::errors::InvalidArgument(
+            "source_num is the sequence number of the"
+            "first decoding step, indicating by Input(Ids)[0].lod[0].size. "
+            "The number of source_num should be larger than"
+            "0, but received %d. ",
+            source_num));
 
     for (size_t i = 0; i < step_num; ++i) {
-      PADDLE_ENFORCE_EQ(ids->at(i).lod().size(), 2UL,
-                        "Level of LodTensor should be 2");
+      PADDLE_ENFORCE_EQ(
+          ids->at(i).lod().size(), 2UL,
+          platform::errors::InvalidArgument(
+              "For the i step in beam search steps,"
+              "the size of Input(Ids)[i].lod() should larger than 2,"
+              "but received %d. ",
+              ids->at(i).lod().size()));
     }
 
     size_t beam_size = ctx.Attr<int>("beam_size");
@@ -190,14 +207,14 @@ hypothesis has.
 class BeamSearchDecodeInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext* context) const override {
-    PADDLE_ENFORCE(context->HasInput("Ids"),
-                   "BeamSearchDecodeOp must have input Ids");
-    PADDLE_ENFORCE(context->HasInput("Scores"),
-                   "BeamSearchDecodeOp must have input Scores");
-    PADDLE_ENFORCE(context->HasOutput("SentenceIds"),
-                   "BeamSearchDecodeOp must have output SentenceIds");
-    PADDLE_ENFORCE(context->HasOutput("SentenceScores"),
-                   "BeamSearchDecodeOp must have output SentenceScores");
+    OP_INOUT_CHECK(context->HasInput("Ids"), "Input", "Ids",
+                   "BeamSearchDecode");
+    OP_INOUT_CHECK(context->HasInput("Scores"), "Input", "Scores",
+                   "BeamSearchDecode");
+    OP_INOUT_CHECK(context->HasOutput("SentenceIds"), "Output", "SentenceIds",
+                   "BeamSearchDecode");
+    OP_INOUT_CHECK(context->HasOutput("SentenceScores"), "Output",
+                   "SentenceScores", "BeamSearchDecode");
   }
 };
 

paddle/fluid/operators/beam_search_decode_op.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 
 #include <algorithm>
+#include <memory>
 #include <vector>
 
 #include "paddle/fluid/framework/lod_tensor_array.h"
@@ -82,7 +83,15 @@ void BeamSearchDecoder<T>::ConvertSentenceVectorToLodTensor(
     LoDTensor* score_tensor, bool reverse, bool sort_by_score) const {
   size_t src_num = sentence_vector_list.size();
 
-  PADDLE_ENFORCE_NE(src_num, 0, "src_num should not be 0");
+  PADDLE_ENFORCE_NE(
+      src_num, 0,
+      platform::errors::InvalidArgument(
+          "src_num is the sequence number of the first decoding step"
+          ", indicating by Input(Ids)[0].lod[0].size."
+          "src_num has wrong value."
+          "src_num should not be 0,"
+          "But received %d.",
+          src_num));
 
   std::vector<size_t> source_level_lod = {0};
   std::vector<size_t> sentence_level_lod = {0};
@@ -144,9 +153,16 @@ void BeamSearchDecoder<T>::Backtrace(const LoDTensorArray& step_ids,
                                      const LoDTensorArray& step_scores,
                                      LoDTensor* id_tensor,
                                      LoDTensor* score_tensor) const {
-  PADDLE_ENFORCE(!step_ids.empty(), "step num should be larger than 0");
-  PADDLE_ENFORCE_EQ(step_ids.size(), step_scores.size(),
-                    "step_ids and step_scores should be the same");
+  PADDLE_ENFORCE_NE(
+      step_ids.empty(), true,
+      platform::errors::InvalidArgument("Input(Ids) should not be empty."
+                                        "But the Input(Ids) is empty."));
+  PADDLE_ENFORCE_EQ(
+      step_ids.size(), step_scores.size(),
+      platform::errors::InvalidArgument(
+          "The size of Input(Ids) and Input(Scores) should be "
+          "the same. But the size of Input(Ids) and Input(Scores) "
+          "are not equal."));
   const size_t step_num = step_ids.size();
   const size_t src_num = step_ids.at(0).lod().at(kSourceLevel).size() - 1;
   std::vector<SentenceVector<T>> sentence_vector_list(

