oops, kept forgetting to actually pass in lang/tone tokens (despite not really using these at the moment)

vall_e/models/ar_nar.py

@@ -154,7 +154,14 @@ class AR_NAR(Base):
 
 					quant_levels = [ generate(quant_level_range[0], quant_level_range[1]) for i in range(batch_size) ]
 
+				# these two are techinically equivalent if the audio embeddings handle things properly
 				resps_list = [r[..., 0] if l == 0 else r[..., :l+1] for r, l in zip(resps_list, quant_levels)]
+				stop_sequence = torch.Tensor([self.stop_token]).to(device=device, dtype=torch.int16)
+
+				"""
+				resps_list = [r[..., :l+1] for r, l in zip(resps_list, quant_levels)]
+				stop_sequence = torch.Tensor([[self.stop_token] * 1]).to(device=device, dtype=torch.int16)
+				"""
 				
 				for i in range(batch_size):
 					# cap quant_level if it exceeds its corresponding resp/prom
@@ -170,8 +177,7 @@ class AR_NAR(Base):
 
 					# append stop tokens for AR
 					# could technically do it in the .inputs call
-					resps_list[i] = torch.cat([resps_list[i], torch.Tensor([self.stop_token]).to(device=device, dtype=torch.int16) ])
+					resps_list[i] = torch.cat([ resps_list[i], stop_sequence ])
-
 
 				inputs = self.inputs(
 					text_list=text_list,
@@ -186,7 +192,7 @@ class AR_NAR(Base):
 
 				return super().forward(
 					inputs=inputs,
-					quant_levels=quant_levels,
+					quant_levels=quant_levels, # could technically just grab this from the above inputs since they're included as an RVQ level token
 				)
 			
 			# is NAR

vall_e/models/base.py

@@ -33,6 +33,10 @@ from ..samplers import reptition_penalize, length_penalize, ban_tokens, top_k_to
 
 from ..emb.qnt import encode_as_embedding
 
+"""
+from ..utils.pattern import DelayedPatternProvider, VALLEPattern
+"""
+
 def _create_mask(l, device):
 	"""1 is valid region and 0 is invalid."""
 	seq = torch.arange(max(l), device=device).unsqueeze(0)  # (1 t)
@@ -331,6 +335,38 @@ class Base(nn.Module):
 			return 1.0
 		return self.config.loss_factors[k] if k in self.config.loss_factors else 1.0
 
+	# these probably need to live in an interleaved model, as pattern-ing is targeted for a sole AR model
+	"""
+	def codes_to_pattern(self, codes):
+		# expand if not batched
+		if codes.dim() == 2:
+			codes = codes.unsqueeze(0)
+		# [batch, timestep, rvq level] (B, T, K) =>  [batch, rvq level, timestep] (B, K, T)
+		codes = codes.permute(0, 2, 1)
+		
+		B, K, T = codes.shape
+		
+		# map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens
+		pattern = self.pattern_provider.get_pattern(T)
+		sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence(
+			codes.contiguous(), self.stop_token, keep_only_valid_steps=False,
+		)
+
+		# (B, K, T) => (B, T, K)
+		return sequence_codes.permute(0, 2, 1)
+
+	def logits_from_pattern(self, logits, pattern):
+		logits = logits.permute(0, 3, 1, 2)  # [B, card, K, S]
+
+		logits, logits_indexes, logits_mask = pattern.revert_pattern_logits(
+			logits, float('nan'), keep_only_valid_steps=False
+		)
+		logits = logits.permute(0, 2, 3, 1)  # [B, K, T, card]
+		logits_mask = logits_mask[None, :, :].expand(B, -1, -1)  # [K, T] -> [B, K, T]
+
+		return logits, logits_mask
+	"""
+
 	def __init__(
 		self,
 		
@@ -814,6 +850,7 @@ class Base(nn.Module):
 
 		return x, state, aux_loss
 
+	# takes a bunch of separate lists and parses them into an ordered array of tuples to guide input sequence creation
 	def inputs(
 		self,
 		text_list: list[Tensor],
@@ -835,33 +872,58 @@ class Base(nn.Module):
 			quant_level = quant_levels[i] if quant_levels is not None else 0
 			task_type = task_list[i] if task_list is not None else "tts"
 
+			# insert task type as a string
 			inputs[i].append( ( "task", task_type ) )
 
