549 lines
28 KiB
Python
549 lines
28 KiB
Python
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# https://github.com/facebookresearch/audiocraft/blob/adf0b04a4452f171970028fcf80f101dd5e26e19/audiocraft/modules/codebooks_patterns.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed per https://github.com/facebookresearch/audiocraft/blob/adf0b04a4452f171970028fcf80f101dd5e26e19/LICENSE
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from collections import namedtuple
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from dataclasses import dataclass
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from functools import lru_cache
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import logging
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import typing as tp
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from abc import ABC, abstractmethod
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import torch
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LayoutCoord = namedtuple('LayoutCoord', ['t', 'q']) # (timestep, codebook index)
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PatternLayout = tp.List[tp.List[LayoutCoord]] # Sequence of coordinates
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logger = logging.getLogger(__name__)
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@dataclass
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class Pattern:
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"""Base implementation of a pattern over a sequence with multiple codebooks.
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The codebook pattern consists in a layout, defining for each sequence step
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the list of coordinates of each codebook timestep in the resulting interleaved sequence.
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The first item of the pattern is always an empty list in order to properly insert a special token
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to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern
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and ``timesteps`` the number of timesteps corresponding to the original sequence.
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The pattern provides convenient methods to build and revert interleaved sequences from it:
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``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T]
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to the interleaved sequence of shape [B, K, S] applying the pattern, with B being the batch size,
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K being the number of codebooks, T the number of original timesteps and S the number of sequence steps
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for the output sequence. The unfilled positions are replaced with a special token and the built sequence
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is returned along with a mask indicating valid tokens.
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``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment
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of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask
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to fill and specify invalid positions if needed.
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See the dedicated methods for more details.
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"""
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# Pattern layout, for each sequence step, we have a list of coordinates
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# corresponding to the original codebook timestep and position.
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# The first list is always an empty list in order to properly insert
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# a special token to start with.
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layout: PatternLayout
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timesteps: int
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n_q: int
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def __post_init__(self):
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assert len(self.layout) > 0
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self._validate_layout()
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self._build_reverted_sequence_scatter_indexes = lru_cache(100)(self._build_reverted_sequence_scatter_indexes)
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self._build_pattern_sequence_scatter_indexes = lru_cache(100)(self._build_pattern_sequence_scatter_indexes)
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logger.info("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout))
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def _validate_layout(self):
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"""Runs checks on the layout to ensure a valid pattern is defined.
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A pattern is considered invalid if:
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- Multiple timesteps for a same codebook are defined in the same sequence step
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- The timesteps for a given codebook are not in ascending order as we advance in the sequence
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(this would mean that we have future timesteps before past timesteps).
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"""
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q_timesteps = {q: 0 for q in range(self.n_q)}
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for s, seq_coords in enumerate(self.layout):
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if len(seq_coords) > 0:
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qs = set()
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for coord in seq_coords:
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qs.add(coord.q)
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last_q_timestep = q_timesteps[coord.q]
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assert coord.t >= last_q_timestep, \
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f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}"
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q_timesteps[coord.q] = coord.t
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# each sequence step contains at max 1 coordinate per codebook
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assert len(qs) == len(seq_coords), \
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f"Multiple entries for a same codebook are found at step {s}"
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@property
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def num_sequence_steps(self):
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return len(self.layout) - 1
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@property
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def max_delay(self):
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max_t_in_seq_coords = 0
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for seq_coords in self.layout[1:]:
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for coords in seq_coords:
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max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1)
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return max_t_in_seq_coords - self.timesteps
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@property
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def valid_layout(self):
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valid_step = len(self.layout) - self.max_delay
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return self.layout[:valid_step]
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def starts_with_special_token(self):
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return self.layout[0] == []
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def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None):
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"""Get codebook coordinates in the layout that corresponds to the specified timestep t
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and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step
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and the actual codebook coordinates.
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"""
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assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps"
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if q is not None:
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assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks"
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coords = []
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for s, seq_codes in enumerate(self.layout):
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for code in seq_codes:
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if code.t == t and (q is None or code.q == q):
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coords.append((s, code))
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return coords
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def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]:
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return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)]
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def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]:
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steps_with_timesteps = self.get_steps_with_timestep(t, q)
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return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None
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def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool,
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device: tp.Union[torch.device, str] = 'cpu'):
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"""Build scatter indexes corresponding to the pattern, up to the provided sequence_steps.
