test_rnn_decode_api.py

# Copyright (c) 2020 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 random
import unittest
import numpy as np

import paddle
import paddle.nn as nn
from paddle import Model, set_device
from paddle.static import InputSpec as Input
from paddle.fluid.dygraph import Layer
from paddle.nn import BeamSearchDecoder, dynamic_decode

import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.core as core

from paddle.fluid.executor import Executor
from paddle.fluid.framework import _test_eager_guard

paddle.enable_static()


class EncoderCell(layers.RNNCell):

    def __init__(self, num_layers, hidden_size, dropout_prob=0.):
        self.num_layers = num_layers
        self.hidden_size = hidden_size
        self.dropout_prob = dropout_prob
        self.lstm_cells = [
            layers.LSTMCell(hidden_size) for i in range(num_layers)
        ]

    def call(self, step_input, states):
        new_states = []
        for i in range(self.num_layers):
            out, new_state = self.lstm_cells[i](step_input, states[i])
            step_input = layers.dropout(
                out, self.dropout_prob) if self.dropout_prob > 0 else out
            new_states.append(new_state)
        return step_input, new_states

    @property
    def state_shape(self):
        return [cell.state_shape for cell in self.lstm_cells]


class DecoderCell(layers.RNNCell):

    def __init__(self, num_layers, hidden_size, dropout_prob=0.):
        self.num_layers = num_layers
        self.hidden_size = hidden_size
        self.dropout_prob = dropout_prob
        self.lstm_cells = [
            layers.LSTMCell(hidden_size) for i in range(num_layers)
        ]

    def attention(self, hidden, encoder_output, encoder_padding_mask):
        query = layers.fc(hidden,
                          size=encoder_output.shape[-1],
                          bias_attr=False)
        attn_scores = layers.matmul(layers.unsqueeze(query, [1]),
                                    encoder_output,
                                    transpose_y=True)
        if encoder_padding_mask is not None:
            attn_scores = layers.elementwise_add(attn_scores,
                                                 encoder_padding_mask)
        attn_scores = layers.softmax(attn_scores)
        attn_out = layers.squeeze(layers.matmul(attn_scores, encoder_output),
                                  [1])
        attn_out = layers.concat([attn_out, hidden], 1)
        attn_out = layers.fc(attn_out, size=self.hidden_size, bias_attr=False)
        return attn_out

    def call(self,
             step_input,
             states,
             encoder_output,
             encoder_padding_mask=None):
        lstm_states, input_feed = states
        new_lstm_states = []
        step_input = layers.concat([step_input, input_feed], 1)
        for i in range(self.num_layers):
            out, new_lstm_state = self.lstm_cells[i](step_input, lstm_states[i])
            step_input = layers.dropout(
                out, self.dropout_prob) if self.dropout_prob > 0 else out
            new_lstm_states.append(new_lstm_state)
        out = self.attention(step_input, encoder_output, encoder_padding_mask)
        return out, [new_lstm_states, out]


class Encoder(object):

    def __init__(self, num_layers, hidden_size, dropout_prob=0.):
        self.encoder_cell = EncoderCell(num_layers, hidden_size, dropout_prob)

    def __call__(self, src_emb, src_sequence_length):
        encoder_output, encoder_final_state = layers.rnn(
            cell=self.encoder_cell,
            inputs=src_emb,
            sequence_length=src_sequence_length,
            is_reverse=False)
        return encoder_output, encoder_final_state


class Decoder(object):

    def __init__(self,
                 num_layers,
                 hidden_size,
                 dropout_prob,
                 decoding_strategy="infer_sample",
                 max_decoding_length=20):
        self.decoder_cell = DecoderCell(num_layers, hidden_size, dropout_prob)
        self.decoding_strategy = decoding_strategy
        self.max_decoding_length = None if (
            self.decoding_strategy == "train_greedy") else max_decoding_length

    def __call__(self, decoder_initial_states, encoder_output,
                 encoder_padding_mask, **kwargs):
        output_layer = kwargs.pop("output_layer", None)
        if self.decoding_strategy == "train_greedy":
            # for teach-forcing MLE pre-training
            helper = layers.TrainingHelper(**kwargs)
        elif self.decoding_strategy == "infer_sample":
            helper = layers.SampleEmbeddingHelper(**kwargs)
        elif self.decoding_strategy == "infer_greedy":
            helper = layers.GreedyEmbeddingHelper(**kwargs)

        if self.decoding_strategy == "beam_search":
            beam_size = kwargs.get("beam_size", 4)
            encoder_output = layers.BeamSearchDecoder.tile_beam_merge_with_batch(
                encoder_output, beam_size)
            encoder_padding_mask = layers.BeamSearchDecoder.tile_beam_merge_with_batch(
                encoder_padding_mask, beam_size)
            decoder = layers.BeamSearchDecoder(cell=self.decoder_cell,
                                               output_fn=output_layer,
                                               **kwargs)
        else:
            decoder = layers.BasicDecoder(self.decoder_cell,
                                          helper,
                                          output_fn=output_layer)

