tfm.nlp.ops.SamplingModule

Implementation for sampling strategies (go/decoding-tf-nlp).

tfm.nlp.ops.SamplingModule(
    symbols_to_logits_fn,
    vocab_size: int,
    max_decode_length: int,
    eos_id: int,
    padded_decode: bool,
    length_normalization_fn: Optional[Callable[[int, tf.DType], float]] = None,
    top_k=0,
    top_p=1.0,
    sample_temperature=0.0,
    enable_greedy: bool = True,
    dtype: tf.DType = tf.float32,
    decoding_name: Optional[str] = None,
    extra_cache_output: bool = False
)

Methods

`generate`

View source

generate(
    initial_ids: tf.Tensor,
    initial_cache: Dict[str, tf.Tensor],
    initial_log_probs: Optional[tf.Tensor] = None
) -> Output

Implements the decoding strategy (beam_search or sampling).

Args
`initial_ids`	initial ids to pass into the symbols_to_logits_fn. int tensor with shape [batch_size, 1]
`initial_cache`	dictionary for caching model outputs from previous step.
`initial_log_probs`	Optionally initial log probs if there is a prefix sequence we want to start to decode from.

Returns
Tuple of tensors representing finished_sequence: shape [batch, max_seq_length] finished_scores: [batch] first_cache: The cache after init token

`inf`

View source

inf()

Returns a value close to infinity, but is still finite in dtype.

This is useful to get a very large value that is still zero when multiplied by zero. The floating-point "Inf" value is NaN when multiplied by zero.

Returns
A very large value.