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Args:
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timesteps (int): Maximum number of timesteps steps to consider.
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keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps.
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device (torch.device or str): Device for created tensors.
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Returns:
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indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S].
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mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S].
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"""
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assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
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assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern"
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# use the proper layout based on whether we limit ourselves to valid steps only or not,
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# note that using the valid_layout will result in a truncated sequence up to the valid steps
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ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
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# single item indexing being super slow with pytorch vs. numpy, so we use numpy here
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indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy()
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mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy()
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# fill indexes with last sequence step value that will correspond to our special token
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# the last value is n_q * timesteps as we have flattened z and append special token as the last token
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# which will correspond to the index: n_q * timesteps
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indexes[:] = n_q * timesteps
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# iterate over the pattern and fill scattered indexes and mask
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for s, sequence_coords in enumerate(ref_layout):
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for coords in sequence_coords:
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if coords.t < timesteps:
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indexes[coords.q, s] = coords.t + coords.q * timesteps
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mask[coords.q, s] = 1
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indexes = torch.from_numpy(indexes).to(device)
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mask = torch.from_numpy(mask).to(device)
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return indexes, mask
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def build_pattern_sequence(self, z: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
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"""Build sequence corresponding to the pattern from the input tensor z.
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The sequence is built using up to sequence_steps if specified, and non-pattern
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coordinates are filled with the special token.
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Args:
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z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T].
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special_token (int): Special token used to fill non-pattern coordinates in the new sequence.
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keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
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Steps that are beyond valid steps will be replaced by the special_token in that case.
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Returns:
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values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S
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corresponding either to the sequence_steps if provided, otherwise to the length of the pattern.
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indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S].
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mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S].
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"""
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B, K, T = z.shape
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indexes, mask = self._build_pattern_sequence_scatter_indexes(
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T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device)
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)
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z = z.view(B, -1)
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# we append the special token as the last index of our flattened z tensor
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z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1)
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values = z[:, indexes.view(-1)]
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values = values.view(B, K, indexes.shape[-1])
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return values, indexes, mask
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def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int,
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keep_only_valid_steps: bool = False,
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is_model_output: bool = False,
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device: tp.Union[torch.device, str] = 'cpu'):
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"""Builds scatter indexes required to retrieve the original multi-codebook sequence
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from interleaving pattern.
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Args:
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sequence_steps (int): Sequence steps.
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n_q (int): Number of codebooks.
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keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
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Steps that are beyond valid steps will be replaced by the special_token in that case.
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is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not.
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device (torch.device or str): Device for created tensors.
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Returns:
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indexes (torch.Tensor): Indexes for reconstructing the output, of shape [K, T].
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mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
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"""
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ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
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# TODO(jade): Do we want to further truncate to only valid timesteps here as well?
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timesteps = self.timesteps
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assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
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assert sequence_steps <= len(ref_layout), \
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f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}"
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# ensure we take the appropriate indexes to keep the model output from the first special token as well
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if is_model_output and self.starts_with_special_token():
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ref_layout = ref_layout[1:]
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# single item indexing being super slow with pytorch vs. numpy, so we use numpy here
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indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy()
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mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy()
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# fill indexes with last sequence step value that will correspond to our special token
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indexes[:] = n_q * sequence_steps
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for s, sequence_codes in enumerate(ref_layout):
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if s < sequence_steps:
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for code in sequence_codes:
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if code.t < timesteps:
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indexes[code.q, code.t] = s + code.q * sequence_steps
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mask[code.q, code.t] = 1
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indexes = torch.from_numpy(indexes).to(device)
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mask = torch.from_numpy(mask).to(device)
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return indexes, mask
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def revert_pattern_sequence(self, s: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
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"""Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving.
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The sequence is reverted using up to timesteps if specified, and non-pattern coordinates
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are filled with the special token.
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Args:
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s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S].
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special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence.
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Returns:
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values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, T] with T
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corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise.
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indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T].
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mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
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"""
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B, K, S = s.shape
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indexes, mask = self._build_reverted_sequence_scatter_indexes(
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S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device)
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)
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s = s.view(B, -1)
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# we append the special token as the last index of our flattened z tensor
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s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1)
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values = s[:, indexes.view(-1)]
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values = values.view(B, K, indexes.shape[-1])
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return values, indexes, mask
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def revert_pattern_logits(self, logits: torch.Tensor, special_token: float, keep_only_valid_steps: bool = False):
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"""Revert model logits obtained on a sequence built from the pattern
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back to a tensor matching the original sequence.
