rnnt_decode.py

# Copyright      2022  Xiaomi Corp.       (author: Wei Kang)
#
# See ../../../LICENSE for clarification regarding multiple authors
#
# 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.

from typing import List
from typing import Tuple

import k2
import torch
import _k2

from k2 import Fsa
from k2 import RaggedShape
from k2 import RaggedTensor
from torch import Tensor
from .ops import index_select
from _k2 import RnntDecodingConfig


class RnntDecodingStream(object):
    def __init__(self, fsa: Fsa) -> None:
        """Create a new rnnt decoding stream.

        Every sequence(wave data) needs a decoding stream, this function is
        expected to be called when a new sequence comes. We support different
        decoding graphs for different streams.

        Args:
          fsa:
            The decoding graph used in this stream.

        Returns:
          A rnnt decoding stream object, which will be combined into
          :class:`RnntDecodingStreams` to do decoding together with other
          sequences in parallel.
        """
        self.fsa = fsa
        self.stream = _k2.create_rnnt_decoding_stream(fsa.arcs)
        self.device = fsa.device

    def __str__(self) -> str:
        """Return a string representation of this object

        For visualization and debug only.
        """
        return f"{self.stream}, device : {self.device}\n"


class RnntDecodingStreams(object):
    """See https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py  # noqa
    for how this class is used in RNN-T decoding.
    """

    def __init__(
        self, src_streams: List[RnntDecodingStream], config: RnntDecodingConfig
    ) -> None:
        """
        Combines multiple RnntDecodingStream objects to create a
        RnntDecodingStreams object, then all these RnntDecodingStreams can do
        decoding in parallel.

        Args:
          src_streams:
            A list of RnntDecodingStream object to be combined.
          config:
            A configuration object which contains decoding parameters like
            `vocab-size`, `decoder_history_len`, `beam`, `max_states`,
            `max_contexts` etc.

        Returns:
          Return a RnntDecodingStreams object.
        """
        assert len(src_streams) > 0
        self.num_streams = len(src_streams)
        self.src_streams = src_streams
        self.device = self.src_streams[0].device
        streams = [x.stream for x in self.src_streams]
        self.streams = _k2.RnntDecodingStreams(streams, config)

    def __str__(self) -> str:
        """Return a string representation of this object

        For visualization and debug only.
        """
        s = f"num_streams : {self.num_streams}\n"
        for i in range(self.num_streams):
            s += f"stream[{i}] : {self.src_streams[i]}"
        return s

    def get_contexts(self) -> Tuple[RaggedShape, Tensor]:
        """
        This function must be called prior to evaluating the joiner network
        for a particular frame.  It tells the calling code for which contexts
        it must evaluate the joiner network.

        Returns:
          Return a two-element tuple containing a RaggedShape and a tensor.

          shape:
            A RaggedShape with 2 axes, representing [stream][context].

          contexts:
            A tensor of shape [tot_contexts][decoder_history_len], where
            tot_contexts == shape->TotSize(1) and decoder_history_len comes from
            the config, it represents the number of symbols in the context of
            the decoder network (assumed to be finite). It contains the token
            ids into the vocabulary(i.e. `0 <= value < vocab_size`).
            Its dtype is torch.int32.
        """
        return self.streams.get_contexts()

    def advance(self, logprobs: Tensor) -> None:
        """
        Advance decoding streams by one frame.

        Args:
          logprobs:
            A tensor of shape [tot_contexts][num_symbols], containing log-probs
            of symbols given the contexts output by `get_contexts()`. It
            satisfies `logprobs.Dim0() == shape.TotSize(1)`, shape is returned
            by `get_contexts()`.
        """
        self.streams.advance(logprobs)

    def terminate_and_flush_to_streams(self) -> None:
        """
        Terminate the decoding process of current RnntDecodingStreams object.
        It will update the decoding states and store the decoding results
        currently got to each of the individual streams.

        Note:
          We can not decode with this object anymore after calling
          terminate_and_flush_to_streams().
        """
        self.streams.terminate_and_flush_to_streams()

    def format_output(
            self,
            num_frames: List[int],
            allow_partial: bool = False,
            log_probs: torch.Tensor = None,
            t2s2c_shape: RaggedShape = None,
    ) -> Fsa:
        """
        Generate the lattice Fsa currently got.

        Note:
          The attributes of the generated lattice is a union of the attributes
          of all the decoding graphs. For example, if `self` contains three
          individual stream, each stream has its own decoding graphs, graph[0]
          has attributes attr1, attr2; graph[1] has attributes attr1, attr3;
          graph[2] has attributes attr3, attr4; then the generated lattice has
          attributes attr1, attr2, attr3, attr4.