paddle/fluid/operators/beam_search_decode_op_test.cc

@@ -35,10 +35,17 @@ void GenerateExample(const std::vector<size_t>& level_0,
                      const std::vector<int>& data, LoDTensorArray* ids,
                      LoDTensorArray* scores) {
   PADDLE_ENFORCE_EQ(level_0.back(), level_1.size() - 1,
-                    "source level is used to describe candidate set");
+                    platform::errors::InvalidArgument(
+                        "source level is used to describe candidate set"
+                        ", so it's element should less than levle_1 length. "
+                        "And the value of source"
+                        "level is %d. ",
+                        level_1.size() - 1));
   PADDLE_ENFORCE_EQ(level_1.back(), data.size(),
+                    platform::errors::InvalidArgument(
                         "the lowest level is used to describe data"
-                    ", so it's last element should be data length");
+                        ", so it's last element should be data length %d. ",
+                        data.size()));
 
   CPUPlace place;
 

paddle/fluid/operators/beam_search_op.cc

@@ -89,13 +89,11 @@ class BeamSearchOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext *ctx) const override {
     for (const std::string &arg :
          std::vector<std::string>({"pre_ids", "scores"})) {
-      PADDLE_ENFORCE(ctx->HasInput(arg), "BeamSearch need input argument '%s'",
-                     arg);
+      OP_INOUT_CHECK(ctx->HasInput(arg), "Input", arg, "BeamSeach");
     }
     for (const std::string &arg :
          std::vector<std::string>({"selected_ids", "selected_scores"})) {
-      PADDLE_ENFORCE(ctx->HasOutput(arg),
-                     "BeamSearch need output argument '%s'", arg);
+      OP_INOUT_CHECK(ctx->HasOutput(arg), "Output", arg, "BeamSeach");
     }
   }
 

paddle/fluid/operators/beam_search_op.h

@@ -29,9 +29,15 @@ class BeamSearchOpKernel : public framework::OpKernel<T> {
     auto* pre_ids = context.Input<framework::LoDTensor>("pre_ids");
     auto* pre_scores = context.Input<framework::LoDTensor>("pre_scores");
 
-    PADDLE_ENFORCE_NOT_NULL(scores);
-    PADDLE_ENFORCE_NOT_NULL(pre_ids);
-    PADDLE_ENFORCE_NOT_NULL(pre_scores);
+    PADDLE_ENFORCE_NOT_NULL(scores,
+                            platform::errors::NotFound(
+                                "Input(scores) of BeamSearchOp is not found."));
+    PADDLE_ENFORCE_NOT_NULL(
+        pre_ids, platform::errors::NotFound(
+                     "Input(pre_ids) of BeamSearchOp is not found."));
+    PADDLE_ENFORCE_NOT_NULL(
+        pre_scores, platform::errors::NotFound(
+                        "Input(pre_scores) of BeamSearchOp is not found."));
 
     size_t level = context.Attr<int>("level");
     size_t beam_size = context.Attr<int>("beam_size");
@@ -42,8 +48,14 @@ class BeamSearchOpKernel : public framework::OpKernel<T> {
     auto selected_scores =
         context.Output<framework::LoDTensor>("selected_scores");
     auto* parent_idx = context.Output<framework::Tensor>("parent_idx");
-    PADDLE_ENFORCE_NOT_NULL(selected_ids);
-    PADDLE_ENFORCE_NOT_NULL(selected_scores);
+    PADDLE_ENFORCE_NOT_NULL(
+        selected_ids,
+        platform::errors::NotFound(
+            "Output(selected_scores) of BeamSearchOp is not found."));
+    PADDLE_ENFORCE_NOT_NULL(
+        selected_scores,
+        platform::errors::NotFound(
+            "Output(parent_idx) of BeamSearchOp is not found."));
 