-			# <text><sep><rvq lvl><sep><prom><sep><resp>
+			# Base-line TTS task
+			# Sequence: <text><sep><rvq lvl><sep><prom><sep><resp>
 			if task_type == "tts":
+				# insert the text prompt
 				if text_list is not None:
 					inputs[i].append( ( "text", text_list[i] ) )
+				# insert lang token if we're trained for it
+				if "lang" in self.capabilities and lang_list is not None:
+					inputs[i].append( ( "lang", lang_list[i] ) )
+				# insert RVQ level guidance token if the model is versioned for it
 				if self.rvq_l_emb is not None:
 					inputs[i].append( ( "quant_level", torch.Tensor([ quant_level ]).to(device=device, dtype=torch.int16) ) )
+				# insert input audio prompt
 				if proms_list is not None:
 					inputs[i].append( ( "prom", proms_list[i] ) )
+				# insert tone token if we're trained for it
+				if "tone" in self.capabilities and tone_list is not None:
+					inputs[i].append( ( "tone", tone_list[i] ) )
+				# insert the current output response
 				if resps_list is not None:
 					inputs[i].append( ( "resp", resps_list[i] ) )
-			# <text><sep><rvq lvl><prom><sep><len>
+		
+			# Audio length prediction task
+			# Sequence: <text><sep><rvq lvl><prom><sep><len>
 			elif task_type == "len":
 				# throw an error so we don't silently train without this
 				if self.len_emb is None:
 					raise Exception(f"Requesting task `{task_type}` but corresponding embedding is not defined.")
+
+				# insert the text prompt
 				if text_list is not None:
 					inputs[i].append( ( "text", text_list[i] ) )
+				# insert lang token if we're trained for it
+				if "lang" in self.capabilities and lang_list is not None:
+					inputs[i].append( ( "lang", lang_list[i] ) )
 				# technically will always be level 0 but for the sake of keeing the input formatting coherent...
 				if self.rvq_l_emb is not None:
 					# override to 0 (I don't know if this change propagates, I'm not familiar with when python passes by (copied) value or reference)
 					quant_levels[i] = 0
 					inputs[i].append( ( "quant_level", torch.Tensor([ 0 ]).to(device=device, dtype=torch.int16) ) )
+				
+				# insert input audio prompt
 				if proms_list is not None:
 					inputs[i].append( ( "prom", proms_list[i] ) )
+				# insert tone token if we're trained for it
+				if "tone" in self.capabilities and tone_list is not None:
+					inputs[i].append( ( "tone", tone_list[i] ) )
 
+				# insert output length tokens (if it exists)
 				if len_list is not None:
 					inputs[i].append( ( "len", len_list[i] ) )
 				# "encode" length to tokens for 0-9 + stop
@@ -917,7 +979,10 @@ class Base(nn.Module):
 					else:
 						# get RVQ level 0, or up to targetted RVQ level inference
 						if self.version <= 4:
-							embedding = self.resps_emb( input if quant_level == 0 else input[:, :quant_level], quant_level )
+							embedding = self.resps_emb(
+								input if quant_level == 0 else input[:, :quant_level],
+								quant_level
+							)
 						else:
 							embedding = self.resps_emb(
 								input if input.dim() == 1 or quant_level == 0 else input[:, :quant_level],
@@ -935,6 +1000,8 @@ class Base(nn.Module):
 
 		return x_list
 
+	# creates position ids from a given input list
+	# if not unified_position_ids, then each input segment will have its own sequence
 	def inputs_to_position_ids(
 		self,
 		inputs: list,

vall_e/utils/pattern.py

@@ -0,0 +1,549 @@
+# https://github.com/facebookresearch/audiocraft/blob/adf0b04a4452f171970028fcf80f101dd5e26e19/audiocraft/modules/codebooks_patterns.py
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed per https://github.com/facebookresearch/audiocraft/blob/adf0b04a4452f171970028fcf80f101dd5e26e19/LICENSE
+
+from collections import namedtuple
+from dataclasses import dataclass
+from functools import lru_cache
+import logging
+import typing as tp
+
+from abc import ABC, abstractmethod
+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 = 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: 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: 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.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])
+        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
+                        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, card, K, S = logits.shape
+        indexes, mask = self._build_reverted_sequence_scatter_indexes(
+            S, K, keep_only_valid_steps, is_model_output=True, device=logits.device
+        )
+        logits = logits.reshape(B, card, -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, card, K, 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: 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)