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This method is similar to ``revert_pattern_sequence`` with the following specificities:
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1. It is designed to work with the extra cardinality dimension
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2. We return the logits for the first sequence item that matches the special_token and
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which matching target in the original sequence is the first item of the sequence,
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while we skip the last logits as there is no matching target
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"""
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B, card, K, S = logits.shape
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indexes, mask = self._build_reverted_sequence_scatter_indexes(
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S, K, keep_only_valid_steps, is_model_output=True, device=logits.device
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)
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logits = logits.reshape(B, card, -1)
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# we append the special token as the last index of our flattened z tensor
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logits = torch.cat([logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1) # [B, card, K x S]
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values = logits[:, :, indexes.view(-1)]
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values = values.view(B, card, K, indexes.shape[-1])
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return values, indexes, mask
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class CodebooksPatternProvider(ABC):
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"""Abstraction around providing pattern for interleaving codebooks.
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The CodebooksPatternProvider abstraction allows to implement various strategies to
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define interleaving pattern of sequences composed of multiple codebooks. For a given
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number of codebooks `n_q`, the pattern provider can generate a specified pattern
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corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern
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can be used to construct a new sequence from the original codes respecting the specified
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pattern. The pattern is defined as a list of list of code coordinates, code coordinate
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being a tuple with the original timestep and codebook to build the new sequence.
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Note that all patterns must start with an empty list that is then used to insert a first
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sequence step of special tokens in the newly generated sequence.
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Args:
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n_q (int): number of codebooks.
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cached (bool): if True, patterns for a given length are cached. In general
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that should be true for efficiency reason to avoid synchronization points.
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"""
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def __init__(self, n_q: int, cached: bool = True):
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assert n_q > 0
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self.n_q = n_q
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self.get_pattern = lru_cache(100)(self.get_pattern) # type: ignore
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@abstractmethod
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def get_pattern(self, timesteps: int) -> Pattern:
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"""Builds pattern with specific interleaving between codebooks.
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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: tp.Optional[tp.List[int]] = 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)
|
||
|
|
|
||
|
|
|
||
|
|
class ParallelPatternProvider(DelayedPatternProvider):
|
||
|
|
"""Provider for parallel pattern across codebooks.
|
||
|
|
This pattern provider is a special case of the delayed pattern with actually no delay,
|
||
|
|
hence delays=repeat(0, n_q).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
n_q (int): Number of codebooks.
|
||
|
|
empty_initial (int): Prepend with N empty list of coordinates.
|
||
|
|
"""
|
||
|
|
def __init__(self, n_q: int, empty_initial: int = 0):
|
||
|
|
super().__init__(n_q, [0] * n_q, empty_initial=empty_initial)
|
||
|
|
|
||
|
|
|
||
|
|
class UnrolledPatternProvider(CodebooksPatternProvider):
|
||
|
|
"""Provider for unrolling codebooks pattern.
|
||
|
|
This pattern provider enables to represent the codebook flattened completely or only to some extend
|
||
|
|
while also specifying a given delay between the flattened codebooks representation, allowing to
|
||
|
|
unroll the codebooks in the sequence.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
1. Flattening of the codebooks.
|
||
|
|
By default, the pattern provider will fully flatten the codebooks such as flattening=range(n_q),
|
||
|
|
taking n_q = 3 and timesteps = 4:
|
||
|
|
[[1, 2, 3, 4],
|
||
|
|
[1, 2, 3, 4],
|
||
|
|
[1, 2, 3, 4]]
|
||
|
|
will result into:
|
||
|
|
[[S, S, 1, S, S, 2, S, S, 3, S, S, 4],
|
||
|
|
[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
|
||
|
|
[1, S, S, 2, S, S, 3, S, S, 4, S, S]]
|
||
|
|
2. Partial flattening of the codebooks. The ``flattening`` parameter allows to specify the inner step
|
||
|
|
for each of the codebook, allowing to define which codebook to flatten (or keep in parallel), for example
|
||
|
|
taking n_q = 3, timesteps = 4 and flattening = [0, 1, 1]:
|
||
|
|
[[1, 2, 3, 4],
|
||
|
|
[1, 2, 3, 4],
|
||
|
|
[1, 2, 3, 4]]
|
||
|
|
will result into:
|
||
|
|
[[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
|
||
|
|
[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
|
||
|
|
[1, S, S, 2, S, S, 3, S, S, 4, S, S]]
|
||
|
|
3. Flattening with delay. The ``delay`` parameter allows to further unroll the sequence of codebooks
|
||
|
|
allowing to specify the delay per codebook. Note that the delay between codebooks flattened to the
|
||
|
|
same inner timestep should be coherent. For example, taking n_q = 3, timesteps = 4, flattening = [0, 1, 1]
|
||
|
|
and delays = [0, 3, 3]:
|
||
|
|
[[1, 2, 3, 4],
|
||
|
|
[1, 2, 3, 4],
|
||
|
|
[1, 2, 3, 4]]
|
||
|
|
will result into:
|
||
|
|
[[S, S, S, 1, S, 2, S, 3, S, 4],
|
||
|
|
[S, S, S, 1, S, 2, S, 3, S, 4],
|
||
|
|
[1, 2, 3, S, 4, S, 5, S, 6, S]]
|
||
|
|
|
||
|
|
Args:
|
||
|
|
n_q (int): Number of codebooks.