     math::BeamSearchFunctor<DeviceContext, T> alg;
     alg(context.template device_context<DeviceContext>(), pre_ids, pre_scores,

paddle/fluid/operators/sequence_ops/sequence_mask_op.cc

@@ -23,8 +23,8 @@ class SequenceMaskOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must exist");
-    PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) must exist");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SequenceMask");
+    OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Y", "SequenceMask");
 
     int maxlen = ctx->Attrs().Get<int>("maxlen");
     auto dim = framework::vectorize<int>(ctx->GetInputDim("X"));

paddle/fluid/operators/sequence_ops/sequence_mask_op.h

@@ -80,7 +80,10 @@ class SequenceMaskKernel : public framework::OpKernel<Tx> {
     int maxlen = ctx.Attr<int>("maxlen");
     if (ctx.HasInput("MaxLenTensor")) {
       auto max_len_tensor = ctx.Input<Tensor>("MaxLenTensor");
-      PADDLE_ENFORCE(max_len_tensor != NULL, "MaxLenTensor is NULL");
+      PADDLE_ENFORCE_NOT_NULL(max_len_tensor,
+                              platform::errors::InvalidArgument(
+                                  "Input(MaxLenTensor) should not be NULL."
+                                  "But received Input(MaxLenTensor) is NULL"));
       if (platform::is_gpu_place(max_len_tensor->place())) {
         framework::Tensor temp;
         TensorCopySync(*max_len_tensor, platform::CPUPlace(), &temp);
@@ -93,8 +96,12 @@ class SequenceMaskKernel : public framework::OpKernel<Tx> {
       y_dim.push_back(maxlen);
       y->Resize(framework::make_ddim(y_dim));
 
-      PADDLE_ENFORCE_GT(maxlen, 0,
-                        "MaxLenTensor value should be greater than 0");
+      PADDLE_ENFORCE_GT(
+          maxlen, 0,
+          platform::errors::InvalidArgument(
+              "Input(MaxLenTensor) value should be greater than 0. But "
+              "received Input(MaxLenTensor) value = %d.",
+              maxlen));
     }
 
     auto *x_data = x->data<Tx>();

python/paddle/fluid/layers/rnn.py

@@ -57,6 +57,7 @@ __all__ = [
 
 class RNNCell(object):
     """
+
     RNNCell is the base class for abstraction representing the calculations
     mapping the input and state to the output and new state. It is suitable to
     and mostly used in RNN.
@@ -221,6 +222,7 @@ class RNNCell(object):
 
 class GRUCell(RNNCell):
     """
+
     Gated Recurrent Unit cell. It is a wrapper for 
     `fluid.contrib.layers.rnn_impl.BasicGRUUnit` to make it adapt to RNNCell.
 
@@ -317,6 +319,7 @@ class GRUCell(RNNCell):
 
 class LSTMCell(RNNCell):
     """
+
     Long-Short Term Memory cell. It is a wrapper for 
     `fluid.contrib.layers.rnn_impl.BasicLSTMUnit` to make it adapt to RNNCell.
 
@@ -431,6 +434,7 @@ def rnn(cell,
         is_reverse=False,
         **kwargs):
     """
+
     rnn creates a recurrent neural network specified by RNNCell `cell`,
     which performs :code:`cell.call()` repeatedly until reaches to the maximum
     length of `inputs`.
@@ -575,6 +579,7 @@ def rnn(cell,
 
 class Decoder(object):
     """
+
     Decoder is the base class for any decoder instance used in `dynamic_decode`.
     It provides interface for output generation for one time step, which can be
     used to generate sequences. 
@@ -664,6 +669,7 @@ class Decoder(object):
 