        (decoder_output, decoder_final_state,
         dec_seq_lengths) = layers.dynamic_decode(
             decoder,
             inits=decoder_initial_states,
             max_step_num=self.max_decoding_length,
             encoder_output=encoder_output,
             encoder_padding_mask=encoder_padding_mask,
             impute_finished=False  # for test coverage
             if self.decoding_strategy == "beam_search" else True,
             is_test=True if self.decoding_strategy == "beam_search" else False,
             return_length=True)
        return decoder_output, decoder_final_state, dec_seq_lengths


class Seq2SeqModel(object):
    """Seq2Seq model: RNN encoder-decoder with attention"""

    def __init__(self,
                 num_layers,
                 hidden_size,
                 dropout_prob,
                 src_vocab_size,
                 trg_vocab_size,
                 start_token,
                 end_token,
                 decoding_strategy="infer_sample",
                 max_decoding_length=20,
                 beam_size=4):
        self.start_token, self.end_token = start_token, end_token
        self.max_decoding_length, self.beam_size = max_decoding_length, beam_size
        self.src_embeder = paddle.nn.Embedding(
            src_vocab_size,
            hidden_size,
            weight_attr=fluid.ParamAttr(name="source_embedding"))
        self.trg_embeder = paddle.nn.Embedding(
            trg_vocab_size,
            hidden_size,
            weight_attr=fluid.ParamAttr(name="target_embedding"))
        self.encoder = Encoder(num_layers, hidden_size, dropout_prob)
        self.decoder = Decoder(num_layers, hidden_size, dropout_prob,
                               decoding_strategy, max_decoding_length)
        self.output_layer = lambda x: layers.fc(x,
                                                size=trg_vocab_size,
                                                num_flatten_dims=len(x.shape) -
                                                1,
                                                param_attr=fluid.ParamAttr(),
                                                bias_attr=False)

    def __call__(self, src, src_length, trg=None, trg_length=None):
        # encoder
        encoder_output, encoder_final_state = self.encoder(
            self.src_embeder(src), src_length)

        decoder_initial_states = [
            encoder_final_state,
            self.decoder.decoder_cell.get_initial_states(
                batch_ref=encoder_output, shape=[encoder_output.shape[-1]])
        ]
        src_mask = layers.sequence_mask(src_length,
                                        maxlen=layers.shape(src)[1],
                                        dtype="float32")
        encoder_padding_mask = (src_mask - 1.0) * 1e9
        encoder_padding_mask = layers.unsqueeze(encoder_padding_mask, [1])

        # decoder
        decoder_kwargs = {
            "inputs": self.trg_embeder(trg),
            "sequence_length": trg_length,
        } if self.decoder.decoding_strategy == "train_greedy" else (
            {
                "embedding_fn": self.trg_embeder,
                "beam_size": self.beam_size,
                "start_token": self.start_token,
                "end_token": self.end_token
            } if self.decoder.decoding_strategy == "beam_search" else {
                "embedding_fn":
                self.trg_embeder,
                "start_tokens":
                layers.fill_constant_batch_size_like(input=encoder_output,
                                                     shape=[-1],
                                                     dtype=src.dtype,
                                                     value=self.start_token),
                "end_token":
                self.end_token
            })
        decoder_kwargs["output_layer"] = self.output_layer

