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Sequence Sampling

This tutorial demonstrates how to sample sequences using a pre-trained language model in the following two ways:

  • with beam search sampler, and
  • with sequence sampler

Let's use V to denote the vocabulary size, and T to denote the sequence length. Given a language model, we can sample sequences according to the probability that they would occur according to our model. At each time step, a language model predicts the likelihood of each word occuring, given the context from prior time steps. The outputs at any time step can be any word from the vocabulary whose size is V and thus the number of all possible outcomes for a sequence of length T is thus V^T.

While sometimes we might want to sample sentences according to their probability of occuring, at other times we want to find the sentences that are most likely to occur. This is especially true in the case of language translation where we don't just want to see a translation. We want the best translation. While finding the optimal outcome quickly becomes intractable as time step increases, there are still many ways to sample reasonably good sequences. GluonNLP provides two samplers for generating from a language model: BeamSearchSampler and SequenceSampler.

Load Pretrained Language Model

First, let's load a pretrained language model, from which we will sample sequences from.

import mxnet as mx
import gluonnlp as nlp
ctx = mx.cpu()
lm_model, vocab = nlp.model.get_model(name='awd_lstm_lm_1150',

Sampling a Sequence with BeamSearchSampler

To overcome the exponential complexity in sequence decoding, beam search decodes greedily, keeping those sequences that are most likely based on the probability up to the current time step. The size of this subset is called the beam size. Suppose the beam size is K and the output vocabulary size is V. When selecting the beams to keep, the beam search algorithm first predict all possible successor words from the previous K beams, each of which has V possible outputs. This becomes a total of K*V paths. Out of these K*V paths, beam search ranks them by their score keeping only the top K paths.

Let's take a look how to construct a BeamSearchSampler. The nlp.model.BeamSearchSampler class takes the following arguments for customization and extension:

  • beam_size : the beam size.
  • decoder : callable function of the one-step-ahead decoder.
  • eos_id : id of the EOS token.
  • scorer : the score function used in beam search.
  • max_length: the maximum search length.

Scorer Function

In this tutorial, we will use the BeamSearchScorer the as scorer, which implements the scoring function with length penalty in Google NMT paper:

scorer = nlp.model.BeamSearchScorer(alpha=0, K=5, from_logits=False)

Decoder Function

Next, we define the decoder based on the pretrained language model.

class LMDecoder(object):
    def __init__(self, model):
        self._model = model
    def __call__(self, inputs, states):
        outputs, states = self._model(mx.nd.expand_dims(inputs, axis=0), states)
        return outputs[0], states
    def state_info(self, *arg, **kwargs):
        return self._model.state_info(*arg, **kwargs)
decoder = LMDecoder(lm_model)

Beam Search Sampler

Given a scorer and decoder, we are ready to create a sampler. We use symbol . to indicate the end of sentence (EOS). We can use vocab to get the index of the EOS, and then feed the index to the sampler. The following codes shows how to construct a beam search sampler. We will create a sampler with 4 beams and a maximum sample length of 20.

eos_id = vocab['.']
beam_sampler = nlp.model.BeamSearchSampler(beam_size=5,

Generate Sequences with Beam Search

Next, we are going to generate sentences starting with "I love it" using beam search first. We feed ['I', 'Love'] to the language model to get the initial states and set the initial input to be the word 'it'. We will then print the top-3 generations.

bos = 'I love it'.split()
bos_ids = [vocab[ele] for ele in bos]
begin_states = lm_model.begin_state(batch_size=1, ctx=ctx)
if len(bos_ids) > 1:
    _, begin_states = lm_model(mx.nd.expand_dims(mx.nd.array(bos_ids[:-1]), axis=1),
inputs = mx.nd.full(shape=(1,), ctx=ctx, val=bos_ids[-1])
def generate_sequences(sampler, inputs, begin_states, num_print_outcomes):
    samples, scores, valid_lengths = sampler(inputs, begin_states)
    samples = samples[0].asnumpy()
    scores = scores[0].asnumpy()
    valid_lengths = valid_lengths[0].asnumpy()
    print('Generation Result:')
    for i in range(num_print_outcomes):
        sentence = bos[:-1]
        for ele in samples[i][:valid_lengths[i]]:
        print([' '.join(sentence), scores[i]])
generate_sequences(beam_sampler, inputs, begin_states, 5)

Sampling a Sequence with SequenceSampler

The previous generation results may look a bit borning. Now, let's use sequence sampler to get some more interesting results.

A SequenceSampler samples from the contextual multinomial distribution produced by the language model at each time step. Since we may want to control how "sharp" the distribution is to tradeoff diversity with correctness, we can use the temperature option in SequenceSampler, which controls the temperature of the softmax function.

For each input same, sequence sampler can sample multiple independent sequences at once. The number of independent sequences to sample can be specified through the argument beam_size.

seq_sampler = nlp.model.SequenceSampler(beam_size=5,

Generate Sequences with Sequence Sampler

Now, use the sequence sampler created to sample sequences based on the same inputs used previously.

generate_sequences(seq_sampler, inputs, begin_states, 5)


  • Tweak alpha and K in BeamSearchScorer, how are the results changed?
  • Try different samples to decode.