 class BeamSearchDecoder(Decoder):
     """
+
     Decoder with beam search decoding strategy. It wraps a cell to get probabilities,
     and follows a beam search step to calculate scores and select candidate
     token ids for each decoding step.
@@ -1127,6 +1133,7 @@ def dynamic_decode(decoder,
                    return_length=False,
                    **kwargs):
     """
+
     Dynamic decoding performs :code:`decoder.step()` repeatedly until the returned
     Tensor indicating finished status contains all True values or the number of
     decoding step reaches to :attr:`max_step_num`.
@@ -1941,6 +1948,7 @@ def dynamic_lstm(input,
                  dtype='float32',
                  name=None):
     """
+
     **Note**:
         1. This OP only supports LoDTensor as inputs. If you need to deal with Tensor, please use :ref:`api_fluid_layers_lstm` .
         2. In order to improve efficiency, users must first map the input of dimension [T, hidden_size] to input of [T, 4 * hidden_size], and then pass it to this OP.
@@ -2111,6 +2119,7 @@ def lstm(input,
          default_initializer=None,
          seed=-1):
     """
+
     **Note**:
         This OP only supports running on GPU devices.
 
@@ -2296,6 +2305,7 @@ def dynamic_lstmp(input,
                   cell_clip=None,
                   proj_clip=None):
     """
+
     **Note**:
         1. In order to improve efficiency, users must first map the input of dimension [T, hidden_size] to input of [T, 4 * hidden_size], and then pass it to this OP.
 
@@ -2505,6 +2515,7 @@ def dynamic_gru(input,
                 h_0=None,
                 origin_mode=False):
     """
+
     **Note: The input type of this must be LoDTensor. If the input type to be
     processed is Tensor, use** :ref:`api_fluid_layers_StaticRNN` .
 
@@ -2665,6 +2676,7 @@ def gru_unit(input,
              gate_activation='sigmoid',
              origin_mode=False):
     """
+
     Gated Recurrent Unit (GRU) RNN cell. This operator performs GRU calculations for
     one time step and it supports these two modes:
 
@@ -2821,6 +2833,7 @@ def beam_search(pre_ids,
                 name=None,
                 return_parent_idx=False):
     """
+
     Beam search is a classical algorithm for selecting candidate words in a
     machine translation task.
 
@@ -2922,6 +2935,12 @@ def beam_search(pre_ids,
                 beam_size=beam_size,
                 end_id=end_id)
     """
+    check_variable_and_dtype(pre_ids, 'pre_ids', ['int64'], 'beam_search')
+    check_variable_and_dtype(pre_scores, 'pre_scores', ['float32', 'float64'],
+                             'beam_search')
+    check_type(ids, 'ids', (Variable, type(None)), 'beam_search')
+    check_variable_and_dtype(scores, 'scores', ['float32', 'float64'],
+                             'beam_search')
     helper = LayerHelper('beam_search', **locals())
     score_type = pre_scores.dtype
     id_type = pre_ids.dtype
@@ -2962,6 +2981,7 @@ def beam_search(pre_ids,
 
 def beam_search_decode(ids, scores, beam_size, end_id, name=None):
     """
+
     This operator is used after beam search has completed. It constructs the
     full predicted sequences for each sample by walking back along the search
     paths stored in lod of ``ids`` . The result sequences are stored in a
@@ -3015,6 +3035,9 @@ def beam_search_decode(ids, scores, beam_size, end_id, name=None):
             finished_ids, finished_scores = fluid.layers.beam_search_decode(
                 ids, scores, beam_size=5, end_id=0)
     """
+    check_variable_and_dtype(ids, 'ids', ['int64'], 'beam_search_encode')
+    check_variable_and_dtype(scores, 'scores', ['float32'],
+                             'beam_search_encode')
     helper = LayerHelper('beam_search_decode', **locals())
     sentence_ids = helper.create_variable_for_type_inference(dtype=ids.dtype)
     sentence_scores = helper.create_variable_for_type_inference(dtype=ids.dtype)
@@ -3041,6 +3064,7 @@ def lstm_unit(x_t,
               bias_attr=None,
               name=None):
     """
+
     Long-Short Term Memory (LSTM) RNN cell. This operator performs LSTM calculations for
     one time step, whose implementation is based on calculations described in `RECURRENT
     NEURAL NETWORK REGULARIZATION <http://arxiv.org/abs/1409.2329>`_  .

python/paddle/fluid/layers/sequence_lod.py

@@ -314,7 +314,7 @@ def sequence_pool(input, pool_type, is_test=False, pad_value=0.0):
             where 1.=1., 5.=3. + 2., 4.=4., 0.0=pad_value, 12.=6. + 5. + 1.
 