|
||
|
|
flattening (list of int, optional): Flattening schema over the codebooks. If not defined,
|
||
|
|
the codebooks will be flattened to 1 codebook per step, meaning that the sequence will
|
||
|
|
have n_q extra steps for each timestep.
|
||
|
|
delays (list of int, optional): Delay for each of the codebooks. If not defined,
|
||
|
|
no delay is added and therefore will default to [0] * ``n_q``.
|
||
|
|
Note that two codebooks that will be flattened to the same inner step
|
||
|
|
should have the same delay, otherwise the pattern is considered as invalid.
|
||
|
|
"""
|
||
|
|
FlattenedCodebook = namedtuple('FlattenedCodebook', ['codebooks', 'delay'])
|
||
|
|
|
||
|
|
def __init__(self, n_q: int, flattening: tp.Optional[tp.List[int]] = None,
|
||
|
|
delays: tp.Optional[tp.List[int]] = None):
|
||
|
|
super().__init__(n_q)
|
||
|
|
if flattening is None:
|
||
|
|
flattening = list(range(n_q))
|
||
|
|
if delays is None:
|
||
|
|
delays = [0] * n_q
|
||
|
|
assert len(flattening) == n_q
|
||
|
|
assert len(delays) == n_q
|
||
|
|
assert sorted(flattening) == flattening
|
||
|
|
assert sorted(delays) == delays
|
||
|
|
self._flattened_codebooks = self._build_flattened_codebooks(delays, flattening)
|
||
|
|
self.max_delay = max(delays)
|
||
|
|
|
||
|
|
def _build_flattened_codebooks(self, delays: tp.List[int], flattening: tp.List[int]):
|
||
|
|
"""Build a flattened codebooks representation as a dictionary of inner step
|
||
|
|
and the actual codebook indices corresponding to the flattened codebook. For convenience, we
|
||
|
|
also store the delay associated to the flattened codebook to avoid maintaining an extra mapping.
|
||
|
|
"""
|
||
|
|
flattened_codebooks: dict = {}
|
||
|
|
for q, (inner_step, delay) in enumerate(zip(flattening, delays)):
|
||
|
|
if inner_step not in flattened_codebooks:
|
||
|
|
flat_codebook = UnrolledPatternProvider.FlattenedCodebook(codebooks=[q], delay=delay)
|
||
|
|
else:
|
||
|
|
flat_codebook = flattened_codebooks[inner_step]
|
||
|
|
assert flat_codebook.delay == delay, (
|
||
|
|
"Delay and flattening between codebooks is inconsistent: ",
|
||
|
|
"two codebooks flattened to the same position should have the same delay."
|
||
|
|
)
|
||
|
|
flat_codebook.codebooks.append(q)
|
||
|
|
flattened_codebooks[inner_step] = flat_codebook
|
||
|
|
return flattened_codebooks
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _num_inner_steps(self):
|
||
|
|
"""Number of inner steps to unroll between timesteps in order to flatten the codebooks.
|
||
|
|
"""
|
||
|
|
return max([inner_step for inner_step in self._flattened_codebooks.keys()]) + 1
|
||
|
|
|
||
|
|
def num_virtual_steps(self, timesteps: int) -> int:
|
||
|
|
return timesteps * self._num_inner_steps + 1
|
||
|
|
|
||
|
|
def get_pattern(self, timesteps: int) -> Pattern:
|
||
|
|
"""Builds pattern for delay across codebooks.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
timesteps (int): Total number of timesteps.