        (decoder_output, decoder_final_state,
         dec_seq_lengths) = self.decoder(decoder_initial_states, encoder_output,
                                         encoder_padding_mask, **decoder_kwargs)
        if self.decoder.decoding_strategy == "beam_search":  # for inference
            return decoder_output
        logits, samples, sample_length = (decoder_output.cell_outputs,
                                          decoder_output.sample_ids,
                                          dec_seq_lengths)
        probs = layers.softmax(logits)
        return probs, samples, sample_length


class PolicyGradient(object):
    """policy gradient"""

    def __init__(self, lr=None):
        self.lr = lr

    def learn(self, act_prob, action, reward, length=None):
        """
        update policy model self.model with policy gradient algorithm
        """
        self.reward = fluid.layers.py_func(func=reward_func,
                                           x=[action, length],
                                           out=reward)
        neg_log_prob = layers.cross_entropy(act_prob, action)
        cost = neg_log_prob * reward
        cost = (layers.reduce_sum(cost) / layers.reduce_sum(length)
                ) if length is not None else layers.reduce_mean(cost)
        optimizer = fluid.optimizer.Adam(self.lr)
        optimizer.minimize(cost)
        return cost


def reward_func(samples, sample_length):
    """toy reward"""

    def discount_reward(reward, sequence_length, discount=1.):
        return discount_reward_1d(reward, sequence_length, discount)

    def discount_reward_1d(reward, sequence_length, discount=1., dtype=None):
        if sequence_length is None:
            raise ValueError(
                'sequence_length must not be `None` for 1D reward.')
        reward = np.array(reward)
        sequence_length = np.array(sequence_length)
        batch_size = reward.shape[0]
        max_seq_length = np.max(sequence_length)
        dtype = dtype or reward.dtype
        if discount == 1.:
            dmat = np.ones([batch_size, max_seq_length], dtype=dtype)
        else:
            steps = np.tile(np.arange(max_seq_length), [batch_size, 1])
            mask = np.asarray(steps < (sequence_length - 1)[:, None],
                              dtype=dtype)
            # Make each row = [discount, ..., discount, 1, ..., 1]
            dmat = mask * discount + (1 - mask)
            dmat = np.cumprod(dmat[:, ::-1], axis=1)[:, ::-1]
        disc_reward = dmat * reward[:, None]
        disc_reward = mask_sequences(disc_reward, sequence_length, dtype=dtype)
        return disc_reward

    def mask_sequences(sequence, sequence_length, dtype=None, time_major=False):
        sequence = np.array(sequence)
        sequence_length = np.array(sequence_length)
        rank = sequence.ndim
        if rank < 2:
            raise ValueError("`sequence` must be 2D or higher order.")
        batch_size = sequence.shape[0]
        max_time = sequence.shape[1]
        dtype = dtype or sequence.dtype
        if time_major:
            sequence = np.transpose(sequence, axes=[1, 0, 2])
        steps = np.tile(np.arange(max_time), [batch_size, 1])
        mask = np.asarray(steps < sequence_length[:, None], dtype=dtype)
        for _ in range(2, rank):
            mask = np.expand_dims(mask, -1)
        sequence = sequence * mask
        if time_major:
            sequence = np.transpose(sequence, axes=[1, 0, 2])
        return sequence

    samples = np.array(samples)
    sample_length = np.array(sample_length)
    # length reward
    reward = (5 - np.abs(sample_length - 5)).astype("float32")
    # repeat punishment to trapped into local minima getting all same words
    # beam search to get more than one sample may also can avoid this
    for i in range(reward.shape[0]):
        reward[i] += -10 if sample_length[i] > 1 and np.all(
            samples[i][:sample_length[i] - 1] == samples[i][0]) else 0
    return discount_reward(reward, sample_length, discount=1.).astype("float32")


class MLE(object):
    """teacher-forcing MLE training"""

    def __init__(self, lr=None):
        self.lr = lr

    def learn(self, probs, label, weight=None, length=None):
        loss = layers.cross_entropy(input=probs, label=label, soft_label=False)
        max_seq_len = layers.shape(probs)[1]
        mask = layers.sequence_mask(length, maxlen=max_seq_len, dtype="float32")
        loss = loss * mask
        loss = layers.reduce_mean(loss, dim=[0])
        loss = layers.reduce_sum(loss)
        optimizer = fluid.optimizer.Adam(self.lr)
        optimizer.minimize(loss)
        return loss


class SeqPGAgent(object):

    def __init__(self,
                 model_cls,
                 alg_cls=PolicyGradient,
                 model_hparams={},
                 alg_hparams={},
                 executor=None,
                 main_program=None,
                 startup_program=None,
                 seed=None):
        self.main_program = fluid.Program(
        ) if main_program is None else main_program
        self.startup_program = fluid.Program(
        ) if startup_program is None else startup_program
        if seed is not None:
            self.main_program.random_seed = seed
            self.startup_program.random_seed = seed
        self.build_program(model_cls, alg_cls, model_hparams, alg_hparams)
        self.executor = executor