     Args:
-        input (variable): LoDTensor with lod_level no more than 2. The data type should be float32.
+        input (variable): LoDTensor with lod_level no more than 2. The data type should be float32 or float64.
         pool_type (str): The pooling type that supports average, sum, sqrt, max, last or first.
         is_test (bool): Only works when :attr:`pool_type` is max. If set False, a temporary Tenosr maxIndex is
             created to record the index information corresponding to the maximum value, which is used for backward
@@ -322,7 +322,7 @@ def sequence_pool(input, pool_type, is_test=False, pad_value=0.0):
         pad_value (float): Used to pad the pooling result for empty input sequence. Default: 0.0
 
     Returns:
-        Variable: LoDTensor after pooling with data type float32.
+        Variable: LoDTensor after pooling with data type float32 or float64.
 
     Examples:
 
@@ -464,10 +464,10 @@ def sequence_first_step(input):
             where 1.=first(1.), 3.=first(3., 2.), 4.=first(4.), 0.0 = pad_value, 6.=first(6., 5., 1.)
 
     Args:
-        input(Variable): LoDTensor with lod_level no more than 2. The data type should be float32.
+        input(Variable): LoDTensor with lod_level no more than 2. The data type should be float32 or float64.
 
     Returns:
-        Variable: LoDTensor consist of the sequence's first step vector. The data type is float32.
+        Variable: LoDTensor consist of the sequence's first step vector. The data type is float32 or float64.
 
     Examples:
 
@@ -477,6 +477,8 @@ def sequence_first_step(input):
              x = fluid.data(name='x', shape=[None, 10], dtype='float32', lod_level=1)
              x_first_step = fluid.layers.sequence_first_step(input=x)
     """
+    check_variable_and_dtype(input, 'input', ['float32', 'float64'],
+                             'sequence_first_step')
     return sequence_pool(input=input, pool_type="first")
 
 
@@ -530,6 +532,8 @@ def sequence_last_step(input):
              x = fluid.data(name='x', shape=[None, 10], dtype='float32', lod_level=1)
              x_last_step = fluid.layers.sequence_last_step(input=x)
     """
+    check_variable_and_dtype(input, 'input', ['float32', 'float64'],
+                             'sequence_last_step')
     return sequence_pool(input=input, pool_type="last")
 
 

python/paddle/fluid/tests/unittests/sequence/test_sequence_first_step.py

+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 paddle.fluid as fluid
+from paddle.fluid.framework import convert_np_dtype_to_dtype_, Program, program_guard
+import paddle.fluid.core as core
+import numpy as np
+import copy
+import unittest
+import sys
+sys.path.append("../")
+from op_test import OpTest
+
+
+class TestSequenceFirstStepOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+
+            def test_Variable():
+                # the input must be Variable
+                input_data = np.random.randint(1, 5, [4]).astype("int64")
+                fluid.layers.sequence_last_step(input_data)
+
+            self.assertRaises(TypeError, test_Variable)
+
+            def test_input_dtype():
+                # the dtype of input must be int64
+                type_data = fluid.layers.data(
+                    name='type_data',
+                    shape=[7, 1],
+                    append_batch_size=False,
+                    dtype='int64',
+                    lod_level=1)
+                fluid.layers.sequence_last_step(type_data)
+
+            self.assertRaises(TypeError, test_input_dtype)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/sequence/test_sequence_last_step.py

+# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 paddle.fluid as fluid
+from paddle.fluid.framework import convert_np_dtype_to_dtype_, Program, program_guard
+import paddle.fluid.core as core
+import numpy as np
+import copy
+import unittest
+import sys
+sys.path.append("../")
+from op_test import OpTest
+
+
+class TestSequenceLastStepOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+
+            def test_Variable():
+                # the input must be Variable
+                input_data = np.random.randint(1, 5, [4]).astype("int64")
+                fluid.layers.sequence_last_step(input_data)
+
+            self.assertRaises(TypeError, test_Variable)
+
+            def test_input_dtype():
+                # the dtype of input must be int64
+                type_data = fluid.layers.data(
+                    name='type_data',
+                    shape=[7, 1],
+                    append_batch_size=False,
+                    dtype='int64',
+                    lod_level=1)
+                fluid.layers.sequence_last_step(type_data)
+
+            self.assertRaises(TypeError, test_input_dtype)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/sequence/test_sequence_mask.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import paddle.fluid as fluid
-from paddle.fluid.framework import convert_np_dtype_to_dtype_
+from paddle.fluid.framework import convert_np_dtype_to_dtype_, Program, program_guard
 import paddle.fluid.core as core
 import numpy as np
 import copy
@@ -154,5 +154,17 @@ class SequenceMaskTest5_tensor_attr(SequenceMaskTestBase_tensor_attr):
         self.mask_dtype = 'float64'
 
 
+class TestSequenceMaskOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            input_data = np.random.uniform(1, 5, [4]).astype("float32")
+
+            def test_Variable():
+                # the input must be Variable
+                fluid.layers.sequence_mask(input_data, maxlen=4)
+
+            self.assertRaises(TypeError, test_Variable)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_beam_search_decode_op.py

@@ -19,6 +19,8 @@ import unittest
 import numpy as np
 import paddle.fluid.core as core
 from paddle.fluid.op import Operator
+import paddle.fluid as fluid
+from paddle.fluid.framework import Program, program_guard
 
 
 class TestBeamSearchDecodeOp(unittest.TestCase):
@@ -110,5 +112,46 @@ class TestBeamSearchDecodeOpGPU(TestBeamSearchDecodeOp):
         self.place = core.CUDAPlace(0)
 
 
+class TestBeamSearchDecodeOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+
+            def test_id_Variable():
+                # the input pre_ids must be Variable
+                test_ids = np.random.randint(1, 5, [5, 1]).astype("int64")
+                scores = fluid.layers.create_array(dtype='float32')
+                fluid.layers.beam_search_decode(
+                    test_ids, scores, beam_size=5, end_id=0)
+
+            self.assertRaises(TypeError, test_id_Variable)
+
+            def test_score_Variable():
+                # the input pre_scores must be Variable
+                ids = fluid.layers.create_array(dtype='int64')
+                test_scores = np.random.uniform(1, 5, [5, 1]).astype("float32")
+                fluid.layers.beam_search_decode(
+                    ids, test_scores, beam_size=5, end_id=0)
+
+            self.assertRaises(TypeError, test_score_Variable)
+
+            def test_id_dtype():
+                # the dtype of input pre_ids must be int64
+                type_ids = fluid.layers.create_array(dtype='float32')
+                scores = fluid.layers.create_array(dtype='float32')
+                fluid.layers.beam_search_decode(
+                    type_ids, scores, beam_size=5, end_id=0)
+
+            self.assertRaises(TypeError, test_id_dtype)
+
+            def test_score_dtype():
+                # the dtype of input pre_scores must be float32
+                ids = fluid.layers.create_array(dtype='int64')
+                type_scores = fluid.layers.create_array(dtype='int64')
+                fluid.layers.beam_search_decode(
+                    ids, type_scores, beam_size=5, end_id=0)
+
+            self.assertRaises(TypeError, test_score_dtype)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_beam_search_op.py

@@ -19,6 +19,8 @@ from paddle.fluid.op import Operator, DynamicRecurrentOp
 import paddle.fluid.core as core
 import unittest
 import numpy as np
+import paddle.fluid as fluid
+from paddle.fluid.framework import Program, program_guard
 
 
 def create_tensor(scope, name, np_data):
@@ -96,5 +98,108 @@ class BeamSearchOpTester(unittest.TestCase):
         tensor.set_lod(self.lod)
 
