audiocraft/codebooks_patterns.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from collections import namedtuple
from dataclasses import dataclass

import logging
import typing as tp
import torch

LayoutCoord = namedtuple('LayoutCoord', ['t', 'q'])  # (timestep, codebook index)
PatternLayout = tp.List[tp.List[LayoutCoord]]  # Sequence of coordinates
logger = logging.getLogger(__name__)


@dataclass
class Pattern:
    """Base implementation of a pattern over a sequence with multiple codebooks.

    The codebook pattern consists in a layout, defining for each sequence step
    the list of coordinates of each codebook timestep in the resulting interleaved sequence.
    The first item of the pattern is always an empty list in order to properly insert a special token
    to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern
    and ``timesteps`` the number of timesteps corresponding to the original sequence.

    The pattern provides convenient methods to build and revert interleaved sequences from it:
    ``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T]
        to the interleaved sequence of shape [B, K, S] applying the pattern, with B being the batch size,
        K being the number of codebooks, T the number of original timesteps and S the number of sequence steps
        for the output sequence. The unfilled positions are replaced with a special token and the built sequence
        is returned along with a mask indicating valid tokens.
    ``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment
        of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask
        to fill and specify invalid positions if needed.
    See the dedicated methods for more details.
    """
    # Pattern layout, for each sequence step, we have a list of coordinates
    # corresponding to the original codebook timestep and position.
    # The first list is always an empty list in order to properly insert
    # a special token to start with.
    layout: PatternLayout
    timesteps: int
    n_q: int

    def __post_init__(self):
        # assert len(self.layout) > 0
        # self._validate_layout()   # 
        self._build_reverted_sequence_scatter_indexes = self._build_reverted_sequence_scatter_indexes
        self._build_pattern_sequence_scatter_indexes = self._build_pattern_sequence_scatter_indexes
        print("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout))

    @property
    def max_delay(self):
        max_t_in_seq_coords = 0
        for seq_coords in self.layout[1:]:
            for coords in seq_coords:
                max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1)
        return max_t_in_seq_coords - self.timesteps

    @property
    def valid_layout(self):
        valid_step = len(self.layout) - self.max_delay
        return self.layout[:valid_step]

    def starts_with_special_token(self):
        return self.layout[0] == []

    def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None):
        """Get codebook coordinates in the layout that corresponds to the specified timestep t
        and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step
        and the actual codebook coordinates.
        """
        assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps"
        if q is not None:
            assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks"
        coords = []
        for s, seq_codes in enumerate(self.layout):
            for code in seq_codes:
                if code.t == t and (q is None or code.q == q):
                    coords.append((s, code))
        return coords

    def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]:
        return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)]

    def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]:
        steps_with_timesteps = self.get_steps_with_timestep(t, q)
        return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None

    def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool,
                                                device: tp.Union[torch.device, str] = 'cpu'):
        """Build scatter indexes corresponding to the pattern, up to the provided sequence_steps.

        Args:
            timesteps (int): Maximum number of timesteps steps to consider.
            keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps.
            device (torch.device or str): Device for created tensors.
        Returns:
            indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S].
            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S].
        """
        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
        assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern"
        # use the proper layout based on whether we limit ourselves to valid steps only or not,
        # note that using the valid_layout will result in a truncated sequence up to the valid steps
        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
        indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy()
        mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy()
        # fill indexes with last sequence step value that will correspond to our special token
        # the last value is n_q * timesteps as we have flattened z and append special token as the last token
        # which will correspond to the index: n_q * timesteps
        indexes[:] = n_q * timesteps
        # iterate over the pattern and fill scattered indexes and mask
        for s, sequence_coords in enumerate(ref_layout):
            for coords in sequence_coords:
                if coords.t < timesteps:
                    indexes[coords.q, s] = coords.t + coords.q * timesteps
                    mask[coords.q, s] = 1
        indexes = torch.from_numpy(indexes).to(device)
        mask = torch.from_numpy(mask).to(device)
        return indexes, mask

    def build_pattern_sequence(self, 
                               z, 
                               special_token, 
                               keep_only_valid_steps=False):
        B, K, T = z.shape
        indexes, mask = self._build_pattern_sequence_scatter_indexes(
            T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device)
        )
        z = z.view(B, -1)
        # we append the special token as the last index of our flattened z tensor
        z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1)
        values = z[:, indexes.view(-1)]
        values = values.view(B, K, indexes.shape[-1])
        
        # print(values.shape, indexes.shape, mask.shape, 'BUILD PATTERN')
        # --
        # torch.Size([1, 4, 39]) torch.Size([4, 39]) torch.Size([4, 39]) BUILD PATTERN
        
        return values, indexes, mask

    def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int,
                                                 keep_only_valid_steps: bool = False,
                                                 is_model_output: bool = False,
                                                 device: tp.Union[torch.device, str] = 'cpu'):
        """Builds scatter indexes required to retrieve the original multi-codebook sequence
        from interleaving pattern.