    def build_program(self, model_cls, alg_cls, model_hparams, alg_hparams):
        with fluid.program_guard(self.main_program, self.startup_program):
            source = fluid.data(name="src", shape=[None, None], dtype="int64")
            source_length = fluid.data(name="src_sequence_length",
                                       shape=[None],
                                       dtype="int64")
            # only for teacher-forcing MLE training
            target = fluid.data(name="trg", shape=[None, None], dtype="int64")
            target_length = fluid.data(name="trg_sequence_length",
                                       shape=[None],
                                       dtype="int64")
            label = fluid.data(name="label",
                               shape=[None, None, 1],
                               dtype="int64")
            self.model = model_cls(**model_hparams)
            self.alg = alg_cls(**alg_hparams)
            self.probs, self.samples, self.sample_length = self.model(
                source, source_length, target, target_length)
            self.samples.stop_gradient = True
            self.reward = fluid.data(
                name="reward",
                shape=[None, None],  # batch_size, seq_len
                dtype=self.probs.dtype)
            self.samples.stop_gradient = False
            self.cost = self.alg.learn(self.probs, self.samples, self.reward,
                                       self.sample_length)

        # to define the same parameters between different programs
        self.pred_program = self.main_program._prune_with_input(
            [source.name, source_length.name],
            [self.probs, self.samples, self.sample_length])

    def predict(self, feed_dict):
        samples, sample_length = self.executor.run(
            self.pred_program,
            feed=feed_dict,
            fetch_list=[self.samples, self.sample_length])
        return samples, sample_length

    def learn(self, feed_dict, fetch_list):
        results = self.executor.run(self.main_program,
                                    feed=feed_dict,
                                    fetch_list=fetch_list)
        return results


class TestDynamicDecode(unittest.TestCase):

    def setUp(self):
        np.random.seed(123)
        self.model_hparams = {
            "num_layers": 2,
            "hidden_size": 32,
            "dropout_prob": 0.1,
            "src_vocab_size": 100,
            "trg_vocab_size": 100,
            "start_token": 0,
            "end_token": 1,
            "decoding_strategy": "infer_greedy",
            "max_decoding_length": 10
        }

        self.iter_num = iter_num = 2
        self.batch_size = batch_size = 4
        src_seq_len = 10
        trg_seq_len = 12
        self.data = {
            "src":
            np.random.randint(
                2, self.model_hparams["src_vocab_size"],
                (iter_num * batch_size, src_seq_len)).astype("int64"),
            "src_sequence_length":
            np.random.randint(1, src_seq_len,
                              (iter_num * batch_size, )).astype("int64"),
            "trg":
            np.random.randint(
                2, self.model_hparams["src_vocab_size"],
                (iter_num * batch_size, trg_seq_len)).astype("int64"),
            "trg_sequence_length":
            np.random.randint(1, trg_seq_len,
                              (iter_num * batch_size, )).astype("int64"),
            "label":
            np.random.randint(
                2, self.model_hparams["src_vocab_size"],
                (iter_num * batch_size, trg_seq_len, 1)).astype("int64"),
        }

        place = core.CUDAPlace(
            0) if core.is_compiled_with_cuda() else core.CPUPlace()
        self.exe = Executor(place)

    def test_mle_train(self):
        paddle.enable_static()
        self.model_hparams["decoding_strategy"] = "train_greedy"
        agent = SeqPGAgent(model_cls=Seq2SeqModel,
                           alg_cls=MLE,
                           model_hparams=self.model_hparams,
                           alg_hparams={"lr": 0.001},
                           executor=self.exe,
                           main_program=fluid.Program(),
                           startup_program=fluid.Program(),
                           seed=123)
        self.exe.run(agent.startup_program)
        for iter_idx in range(self.iter_num):
            reward, cost = agent.learn(
                {
                    "src":
                    self.data["src"][iter_idx * self.batch_size:(iter_idx + 1) *
                                     self.batch_size, :],
                    "src_sequence_length":
                    self.data["src_sequence_length"][iter_idx * self.batch_size:
                                                     (iter_idx + 1) *
                                                     self.batch_size],
                    "trg":
                    self.data["trg"][iter_idx * self.batch_size:(iter_idx + 1) *
                                     self.batch_size, :],
                    "trg_sequence_length":
                    self.data["trg_sequence_length"][iter_idx * self.batch_size:
                                                     (iter_idx + 1) *
                                                     self.batch_size],
                    "label":
                    self.data["label"][iter_idx *
                                       self.batch_size:(iter_idx + 1) *
                                       self.batch_size]
                },
                fetch_list=[agent.cost, agent.cost])
            print("iter_idx: %d, reward: %f, cost: %f" %
                  (iter_idx, reward.mean(), cost))