 
+class TestBeamSearchOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            pre_ids = fluid.data(
+                name='pre_id', shape=[1], lod_level=2, dtype='int64')
+            pre_scores = fluid.data(
+                name='pre_scores', shape=[1], lod_level=2, dtype='float32')
+            probs = fluid.data(name='probs', shape=[10000], dtype='float32')
+            topk_scores, topk_indices = fluid.layers.topk(probs, k=4)
+            accu_scores = fluid.layers.elementwise_add(
+                x=fluid.layers.log(x=topk_scores),
+                y=fluid.layers.reshape(
+                    pre_scores, shape=[-1]),
+                axis=0)
+
+            def test_preids_Variable():
+                # the input pre_ids must be Variable
+                preids_data = np.random.randint(1, 5, [5, 1]).astype("int64")
+                fluid.layers.beam_search(
+                    pre_ids=preids_data,
+                    pre_scores=pre_scores,
+                    ids=topk_indices,
+                    scores=accu_scores,
+                    beam_size=4,
+                    end_id=1)
+
+            self.assertRaises(TypeError, test_preids_Variable)
+
+            def test_prescores_Variable():
+                # the input pre_scores must be Variable
+                prescores_data = np.random.uniform(1, 5,
+                                                   [5, 1]).astype("float32")
+                fluid.layers.beam_search(
+                    pre_ids=pre_ids,
+                    pre_scores=prescores_data,
+                    ids=topk_indices,
+                    scores=accu_scores,
+                    beam_size=4,
+                    end_id=1)
+
+            self.assertRaises(TypeError, test_prescores_Variable)
+
+            def test_ids_Variable():
+                # the input ids must be Variable or None
+                ids_data = np.random.randint(1, 5, [5, 1]).astype("int64")
+                fluid.layers.beam_search(
+                    pre_ids=pre_ids,
+                    pre_scores=pre_scores,
+                    ids=ids_data,
+                    scores=accu_scores,
+                    beam_size=4,
+                    end_id=1)
+
+            self.assertRaises(TypeError, test_ids_Variable)
+
+            def test_scores_Variable():
+                # the input scores must be Variable
+                scores_data = np.random.uniform(1, 5, [5, 1]).astype("float32")
+                fluid.layers.beam_search(
+                    pre_ids=pre_ids,
+                    pre_scores=pre_scores,
+                    ids=topk_indices,
+                    scores=scores_data,
+                    beam_size=4,
+                    end_id=1)
+
+            self.assertRaises(TypeError, test_scores_Variable)
+
+            def test_preids_dtype():
+                # the dtype of input pre_ids must be int64
+                preids_type_data = fluid.data(
+                    name='preids_type_data',
+                    shape=[1],
+                    lod_level=2,
+                    dtype='float32')
+                fluid.layers.beam_search(
+                    pre_ids=preids_type_data,
+                    pre_scores=pre_scores,
+                    ids=topk_indices,
+                    scores=accu_scores,
+                    beam_size=4,
+                    end_id=1)
+
+            self.assertRaises(TypeError, test_preids_dtype)
+
+            def test_prescores_dtype():
+                # the dtype of input pre_scores must be float32
+                prescores_type_data = fluid.data(
+                    name='prescores_type_data',
+                    shape=[1],
+                    lod_level=2,
+                    dtype='int64')
+                fluid.layers.beam_search(
+                    pre_ids=pre_ids,
+                    pre_scores=prescores_type_data,
+                    ids=topk_indices,
+                    scores=accu_scores,
+                    beam_size=4,
+                    end_id=1)
+
+            self.assertRaises(TypeError, test_prescores_dtype)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_rnn_cell_api.py

@@ -541,7 +541,7 @@ def def_seq2seq_model(num_layers, hidden_size, dropout_prob, src_vocab_size,
     loss = layers.unsqueeze(loss, axes=[2])
     max_tar_seq_len = layers.shape(target)[1]
     tar_mask = layers.sequence_mask(
-        target_length, maxlen=max_tar_seq_len, dtype="float")
+        target_length, maxlen=max_tar_seq_len, dtype="float32")
     loss = loss * tar_mask
     loss = layers.reduce_mean(loss, dim=[0])
     loss = layers.reduce_sum(loss)