|
||
|
|
"""
|
||
|
|
# the PatternLayout is built as a tuple of sequence position and list of coordinates
|
||
|
|
# so that it can be reordered properly given the required delay between codebooks of given timesteps
|
||
|
|
indexed_out: list = [(-1, [])]
|
||
|
|
max_timesteps = timesteps + self.max_delay
|
||
|
|
for t in range(max_timesteps):
|
||
|
|
# for each timestep, we unroll the flattened codebooks,
|
||
|
|
# emitting the sequence step with the corresponding delay
|
||
|
|
for step in range(self._num_inner_steps):
|
||
|
|
if step in self._flattened_codebooks:
|
||
|
|
# we have codebooks at this virtual step to emit
|
||
|
|
step_codebooks = self._flattened_codebooks[step]
|
||
|
|
t_for_q = t + step_codebooks.delay
|
||
|
|
coords = [LayoutCoord(t, q) for q in step_codebooks.codebooks]
|
||
|
|
if t_for_q < max_timesteps and t < max_timesteps:
|
||
|
|
indexed_out.append((t_for_q, coords))
|
||
|
|
else:
|
||
|
|
# there is no codebook in this virtual step so we emit an empty list
|
||
|
|
indexed_out.append((t, []))
|
||
|
|
out = [coords for _, coords in sorted(indexed_out)]
|
||
|
|
return Pattern(out, n_q=self.n_q, timesteps=timesteps)
|
||
|
|
|
||
|
|
|
||
|
|
class CoarseFirstPattern(CodebooksPatternProvider):
|
||
|
|
"""First generates all the codebooks #1 (e.g. coarser), then the remaining ones,
|
||
|
|
potentially with delays.
|
||
|
|
|
||
|
|
..Warning:: You must always generate the full training duration at test time, for instance,
|
||
|
|
30 seconds, as otherwise, the fine codebooks will start being generated in an unexpected
|
||
|
|
location. This is due to the non causality of the remaining codebooks with respect to
|
||
|
|
the first ones.
|
||
|
|
|
||
|
|
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.
|
||
|
|
"""
|
||
|
|
def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None):
|
||
|
|
super().__init__(n_q)
|
||
|
|
if delays is None:
|
||
|
|
delays = [0] * (n_q - 1)
|
||
|
|
self.delays = delays
|
||
|
|
assert len(self.delays) == self.n_q - 1
|
||
|
|
assert sorted(self.delays) == self.delays
|
||
|
|
|
||
|
|
def get_pattern(self, timesteps: int) -> Pattern:
|
||
|
|
out: PatternLayout = [[]]
|
||
|
|
for t in range(timesteps):
|
||
|
|
out.append([LayoutCoord(t, 0)])
|
||
|
|
max_delay = max(self.delays)
|
||
|
|
for t in range(timesteps + max_delay):
|
||
|
|
v = []
|
||
|
|
for q, delay in enumerate(self.delays):
|
||
|
|
t_for_q = t - delay
|
||
|
|
if t_for_q >= 0:
|
||
|
|
v.append(LayoutCoord(t_for_q, q + 1))
|
||
|
|
out.append(v)
|
||
|
|
return Pattern(out, n_q=self.n_q, timesteps=timesteps)
|
||
|
|
|
||
|
|
|
||
|
|
class MusicLMPattern(CodebooksPatternProvider):
|
||
|
|
"""Almost MusicLM style pattern. This is equivalent to full flattening
|
||
|
|
but in a different order.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
n_q (int): Number of codebooks.
|
||
|
|
group_by (int): Number of codebooks to group together.
|
||
|
|
"""
|
||
|
|
def __init__(self, n_q: int, group_by: int = 2):
|
||
|
|
super().__init__(n_q)
|
||
|
|
self.group_by = group_by
|
||
|
|
|
||
|
|
def get_pattern(self, timesteps: int) -> Pattern:
|
||
|
|
out: PatternLayout = [[]]
|
||
|
|
for offset in range(0, self.n_q, self.group_by):
|
||
|
|
for t in range(timesteps):
|
||
|
|
for q in range(offset, offset + self.group_by):
|
||
|
|
out.append([LayoutCoord(t, q)])
|
||
|
|
return Pattern(out, n_q=self.n_q, timesteps=timesteps)
|