    def test_greedy_train(self):
        paddle.enable_static()
        self.model_hparams["decoding_strategy"] = "infer_greedy"
        agent = SeqPGAgent(model_cls=Seq2SeqModel,
                           alg_cls=PolicyGradient,
                           model_hparams=self.model_hparams,
                           alg_hparams={"lr": 0.001},
                           executor=self.exe,
                           main_program=fluid.Program(),
                           startup_program=fluid.Program(),
                           seed=123)
        self.exe.run(agent.startup_program)
        for iter_idx in range(self.iter_num):
            reward, cost = agent.learn(
                {
                    "src":
                    self.data["src"][iter_idx * self.batch_size:(iter_idx + 1) *
                                     self.batch_size, :],
                    "src_sequence_length":
                    self.data["src_sequence_length"][iter_idx * self.batch_size:
                                                     (iter_idx + 1) *
                                                     self.batch_size]
                },
                fetch_list=[agent.reward, agent.cost])
            print("iter_idx: %d, reward: %f, cost: %f" %
                  (iter_idx, reward.mean(), cost))

    def test_sample_train(self):
        paddle.enable_static()
        self.model_hparams["decoding_strategy"] = "infer_sample"
        agent = SeqPGAgent(model_cls=Seq2SeqModel,
                           alg_cls=PolicyGradient,
                           model_hparams=self.model_hparams,
                           alg_hparams={"lr": 0.001},
                           executor=self.exe,
                           main_program=fluid.Program(),
                           startup_program=fluid.Program(),
                           seed=123)
        self.exe.run(agent.startup_program)
        for iter_idx in range(self.iter_num):
            reward, cost = agent.learn(
                {
                    "src":
                    self.data["src"][iter_idx * self.batch_size:(iter_idx + 1) *
                                     self.batch_size, :],
                    "src_sequence_length":
                    self.data["src_sequence_length"][iter_idx * self.batch_size:
                                                     (iter_idx + 1) *
                                                     self.batch_size]
                },
                fetch_list=[agent.reward, agent.cost])
            print("iter_idx: %d, reward: %f, cost: %f" %
                  (iter_idx, reward.mean(), cost))

    def test_beam_search_infer(self):
        paddle.set_default_dtype("float32")
        paddle.enable_static()
        self.model_hparams["decoding_strategy"] = "beam_search"
        main_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(main_program, startup_program):
            source = fluid.data(name="src", shape=[None, None], dtype="int64")
            source_length = fluid.data(name="src_sequence_length",
                                       shape=[None],
                                       dtype="int64")
            model = Seq2SeqModel(**self.model_hparams)
            output = model(source, source_length)

        self.exe.run(startup_program)
        for iter_idx in range(self.iter_num):
            trans_ids = self.exe.run(
                program=main_program,
                feed={
                    "src":
                    self.data["src"][iter_idx * self.batch_size:(iter_idx + 1) *
                                     self.batch_size, :],
                    "src_sequence_length":
                    self.data["src_sequence_length"][iter_idx *
                                                     self.batch_size:(iter_idx +
                                                                      1) *
                                                     self.batch_size]
                },
                fetch_list=[output])[0]

    def func_dynamic_basic_decoder(self):
        paddle.disable_static()
        src = paddle.to_tensor(np.random.randint(8, size=(8, 4)))
        src_length = paddle.to_tensor(np.random.randint(8, size=(8)))
        model = Seq2SeqModel(**self.model_hparams)
        probs, samples, sample_length = model(src, src_length)
        paddle.enable_static()

    def test_dynamic_basic_decoder(self):
        with _test_eager_guard():
            self.func_dynamic_basic_decoder()
        self.func_dynamic_basic_decoder()


class ModuleApiTest(unittest.TestCase):

    @classmethod
    def setUpClass(cls):
        cls._np_rand_state = np.random.get_state()
        cls._py_rand_state = random.getstate()
        cls._random_seed = 123
        np.random.seed(cls._random_seed)
        random.seed(cls._random_seed)

        cls.model_cls = type(
            cls.__name__ + "Model", (Layer, ), {
                "__init__": cls.model_init_wrapper(cls.model_init),
                "forward": cls.model_forward
            })

    @classmethod
    def tearDownClass(cls):
        np.random.set_state(cls._np_rand_state)
        random.setstate(cls._py_rand_state)

