# 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)