        Args:
          num_frames:
            A List containing the number of frames we want to gather for each
            stream (note: the frames we have ever received for the corresponding
            stream). It MUST satisfy `len(num_frames) == self.num_streams`.
          allow_partial:
            If true and there is no final state active, we will treat all the
            states on the last frame to be final state.
            If false, we only care about the real final state in the
            decoding graph on the last frame when generating lattice.
            Default False.
          log_probs:
            A tensor of shape [t2s2c_shape.tot_size(2)][num_symbols].
            It's a stacked tensor of logprobs passed to function `advance`
            during decoding.
          t2s2c_shape:
            It is short for time2stream2context_shape,
            which describes shape of log_probs used to generate lattice.
            Used to generate arc_map_token
            and make the whole decoding process differentiable.

        Returns:
          Return the lattice Fsa with all the attributes propagated.
          The returned Fsa has 3 axes with `fsa.dim0==self.num_streams`.
        """
        assert len(num_frames) == self.num_streams

        if log_probs is not None:
            assert t2s2c_shape is not None
            assert t2s2c_shape.tot_size(2) == log_probs.shape[0]
            ragged_arcs, out_map, arc_map_token = self.streams.format_output(
                num_frames, allow_partial, t2s2c_shape
            )
        else:
            ragged_arcs, out_map = self.streams.format_output(
                num_frames, allow_partial
            )
        fsa = Fsa(ragged_arcs)

        # propagate attributes
        tensor_attr_info = dict()
        # gather the attributes info of all the decoding graphs,
        for i in range(self.num_streams):
            src = self.src_streams[i].fsa
            for name, value in src.named_tensor_attr(include_scores=False):
                if name not in tensor_attr_info:
                    filler = 0
                    if isinstance(value, Tensor):
                        filler = float(src.get_filler(name))
                        dtype = value.dtype
                        tensor_type = "Tensor"
                    else:
                        assert isinstance(value, k2.RaggedTensor)
                        # Only integer types ragged attributes are supported now
                        assert value.dtype == torch.int32
                        assert value.num_axes == 2
                        dtype = torch.int32
                        tensor_type = "RaggedTensor"
                    tensor_attr_info[name] = {
                        "filler": filler,
                        "dtype": dtype,
                        "tensor_type": tensor_type,
                    }
        # combine the attributes propagating from different decoding graphs
        for name, info in tensor_attr_info.items():
            values = list()
            start = 0
            for i in range(self.num_streams):
                src = self.src_streams[i].fsa
                device = self.device
                num_arcs = fsa[i].num_arcs
                arc_map = out_map[start: start + num_arcs]
                start = start + num_arcs
                if hasattr(src, name):
                    value = getattr(src, name)
                    if info["tensor_type"] == "Tensor":
                        assert isinstance(value, Tensor)
                        new_value = index_select(
                            value, arc_map, default_value=filler
                        )
                    else:
                        assert isinstance(value, RaggedTensor)
                        # Only integer types ragged attributes are supported now
                        assert value.num_axes == 2
                        assert value.dtype == torch.int32
                        new_value, _ = value.index(
                            arc_map, axis=0, need_value_indexes=False
                        )
                else:
                    if info["tensor_type"] == "Tensor":
                        # fill with filler value
                        new_value = torch.tensor(
                            [filler] * num_arcs,
                            dtype=info["dtype"],
                            device=device,
                        )
                    else:
                        # fill with empty RaggedTensor
                        new_value = RaggedTensor(
                            torch.empty(
                                (num_arcs, 0),
                                dtype=info["dtype"],
                                device=device,
                            )
                        )
                values.append(new_value)
            if info["tensor_type"] == "Tensor":
                new_value = torch.cat(values)
            else:
                new_value = k2.ragged.cat(values, axis=0)
            setattr(fsa, name, new_value)

        # set non_tensor_attrs
        for i in range(self.num_streams):
            src = self.src_streams[i].fsa
            for name, value in src.named_non_tensor_attr():
                setattr(fsa, name, value)

        if log_probs is not None:
            # Make fsa.scores tracked by autograd
            # to make the whole decoding process differentiable.
            scores_tracked_by_autograd = index_select(
                log_probs.reshape(-1), arc_map_token, default_value=0.0)

            # Decoding graph may contain non-zero scores on arcs.
            graph_scores = \
                fsa.scores.detach() - scores_tracked_by_autograd.detach()
            scores_tracked_by_autograd = \
                scores_tracked_by_autograd + graph_scores
            fsa.scores = scores_tracked_by_autograd
        return fsa