    @staticmethod
    def model_init_wrapper(func):

        def __impl__(self, *args, **kwargs):
            Layer.__init__(self)
            func(self, *args, **kwargs)

        return __impl__

    @staticmethod
    def model_init(model, *args, **kwargs):
        raise NotImplementedError(
            "model_init acts as `Model.__init__`, thus must implement it")

    @staticmethod
    def model_forward(model, *args, **kwargs):
        return model.module(*args, **kwargs)

    def make_inputs(self):
        # TODO(guosheng): add default from `self.inputs`
        raise NotImplementedError(
            "model_inputs makes inputs for model, thus must implement it")

    def setUp(self):
        """
        For the model which wraps the module to be tested:
            Set input data by `self.inputs` list
            Set init argument values by `self.attrs` list/dict
            Set model parameter values by `self.param_states` dict
            Set expected output data by `self.outputs` list
        We can create a model instance and run once with these.
        """
        self.inputs = []
        self.attrs = {}
        self.param_states = {}
        self.outputs = []

    def _calc_output(self, place, mode="test", dygraph=True):
        if dygraph:
            fluid.enable_dygraph(place)
        else:
            fluid.disable_dygraph()
        gen = paddle.seed(self._random_seed)
        paddle.framework.random._manual_program_seed(self._random_seed)
        scope = fluid.core.Scope()
        with fluid.scope_guard(scope):
            layer = self.model_cls(**self.attrs) if isinstance(
                self.attrs, dict) else self.model_cls(*self.attrs)
            model = Model(layer, inputs=self.make_inputs())
            model.prepare()
            if self.param_states:
                model.load(self.param_states, optim_state=None)
            return model.predict_batch(self.inputs)

    def check_output_with_place(self, place, mode="test"):
        dygraph_output = self._calc_output(place, mode, dygraph=True)
        stgraph_output = self._calc_output(place, mode, dygraph=False)
        expect_output = getattr(self, "outputs", None)
        for actual_t, expect_t in zip(dygraph_output, stgraph_output):
            np.testing.assert_allclose(actual_t, expect_t, rtol=1e-05, atol=0)
        if expect_output:
            for actual_t, expect_t in zip(dygraph_output, expect_output):
                np.testing.assert_allclose(actual_t,
                                           expect_t,
                                           rtol=1e-05,
                                           atol=0)

    def check_output(self):
        devices = ["CPU", "GPU"] if fluid.is_compiled_with_cuda() else ["CPU"]
        for device in devices:
            place = set_device(device)
            self.check_output_with_place(place)


class TestBeamSearch(ModuleApiTest):

    def setUp(self):
        paddle.set_default_dtype("float64")
        shape = (8, 32)
        self.inputs = [
            np.random.random(shape).astype("float64"),
            np.random.random(shape).astype("float64")
        ]
        self.outputs = None
        self.attrs = {
            "vocab_size": 100,
            "embed_dim": 32,
            "hidden_size": 32,
        }
        self.param_states = {}

    @staticmethod
    def model_init(self,
                   vocab_size,
                   embed_dim,
                   hidden_size,
                   bos_id=0,
                   eos_id=1,
                   beam_size=4,
                   max_step_num=20):
        embedder = paddle.fluid.dygraph.Embedding(size=[vocab_size, embed_dim],
                                                  dtype="float64")
        output_layer = nn.Linear(hidden_size, vocab_size)
        cell = nn.LSTMCell(embed_dim, hidden_size)
        self.max_step_num = max_step_num
        self.beam_search_decoder = BeamSearchDecoder(cell,
                                                     start_token=bos_id,
                                                     end_token=eos_id,
                                                     beam_size=beam_size,
                                                     embedding_fn=embedder,
                                                     output_fn=output_layer)

    @staticmethod
    def model_forward(model, init_hidden, init_cell):
        return dynamic_decode(model.beam_search_decoder,
                              [init_hidden, init_cell],
                              max_step_num=model.max_step_num,
                              impute_finished=True,
                              is_test=True)[0]

    def make_inputs(self):
        inputs = [
            Input([None, self.inputs[0].shape[-1]], "float64", "init_hidden"),
            Input([None, self.inputs[1].shape[-1]], "float64", "init_cell"),
        ]
        return inputs

    def func_check_output(self):
        self.setUp()
        self.make_inputs()
        self.check_output()

    def test_check_output(self):
        with _test_eager_guard():
            self.func_check_output()
        self.func_check_output()


if __name__ == '__main__':
    unittest.main()