vui/patterns.py

import logging
from abc import ABC, abstractmethod
from collections import namedtuple
from dataclasses import dataclass
from functools import lru_cache

import torch
import torch.nn.functional as F


def apply_delay_pattern(codes: torch.Tensor, mask_token: int):
    codes = F.pad(codes, (0, codes.shape[1] + 1), value=mask_token)
    return torch.stack([codes[:, k].roll(k + 1) for k in range(codes.shape[1])], dim=1)


def revert_delay_pattern(codes: torch.Tensor):
    _, n_q, seq_len = codes.shape
    return torch.stack(
        [codes[:, k, k + 1 : seq_len - n_q + k] for k in range(n_q)], dim=1
    )


LayoutCoord = namedtuple("LayoutCoord", ["t", "q"])  # (timestep, codebook index)
PatternLayout = list[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 = lru_cache(100)(
            self._build_reverted_sequence_scatter_indexes
        )
        self._build_pattern_sequence_scatter_indexes = lru_cache(100)(
            self._build_pattern_sequence_scatter_indexes
        )
        logger.info(
            "New pattern, time steps: %d, sequence steps: %d",
            self.timesteps,
            len(self.layout),
        )

    def _validate_layout(self):
        """Runs checks on the layout to ensure a valid pattern is defined.
        A pattern is considered invalid if:
            - Multiple timesteps for a same codebook are defined in the same sequence step
            - The timesteps for a given codebook are not in ascending order as we advance in the sequence
              (this would mean that we have future timesteps before past timesteps).
        """
        q_timesteps = {q: 0 for q in range(self.n_q)}
        for s, seq_coords in enumerate(self.layout):
            if len(seq_coords) > 0:
                qs = set()
                for coord in seq_coords:
                    qs.add(coord.q)
                    last_q_timestep = q_timesteps[coord.q]
                    assert (
                        coord.t >= last_q_timestep
                    ), f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}"
                    q_timesteps[coord.q] = coord.t
                # each sequence step contains at max 1 coordinate per codebook
                assert len(qs) == len(
                    seq_coords
                ), f"Multiple entries for a same codebook are found at step {s}"

    @property
    def num_sequence_steps(self):
        return len(self.layout) - 1

    @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: int | None = 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: int | None = None) -> 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: int | None = None) -> int | None:
        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: 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: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False
    ):
        """Build sequence corresponding to the pattern from the input tensor z.
        The sequence is built using up to sequence_steps if specified, and non-pattern
        coordinates are filled with the special token.

        Args:
            z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T].
            special_token (int): Special token used to fill non-pattern coordinates in the new sequence.
            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.
        Returns:
            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S
                corresponding either to the sequence_steps if provided, otherwise to the length of the pattern.
            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S].
            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S].
        """
        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.reshape(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])
        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: 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
        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
                        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: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False
    ):
        """Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving.
        The sequence is reverted using up to timesteps if specified, and non-pattern coordinates
        are filled with the special token.

        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
                corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise.
            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].
        """
        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

    def revert_pattern_logits(
        self,
        logits: torch.Tensor,
        special_token: float,
        keep_only_valid_steps: bool = False,
    ):
        """Revert model logits obtained on a sequence built from the pattern
        back to a tensor matching the original sequence.

        This method is similar to ``revert_pattern_sequence`` with the following specificities:
        1. It is designed to work with the extra cardinality dimension
        2. We return the logits for the first sequence item that matches the special_token and
        which matching target in the original sequence is the first item of the sequence,
        while we skip the last logits as there is no matching target
        """
        B, n, Q, S = logits.shape
        indexes, mask = self._build_reverted_sequence_scatter_indexes(
            S, Q, keep_only_valid_steps, is_model_output=True, device=logits.device
        )
        logits = logits.reshape(B, n, -1)
        # we append the special token as the last index of our flattened z tensor
        logits = torch.cat(
            [logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1
        )  # [B, card, K x S]
        values = logits[:, :, indexes.view(-1)]
        values = values.view(B, n, Q, indexes.shape[-1])
        return values, indexes, mask


class CodebooksPatternProvider(ABC):
    """Abstraction around providing pattern for interleaving codebooks.

    The CodebooksPatternProvider abstraction allows to implement various strategies to
    define interleaving pattern of sequences composed of multiple codebooks. For a given
    number of codebooks `n_q`, the pattern provider can generate a specified pattern
    corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern
    can be used to construct a new sequence from the original codes respecting the specified
    pattern. The pattern is defined as a list of list of code coordinates, code coordinate
    being a tuple with the original timestep and codebook to build the new sequence.
    Note that all patterns must start with an empty list that is then used to insert a first
    sequence step of special tokens in the newly generated sequence.

    Args:
        n_q (int): number of codebooks.
        cached (bool): if True, patterns for a given length are cached. In general
            that should be true for efficiency reason to avoid synchronization points.
    """

    def __init__(self, n_q: int, cached: bool = True):
        assert n_q > 0
        self.n_q = n_q
        self.get_pattern = lru_cache(100)(self.get_pattern)  # type: ignore

    @abstractmethod
    def get_pattern(self, timesteps: int) -> Pattern:
        """Builds pattern with specific interleaving between codebooks.

        Args:
            timesteps (int): Total number of timesteps.
        """
        raise NotImplementedError()


class DelayedPatternProvider(CodebooksPatternProvider):
    """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: int,
        delays: list[int] | None = None,
        flatten_first: int = 0,
        empty_initial: int = 0,
    ):
        super().__init__(n_q)
        if delays is None:
            delays = list(range(n_q))
        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: int) -> Pattern:
        omit_special_token = self.empty_initial < 0
        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)
        return Pattern(out, n_q=self.n_q, timesteps=timesteps)


if __name__ == "__main__":
    # Tried to use the simple patterns to train and something very odd happened.
    Q = 4

    codes = torch.randint(0, 1000, (1, Q, 100))
    pcodes = apply_delay_pattern(codes, 1001)
    provider = DelayedPatternProvider(Q)
    provider = provider.get_pattern(100)
    pcodes2 = provider.build_pattern_sequence(codes, 1001)
    breakpoint()