        Args:
            sequence_steps (int): Sequence steps.
            n_q (int): Number of codebooks.
            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
                Steps that are beyond valid steps will be replaced by the special_token in that case.
            is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not.
            device (torch.device or str): Device for created tensors.
        Returns:
            indexes (torch.Tensor): Indexes for reconstructing the output, of shape [K, T].
            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
        """
        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
        # TODO(jade): Do we want to further truncate to only valid timesteps here as well?
        timesteps = self.timesteps
        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
        assert sequence_steps <= len(ref_layout), \
            f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}"

        # ensure we take the appropriate indexes to keep the model output from the first special token as well
        if is_model_output and self.starts_with_special_token():
            ref_layout = ref_layout[1:]

        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
        indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy()
        mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy()
        # fill indexes with last sequence step value that will correspond to our special token
        indexes[:] = n_q * sequence_steps
        for s, sequence_codes in enumerate(ref_layout):
            if s < sequence_steps:
                for code in sequence_codes:
                    if code.t < timesteps:
                        indexes[code.q, code.t] = s + code.q * sequence_steps  # oh the jump - so are the codes linearised
                        mask[code.q, code.t] = 1
        indexes = torch.from_numpy(indexes).to(device)
        mask = torch.from_numpy(mask).to(device)
        return indexes, mask

    def revert_pattern_sequence(self,
                                s,
                                special_token,
                                keep_only_valid_steps=False):
        """SPECIAL TOKEN NOT DELETED HERE !!!!

        Args:
            s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S].
            special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence.
        Returns:
            values (torch.Tensor) : Interleaved sequence matching the pattern, of shape [B, K, T] with T
            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T].
            mask (torch.Tensor)   : Mask corresponding to indexes that matches valid indexes of shape [K, T].
                                    shall this mask delete special token id;
        """
        B, K, S = s.shape
        indexes, mask = self._build_reverted_sequence_scatter_indexes(
            S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device)
        )
        s = s.view(B, -1)
        # we append the special token as the last index of our flattened z tensor
        s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1)
        values = s[:, indexes.view(-1)]
        values = values.view(B, K, indexes.shape[-1])
        
        return values, indexes, mask
    
    
    


class DelayedPatternProvider():
    """Provider for delayed pattern across delayed codebooks.
    Codebooks are delayed in the sequence and sequence steps will contain codebooks
    from different timesteps.

    Example:
        Taking timesteps=4 and n_q=3, delays=None, the multi-codebook sequence:
        [[1, 2, 3, 4],
        [1, 2, 3, 4],
        [1, 2, 3, 4]]
        The resulting sequence obtained from the returned pattern is:
        [[S, 1, 2, 3, 4],
        [S, S, 1, 2, 3],
        [S, S, S, 1, 2]]
        (with S being a special token)

    Args:
        n_q (int): Number of codebooks.
        delays (list of int, optional): Delay for each of the codebooks.
            If delays not defined, each codebook is delayed by 1 compared to the previous one.
        flatten_first (int): Flatten the first N timesteps.
        empty_initial (int): Prepend with N empty list of coordinates.
    """
    def __init__(self, 
                 n_q,
                 delays,
                 flatten_first=0, 
                 empty_initial=0):
        self.n_q = n_q
        if delays is None:
            delays = list(range(n_q))
        print(f'{delays=}  PATTERN __ini')    
        self.delays = delays
        self.flatten_first = flatten_first
        self.empty_initial = empty_initial
        assert len(self.delays) == self.n_q
        assert sorted(self.delays) == self.delays

    def get_pattern(self, timesteps):
        # get_pattern for desired length?
        # print(f'{timesteps=} GET_PATTERn')   # 35
        # print(f'{self.empty_initial=}')
        omit_special_token = self.empty_initial < 0   # False as initial = 0 unset
        
        out: PatternLayout = [] if omit_special_token else [[]]
        max_delay = max(self.delays)
        if self.empty_initial:
            out += [[] for _ in range(self.empty_initial)]
        if self.flatten_first:
            for t in range(min(timesteps, self.flatten_first)):
                for q in range(self.n_q):
                    out.append([LayoutCoord(t, q)])
        for t in range(self.flatten_first, timesteps + max_delay):
            v = []
            for q, delay in enumerate(self.delays):
                t_for_q = t - delay
                if t_for_q >= self.flatten_first:
                    v.append(LayoutCoord(t_for_q, q))
            out.append(v)
        # print(self.n_q, 'N_Q in PATTERN')  # 4 N_Q in PATTERN
        return Pattern(out, n_q=self.n_q, timesteps=timesteps)